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Publications (10 of 23) Show all publications
Nguyen, C., Klein, C. & Elmroth, E. (2019). Multivariate LSTM-Based Location-Aware Workload Prediction for Edge Data Centers. In: Proceedings, 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing: . Paper presented at CCGrid 2019, 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (IEEE/ACM CCGrid 2019), 14-17 May, Larnaca, Cyprus (pp. 341-350). IEEE
Open this publication in new window or tab >>Multivariate LSTM-Based Location-Aware Workload Prediction for Edge Data Centers
2019 (English)In: Proceedings, 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE, 2019, p. 341-350Conference paper, Published paper (Refereed)
Abstract [en]

Mobile Edge Clouds (MECs) is a promising computing platform to overcome challenges for the success of bandwidth-hungry, latency-critical applications by distributing computing and storage capacity in the edge of the network as Edge Data Centers (EDCs) within the close vicinity of end-users. Due to the heterogeneous distributed resource capacity in EDCs, the application deployment flexibility coupled with the user mobility, MECs bring significant challenges to control resource allocation and provisioning. In order to develop a self-managed system for MECs which efficiently decides how much and when to activate scaling, where to place and migrate services, it is crucial to predict its workload characteristics, including variations over time and locality. To this end, we present a novel location-aware workload predictor for EDCs. Our approach leverages the correlation among workloads of EDCs in a close physical distance and applies multivariate Long Short-Term Memory network to achieve on-line workload predictions for each EDC. The experiments with two real mobility traces show that our proposed approach can achieve better prediction accuracy than a state-of-the art location-unaware method (up to 44%) and a location-aware method (up to 17%). Further, through an intensive performance measurement using various input shaking methods, we substantiate that the proposed approach achieves a reliable and consistent performance.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Mobile Edge Cloud, Edge Data Center, ResourceManagement, Workload Prediction, Location-aware, MachineLearning
National Category
Computer Systems
Research subject
Computer Systems
Identifiers
urn:nbn:se:umu:diva-159540 (URN)10.1109/CCGRID.2019.00048 (DOI)000483058700039 ()2-s2.0-85069517887 (Scopus ID)978-1-7281-0912-1 (ISBN)978-1-7281-0913-8 (ISBN)
Conference
CCGrid 2019, 19th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (IEEE/ACM CCGrid 2019), 14-17 May, Larnaca, Cyprus
Available from: 2019-05-30 Created: 2019-05-30 Last updated: 2019-09-27Bibliographically approved
Nguyen, C. L., Mehta, A., Klein, C. & Elmroth, E. (2019). Why Cloud Applications Are not Ready for the Edge (yet). In: 4th ACM/IEEE Symposium on Edge Computing: . Paper presented at 4th ACM/IEEE Symposium on Edge Computing (SEC 2019). IEEE
Open this publication in new window or tab >>Why Cloud Applications Are not Ready for the Edge (yet)
2019 (English)In: 4th ACM/IEEE Symposium on Edge Computing, IEEE, 2019Conference paper, Published paper (Other academic)
Abstract [en]

Mobile Edge Clouds (MECs) are distributed platforms in which distant data-centers are complemented with computing and storage capacity located at the edge of the network. With such high resource distribution, MECs potentially fulfill the need of low latency and high bandwidth to offer an improved user experience.

As modern cloud applications are increasingly architected as collections of small, independently deployable services, it enables them to be flexibly deployed in various configurations combining resources from both centralized datacenters and edge location. Therefore, one might expect them to be well-placed to benefit from the advantage of MECs in order to reduce the service response time.In this paper, we quantify the benefits of deploying such cloud micro-service applications on MECs. Using two popular benchmarks, we show that, against conventional wisdom, end-to-end latency does not improve significantly even when most application services are deployed in the edge location. We developed a profiler to better understand this phenomenon, allowing us to develop recommendations for adapting applications to MECs. Further, by quantifying the gains of those recommendations, we show that the performance of an application can be made to reach the ideal scenario, in which the latency between an edge datacenter and a remote datacenter has no impact on the application performance.

