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Collaborative Service Placement, Task Scheduling, and Resource Allocation for Task Offloading With Edge-Cloud Cooperation
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AI
DOI：10.1109/TMC.2022.3219261.png)
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Abstract
En 中文
In an edge-cloud cooperative computing network, the task offloading performance can be further improved by the edge-cloud and edge-edge cooperation, in which the tasks can be offloaded from an edge server to the cloud server or another edge server. Such edge-cloud cooperative task offloading can jointly utilize the resources of all the edge servers and the cloud server. This paper proposes a collaborative service placement, task scheduling, computing resource allocation, and transmission rate allocation scheme for a multi-task and multi-service scenario with edge-cloud cooperation. The objective of our optimization problem is to minimize the total task processing delay while guaranteeing long-term task queuing stability. Considering the high complexity of the original optimization problem, we transform the problem into a deterministic problem for each time slot based on the Lyapunov optimization. Then, we design an iterative algorithm to obtain the whole solution to the problem efficiently based on a hybrid method using multiple numerical techniques. Further, considering the inherent difference in the optimization periods of the service placement, resource allocation, and task scheduling sub-problems, we design a multi-timescale algorithm to solve the sub-problems with different optimization periods. The complexity of the proposed algorithms is analyzed, and extensive simulations are conducted by varying multiple crucial parameters. The superiority of our scheme is demonstrated in comparison with 4 other schemes.
Keywords:
Task analysis
Servers
Resource management
Optimization
Cloud computing
Processor scheduling
Stability analysis
Service placement
task offloading
resource allocation
edge computing
cloud computing
Journal
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9.2
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5.6K
Citations:
1.8W