Hårek Haugerud

• Gorosito, Martin; Yazidi, Anis; Hermansen, Åsmund; Berg, Bjørnar; Øiestad, Britt Elin & Grotle, Margreth [Show all 8 contributors for this article] (2026). Exploring risk factors for long-term sickness absence during emerging adulthood: Continuous and discrete time models using Young-HUNT data on psychological distress and chronic pain. International Journal of Medical Informatics. ISSN 1386-5056. 219. doi: 10.1016/j.ijmedinf.2026.106551. Full text in Research Archive Show summary

  Introduction: Long-term sickness absence (LTSA) in young adults has important consequences for labour market participation and future work disability. Chronic pain and psychological distress are key risk factors and frequently co-occur, yet their combined impact during adolescence on later LTSA remains insufficiently understood. This study aims to explore factors that influence adolescents’ and young people’s risk of receiving LTSA benefits during emerging adulthood. Methods: This longitudinal study used data from the Young-HUNT1 (1995–1997; n = 8736) and Young-HUNT3 (2006–2008; n = 7935) cohorts linked to Norwegian registry data and followed into early adulthood. The outcome was time to LTSA (90 or 180 days). Associations were examined using Cox proportional hazards models and Kaplan–Meier analyses. Continuous- and discrete-time models were developed and evaluated using the concordance index, time-dependent AUC, and integrated Brier score. Risk factors were analysed using SurvSHAP, SHAP, and regression-based methods. Results: Chronic pain and co-occurring pain and psychological distress were consistently associated with increased LTSA risk (adjusted HRs between 1.3 and 1.5 for pain and between 1.6 and 1.7 for co-occurrence). In contrast, psychological distress alone showed no consistent association. Model performance was moderate and similar across approaches (C-index between 0.63 and 0.67). Key predictors included female sex, low parental education, chronic pain, poor perceived health, and indicators of early health problems. Conclusion: Adolescent chronic pain, particularly when co-occurring with psychological distress, is an important predictor of LTSA in early adulthood. While absolute LTSA levels may vary across cohorts, underlying risk patterns remain stable. More complex models did not outperform traditional approaches. These findings highlight the importance of early-life conditions and support early identification and intervention to reduce later work absence.

• Gorosito, Martin; Berg, Bjørnar; Yazidi, Anis; Hermansen, Åsmund; Øiestad, Britt Elin & Grotle, Margreth [Show all 8 contributors for this article] (2025). Disability pension during emerging adulthood: Insights from the young-HUNT study on psychological distress, chronic pain, and policy reform. International Journal of Medical Informatics. ISSN 1386-5056. 207. doi: 10.1016/j.ijmedinf.2025.106216. Full text in Research Archive Show summary

  Introduction: Disability pension (DP) among young adults has steadily increased in Norway, with chronic pain and psychological distress among the main causes. This study examined the association between co-occurring psychological distress and chronic pain in adolescence and the risk of DP during emerging adulthood, before and after the Norwegian Labour and Welfare Administration (NAV) reform. Machine learning survival models were also developed to predict early DP and identify consistent risk factors across cohorts. Methods: Data from two general population surveys in 1995–1997 (Young-HUNT1) and 2006–2008 (Young-HUNT3) were linked to public registry data for DP, with a mean follow-up of 12 years. These represent populations pre- and post-NAV reform, respectively. Crude and adjusted Cox proportional hazards models tested associations between chronic pain, psychological distress and their co-occurrence with early DP. Machine learning survival models were developed using common predictors across cohorts and validated temporally. SurvSHAP(t) and hazard ratios were used for explainable risk factor analysis. Results: Co-occurring psychological distress and chronic pain were associated with increased early DP risk in Young-HUNT3 (HR = 2.25, [95 % CI 1.62 to 3.14]) but not in Young-HUNT1. Tree-based machine learning models showed strong predictive performance and generalizability over time, with ExtraSurvivalTrees achieving a validation c-index of 0.75 [95 % CI 0.74–0.76]. Consistent risk factors included low social integration, limited physical activity, and low parental education in both cohorts, while additional factors post-reform included low self-esteem, poor self-rated health, and negative school experiences. Conclusion: Adolescents with co-occurring psychological distress and chronic pain face an elevated risk of early DP. Early identification and support are crucial, as risk factors extend beyond medical conditions. Findings also suggest that NAV policy changes may have influenced DP receipt rates among young adults.

• Gorosito, Martin; Haugerud, Hårek; Berg, Bjørnar; Yazidi, Anis; Øiestad, Britt Elin & Sivertsen, Børge [Show all 8 contributors for this article] (2025). Chronic pain, psychological distress, and their co-occurrence in Norwegian young adults: Insights from machine learning and explainable AI. International Journal of Medical Informatics. ISSN 1386-5056. 205. doi: 10.1016/j.ijmedinf.2025.106108. Full text in Research Archive Show summary

  Introduction Chronic pain and psychological distress significantly impact young adults' health, yet little is known about their associated factors when they co-occur. This study applied machine learning classification models and explainable AI to uncover associations across diverse factors with chronic pain, psychological distress, and their co-occurrence in young adults. It also examined how associations vary when these conditions occur individually versus when they co-occur. Variable clusters were also obtained to identify latent subgroups. Methods Cross-sectional data from the Norwegian Students' Health and Well-being Study (SHoT2018) were utilised. Three classification models (Logistic Regression, Tsetlin Machine, CatBoost) were developed separately for three mutually exclusive groups (chronic pain only, psychological distress only, and their co-occurrence), against a healthy group. Model performance was assessed using Area Under the Curve (AUC) and internal-external cross-validation using geographical regions as folds. Shapley values and odds ratios were applied to identify factors associated with conditions, with associations compared across them. Clause rules were obtained to search for variable clusters. Results A total of 34,469 participants (mean age 22.9 years; 67.4% female) were included. Chronic pain, psychological distress, and co-occurrence were reported by 27.2%, 12.7%, and 26.7%, respectively. Models performed consistently across regions (AUC 0.70–0.72 for chronic pain, 0.92–0.93 for psychological distress, and 0.94–0.96 for co-occurrence. Explainable AI analyses identified females, perceived health status, and sleep disorders as factors associated with all conditions. Associations shifted when conditions co-occurred versus individually, with some negating each other while others combined, revealing distinct patterns for the co-occurring group. Conclusion The findings underscore the importance of studying these conditions both individually and simultaneously. Machine learning and explainable AI revealed unique associations that can help with early identification of subjects at risk.

• Gambin, Angel; Yazidi, Anis; Vasilakos, Athanasios; Haugerud, Hårek & Djenouri, Youcef (2024). Deepfakes: current and future trends. Artificial Intelligence Review. ISSN 0269-2821. 57(3), p. 1–32. doi: 10.1007/s10462-023-10679-x. Full text in Research Archive Show summary

  Advances in Deep Learning (DL), Big Data and image processing have facilitated online disinformation spreading through Deepfakes. This entails severe threats including public opinion manipulation, geopolitical tensions, chaos in financial markets, scams, defamation and identity theft among others. Therefore, it is imperative to develop techniques to prevent, detect, and stop the spreading of deepfake content. Along these lines, the goal of this paper is to present a big picture perspective of the deepfake paradigm, by reviewing current and future trends. First, a compact summary of DL techniques used for deepfakes is presented. Then, a review of the fight between generation and detection techniques is elaborated. Moreover, we delve into the potential that new technologies, such as distributed ledgers and blockchain, can offer with regard to cybersecurity and the fight against digital deception. Two scenarios of application, including online social networks engineering attacks and Internet of Things, are reviewed where main insights and open challenges are tackled. Finally, future trends and research lines are discussed, pointing out potential key agents and technologies.