This work thus presents ways of adapting cloud-native applications to take advantage of MECs and provides guidance for developing MEC-native applications. We believe that both these elements are necessary to drive MEC adoption.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Mobile Edge Clouds, Edge Latency, Mobile Application Development, Micro-service, Profiling
National Category
Computer Sciences
Identifiers
urn:nbn:se:umu:diva-162930 (URN)
Conference
4th ACM/IEEE Symposium on Edge Computing (SEC 2019)
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)
Available from: 2019-09-02 Created: 2019-09-02 Last updated: 2019-10-29
Tesfatsion, S. K., Klein, C. & Tordsson, J. (2018). Virtualization Techniques Compared: Performance, Resource, and Power Usage Overheads in Clouds. Paper presented at ACM/SPEC Internation Conference on Performance Engineering (ICPE).
Open this publication in new window or tab >>Virtualization Techniques Compared: Performance, Resource, and Power Usage Overheads in Clouds
2018 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Virtualization solutions based on hypervisors or containers are enabling technologies

for scalable, flexible, and cost-effective resource sharing. As the fundamental

limitations of each technology are yet to be understood, they need to be regularly

reevaluated to better understand the trade-off provided by latest technological advances.

This paper presents an in-depth quantitative analysis of virtualization

overheads in these two groups of systems and their gaps relative to native environments

based on a diverse set of workloads that stress CPU, memory, storage,

and networking resources. KVM and XEN are used to represent hypervisor-based

virtualization, and LXC and Docker for container-based platforms. The systems

were evaluated with respect to several cloud resource management dimensions including

performance, isolation, resource usage, energy efficiency, start-up time,

and density. Our study is useful both to practitioners to understand the current

state of the technology in order to make the right decision in the selection, operation

and/or design of platforms and to scholars to illustrate how these technologies

evolved over time.

National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-145924 (URN)
Conference
ACM/SPEC Internation Conference on Performance Engineering (ICPE)
Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-06-09
Filieri, A., Maggio, M., Angelopoulos, K., D’ippolito, N., Gerostathopoulos, I., Hempel, A. B., . . . Vogel, T. (2017). Control Strategies for Self-Adaptive Software Systems. ACM Transactions on Autonomous and Adaptive Systems, 11(4), Article ID 24.
Open this publication in new window or tab >>Control Strategies for Self-Adaptive Software Systems
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2017 (English)In: ACM Transactions on Autonomous and Adaptive Systems, ISSN 1556-4665, E-ISSN 1556-4703, Vol. 11, no 4, article id 24Article in journal (Refereed) Published
Abstract [en]

The pervasiveness and growing complexity of software systems are challenging software engineering to design systems that can adapt their behavior to withstand unpredictable, uncertain, and continuously changing execution environments. Control theoretical adaptation mechanisms have received growing interest from the software engineering community in the last few years for their mathematical grounding, allowing formal guarantees on the behavior of the controlled systems. However, most of these mechanisms are tailored to specific applications and can hardly be generalized into broadly applicable software design and development processes.

This article discusses a reference control design process, from goal identification to the verification and validation of the controlled system. A taxonomy of the main control strategies is introduced, analyzing their applicability to software adaptation for both functional and nonfunctional goals. A brief extract on how to deal with uncertainty complements the discussion. Finally, the article highlights a set of open challenges, both for the software engineering and the control theory research communities.

Keywords
Self-adaptive software, control theory, non-functional properties, formal methods
National Category
Software Engineering Control Engineering
Identifiers
urn:nbn:se:umu:diva-135693 (URN)10.1145/3024188 (DOI)000395848000005 ()
Projects
Wallenberg Autonomous Systems and Software Program
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2018-06-09Bibliographically approved
Shahrad, M., Klein, C., Zheng, L., Chiang, M., Elmroth, E. & Wentzlaf, D. (2017). Incentivizing Self-Capping to Increase Cloud Utilization. In: Proceedings of the 2017 Symposium on Cloud Computing (SOCC '17): . Paper presented at ACM Symposium on Cloud Computing 2017 (SoCC '17) (pp. 52-65). Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Incentivizing Self-Capping to Increase Cloud Utilization
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2017 (English)In: Proceedings of the 2017 Symposium on Cloud Computing (SOCC '17), Association for Computing Machinery (ACM), 2017, p. 52-65Conference paper, Published paper (Refereed)
Abstract [en]

Cloud Infrastructure as a Service (IaaS) providers continually seek higher resource utilization to better amortize capital costs. Higher utilization not only can enable higher profit for IaaS providers but also provides a mechanism to raise energy efficiency; therefore creating greener cloud services. Unfortunately, achieving high utilization is difficult mainly due to infrastructure providers needing to maintain spare capacity to service demand fluctuations.

