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Ali-Eldin, A.
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Publications (10 of 24) Show all publications
Bauer, A., Herbst, N., Spinner, S., Ali-Eldin, A. & Kounev, S. (2019). Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field. IEEE Transactions on Parallel and Distributed Systems, 30(4), 800-813
Open this publication in new window or tab >>Chameleon: A Hybrid, Proactive Auto-Scaling Mechanism on a Level-Playing Field
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2019 (English)In: IEEE Transactions on Parallel and Distributed Systems, ISSN 1045-9219, E-ISSN 1558-2183, Vol. 30, no 4, p. 800-813Article in journal (Refereed) Published
Abstract [en]

Auto-scalers for clouds promise stable service quality at low costs when facing changing workload intensity. The major public cloud providers provide trigger-based auto-scalers based on thresholds. However, trigger-based auto-scaling has reaction times in the order of minutes. Novel auto-scalers from literature try to overcome the limitations of reactive mechanisms by employing proactive prediction methods. However, the adoption of proactive auto-scalers in production is still very low due to the high risk of relying on a single proactive method. This paper tackles the challenge of reducing this risk by proposing a new hybrid auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism. Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation to calculate the required resource consumption per work unit without the need for application instrumentation. We benchmark Chameleon against five different state-of-the-art proactive and reactive auto-scalers one in three different private and public cloud environments. We generate five different representative workloads each taken from different real-world system traces. Overall, Chameleon achieves the best scaling behavior based on user and elasticity performance metrics, analyzing the results from 400 hours aggregated experiment time.

Place, publisher, year, edition, pages
IEEE Computer Society, 2019
Keywords
Auto-scaling, elasticity, workload forecasting, service demand estimation, IaaS cloud, benchmarking, metrics
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-157732 (URN)10.1109/TPDS.2018.2870389 (DOI)000461343700007 ()2-s2.0-85053342911 (Scopus ID)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-04-10Bibliographically approved
Krzywda, J., Ali-Eldin, A., Wadbro, E., Östberg, P.-O. & Elmroth, E. (2018). ALPACA: Application Performance Aware Server Power Capping. In: ICAC 2018: 2018 IEEE International Conference on Autonomic Computing (ICAC), Trento, Italy, September 3-7, 2018. Paper presented at 15th IEEE International Conference on Autonomic Computing (ICAC 2018) (pp. 41-50). IEEE Computer Society
Open this publication in new window or tab >>ALPACA: Application Performance Aware Server Power Capping
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2018 (English)In: ICAC 2018: 2018 IEEE International Conference on Autonomic Computing (ICAC), Trento, Italy, September 3-7, 2018, IEEE Computer Society, 2018, p. 41-50Conference paper, Published paper (Refereed)
Abstract [en]

Server power capping limits the power consumption of a server to not exceed a specific power budget. This allows data center operators to reduce the peak power consumption at the cost of performance degradation of hosted applications. Previous work on server power capping rarely considers Quality-of-Service (QoS) requirements of consolidated services when enforcing the power budget. In this paper, we introduce ALPACA, a framework to reduce QoS violations and overall application performance degradation for consolidated services. ALPACA reduces unnecessary high power consumption when there is no performance gain, and divides the power among the running services in a way that reduces the overall QoS degradation when the power is scarce. We evaluate ALPACA using four applications: MediaWiki, SysBench, Sock Shop, and CloudSuite’s Web Search benchmark. Our experiments show that ALPACA reduces the operational costs of QoS penalties and electricity by up to 40% compared to a non optimized system. 