• Ojha, Jaya; Haugerud, Hårek; Yazidi, Anis & Lind, Pedro (2024). Exploring Interpretable AI Methods for ECG Data Classification. In for, Computing Mach Association (Eds.), ICDAR 2024 Proceedings. Association for Computing Machinery (ACM). ISSN 9798400705496. p. 11–18. doi: 10.1145/3643488.3660294. Full text in Research Archive Show summary

  We address ECG data classification, using methods from explainable artificial intelligence (XAI). In particular, we focus on the extended performance of the ST-CNN-5 model compared to established models. The model showcases slight improvement in accuracy suggesting the potential of this new model to provide more reliable predictions compared to other models. However, lower values of the specificity and area-under-curve metrics highlight the need to thoroughly evaluate the strengths and weaknesses of the extended model compared to other models. For the interpretability analysis, we use Shapley Additive Explanations (SHAP), Gradient-weighted Class Activation Mapping (GradCAM), and Local Interpretable Model-agnostic Explanations (LIME) methods. In particular, we show that the new model exhibits improved explainability in its GradCAM explanations compared to the former model. SHAP effectively highlights crucial ECG features, better than GradCAM and LIME. The latter methods exhibit inferior performance, particularly in capturing nuanced patterns associated with certain cardiac conditions. By using distinctive methods in the interpretability analysis, we provide a systematic discussion about which ECG features are better - or worse - uncovered by each method.

• Berg, Bjørnar; Gorosito, Martin; Fjeld, Olaf; Haugerud, Hårek; Storheim, Kjersti & Solberg, Tore [Show all 7 contributors for this article] (2024). Machine Learning Models for Predicting Disability and Pain Following Lumbar Disc Herniation Surgery. JAMA Network Open. 7(2), p. e2355024–e2355024. doi: 10.1001/jamanetworkopen.2023.55024. Full text in Research Archive Show summary

  IMPORTANCE Lumber disc herniation surgery can reduce pain and disability. However, a sizable minority of individuals experience minimal benefit, necessitating the development of accurate prediction models.<p> <p>OBJECTIVE To develop and validate prediction models for disability and pain 12 months after lumbar disc herniation surgery. <p>DESIGN, SETTING, AND PARTICIPANTS A prospective, multicenter, registry-based prognostic study was conducted on a cohort of individuals undergoing lumbar disc herniation surgery from January 1, 2007, to May 31, 2021. Patients in the Norwegian Registry for Spine Surgery from all public and private hospitals in Norway performing spine surgery were included. Data analysis was performed from January to June 2023. <p>EXPOSURES Microdiscectomy or open discectomy. <p>MAIN OUTCOMES AND MEASURES Treatment success at 12 months, defined as improvement in Oswestry Disability Index (ODI) of 22 points or more; Numeric Rating Scale (NRS) back pain improvement of 2 or more points, and NRS leg pain improvement of 4 or more points. Machine learning models were trained for model development and internal-external cross-validation applied over geographic regions to validate the models. Model performance was assessed through discrimination (C statistic) and calibration (slope and intercept). RESULTS Analysis included 22 707 surgical cases (21 161 patients) (ODI model) (mean [SD] age, 47.0 [14.0] years; 12 952 [57.0%] males). Treatment nonsuccess was experienced by 33% (ODI), 27% (NRS back pain), and 31% (NRS leg pain) of the patients. In internal-external cross-validation, the selected machine learning models showed consistent discrimination and calibration across all 5 regions. The C statistic ranged from 0.81 to 0.84 (pooled random-effects meta-analysis estimate, 0.82; 95% CI, 0.81-0.84) for the ODI model. Calibration slopes (point estimates, 0.94-1.03; pooled estimate, 0.99; 95% CI, 0.93-1.06) and calibration intercepts (point estimates, −0.05 to 0.11; pooled estimate, 0.01; 95% CI, −0.07 to 0.10) were also consistent across regions. For NRS back pain, the C statistic ranged from 0.75 to 0.80 (pooled estimate, 0.77; 95% CI, 0.75-0.79); for NRS leg pain, the C statistic ranged from 0.74 to 0.77 (pooled estimate, 0.75; 95% CI, 0.74-0.76). Only minor heterogeneity was found in calibration slopes and intercepts. <p>CONCLUSION The findings of this study suggest that the models developed can inform patients and clinicians about individual prognosis and aid in surgical decision-making.

• Aoueileyine, Mohamed Ould-Elhassen; Bennouri, Hajar; Berqia, Amine; Lind, Pedro; Haugerud, Hårek & Krejcar, Ondrej [Show all 8 contributors for this article] (2023). Quality of Monitoring Optimization in Underwater Sensor Networks through a Multiagent Diversity-Based Gradient Approach. Sensors. 23(8). doi: 10.3390/s23083877. Full text in Research Archive Show summary

  Due to the complex underwater environment, conventional measurement and sensing methods used for land are difficult to apply directly in the underwater environment. Especially for seabed topography, it is impossible to perform long-distance and accurate detection by electromagnetic waves. Therefore, various types of acoustic and even optical sensing devices for underwater applications have been used. Equipped with submersibles, these underwater sensors can detect a wide underwater range accurately. In addition, the development of sensor technology will be modified and optimized according to the needs of ocean exploitation. In this paper, we propose a multiagent approach for optimizing the quality of monitoring (QoM) in underwater sensor networks. Our framework aspires to optimize the QoM by resorting to the machine learning concept of diversity. We devise a multiagent optimization procedure which is able to both reduce the redundancy among the sensor readings and maximize the diversity in a distributed and adaptive manner. The mobile sensor positions are adjusted iteratively using a gradient type of updates. The overall framework is tested through simulations based on realistic environment conditions. The proposed approach is compared to other placement approaches and is found to achieve a higher QoM with a smaller number of sensors.

• Palumbo, Fabrizio; Buji, Adam; Yazidi, Anis & Haugerud, Hårek (2022). Genetic Algorithms For Tightening Security. In Ukjent, Ukjent (Eds.), Proceedings of the 14th IFIP Wireless and Mobile Networking Conference. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783903176522. doi: 10.23919/WMNC56391.2022.9954297. Full text in Research Archive Show summary

  Proper configuration of operating systems and program parameters is known to be a key security factor in order to remove vulnerabilities. It is known that vulnerabilities can be caused by a human misconfiguration or by an improper chain of parameter settings. It is impossible to find an optimal combination manually due to the enormous number of possible configurations. In this article, we resort to a Genetic Algorithm equipped with a user-defined fitness function in order to compute a configuration of high fitness. Our work presents a two-fold contribution. First, we successfully use a GA to implement a moving target defense by alerting the configuration regularly in order to spoil an attacker's reconnaissance efforts. The GA tightens the security solution by evolving the fitness of the configuration over generations while maintaining diversity within generations across a pool of servers. This resulted in high-quality configurations crucial for a successful moving target defense strategy. Second, we try to find a compromise between tightening the security of the configuration and maintaining the Quality of Service (QoS) on a web server. In practice, usually tightening security on a web server comes at the cost of a decrease in QoS.