Graceful degradation is a self-adaptation technique originally designed for constructing robust services that survive resource shortages. Previous work has shown that graceful degradation can also be used to improve resource utilization in the cloud by absorbing demand fluctuations and reducing spare capacity. In this work, we build a system and pricing model that enables infrastructure providers to incentivize their tenants to use graceful degradation. By using graceful degradation with an appropriate pricing model, the infrastructure provider can realize higher resource utilization while simultaneously, its tenants can increase their profit. Our proposed solution is based on a hybrid model which guarantees both reserved and peak on-demand capacities over flexible periods. It also includes a global dynamic price pair for capacity which remains uniform during each tenant's Service Level Agreement (SLA) term.

We evaluate our scheme using simulations based on real-world traces and also implement a prototype using RUBiS on the Xen hypervisor as an end-to-end demonstration. Our analysis shows that the proposed scheme never hurts a tenant's net profit, but can improve it by as much as 93%. Simultaneously, it can also improve the effective utilization of contracts from 42% to as high as 99%.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017
Keywords
cloud computing, pricing model, economic incentives, utilization, resource management, dynamic pricing, SLA, IaaS
National Category
Computer Systems Computer Sciences
Research subject
Computer Systems; Computer Science
Identifiers
urn:nbn:se:umu:diva-138268 (URN)10.1145/3127479.3128611 (DOI)000414279000005 ()978-1-4503-5028-0 (ISBN)
Conference
ACM Symposium on Cloud Computing 2017 (SoCC '17)
Projects
Wallenberg Autonomous Systems and Software Program
Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-06-09Bibliographically approved
Lakew, E. B., Papadopoulos, A. V., Maggio, M., Klein, C. & Elmroth, E. (2017). KPI-agnostic Control for Fine-Grained Vertical Elasticity. In: 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID): . Paper presented at 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), MAY 14-17, 2017, Madrid, SPAIN (pp. 589-598). IEEE
Open this publication in new window or tab >>KPI-agnostic Control for Fine-Grained Vertical Elasticity
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2017 (English)In: 2017 17TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON CLUSTER, CLOUD AND GRID COMPUTING (CCGRID), IEEE , 2017, p. 589-598Conference paper, Published paper (Refereed)
Abstract [en]

Applications hosted in the cloud have become indispensable in several contexts, with their performance often being key to business operation and their running costs needing to be minimized. To minimize running costs, most modern virtualization technologies such as Linux Containers, Xen, and KVM offer powerful resource control primitives for individual provisioning - that enable adding or removing of fraction of cores and/or megabytes of memory for as short as few seconds. Despite the technology being ready, there is a lack of proper techniques for fine-grained resource allocation, because there is an inherent challenge in determining the correct composition of resources an application needs, with varying workload, to ensure deterministic performance.

This paper presents a control-based approach for the management of multiple resources, accounting for the resource consumption, together with the application performance, enabling fine-grained vertical elasticity. The control strategy ensures that the application meets the target performance indicators, consuming as less resources as possible. We carried out an extensive set of experiments using different applications – interactive with response-time requirements, as well as non-interactive with throughput desires – by varying the workload mixes of each application over time. The results demonstrate that our solution precisely provides guaranteed performance while at the same time avoiding both resource over- and under-provisioning.

