Abstract
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.
Original language | English (US) |
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Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain Duration: Dec 7 2021 → Dec 11 2021 |
Keywords
- data-driven
- Edge computing
- load balancing
- microservice
- resource provisioning
- robustness
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Hardware and Architecture
- Signal Processing