• Haugerud, Hårek; Sobhie, Mohamad & Yazidi, Anis (2022). Tuning of Elasticsearch Configuration: Parameter Optimization Through Simultaneous Perturbation Stochastic Approximation. Frontiers in Big Data. 5. doi: 10.3389/fdata.2022.686416. Full text in Research Archive Show summary

  Elasticsearch is currently the most popular search engine for full-text database management systems. By default, its configuration does not change while it receives data. However, when Elasticsearch stores a large amount of data over time, the default configuration becomes an obstacle to improving performance. In addition, the servers that host Elasticsearch may have limited resources, such as internal memory and CPU. A general solution to these problems is to dynamically tune the configuration parameters of Elasticsearch in order to improve its performance. The sheer number of parameters involved in this configuration makes it a complex task. In this work, we apply the Simultaneous Perturbation Stochastic Approximation method for optimizing Elasticsearch with multiple unknown parameters. Using this algorithm, our implementation optimizes the Elasticsearch configuration parameters by observing the performance and automatically changing the configuration to improve performance. The proposed solution makes it possible to change the configuration parameters of Elasticsearch automatically without having to restart the currently running instance of Elasticsearch. The results show a higher than 40% improvement in the combined data insertion capacity and the system’s response time.

• Kashi, Meraj Mostam; Yazidi, Anis & Haugerud, Hårek (2022). Mitigating yo-yo attacks on cloud auto-scaling. IFIP Wireless and Mobile Networking Conference (WMNC). ISSN 2163-4033. doi: 10.23919/WMNC56391.2022.9954300. Full text in Research Archive
• Jaber, Aws Naser; Fritsch, Lothar & Haugerud, Hårek (2022). Improving Phishing Detection with the Grey Wolf Optimizer, 2022 International Conference on Electronics, Information, and Communication (ICEIC 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665409353. doi: 10.1109/ICEIC54506.2022.9748592. Full text in Research Archive Show summary

  With the recent epidemic of COVID-19-themed scam and phishing, the efficient automated detection of such attacks is crucial. Although many anti-phishing solutions, such as lists and similarity and heuristic-based approaches detect attacks, methods still can be improved. Classification accuracy is highly dependent on the feature selection method used to select appropriate features for classification. In this article, a multi-objective grey wolf optimizer is used to select proper features for classifying phishing websites through a variational autoencoder. Our results indicate the superiority of the classification rate compared with related work: A classification rate of 97.49%, is obtained, thereby suggesting the feasibility of evaluating our work.

• Haugerud, Hårek; Tran, Huy Nhut; Aitsaadi, Nadjib & Yazidi, Anis (2021). A dynamic and scalable parallel Network Intrusion Detection System using intelligent rule ordering and Network Function Virtualization. Future Generation Computer Systems. ISSN 0167-739X. 124, p. 254–267. doi: 10.1016/j.future.2021.05.037. Full text in Research Archive Show summary

  A Network Intrusion Detection System (NIDS) is a fundamental security tool. However, under heavy network traffic, a NIDS might become a bottleneck. In an overloaded state, incoming and outgoing packets in the network might suffer from long delays since previous packets are still being inspected, and eventually the NIDS starts to drop packets when it runs out of hardware resources. Although many solutions have been suggested in the literature to counter this problem, they are not completely reliable as each of them has limitations. This paper investigates the design of a lightweight elastic architecture which allows parallel processing in an existing NIDS while maintaining the filtering integrity. Furthermore, we propose two adaptive algorithms which dynamically adjust and divide the signature rules evenly across NIDS nodes using a node level parallelism method in order to achieve intelligent rule ordering. We test our approaches in real-life settings by implementing a functioning prototype involving different modern networking technologies. The prototype presented is a Network Function Virtualization (NFV) of an intrusion detection system which utilizes Open vSwitch and Docker containers running Snort in order to provide an elastic system. To the best of our knowledge, there has been no work that orchestrates both scaling and rule splitting and re-ordering of IDS signatures as a part of a holistic elastic IDS solution. The results of this study show that the proposed algorithms are able to equally split the IDS workload and thereby enabling the system to scale by adjusting the number of virtual components which analyse the network traffic. At the same time the experiments indicate that the algorithms can be tuned by a single parameter in order to avoid that some packets go unexamined while simultaneously craving a minimum of the dynamically available computer resources.

• Bårli, Eirik Molde; Yazidi, Anis; Viedma, Enrique Herrera & Haugerud, Hårek (2021). DoS and DDoS mitigation using Variational Autoencoders. Computer Networks. ISSN 1389-1286. 199(199). doi: 10.1016/j.comnet.2021.108399. Full text in Research Archive Show summary

  DoS and DDoS attacks have been growing in size and number over the last decade and existing solutions to mitigate these attacks are largely inefficient. Compared to other types of malicious cyber attacks, DoS and DDoS attacks are particularly challenging to combat. Because of their ability to mask themselves as legitimate traffic, it has proven difficult to develop methods to detect these types of attacks on a packet or flow level. In this paper, we explore the potential of Variational Autoencoders to serve as a component within an intelligent security solution that differentiates between normal and malicious traffic. The motivation behind resorting to Variational Autoencoders is that unlike normal encoders that would code an input flow as a single point, they encode a flow as a distribution over the latent space which avoids overfitting. Intuitively, this allows a Variational Autoencoder to not only learn latent representations of seen input features, but to generalize in a way that allows for an interpretation of unseen flows and flow features with slight variations. Two methods based on the ability of Variational Autoencoders to learn latent representations from network traffic flows of both benign and malicious traffic, are proposed. The first method resorts to a classifier based on the latent encodings obtained from Variational Autoencoders learned from traffic traces. The second method is an anomaly detection method, where the Variational Autoencoder is used to learn the abstract feature representations of exclusively legitimate traffic. Anomalies are then filtered out by relying on the reconstruction loss of the Variational Autoencoder. In this sense, the construction loss of the autoencoder is fed as input to a classifier that outputs the class of the traffic including benign and malign, and eventually the attack type. Thus, the second approach operates with two separate training processes on two separate data sources: the first training involving only legitimate traffic, and the second training involving all traffic classes. This is different from the first approach which operates only a single training process on the whole traffic dataset. Thus, the autoencoder of the first approach aspires to learn a general feature representation of the flows while the autoencoder of the second approach aims to exclusively learn a representation of the benign traffic. The second approach is thus more susceptible to finding zero day attacks and discovering new attacks as anomalies. Both of the proposed methods have been thoroughly tested on two separate datasets with a similar feature space. The results show that both methods are promising, with the classifier-based method being slightly superior to the anomaly-based one.

• Upreti, Ramesh; Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Engelstad, Paal E. & Yazidi, Anis (2020). Adaptive Pursuit Learning for Energy‐efficient Target Coverage in Wireless Sensor Networks. Concurrency and Computation: Practice and Experience. ISSN 1532-0626. doi: 10.1002/cpe.5975. Full text in Research Archive Show summary

  With the proliferation of technologies such as wireless sensor networks (WSNs) and the Internet of things (IoT), we are moving towards the era of automation without any human intervention. Sensors are the principal components of the WSNs that bring the idea of IoT into reality. Over the last decade, WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care monitoring, and so on. However, the energy efficiency of the sensor nodes in WSN remains a challenging issue due to the use of a small battery. Moreover, replacing the batteries of the sensor nodes deployed in a hostile environment frequently is not a feasible option. Therefore, intelligent scheduling of the sensor nodes for optimizing its energy-efficient operation and thereby extending the lifetime of WSN has received a lot of research attention lately. In particular, this article investigates extending the lifetime of the WSN in the context of target coverage problems. To tackle this problem, we propose a scheduling technique for WSN based on a novel concept within the theory of learn-ing automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with anLA so that it can autonomously select its proper state, that is, either sleep or active, with an aim to cover all targets with the lowest energy cost possible. Our comprehensive experimental testing of the proposed algorithm not only verifies the efficiency of our algorithm, but it also demonstrates its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

• Haugerud, Hårek; Xue, Noha & Yazidi, Anis (2020). On automated cloud bursting and hybrid cloud setups using Apache Mesos. Concurrency and Computation: Practice and Experience. ISSN 1532-0626. doi: 10.1002/cpe.5662. Full text in Research Archive Show summary

  Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite some commercial solutions being available to build a hybrid cloud, an open‐source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open‐source implementation using the power of Apache Mesos. We build a hybrid cloud on top of multiple cloud platforms, private and public. Through comprehensive experimental results, we show that our solution is able to ensure resource bursting by leveraging the power of the public cloud.