Place, publisher, year, edition, pages
IEEE, 2017
Series
IEEE-ACM International Symposium on Cluster Cloud and Grid Computing, ISSN 2376-4414
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-146250 (URN)10.1109/CCGRID.2017.71 (DOI)000426912900063 ()978-1-5090-6611-7 (ISBN)
Conference
17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), MAY 14-17, 2017, Madrid, SPAIN
Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-06-09Bibliographically approved
Nguyen, C. L., Klein, C. & Elmroth, E. (2017). Location-aware load prediction in edge data centers. In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC): . Paper presented at The 2nd International Conference on Fog and Mobile Edge Computing (FMEC), May 8-11, 2017, Valencia, Spain (pp. 25-31). IEEE
Open this publication in new window or tab >>Location-aware load prediction in edge data centers
2017 (English)In: 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC), IEEE, 2017, p. 25-31Conference paper, Published paper (Other academic)
Abstract [en]

Mobile Edge Cloud (MEC) is a platform complementing traditional centralized clouds, consisting in moving computing and storage capacity closer to users -e. g., as Edge Data Centers (EDC) in base stations -in order to reduce application-level latency and network bandwidth. The bounded coverage radius of base station and limited capacity of each EDC intertwined with user mobility challenge the operator's ability to perform capacity adjustment and planning. To face this challenge, proactive resource provisioning can be performed. The resource usage in each EDC is estimated in advance, which is made available for the decision making to efficiently determine various management actions and ensure that EDCs persistently satisfies the Quality of Service (QoS), while maximizing resource utilization. In this paper, we propose location-aware load prediction. For each EDC, load is not only predicted using its own historical load time series -as done for centralized clouds -but also those of its neighbor EDCs. We employ Vector Autoregression Model (VAR) in which the correlation among adjacent EDCs load time series are exploited. We evaluate our approach using real world mobility traces to simulate load in each EDC and conduct various experiments to evaluate the proposed algorithm. Result shows that our proposed algorithm is able to achieve an average accuracy of up to 93% on EDCs with substantial average load, which slightly improves prediction by 4.3% compared to the state-of-the-art approach. Considering the expected scale of MEC, this translates to substantial cost savings e. g., servers can be shutdown without QoS violation.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
Workload Prediction, Proactive Resource Management, Mobile Edge Cloud, VAR Model, User Mobility
National Category
Computer Systems
Identifiers
urn:nbn:se:umu:diva-135700 (URN)000411731700004 ()978-1-5386-2859-1 (ISBN)
Conference
The 2nd International Conference on Fog and Mobile Edge Computing (FMEC), May 8-11, 2017, Valencia, Spain
Projects
WASP
Available from: 2017-06-02 Created: 2017-06-02 Last updated: 2019-09-02Bibliographically approved
Papadopoulos, A. V., Klein, C., Maggio, M., Dürango, J., Dellkrantz, M., Hernández-Rodriguez, F., . . . Årzén, K.-E. (2016). Control-based load-balancing techniques: Analysis and performance evaluation via a randomized optimization approach. Control Engineering Practice, 52, 24-34
Open this publication in new window or tab >>Control-based load-balancing techniques: Analysis and performance evaluation via a randomized optimization approach
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2016 (English)In: Control Engineering Practice, ISSN 0967-0661, E-ISSN 1873-6939, Vol. 52, p. 24-34Article in journal (Refereed) Published
Abstract [en]

