Collaborative Resource Management and Workloads Scheduling in Cloud-Assisted Mobile Edge Computing across Timescales

TL;DR

The paper tackles the challenge of limited edge resources in cloud-assisted MEC by formulating a joint optimization of service placement, resource provisioning, and workloads scheduling under budget constraints. It proposes RMWS, a two-timescale framework that uses Gibbs sampling for frame-level service placement, alternating minimization for provisioning and shadow scheduling, and a sub-gradient method for slot-level workload scheduling, with rigorous theoretical guarantees and practical algorithms. Key contributions include a MINLP formulation, convergence properties for the Gibbs sampler, convexity proofs for subproblems, and KKT-based solutions, all validated by extensive simulations showing at least 10% latency improvement over baselines. The approach enables efficient edge-cloud cooperation and robust performance under dynamic workloads and service popularity, highlighting tangible benefits for real-world MEC deployments.

Abstract

Due to the limited resource capacity of edge servers and the high purchase costs of edge resources, service providers are facing the new challenge of how to take full advantage of the constrained edge resources for Internet of Things (IoT) service hosting and task scheduling to maximize system performance. In this paper, we study the joint optimization problem on service placement, resource provisioning, and workloads scheduling under resource and budget constraints, which is formulated as a mixed integer non-linear programming problem. Given that the frequent service placement and resource provisioning will significantly increase system configuration costs and instability, we propose a two-timescale framework for resource management and workloads scheduling, named RMWS. RMWS consists of a Gibbs sampling algorithm and an alternating minimization algorithm to determine the service placement and resource provisioning on large timescales. And a sub-gradient descent method has been designed to solve the workload scheduling challenge on small timescales.We conduct comprehensive experiments under different parameter settings. The RMWS consistently ensures a minimum 10% performance enhancement compared to other algorithms, showcasing its superiority. Theoretical proofs are also provided accordingly.

Figures (10)

• Figure 1: System model.
• Figure 2: Joint optimization across two-timescale.
• Figure 3: The flowchart of the RMWS algorithm
• Figure 4: Response time under different budget constraints
• Figure 5: Response time under different numbers of service types