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Characterization and modeling of an edge computing mixed reality workload
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7300-3603
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-1485-0802
2020 (English)In: Journal of Cloud Computing: Advances, Systems and Applications, E-ISSN 2192-113X, Vol. 9, no 1, article id 46Article in journal (Refereed) Published
Abstract [en]

The edge computing paradigm comes with a promise of lower application latency compared to the cloud. Moreover, offloading user device computations to the edge enables running demanding applications on resource-constrained mobile end devices. However, there is a lack of workload models specific to edge offloading using applications as their basis.In this work, we build upon the reconfigurable open-source mixed reality (MR) framework MR-Leo as a vehicle to study resource utilisation and quality of service for a time-critical mobile application that would have to rely on the edge to be widely deployed. We perform experiments to aid estimating the resource footprint and the generated load by MR-Leo, and propose an application model and a statistical workload model for it. The idea is that such empirically-driven models can be the basis of evaluations of edge algorithms within simulation or analytical studies.A comparison with a workload model used in a recent work shows that the computational demand of MR-Leo exhibits very different characteristics from those assumed for MR applications earlier.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 9, no 1, article id 46
Keywords [en]
Edge; fog computing; Mixed reality; Open-source; Empirical performance evaluation; Workload characterization and modeling; Application instrumentation for data collection; Resource footprint
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-169226DOI: 10.1186/s13677-020-00190-xISI: 000560710500001Scopus ID: 2-s2.0-85089493866OAI: oai:DiVA.org:liu-169226DiVA, id: diva2:1466638
Note

Funding Agencies|Swedish National Graduate School in Computer Science (CUGS)

Available from: 2020-09-12 Created: 2020-09-12 Last updated: 2024-09-02Bibliographically approved
In thesis
1. Orchestrating a Resource-aware Edge
Open this publication in new window or tab >>Orchestrating a Resource-aware Edge
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

More and more services are moving to the cloud, attracted by the promise of unlimited resources that are accessible anytime, and are managed by someone else. However, hosting every type of service in large cloud datacenters is not possible or suitable, as some emerging applications have stringent latency or privacy requirements, while also handling huge amounts of data. Therefore, in recent years, a new paradigm has been proposed to address the needs of these applications: the edge computing paradigm. Resources provided at the edge (e.g., for computation and communication) are constrained, hence resource management is of crucial importance.

The incoming load to the edge infrastructure varies both in time and space. Managing the edge infrastructure so that the appropriate resources are available at the required time and location is called orchestrating. This is especially challenging in case of sudden load spikes and when the orchestration impact itself has to be limited. This thesis enables edge computing orchestration with increased resource-awareness by contributing with methods, techniques, and concepts for edge resource management. First, it proposes methods to better understand the edge resource demand. Second, it provides solutions on the supply side for orchestrating edge resources with different characteristics in order to serve edge applications with satisfactory quality of service. Finally, the thesis includes a critical perspective on the paradigm, by considering sustainability challenges.

To understand the demand patterns, the thesis presents a methodology for categorizing the large variety of use cases that are proposed in the literature as potential applications for edge computing. The thesis also proposes methods for characterizing and modeling applications, as well as for gathering traces from real applications and analyzing them. These different approaches are applied to a prototype from a typical edge application domain: Mixed Reality. The important insight here is that application descriptions or models that are not based on a real application may not be giving an accurate picture of the load. This can drive incorrect decisions about what should be done on the supply side and thus waste resources.

Regarding resource supply, the thesis proposes two orchestration frameworks for managing edge resources and successfully dealing with load spikes while avoiding over-provisioning. The first one utilizes mobile edge devices while the second leverages the concept of spare devices. Then, focusing on the request placement part of orchestration, the thesis formalizes it in the case of applications structured as chains of functions (so-called microservices) as an instance of the Traveling Purchaser Problem and solves it using Integer Linear Programming. Two different energy metrics influencing request placement decisions are proposed and evaluated.

Finally, the thesis explores further resource awareness. Sustainability challenges that should be highlighted more within edge computing are collected. Among those related to resource use, the strategy of sufficiency is promoted as a way forward. It involves aiming at only using the needed resources (no more, no less) with a goal of reducing resource usage. Different tools to adopt it are proposed and their use demonstrated through a case study.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. p. 96
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2403
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-207114 (URN)10.3384/9789180757485 (DOI)9789180757478 (ISBN)9789180757485 (ISBN)
Public defence
2024-10-04, Ada Lovelace, B Building, Campus Valla, Linköping, 09:15 (English)
Opponent
Supervisors
Note

Funding agency: The Swedish National Graduate School of Computer Science (CUGS)

Available from: 2024-09-02 Created: 2024-09-02 Last updated: 2024-12-09Bibliographically approved

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Toczé, KlervieNadjm-Tehrani, Simin

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Toczé, KlervieLindqvist, JohanNadjm-Tehrani, Simin
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