Distributed CPU Scheduling Subject to Nonlinear Constraints

TL;DR

This paper develops distributed algorithms for CPU resource allocation in networks of data centers, ensuring solutions are feasible at all times and addressing nonlinear constraints, quantization, and limited capacities.

Contribution

It introduces new conditions for anytime feasibility in nonlinear constrained distributed optimization and proposes solutions applicable to non-quadratic convex functions.

Findings

Converges over directed and undirected networks.

Handles quantization with epsilon-accurate solutions.

Applicable to CPU balancing in cloud data centers.

Abstract

This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints. In many applications, it is required (or desirable) that the solution be anytime feasible in terms of satisfying the sum-preserving global constraint. Motivated by this, sufficient conditions on the nonlinear mapping for anytime feasibility (or non-asymptotic feasibility) are addressed in this paper. For the two proposed distributed solutions, one converges over directed weight-balanced networks and the other one over undirected networks. In particular, we elaborate on uniform quantization and discuss the notion of {\epsilon}-accurate solution, which gives an estimate of how close we can get to the exact optimizer subject to different quantization levels. This work, further, handles general (possibly non-quadratic) strictly convex objective functions with application to CPU allocation among a cloud of data centers via distributed solutions. The results can be used as a coordination mechanism to optimally balance the tasks and CPU resources among a group of networked servers while addressing quantization or limited server capacity. Index Terms: multi-agent systems, sum-preserving resource allocation, distributed optimization, anytime feasibility