Achieving Efficient Distributed Scheduling with Message Queues in the Cloud for Many-Task Computing and High-Performance Computing

Abstract

Task scheduling and execution over large scale, distributed systems plays an important role on achieving good performance and high system utilization. Due to the explosion of parallelism found in today's hardware, applications need to perform over-decomposition to deliver good performance; this over-decomposition is driving job management systems' requirements to support applications with a growing number of tasks with finer granularity. Our goal in this work is to provide a compact, light-weight, scalable, and distributed task execution framework (CloudKon) that builds upon cloud computing building blocks (Amazon EC2, SQS, and DynamoDB). Most of today's state-of-the-art job execution systems have predominantly Master/Slaves architectures, which have inherent limitations, such as scalability issues at extreme scales and single point of failures. On the other hand distributed job management systems are complex, and employ non-trivial load balancing algorithms to maintain good utilization. CloudKon is a distributed job management system that can support both HPC and MTC workloads with millions of tasks/jobs. We compare our work with other state-of-the-art job management systems including Sparrow and MATRIX. The results show that CloudKon delivers better scalability compared to other state-of-the-art systems for some metrics - all with a significantly smaller code-base (5%).