Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective
Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead...
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| Veröffentlicht in: | Processes 2023-05, Vol.11 (5), p.1560 |
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| container_title | Processes |
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| creator | Liu, Ao Yang, Shaoshi Tan, Jingsheng Liang, Zongze Sun, Jiasen Wen, Tao Yan, Hongyan |
| description | Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes. |
| doi_str_mv | 10.3390/pr11051560 |
| format | Article |
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Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.</description><identifier>ISSN: 2227-9717</identifier><identifier>EISSN: 2227-9717</identifier><identifier>DOI: 10.3390/pr11051560</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Analysis ; Cloud computing ; Combinatorial analysis ; Communication ; Computer centers ; Containerization ; Containers ; Edge computing ; Efficiency ; Internet service providers ; Mathematical models ; Mobile computing ; Multiple objective analysis ; Multitasking ; Optimization ; Packing problem ; Placement ; Resource utilization ; Scheduling ; Servers ; Task scheduling ; Virtualization ; Workflow</subject><ispartof>Processes, 2023-05, Vol.11 (5), p.1560</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. 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| source | MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Algorithms Analysis Cloud computing Combinatorial analysis Communication Computer centers Containerization Containers Edge computing Efficiency Internet service providers Mathematical models Mobile computing Multiple objective analysis Multitasking Optimization Packing problem Placement Resource utilization Scheduling Servers Task scheduling Virtualization Workflow |
| title | Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective |
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