Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimization

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Provided by: Massachusetts Institute of Technology
Topic: Cloud
Format: PDF
The scheduling of multitask jobs on clouds is an NP-hard problem. The problem becomes even worse when complex workflows are executed on elastic clouds, such as Amazon EC2 or IBM RC2. The main difficulty lies in the large search space and high overhead for generation of optimal schedules, especially for real-time applications with dynamic workloads. In this paper, a new Iterative Ordinal Optimization (IOO) method is proposed. The ordinal optimization method is applied in each iteration to achieve sub-optimal schedules. IOO aims at generating more efficient schedules from a global perspective over a long period. The authors prove through overhead analysis the advantages in time and space efficiency in using the IOO method. The IOO method is designed to adapt to system dynamism to yield suboptimal performance. In cloud experiments on IBM RC2 cloud, they execute 20,000 tasks in LIGO (Laser Interferometer Gravitational-wave Observatory) verification workflow on 128 virtual machines.
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