Joint Task Dispatching and Bandwidth Allocation with Hard Deadlines in Distributed Serverless Edge Computing Systems

Serverless computing lifts the burden of infrastructure maintenance from application developers and also reduces the usage cost of cloud/edge computing platforms. However, the pay-as-you-go pricing model offered by serverless computing complicates the task dispatching problem in serverless computing...

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Bibliographic Details
Published in:Journal of grid computing Vol. 22; no. 2; p. 51
Main Authors: Sun, Yuan, Zhang, Chen, Huang, Tao
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.06.2024
Springer Nature B.V
Subjects:
Algorithms
Bandwidths
Computer Science
Edge computing
Management of Computing and Information Systems
Network latency
Nodes
Permutations
Pricing
Processor Architectures
User Interfaces and Human Computer Interaction
Hard deadlines
Bandwidth allocation
Serverless computing
Task dispatching
Edge computing
ISSN:1570-7873, 1572-9184
Online Access:Get full text
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Summary:Serverless computing lifts the burden of infrastructure maintenance from application developers and also reduces the usage cost of cloud/edge computing platforms. However, the pay-as-you-go pricing model offered by serverless computing complicates the task dispatching problem in serverless computing systems. Specifically, when the pay-as-you-go pricing model is adopted, the task response latency is not simply the task execution latency, but the cold-start latency and the container image downloading latency should also be considered as part of the task response latency. In this paper, we focus on the joint task dispatching and bandwidth allocation problem with hard deadlines in distributed serverless edge computing systems. To maximize the overall profit, a new algorithm called PN-GRD is presented. PN-GRD first uses a Pointer Network model that is well trained offline to inference a task permutation, which is used to determine the task priority. Then, multiple edge node selection steps are carried out to select an edge node for each task according to the task priority. The final task dispatching and bandwidth allocation is obtained once the best edge node for every task is chosen or none of the edge nodes is suitable to run a task. We validate the performance of PN-GRD through simulations. The results show that PN-GRD outperforms practical baselines in terms of the average overall profit.
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ISSN:1570-7873
1572-9184
DOI:10.1007/s10723-024-09770-6