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Title: Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load
This paper studies how to provision edge computing and network resources for complex microservice-based applications (MSAs) in face of uncertain and dynamic geo-distributed demands. The complex inter-dependencies between distributed microservice components make load balancing for MSAs extremely challenging, and the dynamic geo-distributed demands exacerbate load imbalance and consequently congestion and performance loss. In this paper, we develop an edge resource provisioning model that accurately captures the inter-dependencies between microservices and their impact on load balancing across both computation and communication resources. We also propose a robust formulation that employs explicit risk estimation and optimization to hedge against potential worst-case load fluctuations, with controlled robustness-resource trade-off. Utilizing a data-driven approach, we provide a solution that provides risk estimation with measurement data of past load geo-distributions. Simulations with real-world datasets have validated that our solution provides the important robustness crucially needed in MSAs, and performs superiorly compared to baselines that neglect either network or inter-dependency constraints.
Authors:
; ; ;
Award ID(s):
2007391 2045539
Publication Date:
NSF-PAR ID:
10328845
Journal Name:
IEEE GLOBECOM
Page Range or eLocation-ID:
1 to 6
Sponsoring Org:
National Science Foundation
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