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Series
IEEE Conference Publication, ISSN 2474-0756
Keywords
power capping, performance degradation, power-performance tradeoffs
National Category
Computer Systems
Research subject
business data processing
Identifiers
urn:nbn:se:umu:diva-132428 (URN)10.1109/ICAC.2018.00014 (DOI)978-1-5386-5139-1 (ISBN)
Conference
15th IEEE International Conference on Autonomic Computing (ICAC 2018)
Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2019-08-07Bibliographically approved
Ilyushkin, A., Ali-Eldin, A., Herbst, N., Bauer, A., Papadopoulos, A. V., Epema, D. & Iosup, A. (2018). An Experimental Performance Evaluation of Autoscalers for Complex Workflows. ACM Transactions on Modeling and Performance Evaluation of Computing Systems, 3(2), Article ID 8.
Open this publication in new window or tab >>An Experimental Performance Evaluation of Autoscalers for Complex Workflows
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2018 (English)In: ACM Transactions on Modeling and Performance Evaluation of Computing Systems, ISSN 2376-3639, Vol. 3, no 2, article id 8Article in journal (Refereed) Published
Abstract [en]

Elasticity is one of the main features of cloud computing allowing customers to scale their resources based on the workload. Many autoscalers have been proposed in the past decade to decide on behalf of cloud customers when and how to provision resources to a cloud application based on the workload utilizing cloud elasticity features. However, in prior work, when a new policy is proposed, it is seldom compared to the state-of-the-art, and is often compared only to static provisioning using a predefined quality of service target. This reduces the ability of cloud customers and of cloud operators to choose and deploy an autoscaling policy, as there is seldom enough analysis on the performance of the autoscalers in different operating conditions and with different applications. In our work, we conduct an experimental performance evaluation of autoscaling policies, using as application model workflows, a popular formalism for automating resource management for applications with well-defined yet complex structures. We present a detailed comparative study of general state-of-the-art autoscaling policies, along with two new workflow-specific policies. To understand the performance differences between the seven policies, we conduct various experiments and compare their performance in both pairwise and group comparisons. We report both individual and aggregated metrics. As many workflows have deadline requirements on the tasks, we study the effect of autoscaling on workflow deadlines. Additionally, we look into the effect of autoscaling on the accounted and hourly based charged costs, and we evaluate performance variability caused by the autoscaler selection for each group of workflow sizes. Our results highlight the trade-offs between the suggested policies, how they can impact meeting the deadlines, and how they perform in different operating conditions, thus enabling a better understanding of the current state-of-the-art.

Keywords
Autoscaling, elasticity, scientific workflows, benchmarking, metrics
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-147462 (URN)10.1145/3164537 (DOI)000430350200004 ()
Available from: 2018-05-29 Created: 2018-05-29 Last updated: 2018-06-09Bibliographically approved
Krzywda, J., Ali-Eldin, A., Carlson, T. E., Östberg, P.-O. & Elmroth, E. (2018). Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling. Future generations computer systems, 81, 114-128
Open this publication in new window or tab >>Power-performance tradeoffs in data center servers: DVFS, CPUpinning, horizontal, and vertical scaling
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2018 (English)In: Future generations computer systems, ISSN 0167-739X, E-ISSN 1872-7115, Vol. 81, p. 114-128Article in journal (Refereed) Published
Abstract [en]

Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, horizontal, and vertical scaling, are four techniques that have been proposed as actuators to control the performance and energy consumption on data center servers. This work investigates the utility of these four actuators, and quantifies the power-performance tradeoffs associated with them. Using replicas of the German Wikipedia running on our local testbed, we perform a set of experiments to quantify the influence of DVFS, vertical and horizontal scaling, and CPU pinning on end-to-end response time (average and tail), throughput, and power consumption with different workloads. Results of the experiments show that DVFS rarely reduces the power consumption of underloaded servers by more than 5%, but it can be used to limit the maximal power consumption of a saturated server by up to 20% (at a cost of performance degradation). CPU pinning reduces the power consumption of underloaded server (by up to 7%) at the cost of performance degradation, which can be limited by choosing an appropriate CPU pinning scheme. Horizontal and vertical scaling improves both the average and tail response time, but the improvement is not proportional to the amount of resources added. The load balancing strategy has a big impact on the tail response time of horizontally scaled applications.

