Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load
Data-Driven Edge Resource Provisioning for Inter-Dependent Microservices with Dynamic Load
复制标题
DOI：
10.1109/globecom46510.2021.9685155
复制
发表时间：
2021-12
期刊：
影响因子：
--
通讯作者：
Ruozhou Yu;Szu-Yu Lo;Fangtong Zhou;G. Xue
中科院分区：
文献类型：
--
作者：
Ruozhou Yu;Szu-Yu Lo;Fangtong Zhou;G. Xue
本文面对不确定且动态的地理分布的需求，研究如何为基于微服务的应用程序（MSA）提供边缘计算和网络资源。分布式微服务组件之间的复杂相互依存关系使MSA的负载平衡极具挑战性，而动态的地理分布要求加剧了负载失衡，因此也使拥挤和绩效损失。在本文中，我们开发了一个边缘资源供应模型，该模型可以准确捕获微服务之间的相互依赖性及其对跨计算和通信资源的负载平衡的影响。我们还提出了一种强大的配方，该配方采用明确的风险估计和优化，以对冲潜在的最严重的负载波动，并具有受控的鲁棒性资源权衡。利用数据驱动的方法，我们提供了一种解决方案，该解决方案通过过去负载地理分布的测量数据提供风险估计。具有现实世界数据集的模拟已经验证了我们的解决方案提供了MSA中至关重要的鲁棒性，并且与忽略网络或相互依存约束的基线相比，其性能优越。
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.