• Rønning, Madeleine; Haugerud, Hårek; Yazidi, Anis; Maseng, Torleiv & Hammer, Hugo Lewi (2020). Mitigating DDoS using weight-based geographical clustering. Concurrency and Computation: Practice and Experience. ISSN 1532-0626. 32(11). doi: 10.1002/cpe.5679. Full text in Research Archive Show summary

  Distributed denial of service (DDoS) attacks have for the last two decades been among the greatest threats facing the internet infrastructure. Mitigating DDoS attacks is a particularly challenging task as an attacker tries to conceal a huge amount of traffic inside a legitimate traffic flow. This article proposes to use data mining approaches to find unique hidden data structures which are able to characterize the normal traffic flow. This will serve as a mean for filtering illegitimate traffic under DDoS attacks. In this endeavor, we devise three algorithms built on previously uncharted areas within mitigation techniques where clustering techniques are used to create geographical clusters in regions which are likely to contain legitimate traffic. We argue through extensive experimental results that establishing clusters around this narrative is a superior solution to clustering algorithms which rely on bitwise distances between IP addresses. In addition, the DDoS filtering algorithm is deployed in a virtual Linux environment using Nfqueue and tested in a simulated real‐life DDoS attack.

• Haugerud, Hårek; Svensson, Jonas Krüger & Yazidi, Anis (2020). Autonomous Provisioning of Preemptive Instances in Google Cloud for Maximum Performance Per Dollar. In Essaaidi, Mohamed & Zbakh, Mostapha (Ed.), Proceedings of 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech’20). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728161754. doi: 10.1109/CloudTech49835.2020. Full text in Research Archive
• Fladby, Torgeir; Haugerud, Hårek; Nichele, Stefano; Begnum, Kyrre & Yazidi, Anis (2020). Evading a Machine Learning-based Intrusion Detection System through Adversarial Perturbations. In NN, NN (Eds.), RACS '20: Proceedings of the International Conference on Research in Adaptive and Convergent Systems. Association for Computing Machinery (ACM). ISSN 9781450380256. p. 161–166. doi: 10.1145/3400286.3418252. Full text in Research Archive Show summary

  Machine-learning based Intrusion Detection and Prevention Systems provide significant value to organizations because they can efficiently detect previously unseen variations of known threats, new threats related to known malware or even zero-day malware, unrelated to any other known threats. However, while such systems prove invaluable to security personnel, researchers have observed that data subject to inspection by behavioral analysis can be perturbed in order to evade detection. We investigated the use of adversarial techniques for adapting the communication patterns between botnet malware and control unit in order to evaluate the robustness of an existing Network Behavioral Analysis solution. We implemented a packet parser that let us extract and edit certain properties of network flows and automated an approach for conducting a grey-box testing scheme of Stratosphere Linux IPS. As part of our implementation, we provided several techniques for providing perturbation to network flow parameters, including a Simultaneous Perturbation Stochastic Approximation method, which was able to produce sufficiently perturbed network flow patterns while adhering to an underlying objective function. Our results showed that network flow parameters could indeed be perturbed to ultimately enable evasion of intrusion detection based on the detection models that were used with the Intrusion Detection System. Additionally, we demonstrated that it was possible to combine evading detection with techniques for optimization problems that aimed to minimize the magnitude of perturbation to network flows, effectively enabling adaptive network flow behavior.

• Sangar, Disha; Upreti, Ramesh; Haugerud, Hårek; Begnum, Kyrre & Yazidi, Anis (2020). Stable marriage matching for homogenizing load distribution in cloud data center. Transactions on Large-Scale Data- and Knowledge-Centered Systems. ISSN 1869-1994. 45, p. 172–198. doi: 10.1007/978-3-662-62308-4_7. Full text in Research Archive Show summary

  Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers. In this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center by mapping the problem to a Stable Marriage matching problem. The algorithm judiciously chooses pairs of overloaded and underloaded servers for matching and subsequently VM migrations are performed to reduce load imbalance. For the purpose of comparisons, three different greedy matching algorithms are also introduced. In order to verify the feasibility of our approach in real-life scenarios, we implement our solution on a small test-bed. For the larger scale scenarios, we provide simulation results that demonstrate the efficiency of the algorithm and its ability to yield a near-optimal solution compared to other algorithms. The results are promising, given the low computational footprint of the algorithm.

• Rauniyar, Ashish; Kunwar, Jeevan; Haugerud, Hårek; Yazidi, Anis & Engelstad, Paal E. (2019). Energy Efficient Target Coverage in Wireless Sensor Networks Using Adaptive Learning. In Jemili, Imem & Mosbah, Mohamed (Ed.), Distributed Computing for Emerging Smart Networks. Springer Nature. ISSN 9783030401313. p. 133–147. doi: 10.1007/978-3-030-40131-3_9. Full text in Research Archive Show summary

  Over the past few years, innovation in the development of Wireless Sensor Networks (WSNs) has evolved rapidly. WSNs are being used in many application fields such as target coverage, battlefield surveillance, home security, health care supervision, and many more. However, power usage in WSNs remains a challenging issue due to the low capacity of batteries and the difficulty of replacing or charging them, especially in harsh environments. Therefore, this has led to the development of various architectures and algorithms to deal with optimizing the energy usage of WSNs. In particular, extending the lifetime of the WSNin the context of target coverage problems by resorting to intelligent scheduling has received a lot of research attention. In this paper, we propose a scheduling technique for WSN based on a novel concept within the theory of Learning Automata (LA) called pursuit LA. Each sensor node in the WSN is equipped with an LA so that it can autonomously select its proper state, i.e., either sleep or active with the aim to coverall targets with the lowest energy cost. Through comprehensive experimental testing, we verify the efficiency of our algorithm and its ability to yield a near-optimal solution. The results are promising, given the low computational footprint of the algorithm.

• Yazidi, Anis; Haugerud, Hårek; Ung, Fredrik & Begnum, Kyrre Matthias (2019). Minimum-Impact First: Scheduling Virtual Machines Under Maintenance Scenarios, 11th ACM International Conference on Management of Digital EcoSystems. ACM Publications. ISSN 9781450362382. doi: 10.1145/3297662.3365831. Full text in Research Archive Show summary

  Virtual Machine (VM) migration is an important feature for ensuring smooth operations during maintenance and disaster recovery scenarios. The migration might be inter-site and in such a case the inter-site bandwidth which is typically Wide Area Network (WAN) might be a bottleneck. In such a case, the bandwidth is affected by the amount of inter-VM traffic that becomes separated during the migration process. The amount of separated traffic might not only cause degradation of the of the Quality of Service (QoS) of inter-communicating VMs but can also delay the migration process due to the congestion of the migration link. The state-of-the-art algorithm due to Yazidi et al. is an affinity aware algorithm that does not consider the completion time of the migration. The first stage of our algorithm is identical to Yazidi et al. where we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise a greedy algorithm for controlling the order of the migration groups by considering their inter-group traffic that greedily selects groups with the lowest impact in terms of volume of separated traffic which we denominate Minimum-Impact First (MIF). We also design a latency-aware algorithm that only schedules the quickest migration first. The latter simple heuristic interestingly outperforms legacy works in the case of migration over a non-dedicated link. We find that our MIF algorithm consistently outperforms the state-of-the-art algorithms by a clear margin using real-traffic traces by a margin larger than 40%. We show that the MIF algorithm ensures the lowest amount of separated traffic in both dedicated-link and non-dedicated-link scenarios.