Cloud applications are often subject to unexpected events like flashcrowds and hardware failures. Users that expect a predictable behavior may abandon an unresponsive application when these events occur. Researchers and engineers addressed this problem on two separate fronts: first, they introduced replicas - copies of the application with the same functionality - for redundancy and scalability; second, they added a self-adaptive feature called brownout inside cloud applications to bound response times by modulating user experience. The presence of multiple replicas requires a dedicated component to direct incoming traffic: a load-balancer. Existing load-balancing strategies based on response times interfere with the response time controller developed for brownout-compliant applications. In fact, the brownout approach bounds response times using a control action. Hence, the response time, that was used to aid load-balancing decision, is not a good indicator of how well a replica is performing. To fix this issue, this paper reviews some proposal for brownout-aware load-balancing and provides a comprehensive experimental evaluation that compares them. To provide formal guarantees on the load balancing performance, we use a randomized optimization approach and apply the scenario theory. We perform an extensive set of experiments on a real machine, extending the popular lighttpd web server and load-balancer, and obtaining a production-ready implementation. Experimental results show an improvement of the user experience over Shortest Queue First (SQF)-believed to be near-optimal in the non-adaptive case. The improved user experience is obtained preserving the response time predictability.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Load-balancing, Randomized optimization, Cloud control
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:umu:diva-119368 (URN)10.1016/j.conengprac.2016.03.020 (DOI)000377740300003 ()
Funder
Swedish Research Council, Cloud ControlSwedish Research Council, Power and temperature control for large-scale computing infrastructuresELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2016-04-18 Created: 2016-04-18 Last updated: 2018-06-07Bibliographically approved
Mehta, A., Tärneberg, W., Klein, C., Tordsson, J., Kihl, M. & Elmroth, E. (2016). How beneficial are intermediate layer Data Centers in Mobile Edge Networks?. In: Sameh Elnikety, Peter R. Lewis and Christian Müller-Schloer (Ed.), 2016 IEEE 1st International Workshops on Foundations and Applications of Self-* Systems: . Paper presented at FAS* Foundations and Applications of Self* Systems University of Augsburg, Augsburg, Germany, 12-16 September 2016 (pp. 222-229).
Open this publication in new window or tab >>How beneficial are intermediate layer Data Centers in Mobile Edge Networks?
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2016 (English)In: 2016 IEEE 1st International Workshops on Foundations and Applications of Self-* Systems / [ed] Sameh Elnikety, Peter R. Lewis and Christian Müller-Schloer, 2016, p. 222-229Conference paper, Published paper (Refereed)
Abstract [en]

To reduce the congestion due to the future bandwidth-hungry applications in domains such as Health care, Internet of Things (IoT), etc., we study the benefit of introducing additional Data Centers (DCs) closer to the network edge for the optimal application placement. Our study shows that the edge layer DCs in a Mobile Edge Network (MEN) infrastructure is cost beneficial for the bandwidth-hungry applications having their strong demand locality and in the scenarios where large capacity is deployed at the edge layer DCs. The cost savings for such applications can go up to 67%. Additional intermediate layer DCs close to the root DC can be marginally cost beneficial for the compute intensive applications with medium or low demand locality. Hence, a Telecom Network Operator should start building an edge DC first having capacity up to hundreds of servers at the network edge to cater the emerging bandwidth-hungry applications and to minimize its operational cost.

National Category
Communication Systems
Identifiers
urn:nbn:se:umu:diva-125640 (URN)10.1109/FAS-W.2016.55 (DOI)000391523100042 ()978-1-5090-3651-6 (ISBN)
Conference
FAS* Foundations and Applications of Self* Systems University of Augsburg, Augsburg, Germany, 12-16 September 2016
Available from: 2016-09-13 Created: 2016-09-13 Last updated: 2018-09-04Bibliographically approved
Chaudhry, T., Doblander, C., Dammer, A., Klein, C. & Jacobsen, H.-A. (2016). Retrofitting Admission Control in an Internet-Scale Application.
Open this publication in new window or tab >>Retrofitting Admission Control in an Internet-Scale Application
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2016 (English)Report (Other academic)
Abstract [en]

In this paper we propose a methodology to retrofit admission control in an Internet-scale, production application. Admission control requires less effort to improve the availability of an application, in particular when making it scalable is costly. This can occur due to the integration of 3rd-party legacy code or handling large amounts of data, and is further motivated by lean thinking, which argues for building a minimum viable product to discover customer requirements.

Our main contribution consists in a method to generate an amplified workload, that is realistic enough to test all kinds of what-if scenarios, but does not require an exhaustive transition matrix. This workload generator can then be used to iteratively stress-test the application, identify the next bottleneck and add admission control.

To illustrate the usefulness of the approach, we report on our experience with adding admission control within SimScale, a Software-as-a-Service start-up for engineering simulations, that already features 50,000 users.

Publisher
p. 11
Series
Report / UMINF, ISSN 0348-0542 ; 16.17
Keywords
cloud, admission control, performance, Software-as-a-Service, software engineering
National Category
Computer Systems Software Engineering
Research subject
Computer Systems; Computer Science
Identifiers
urn:nbn:se:umu:diva-125672 (URN)
Available from: 2016-09-14 Created: 2016-09-14 Last updated: 2018-06-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0106-3049

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