Keywords
Power-performance tradeoffs, Dynamic Voltage and Frequency Scaling (DVFS), CPU pinning, Horizontal scaling, Vertical scaling
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-132427 (URN)10.1016/j.future.2017.10.044 (DOI)000423652200010 ()2-s2.0-85033772481 (Scopus ID)
Note

Originally published in thesis in manuscript form.

Available from: 2017-03-13 Created: 2017-03-13 Last updated: 2019-07-02Bibliographically approved
Ali-Eldin, A., Ilyushkin, A., Ghit, B., Herbst, N. R., Papadopoulos, A. & Losup, A. (2016). Which Cloud Auto-Scaler Should I Use for my Application?: Benchmarking Auto-Scaling Algorithms. In: PROCEEDINGS OF THE 2016 ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING (ICPE'16): . Paper presented at 7th ACM/SPEC International Conference on Performance Engineering (ICPE), MAR 12-16, 2016, Delft, NETHERLANDS (pp. 131-132). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Which Cloud Auto-Scaler Should I Use for my Application?: Benchmarking Auto-Scaling Algorithms
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2016 (English)In: PROCEEDINGS OF THE 2016 ACM/SPEC INTERNATIONAL CONFERENCE ON PERFORMANCE ENGINEERING (ICPE'16), Association for Computing Machinery (ACM), 2016, p. 131-132Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2016
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-130265 (URN)10.1145/2851553.2858677 (DOI)000389809200022 ()978-1-4503-4080-9 (ISBN)
Conference
7th ACM/SPEC International Conference on Performance Engineering (ICPE), MAR 12-16, 2016, Delft, NETHERLANDS
Available from: 2017-01-14 Created: 2017-01-14 Last updated: 2018-06-09Bibliographically approved
Ali-Eldin, A., Kihl, M., Tordsson, J. & Elmroth, E. (2015). Analysis and characterization of a Video-on-Demand service workload. In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015: . Paper presented at 6th ACM Multimedia Systems Conference, MMSys 2015; Portland; United States; 18 March 2015 through 20 March 2015; Code 113421 (pp. 189-200). ACM Digital Library
Open this publication in new window or tab >>Analysis and characterization of a Video-on-Demand service workload
2015 (English)In: Proceedings of the 6th ACM Multimedia Systems Conference, MMSys 2015, ACM Digital Library, 2015, p. 189-200Conference paper, Published paper (Refereed)
Abstract [en]

Video-on-Demand (VoD) and video sharing services accountfor a large percentage of the total downstream Internet traf-fic. In order to provide a better understanding of the loadon these services, we analyze and model a workload tracefrom a VoD service provided by a major Swedish TV broad-caster. The trace contains over half a million requests gener-ated by more than 20000 unique users. Among other things,we study the request arrival rate, the inter-arrival time, thespikes in the workload, the video popularity distribution, thestreaming bit-rate distribution and the video duration distri-bution. Our results show that the user and the session ar-rival rates for the TV4 workload does not follow a Poissonprocess. The arrival rate distribution is modeled using a log-normal distribution while the inter-arrival time distributionis modeled using a stretched exponential distribution. Weobserve the “impatient user” behavior where users abandonstreaming sessions after minutes or even seconds of startingthem. Both very popular videos and non-popular videos areparticularly affected by impatient users. We investigate ifthis behavior is an invariant for VoD workloads.