• Khan, Naveed Ashraf; Haugerud, Hårek; Shrestha, Raju & Yazidi, Anis (2019). Optimizing Power and Energy Efficiency in Cloud Computing, 11th ACM International Conference on Management of Digital EcoSystems. ACM Publications. ISSN 9781450362382. doi: 10.5220/0007723503800387. Full text in Research Archive Show summary

  With the exponential growth in cloud computing, the steadily increasing amount of power consumption due to the use of physical and virtual machines is becoming a serious challenge. In this context, we report a study on optimizing the power and energy efficiency of physical and virtual machines in a cloud computing environment. The energy profile of different workloads is thoroughly investigated under different configurations. This paper presents the findings from our study which provides a good understanding of how different workloads affect power and energy efficiency of both physical and virtual machines.

• Sangar, Disha; Haugerud, Hårek; Yazidi, Anis & Begnum, Kyrre Matthias (2019). A Decentralized Approach for Homogenizing Load Distribution in Cloud Data Center Based on Stable Marriage Matching, 11th ACM International Conference on Management of Digital EcoSystems. ACM Publications. ISSN 9781450362382. doi: 10.1145/3297662.3365815. Full text in Research Archive Show summary

  Running a sheer virtualized data center with the help of Virtual Machines (VM) is the de facto-standard in modern data centers. Live migration offers immense flexibility opportunities as it endows the system administrators with tools to seamlessly move VMs across physical machines. Several studies have shown that the resource utilization within a data center is not homogeneous across the physical servers. Load imbalance situations are observed where a significant portion of servers are either in overloaded or underloaded states. Apart from leading to inefficient usage of energy by underloaded servers, this might lead to serious QoS degradation issues in the overloaded servers. In this paper, we propose a lightweight decentralized solution for homogenizing the load across different machines in a data center. In search of better solutions, we have looked outside the field of computer science for inspiration. Inspired by Nobel Peace Prize winners Alvin Roth and Lloyd Shapley's work on Stable Matching [4], we borrow the concept of stable marriage matching problems where we pair pairs of underloaded servers and overloaded servers based on some notion of preferences for the purpose of homogenizing their load through exchange of VMs. Furthermore, our solution is distributed by accommodating this aspect in the original Stable Matching algorithm. We provide some real-life experimental results that demonstrate the efficiency of our approach.

• Yazidi, Anis; Ung, Fredrik; Haugerud, Hårek & Begnum, Kyrre Matthias (2019). Affinity Aware-Scheduling of Live Migration of Virtual Machines Under Maintenance Scenarios. Proceedings from 2019 IEEE Symposium on Computers and Communications (ISCC2019). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728129990. doi: 10.1109/ISCC47284.2019.8969573. Full text in Research Archive Show summary

  During maintenance and disaster recovery scenarios, Virtual Machine (VM) inter-site migrations usually take place over limited bandwidth–typically Wide Area Network (WAN)–which is highly affected by the amount of inter-VM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of inter-communicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise an affinity-based scheduling algorithm for controlling the order of the migration groups by considering their inter-group traffic. Comprehensive real-life experimental results and simulations show that our approach in some cases is able to decrease the volume of separated traffic by a factor larger than 60%.

• Gembala, Bartosz Gembala; Yazidi, Anis; Haugerud, Hårek & Nichele, Stefano (2018). Autonomous configuration of network parameters in operating systems using evolutionary algorithms. In NN, NN (Eds.), Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. Association for Computing Machinery (ACM). ISSN 9781450358859. p. 118–125. doi: 10.1145/3264746.3264799. Full text in Research Archive Show summary

  By default, the Linux network stack is not configured for highspeed large file transfer. The reason behind this is to save memory resources. It is possible to tune the Linux network stack by increasing the network buffers size for high-speed networks that connect server systems in order to handle more network packets. However, there are also several other TCP/IP parameters that can be tuned in an Operating System (OS). In this paper, we leverage Genetic Algorithms (GAs) to devise a system which learns from the history of the network traffic and uses this knowledge to optimize the current performance by adjusting the parameters. This can be done for a standard Linux kernel using sysctl or /proc. For a Virtual Machine (VM), virtually any type of OS can be installed and an image can swiftly be compiled and deployed. By being a sandboxed environment, risky configurations can be tested without the danger of harming the system. Different scenarios for network parameter configurations are thoroughly tested, and an increase of up to 65% throughput speed is achieved compared to the default Linux configuration.

• Yazidi, Anis; Ung, Fredrik; Haugerud, Hårek & Begnum, Kyrre (2018). Effective live migration of virtual machines using partitioning and affinity aware-scheduling. Computers and Electrical Engineering. ISSN 0045-7906. 69, p. 240–255. doi: 10.1016/j.compeleceng.2018.02.025. Full text in Research Archive Show summary

  During maintenance and disaster recovery scenarios, Virtual Machine (VM) inter-site migrations usually take place over limited bandwidth—typically Wide Area Network (WAN)—which is highly affected by the amount of inter-VM traffic that becomes separated during the migration process. This causes both a degradation of the Quality of Service (QoS) of inter communicating VMs and an increase in the total migration time due to congestion of the migration link. We consider the problem of scheduling VM migration in those scenarios. In the first stage, we resort to graph partitioning theory in order to partition the VMs into groups with high intra-group communication. In the second stage, we devise an affinity-based scheduling algorithm for controlling the order of the migration groups by considering their inter-group traffic. Comprehensive simulations and real-life experimental results show that our approach is able to decrease the volume of separated traffic by a factor larger than 30%.

• Hansen, Erik Aaron; Nichele, Stefano; Yazidi, Anis; Haugerud, Hårek; Mofrad, Asieh Abolpour & Alcocer, Alex (2018). Achieving Connectivity between Wide Areas Through Self-Organising Robot Swarms using Embodied Evolution. In Sundaram, Suresh (Eds.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI 2018). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538692752. p. 875–883. doi: 10.1109/ssci.2018.8628944. Full text in Research Archive Show summary

  Abruptions to the communication infrastructuremight occur occasionally, where manual intervention by dedi-cated personnel is needed to fix the interruptions, restoring com-munication abilities. However, sometimes this can be dangerousto the personnel carrying out the task, which can be the case inenvironmental disasters like earthquakes or toxic spills or in theoccurrence of fire. Therefore, personal risks can be minimised ifautonomous robots are deployed to achieve the same outcome: toestablish a communication link between two previously connectedbut distant sites. In this paper, we investigate the deployment ofmobile ad hoc robots which relay traffic between two sites. Inorder to get the robots to locate themselves appropriately, wetake inspiration from self-organisation and emergence in artificiallife, where a common overall goal may be achieved if the correctlocal rules on the agents in the system are invoked. We integratethe aspect of connectivity between two sites into the multirobotsimulation platform known as JBotEvolver. The robot swarm iscomposed of Thymio II robots. In addition, we compare threeheuristics, of which one uses neuroevolution (evolution of neuralnetworks) to show how self-organisation and embodied evolutioncan be used within the integration. Our use of embodiment inrobotic controllers shows promising results and provides solidknowledge and guidelines for further investigations.

