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Why Cloud Applications Are not Ready for the Edge (yet)
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-9156-3364
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0003-0106-3049
Umeå University, Faculty of Science and Technology, Department of Computing Science.
2019 (English)In: Proceedings of the 4th ACM/IEEE Symposium on Edge Computing, IEEE, 2019, p. 250-263Conference 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. Their wide resource distribution enables MECs to 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, they can be flexibly deployed in various configurations that combines resources from both centralized datacenters and edge locations. In principle, such applications should therefore be well-placed to exploit the advantages of MECs so as to reduce service response times.

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. p. 250-263
Keywords [en]
Mobile Edge Clouds, Edge Latency, Mobile Application Development, Micro-service, Profiling
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:umu:diva-162930DOI: 10.1145/3318216.3363298ISBN: 978-1-4503-6733-2 (print)OAI: oai:DiVA.org:umu-162930DiVA, id: diva2:1347841
Conference
The Fourth ACM/IEEE Symposium on Edge Computing, Washington DC, November 7–9, 2019
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2019-09-02 Created: 2019-09-02 Last updated: 2019-11-15Bibliographically approved
In thesis
1. Autonomous resource management for Mobile Edge Clouds
Open this publication in new window or tab >>Autonomous resource management for Mobile Edge Clouds
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:nbn:se:umu:diva-162924 (URN)9789178551163 (ISBN)
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

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Nguyen, Chanh Le TanMehta, AmardeepKlein, CristianElmroth, Erik

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