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Autonomous resource management for Mobile Edge Clouds
Umeå University, Faculty of Science and Technology, Department of Computing Science. (Distributed Systems)ORCID iD: 0000-0002-9156-3364
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Mobile Edge Clouds (MECs) are platforms that complement today's centralized clouds by distributing computing and storage capacity across the edge of the network, in Edge Data Centers (EDCs) located in close proximity to end-users. They are particularly attractive because of their potential benefits for the delivery of bandwidth-hungry, latency-critical applications. However, the control of resource allocation and provisioning in MECs is challenging because of the  heterogeneous distributed resource capacity of EDCs as well as the need for flexibility in application deployment and the dynamic nature of mobile users. To realize the potential of MECs, efficient resource management systems that can deal with these challenges must be designed and built.

This thesis focuses on two problems. The first relates to the fact that it is unrealistic to expect MECs to become successful based solely on MEC-native applications. Thus, to spur the development of MECs, we investigated the benefits MECs can offer to non-MEC-native applications, i.e., applications not specifically engineered for MECs. One class of popular applications that may benefit strongly from deployment on MECs are cloud-native applications, particularly microservice-based applications with high deployment flexibility. We therefore quantified the performance of cloud-native applications deployed using resources from both cloud datacenters and edge locations. We also developed a network communication profiling tool to identify the aspects of these applications that reduce the benefits they derive from deployment on MECs, and proposed design improvements that would allow such applications to better exploit MECs' capabilities.

The second problem examined in this thesis relates to the dynamic nature of resource demand in MECs. To overcome the challenges arising from this dynamicity, we make use of statistical time series models and machine learning techniques to develop two workload prediction models for EDCs that account for both user mobility and the correlation of workload changes among EDCs in close physical proximity.  

Place, publisher, year, edition, pages
Umeå: Institutionen för datavetenskap, Umeå universitet , 2019. , p. 31
Series
Report / UMINF, ISSN 0348-0542 ; 19.07
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-162924ISBN: 9789178551163 (print)OAI: oai:DiVA.org:umu-162924DiVA, id: diva2:1347774
Presentation
2019-09-19, MA121, MIT building, Umeå University, Umeå, 13:15
Opponent
Supervisors
Available from: 2019-09-02 Created: 2019-09-02 Last updated: 2019-09-02Bibliographically approved
List of papers
1. Why Cloud Applications Are not Ready for the Edge (yet)
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: 2019-09-04
2. Location-aware load prediction in edge data centers
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
3. Multivariate LSTM-Based Location-Aware Workload Prediction for Edge Data Centers
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

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