• Rønning, Madeleine; Yazidi, Anis; Haugerud, Hårek & Hammer, Hugo Lewi (2018). Data Mining Approaches for IP Address Clustering. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 221, p. 266–275. doi: 10.1007/978-3-319-74176-5_23. Full text in Research Archive
• Hammer, Hugo Lewi; Yazidi, Anis; Bratterud, Alfred; Haugerud, Hårek & Feng, Boning (2017). Recovering Request Patterns to a CPU Processor from Observed CPU Consumption Data. In Maglaras, Leandros (Eds.), Industrial Networks and Intelligent Systems. Springer. ISSN 9783319525686. p. 14–28. doi: 10.1007/978-3-319-52569-3_2. Full text in Research Archive Show summary

  Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes of the requests are varied to investigate the impact on the computer system. A challenge with such an approach is that we do not know if the artificial requests processes are realistic when the system are applied in a real setting. Motivated by this challenge, we develop a method to estimate the properties of the underlying request processes to the computer system when the system is used in a real setting. In particular we look at the problem of recovering the request patterns to a CPU processor. It turns out that this is a challenging statistical estimation problem since we do not observe the request process (rate and size of the requests) to the CPU directly, but only the average CPU usage in disjoint time intervals. In this paper we demonstrate that, quite astonishingly, we are able to recover the properties of the underlying request process (rate and sizes of the requests) by using specially constructed statistics of the observed CPU data and apply a recently developed statistical framework called Approximate Bayesian Computing.

• Tasoulas, Evangelos; Hammer, Hugo Lewi; Haugerud, Hårek; Yazidi, Anis; Bratterud, Alfred & Feng, Boning (2017). The concept of workload delay as a quality-of-service metric for consolidated cloud environments with deadline requirements. In Gkoulalas-Divanis, Aris (Eds.), 2017 IEEE 16th International Symposium on Network Computing and Applications (NCA). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538614655. p. 1–5. doi: 10.1109/NCA.2017.8171342. Full text in Research Archive Show summary

  Virtual Machine (VM) consolidation in the cloud has received significant research interest. A large body of approaches for VM consolidation in data centers resort to variants of the bin packing problem which tries to minimize the number of deployed physical machines while meeting the Service-Level-Agreement (SLA) constraints. In this paper we introduce the concept of workload delay as a Quality-of-Service (QoS) metric that captures directly the resulting degradation that a cloud user would experience in the case where the SLA is violated. Our results, that are based on real-life trace-based simulations, show that consolidating VMs based on the level of utilization results in little control over the resulting delay, a particularly significant drawback when running jobs with deadline requirements, while we are able to control the delay much better if we take into account our suggested metric of the delay.

• Hammer, Hugo Lewi; Yazidi, Anis; Bratterud, Alfred; Haugerud, Hårek & Feng, Boning (2017). A Queue Model for Reliable Forecasting of Future CPU Consumption. Mobile Networks and Applications. ISSN 1383-469X. p. 1–14. doi: 10.1007/s11036-017-0880-3. Full text in Research Archive Show summary

  Statistical queuing models are popular to analyze a computer systems ability to process different types requests. A common strategy is to run stress tests by sending artificial requests to the system. The rate and sizes of the requests are varied to investigate the impact on the computer system. A challenge with such an approach is that we do not know if the artificial requests processes are realistic when the system is applied in a real setting. Motivated by this challenge, we develop a method to estimate the properties of the underlying request processes to the computer system when the system is used in a real setting. In particular we look at the problem of recovering the request patterns to a CPU processor. It turns out that this is a challenging statistical estimation problem since we do not observe the request process (rate and size of the requests) to the CPU directly, but only the average CPU usage in disjoint time intervals. In this paper we demonstrate that, quite astonishingly, we are able to recover the properties of the underlying request process (rate and sizes of the requests) by using specially constructed statistics of the observed CPU data and apply a recently developed statistical framework called Approximate Bayesian Computing. Further we apply the model to forecast future CPU consumption. Our results show that the model forecast future CPU consumption with less error than both the hidden Markov model (HMM) in (Hammer et al. 2016) and an ARIMA model. Another good property of the queue model is that we can forecast the instantaneous CPU consumption at any time point in the future, while the HMM in (Hammer et al. 2016) and time series models are limited to only forecasting the average CPU consumption in disjoint time intervals.

• Tamrakar, Kabin; Yazidi, Anis & Haugerud, Hårek (2017). Cost efficient batch processing in amazon cloud with deadline awareness. Advanced Information Networking and Applications. ISSN 1550-445X. p. 963–971. doi: 10.1109/AINA.2017.170. Full text in Research Archive Show summary

  Amazon spot instances have become a very popular alternative for cost-saving in the cloud. The spot instances are prone to abrupt termination whenever the spot market price exceeds the bid price. In this paper, spot instances are resorted to in task instances’ group of Amazon Elastic Map Reduce (EMR)clustertoprocessbatchjobswithdeadline.AmazonEMR makes it convenient to process Big Data with the aid of the Hadoop framework. However, the processed intermediate results in the task nodes of the cluster are lost if the spot instances gets terminated which can cause processing delay. The cost efficiency can be realized by exploiting the non-real time nature of batch computingforBigData.Twoalgorithmsaredevisedforachieving cost efficient processing in Hadoop MapReduce. Both algorithms process data in divisions such that abrupt termination of spot instances only affects the last division. Based on monitoring the progress at given checkpoints, task group’s capacity is resized to completetheprocessingwithinthedeadline.Progressismeasured in terms of the number of completed work divisions. The first algorithm begins with some spot instances whose number is initially estimated. To complete processing of all data in time, ondemandinstancesaredeployedafteracertainthresholdtime.The second algorithm starts by using higher number of spot instances than required to complete the work within the given deadline. Therefore, it has higher chance to rely solely on instances during the whole execution of the batch job. On-demand instances are deployed only in case of slow progress caused by termination of the spot instances combined with subsequent unsuccessful bids. The experiments show that both algorithms are able to minimize the processing cost. The second algorithm further minimizes the cost in most cases.

• Xue, Noha; Haugerud, Hårek & Yazidi, Anis (2017). On automated cloud bursting and hybrid cloud setups using Apache Mesos. In Essaaidi, Mohamed (Eds.), 2017 3rd International Conference of Cloud Computing Technologies and Applications (CloudTech). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538611159. p. 74–82. doi: 10.1109/CloudTech.2017.8284707. Full text in Research Archive
• Xue, Noha; Haugerud, Hårek & Yazidi, Anis (2017). Towards a hybrid cloud platform using apache mesos. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 10356 LNCS, p. 143–148. doi: 10.1007/978-3-319-60774-0_12. Full text in Research Archive Show summary

  Hybrid cloud technology is becoming increasingly popular as it merges private and public clouds to bring the best of two worlds together. However, due to the heterogeneous cloud installation, facilitating a hybrid cloud setup is not simple. Despite the availability of some commercial solutions to build a hybrid cloud, an open source implementation is still unavailable. In this paper, we try to bridge the gap by providing an open source implementation by leveraging the power of Apache Mesos. We build a hybrid cloud on the top of multiple cloud platforms, private and public.