Place, publisher, year, edition, pages
ACM Digital Library, 2015
National Category
Engineering and Technology Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-108393 (URN)10.1145/2713168.2713183 (DOI)2-s2.0-84942543893 (Scopus ID)978-1-4503-3351-1 (ISBN)
Conference
6th ACM Multimedia Systems Conference, MMSys 2015; Portland; United States; 18 March 2015 through 20 March 2015; Code 113421
Projects
CACTOSCloud Control
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2018-06-07Bibliographically approved
Elmroth, E., Gardfjall, P., Tordsson, J., Ali-Eldin, A. & L., L. (2015). METHOD, NODE AND COMPUTER PROGRAM FOR ENABLING AUTOMATIC ADAPTATION OF RESOURCE UNITS. se 20150286507.
Open this publication in new window or tab >>METHOD, NODE AND COMPUTER PROGRAM FOR ENABLING AUTOMATIC ADAPTATION OF RESOURCE UNITS
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2015 (English)Patent (Other (popular science, discussion, etc.))
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-112460 (URN)
Patent
SE 20150286507
Available from: 2015-12-08 Created: 2015-12-08 Last updated: 2018-06-07
Papadopoulos, A. V., Ali-Eldin, A., Årzén, K.-E., Tordsson, J. & Elmroth, E. (2015). PEAS: A Performance Evaluation framework for Auto-Scaling strategies in cloud applications.
Open this publication in new window or tab >>PEAS: A Performance Evaluation framework for Auto-Scaling strategies in cloud applications
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2015 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Numerous auto-scaling strategies have been proposed in the last few years for improving various Quality of Service (QoS)indicators of cloud applications, e.g., response time and throughput, by adapting the amount of resources assigned to theapplication to meet the workload demand. However, the evaluation of a proposed auto-scaler is usually achieved throughexperiments under specific conditions, and seldom includes extensive testing to account for uncertainties in the workloads, andunexpected behaviors of the system. These tests by no means can provide guarantees about the behavior of the system in generalconditions. In this paper, we present PEAS, a Performance Evaluation framework for Auto-Scaling strategies in the presenceof uncertainties. The evaluation is formulated as a chance constrained optimization problem, which is solved using scenariotheory. The adoption of such a technique allows one to give probabilistic guarantees of the obtainable performance. Six differentauto-scaling strategies have been selected from the literature for extensive test evaluation, and compared using the proposedframework. We build a discrete event simulator and parameterize it based on real experiments. Using the simulator, each auto-scaler’s performance is evaluated using 796 distinct real workload traces from projects hosted on the Wikimedia foundations’servers, and their performance is compared using PEAS. The evaluation is carried out using different performance metrics,highlighting the flexibility of the framework, while providing probabilistic bounds on the evaluation and the performance of thealgorithms. Our results highlight the problem of generalizing the conclusions of the original published studies and show thatbased on the evaluation criteria, a controller can be shown to be better than other controllers.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108394 (URN)
Funder
Swedish Research CouncilEU, European Research Council
Note

