A resource management technique for processing deadline-constrained multi-stage workflows

Abstract

Abstract The use of cloud computing that provides resources on demand to various types of users, including enterprises as well as engineering and scientific institutions, is growing rapidly. An effective resource management middleware is necessary to harness the power of the underlying distributed hardware in a cloud. Two of the key operations provided by a resource manager are resource allocation (matchmaking) and scheduling. This paper concerns the problem of matchmaking and scheduling an open stream of multi-stage jobs (or workflows) with Service Level Agreements (SLAs) on a cloud or cluster. Multi-stage jobs require service from multiple system resources and are characterized by multiple phases of execution. This paper presents a resource allocation and scheduling technique called RM-DCWF: Resource Management Technique for Deadline-constrained Workflows that can efficiently matchmake and schedule an open stream of multi-stage jobs with SLAs, where each SLA is characterized by an earliest start time, an execution time, and a deadline. A rigorous simulation-based performance evaluation of RM-DCWF is conducted using synthetic workloads derived from real scientific workflows. In addition, the impact of various system and workload parameters on system performance is investigated. The results of this performance evaluation demonstrate the effectiveness of RM-DCWF as captured in a low number of jobs missing their deadlines.

Keywords

• Resource allocation and scheduling on clouds
• Multi-stage jobs with SLAs
• Workflows with SLAs
• Jobs with deadlines