• Morshedi, Maghsoud; Haugerud, Hårek & Begnum, Kyrre (2017). Making the Case for Highly Efficient Multicore Enabled Unikernels With IncludeOS. In NN, NN (Eds.), The Eighth International Conference on Cloud Computing, GRIDs, and Virtualization. International Academy, Research and Industry Association (IARIA). ISSN 9781612085296. p. 10–15. Full text in Research Archive
• Ahmad, Bilal; Yazidi, Anis; Haugerud, Hårek & Farokhi, Soodeh (2017). Orchestrating resource allocation for interactive vs. Batch services using a hybrid controller, Proceedings of the IM 2017 - 2017 IFIP/IEEE International Symposium on Integrated Network Management. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9783901882890. p. 1195–1203. doi: 10.23919/INM.2017.7987461. Full text in Research Archive Show summary

  Cloud service providers are trying to reduce their operating costs while offering their services with a higher quality via resorting to the concept of elasticity. However, the vast majority of related work focuses solely on guaranteeing the quality of service (QoS) of interactive applications such as Web services. Nevertheless, a broad range of applications have different QoS constraints that do not fall under the same class of latency-critical applications. For instance, batch processing possesses QoS requirements that are latency-tolerant and usually defined in terms of job progress. In this sense, a possible manner to quantify the performance of a batch processing application is to estimate its job progress so that to determine if future deadlines can be met. The novelty of this work is two-fold. First, we propose a hybrid controller coordinating resource allocation between interactive and batch applications running at the same infrastructure. The intuition is to deploy a controller for the interactive application at a faster time-scale than the batch application. Second, we bridge the gap between vertical and horizontal scaling under the same framework. In this perspective, vertical scaling is used for small fluctuations in the load, while horizontal scaling handles larger load changes. Comprehensive experimental results demonstrate the feasibility of our approach and its efficiency in ensuring a high CPU utilization across all experiments consisting of 83.70% for the Web service and 89.51% for the batch service, while meeting the respective QoS requirements of both services.

• Kvadsheim, Reidar; Haugerud, Hårek; Hammer, Hugo Lewi; Bratterud, Alfred & Habib, Laurence (2015). Does Clicker Use Improve Exam Scores? A Controlled Randomized Experiment in a Bachelor-Level Course in Software Engineering. International Journal of Engineering Education. ISSN 0949-149X. 31(2), p. 505–520. Full text in Research Archive
• Bratterud, Alfred; Walla, Alf-André; Haugerud, Hårek; Engelstad, Paal E. & Begnum, Kyrre (2015). IncludeOS: A minimal, resource efficient unikernel for cloud services. IEEE International Conference on Cloud Computing Technology and Science (CloudCom). ISSN 2330-2194. p. 250–257. doi: 10.1109/CloudCom.2015.89. Full text in Research Archive Show summary

  The emergence of cloud computing as a ubiquitous platform for elastically scaling services has generated need and opportunity for new types of operating systems. A service that needs to be both elastic and resource efficient needs A) highly specialized components, and B) to run with minimal resource overhead. Classical general purpose operating systems designed for extensive hardware support are by design far from meeting these requirements. In this paper we present IncludeOS, a single tasking library operating system for cloud services, written from scratch in C++. Key features include: extremely small disk-and memory footprint, efficient asynchronous I/O, OS-library where only what your service needs gets included, and only one device driver by default (virtio). As a test case a bootable disk image consisting of a simple DNS server with OS included is shown to require only 158 kb of disk space and to require 5-20% less CPU-time, depending on hardware, compared to the same binary running on Linux.

• Bratterud, Alfred & Haugerud, Hårek (2013). Maximizing hypervisor scalability using minimal virtual machines. In Society, IEEE (Eds.), 2013 IEEE International Conference on Cloud Computing Technology and Science. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9780769550954. p. 218–223. doi: 10.1109/CloudCom.2013.11. Full text in Research Archive Show summary

  The smallest instance offered by Amazon EC2 comes with 615MB memory and a 7.9GB disk image. While small by today's standards, embedded web servers with memory footprints well under 100kB, indicate that there is much to be saved. In this work we investigate how large VM-populations the open Stack hyper visor can be made to sustain, by tuning it for scalability and minimizing virtual machine images. Request-driven Qemu images of 512 byte are written in assembly, and more than 110 000 such instances are successfully booted on a 48 core host, before memory is exhausted. Other factors are shown to dramatically improve scalability, to the point where 10 000 virtual machines consume no more than 2.06% of the hyper visor CPU.

• Tasoulas, Evangelos; Haugerud, Hårek & Begnum, Kyrre (2012). Bayllocator: A proactive system to predict server utilization and dynamically allocate memory resources using Bayesian networks and ballooning. In Rowland, Carolyn (Eds.), Proceedings of LISA ’12: 26th Large Installation System Administration Conference. USENIX Association. ISSN 9781583818169. p. 111–121. Full text in Research Archive Show summary

  With the advent of virtualization and cloud computing, virtualized systems can be found from small companies to service providers and big data centers. All of them use this technology because of the many benefits it has to offer, such as a greener ICT, cost reduction, improved profitability, uptime, flexibility in management, maintenance, disaster recovery, provisioning and more. The main reason for all of these benefits is server consolidation which can be even further improved through dynamic resource allocation techniques. Out of the resources to be allocated, memory is one of the most difficult and requires proper planning, good predictions and proactivity. Many attempts have been made to approach this problem, but most of them are using traditional statistical mathematical methods. In this paper, the application of discrete Bayesian networks is evaluated, to offer probabilistic predictions on system utilization with focus on memory. The tool Bayllocator is built to provide proactive dynamic memory allocation based on the Bayesian predictions, for a set of virtual machines running in a single hypervisor. The results show that Bayesian networks are capable of providing good predictions for system load with proper tuning, and increase performance and consolidation of a single hypervisor. The modularity of the tool gives a great freedom for experimentation and even results to deal with the reactivity of the system can be provided. A survey of the current state-of-the-art in dynamic memory allocation for virtual machines is included in order to provide an overview.

• Bjørgeengen, Jarle & Haugerud, Hårek (2010). Using TCP/IP Traffic Shaping to Achieve iSCSI Service Predictability. In Drunen, Rudi van (Eds.), Proceedings of the 24th Large Installation System Administration Conference, LISA 2010. USENIX Association. ISSN 9781931971713. p. 91–107. Full text in Research Archive Show summary

  This paper addresses the unpredictable service availability of large centralized storage solutions. Fibre Channel is a common connection type for storage area networks (SANs) in enterprise storage and currently there are no standard mechanisms for prioritizing workloads using this technology. However, the increasing use of TCP/IP based network communication in SANs has introduced the possibility of employing well known techniques and tools for prioritizing IP-traffic. A method for throttling traffic to an iSCSI target server is devised: the packet delay throttle, using common TCP/IP traffic shaping techniques. It enables close-to-linear rate reduction for both read and write operations. All throttling is achieved without triggering TCP retransmit timeout and subsequent slow start caused by packet loss. A control mechanism for dynamically adapting throttling values to rapidly changing workloads is implemented using a modified proportional integral derivative (PID) controller. An example prototype of an autonomic resource prioritization framework is designed. The framework identifies and maintains information about resources, their consumers, response time for active consumers and their set of throttleable consumers. The framework is exposed to extreme workload changes and demonstrates high ability to keep read response time below a prede- fined threshold. It exhibits low overhead and resource consumption, promising suitability for large scale operation in production environments.