Submitted

Available from: 2015-09-10 Created: 2015-09-10 Last updated: 2018-06-07
Ali-Eldin Hassan, A. (2015). Workload characterization, controller design and performance evaluation for cloud capacity autoscaling. (Doctoral dissertation). Umeå: Umeå University
Open this publication in new window or tab >>Workload characterization, controller design and performance evaluation for cloud capacity autoscaling
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis studies cloud capacity auto-scaling, or how to provision and release re-sources to a service running in the cloud based on its actual demand using an auto-matic controller. As the performance of server systems depends on the system design,the system implementation, and the workloads the system is subjected to, we focuson these aspects with respect to designing auto-scaling algorithms. Towards this goal,we design and implement two auto-scaling algorithms for cloud infrastructures. Thealgorithms predict the future load for an application running in the cloud. We discussthe different approaches to designing an auto-scaler combining reactive and proactivecontrol methods, and to be able to handle long running requests, e.g., tasks runningfor longer than the actuation interval, in a cloud. We compare the performance ofour algorithms with state-of-the-art auto-scalers and evaluate the controllers’ perfor-mance with a set of workloads. As any controller is designed with an assumptionon the operating conditions and system dynamics, the performance of an auto-scalervaries with different workloads.In order to better understand the workload dynamics and evolution, we analyze a6-years long workload trace of the sixth most popular Internet website. In addition,we analyze a workload from one of the largest Video-on-Demand streaming servicesin Sweden. We discuss the popularity of objects served by the two services, the spikesin the two workloads, and the invariants in the workloads. We also introduce, a mea-sure for the disorder in a workload, i.e., the amount of burstiness. The measure isbased on Sample Entropy, an empirical statistic used in biomedical signal processingto characterize biomedical signals. The introduced measure can be used to charac-terize the workloads based on their burstiness profiles. We compare our introducedmeasure with the literature on quantifying burstiness in a server workload, and showthe advantages of our introduced measure.To better understand the tradeoffs between using different auto-scalers with differ-ent workloads, we design a framework to compare auto-scalers and give probabilisticguarantees on the performance in worst-case scenarios. Using different evaluation cri-teria and more than 700 workload traces, we compare six state-of-the-art auto-scalersthat we believe represent the development of the field in the past 8 years. Knowingthat the auto-scalers’ performance depends on the workloads, we design a workloadanalysis and classification tool that assigns a workload to its most suitable elasticitycontroller out of a set of implemented controllers. The tool has two main components;an analyzer, and a classifier. The analyzer analyzes a workload and feeds the analysisresults to the classifier. The classifier assigns a workload to the most suitable elasticitycontroller based on the workload characteristics and a set of predefined business levelobjectives. The tool is evaluated with a set of collected real workloads, and a set ofgenerated synthetic workloads. Our evaluation results shows that the tool can help acloud provider to improve the QoS provided to the customers.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2015. p. 16
Series
Report / UMINF, ISSN 0348-0542 ; 15.09
Keywords
cloud computing, autoscaling, workloads, performance modeling, controller design
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-108398 (URN)978-91-7601-330-4 (ISBN)
Public defence
2015-10-02, N360, Naturveterhuset Building, Umeå University, Umeå, 14:00 (English)
Opponent
Supervisors
Funder
EU, European Research CouncilSwedish Research Council
Available from: 2015-09-11 Created: 2015-09-10 Last updated: 2018-06-07Bibliographically approved
Ali-Eldin, A., Rezaie, A., Mehta, A., Razroev, S., Sjöstedt-de Luna, S., Seleznjev, O., . . . Elmroth, E. (2014). How will your workload look like in 6 years?: Analyzing Wikimedia's workload. In: Lisa O’Conner (Ed.), Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014): . Paper presented at IC2E 2014, IEEE International Conference on Cloud Engineering, Boston, Massachusetts, 11-14 March 2014 (pp. 349-354). IEEE Computer Society
Open this publication in new window or tab >>How will your workload look like in 6 years?: Analyzing Wikimedia's workload
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2014 (English)In: Proceedings of the 2014 IEEE International Conference on Cloud Engineering (IC2E 2014) / [ed] Lisa O’Conner, IEEE Computer Society, 2014, p. 349-354Conference paper, Published paper (Refereed)
Abstract [en]

Accurate understanding of workloads is key to efficient cloud resource management as well as to the design of large-scale applications. We analyze and model the workload of Wikipedia, one of the world's largest web sites. With descriptive statistics, time-series analysis, and polynomial splines, we study the trend and seasonality of the workload, its evolution over the years, and also investigate patterns in page popularity. Our results indicate that the workload is highly predictable with a strong seasonality. Our short term prediction algorithm is able to predict the workload with a Mean Absolute Percentage Error of around 2%.

Place, publisher, year, edition, pages
IEEE Computer Society, 2014
Series
IEEE, ISSN 2373-3845
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Computing Science
Identifiers
urn:nbn:se:umu:diva-87235 (URN)10.1109/IC2E.2014.50 (DOI)000361018600043 ()978-1-4799-3766-0 (ISBN)
Conference
IC2E 2014, IEEE International Conference on Cloud Engineering, Boston, Massachusetts, 11-14 March 2014
Funder
Swedish Research Council, C0590801eSSENCE - An eScience Collaboration
Available from: 2014-03-25 Created: 2014-03-25 Last updated: 2018-06-08Bibliographically approved
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