Effective Cost Reduction for Elastic Clouds under Spot Instance Pricing Through Adaptive Checkpointing

摘要

Cloud computing users are most concerned about the application turnaround time and the monetary cost involved. For lower monetary costs, less expensive services, like spot instances offered by Amazon, are often made available, albeit to their relatively frequent resource unavailability that leads to on-going execution being evicted, thereby undercutting execution performance. Meanwhile, multithreaded applications may take advantage of elastic resource availability and cost fluctuation inherent to the systems. However, their potential gains on utilizing spot instances would be contingent upon how they handle resource unavailability, calling for an effective checkpointing. This work presents design and implementation of our enhanced adaptive incremental checkpointing (EAIC) for multithreaded applications on the RaaS clouds under spot instance pricing. EAIC model takes into account spot instance revocation events, besides hardware failures, for fast and accurately predicting the desirable points of time to take checkpoints so as to markedly reduce the expected job turnaround time and the monetary cost. The experimental results from our established test bed on PARSEC benchmarks under real spot instance price traces from Amazon EC2 show that EAIC lowers both the application turnaround time and the monetary cost markedly (by up to 58% and 59%, respectively) in comparison to its recent checkpointing counterpart.