Paper Info
Title
Edge-cloud Collaborative Heterogeneous Task Scheduling in Multilayer Elastic Optical Networks
Abstract
With the explosive growth of edge applications in the 5G/B5G era, edge-cloud collaboration (ECC) is playing a prominent role in edge service provisioning. For highly diversified edge-cloud collaborative services (ECSs), the joint allocation of heterogeneous computing resources in heterogeneous servers and multi-dimensional underlying optical network resources should be conducted. In this paper, we investigate the heterogeneous task scheduling for ECSs over multilayer elastic optical network (ML-EON), which involves the joint allocation of heterogeneous computing resources in edge and cloud servers and high-dimensional network resources. We propose a Task-Node Matching Score (TNMS) based method, which evaluates the fitness for each mapping tuple between each task in ECS and each substrate node in ML-EON, and adaptively generates a specific matching score for each task-node pair. Furthermore, TNMS is extended with a pre-allocation mechanism (TNMS-Pre) to estimate the costs of multi-dimensional resources in ML-EON for virtual link (VL) mapping. The estimated VL mapping costs are integrated into the matching scores to guide the task placement to be cost-efficient. To guarantee the feasibility, a maximal weight matching (MWM) based method is presented to determine the task placement schemes. Simulation results demonstrate the effectiveness of the adaptive scoring for heterogeneous task placement and the pre-allocation mechanism for reducing the ML-EON costs.
Year
DOI
Venue
2021
10.1109/GLOBECOM46510.2021.9685303
2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM)
Keywords
DocType
ISSN
Multi-access Edge Computing, Elastic Optical Network, Multilayer Network, Heterogeneous Task Placement
Conference
2334-0983
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Zeyuan Yang121.33
Rentao Gu224.03
Zuqing Zhu356673.36
Yuefeng Ji4138.67