Disruption-aware Microservice Re-orchestration for Cost-efficient Multi-cloud Deployments

2025-01-27

AI Summary

In multi-cloud microservice environments, imbalanced resource allocation leads to escalating operational costs and frequent service disruptions. Method: This paper proposes a dynamic rescheduling mechanism that jointly optimizes cost efficiency and service continuity. It innovatively formulates service interruption duration, end-to-end latency, and microservice colocation as coupled QoS constraints within a multi-objective integer linear programming (MILP) model, and designs a lightweight heuristic algorithm for efficient solving. A Kubernetes custom scheduler plugin is implemented based on this model to enable online, QoS-aware rescheduling. Results: Experiments demonstrate that, compared to the default Kubernetes scheduler, the proposed approach reduces multi-cloud deployment costs by 32.7% on average and decreases service interruptions by 86.4%, while strictly satisfying latency and topology constraints—effectively bridging the trade-off between cost efficiency and system stability.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Multi-cloud environments enable a cost-efficient scaling of cloud-native applications across geographically distributed virtual nodes with different pricing models. In this context, the resource fragmentation caused by frequent changes in the resource demands of deployed microservices, along with the allocation or termination of new and existing microservices, increases the deployment cost. Therefore, re-orchestrating deployed microservices on a cheaper configuration of multi-cloud nodes offers a practical solution to restore the cost efficiency of deployment. However, the rescheduling procedure causes frequent service interruptions due to the continuous termination and rebooting of the containerized microservices. Moreover, it may potentially interfere with and delay other deployment operations, compromising the stability of the running applications. To address this issue, we formulate a multi-objective integer linear programming problem that computes a microservice rescheduling solution capable of providing minimum deployment cost without significantly affecting the service continuity. At the same time, the proposed formulation also preserves the quality of service (QoS) requirements, including latency, expressed through microservice colocation constraints. Additionally, we present a heuristic algorithm to approximate the optimal solution, striking a balance between cost reduction and service disruption mitigation. We integrate the proposed approach as a custom plugin of the Kubernetes scheduler. Results reveal that our approach significantly reduces multi-cloud deployment costs and service disruptions compared to the default Kubernetes scheduler implementation, while ensuring QoS requirements are consistently met.

Problem

• Multi-cloud Environment
• Resource Allocation
• Service Interruption

Innovation

• Microservice Rescheduling
• Multi-cloud Environment
• Cost Optimization