• Burgess, Mark; Reitan, Trond; Haugerud, Hårek & Straumsnes, Sigmund (2002). Measuring system normality. ACM Transactions on Computer Systems. ISSN 0734-2071. 20(2), p. 125–160. Full text in Research Archive
• Andersen, Jens Oluf & Haugerud, Hårek (2002). Ground state of a trapped Bose-Einstein condensate in two dimensions: Beyond the mean-field approximation. Physical Review A. Atomic, Molecular, and Optical Physics (PRA). ISSN 1050-2947. 65(3). doi: 10.1103/PhysRevA.65.033615. Full text in Research Archive Show summary

  We consider the ground state of a trapped Bose-Einstein condensate in two dimensions. In the mean-field approximation the ground-state density profile satisfies the Gross-Pitaevskii equation. We compute the leading quantum corrections to the density profile to second order in an expansion around the Thomas-Fermi limit. By summing the ladder diagrams, we obtain a modified equation that includes gradient corrections.

• Haugerud, Hårek & Straumsnes, Sigmund (2001). Simulation of User-Driven Computer Behaviour. In Burgess, Mark (Eds.), Proceedings of the 15th Conference on Systems Administration (LISA 2001). USENIX. ISSN 9781880446058. p. 101–108. Full text in Research Archive
• Tor, Haugset; Haugerud, Hårek & Ravndal, Finn (1998). Thermodynamics of a weakly interacting Bose–Einstein gas. Annals of Physics. ISSN 0003-4916. 266(1), p. 27–62. doi: 10.1006/aphy.1998.5795. Full text in Research Archive
• Tor, Haugset & Haugerud, Hårek (1998). Exact diagonalization of the Hamiltonian for trapped interacting bosons in lower dimensions. Physical Review A. Atomic, Molecular, and Optical Physics (PRA). ISSN 1050-2947. 57(5), p. 3809–3817. doi: 10.1103/PhysRevA.57.3809. Full text in Research Archive
• Burgess, Mark; Haugerud, Hårek; Strandlie, Are & Holmes, Lewis M. (1998). Object orientation and visualization of physics in two dimensions. Computers in physics. ISSN 0894-1866. 12(3), p. 274–285. Full text in Research Archive Show summary

  We present a generalized framework for cellular/lattice-based visuali zations in two dimensions based on state-of-the-art computing abstrac tions.

• Haugerud, Hårek; Uimin, Gennadi & Selke, Walter (1997). From microscopic description to statistical mechanics of Cu-O chain fragments in high-T c superconductors REBa_2 Cu_3 O_6+x. Physica. C, Superconductivity. ISSN 0921-4534. 275(1), p. 93–109. doi: 10.1016/S0921-4534(97)00002-6. Full text in Research Archive
• Tor, Haugset; Haugerud, Hårek & Oluf, Andersen Jens (1997). Bose-Einstein condensation in anisotropic harmonic traps. Physical Review A. Atomic, Molecular, and Optical Physics (PRA). ISSN 1050-2947. 55(4), p. 2922–2929. doi: 10.1103/PhysRevA.55.2922. Full text in Research Archive Show summary

  We study the thermodynamic behavior of an ideal gas of bosons trapped in a three-dimensional anisotropic harmonic-oscillator potential. The condensate fraction as well as the specific heat is calculated using the Euler-Maclaurin approximation. For a finite number of particles there is no phase transition, but there is a well-defined temperature at which the condensation starts. We also consider condensation in lower dimensions, and, for one-dimensional systems, we discuss the dependence of the condensate fraction and heat capacity on the ensemble used.

• Haugerud, Hårek; Tor, Haugset & Ravndal, Finn (1997). A more accurate analysis of Bose-Einstein condensation in harmonic traps. Physics Letters A. ISSN 0375-9601. 225(1-3), p. 18–22. doi: 10.1016/S0375-9601(96)08842-1. Full text in Research Archive Show summary

  Using the Euler-Maclaurin summation we calculate analytically the internal energy for non-interacting bosons confined within a harmonic oscillator potential. The specific heat shows a sharp λ-like peak indicating a condensation into the ground state at a well-defined transition temperature. Full agreement is obtained with direct numerical calculation of the same quantities. When the number of trapped particles is very large and at temperatures near and above the transition temperature, the results also agree with previous approximate calculations. At extremely low temperatures both the specific heat and the number of particles excited from the condensate are exponentially suppressed.

• Gawiec, P.; Grempel, D.R.; Riiser, Anne-Cecilie; Haugerud, Hårek & Uimin, Gennadi (1996). Theoretical study of Cu-O chain fragments in the oxygen-deficient planes of YBa2Cu3O6+x. I. Microscopic properties. Physical Review B. Condensed Matter and Materials Physics. ISSN 1098-0121. 53(9), p. 5872–5879. doi: 10.1103/PhysRevB.53.5872. Full text in Research Archive Show summary

  We examine some models of the strongly correlated ⋅⋅⋅Cu-O⋅⋅⋅ chain fragments that are the main components of the oxygen-deficient planes of YBa2Cu3O6+x. We use quantum Monte Carlo methods, exact numerical diagonalization as well as an analytical approach in order to study the thermodynamic properties of the chain fragments and the charge and magnetic correlations within them as functions of the concentration of carriers and of the temperature. Only the fermionic degrees of freedom of the chain fragments are considered.

• Grempel, D.R.; Haugerud, Hårek; Uimin, Gennadi & Selke, Walter (1996). The role of Cu−O chain fragments in the REBa2Cu3O6+x high-Tc superconductors. Czechoslovak Journal of Physics. ISSN 0011-4626. 46(2), p. 967–968. doi: 10.1007/BF02583791. Full text in Research Archive
• Haugerud, Hårek & Ravndal, Finn (1995). The two‐dimensional harmonic oscillator at finite temperature and nonzero chemical potential. American Journal of Physics (AJP). ISSN 0002-9505. 63(9), p. 839–844. doi: 10.1119/1.17809. Full text in Research Archive
• Gawiec, P.; Grempel, D.R.; Riiser, Anne-Cecilie; Haugerud, Hårek & Uimin, Gennadi (1994). Physical properties of Cu-O chain fragments within oxygen deficient planes in YBa2Cu3O6+x: a theoretical approach. Physica. C, Superconductivity. ISSN 0921-4534. 235, p. 1273–1274. doi: 10.1016/0921-4534(94)91861-9. Full text in Research Archive
• Haugerud, Hårek; Ravndal, Finn & Uimin, Gennadi (1993). Interacting holes in CuO cyclic chains. Journal of Physics: Condensed Matter. ISSN 0953-8984. 5(37). Full text in Research Archive
• Haugerud, Hårek & Ravndal, Finn (1990). Finite-temperature Gaussian effective potential from a variational principle. Physical Review D. ISSN 1550-7998. 43, p. 2736–2738. doi: 10.1103/PhysRevD.43.2736. Full text in Research Archive Show summary

  Writing the partition function for a scalar quantum field theory as a functional integral, it follows that the finite-temperature Gaussian effective potential is an upper limit to the free energy of the system. Explicit results are given for the anharmonic oscillator at finite temperature.

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• Brodtkorb, André; Begnum, Kyrre Matthias; Chen, Weiqin; Erikson, Håkon; Fritsch, Lothar & Habib, Laurence [Show all 21 contributors for this article] (2024). Fra EDB til KI. 40 år med informasjonsteknologi. Institutt for informasjonsteknologi, OsloMet. ISBN 9788283646382. 160 p. Full text in Research Archive

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• Bratterud, Alfred & Haugerud, Hårek (2013). Maximizing hypervisor scalability using minimal virtual machines. Full text in Research Archive
• Begnum, Kyrre & Haugerud, Hårek (2005). Bruk av virtuelle maskiner i data undervisning. Høgskolen i Oslo og Akershus. Full text in Research Archive

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