Scheduling for Cloud-Based Computing Systems to Support Soft Real-Time Applications

TL;DR

This paper analyzes resource allocation strategies in cloud computing systems supporting soft real-time applications, proposing bounds and policies that improve efficiency and reduce resource usage under QoS constraints.

Contribution

It introduces bounds on feasible QoS regions and evaluates simple priority-based scheduling policies for heterogeneous soft real-time workloads.

Findings

Priority-based greedy scheduling performs near-optimally with large deadlines.

Resource savings are significant compared to reservation-based approaches.

Policies are effective especially when resources are abundant or deadlines are lenient.

Abstract

Cloud-based computing infrastructure provides an efficient means to support real-time processing workloads, e.g., virtualized base station processing, and collaborative video conferencing. This paper addresses resource allocation for a computing system with multiple resources supporting heterogeneous soft real-time applications subject to Quality of Service (QoS) constraints on failures to meet processing deadlines. We develop a general outer bound on the feasible QoS region for non-clairvoyant resource allocation policies, and an inner bound for a natural class of policies based on dynamically prioritizing applications' tasks by favoring those with the largest (QoS) deficits. This provides an avenue to study the efficiency of two natural resource allocation policies: (1) priority-based greedy task scheduling for applications with variable workloads, and (2) priority-based task selection and optimal scheduling for applications with deterministic workloads. The near-optimality of these simple policies emerges when task processing deadlines are relatively large and/or when the number of compute resources is large. Analysis and simulations show substantial resource savings for such policies over reservation-based designs.