Optimizing Service Level Agreements for Autonomic Cloud Bursting Schedulers

Abstract

The practice of computing across two or more data centers separated by the Internet is growing in popularity due to an explosion in scalable computing demands and pay-as-you-go schemes offered on the cloud. While cloud-bursting is addressing this process of scaling up and down across data centers (i.e. between private and public clouds), offering service level guarantees, is a challenge for inter-cloud computation, particularly for best-effort traffic and large files. The parallel workload we address is real-time and involves inter-cloud processing and analysis of images and documents. In our production printing domain, dedicated processing/network resources are cost-prohibitive. Further, the problem is exacerbated by data intensive computing - we encounter huge file sizes atypical of intercloud parallel processing. To address these problems we propose three flavors of autonomic cloud-bursting schedulers that offer probabilistic guarantees on service levels required by customers (such as speed-up and queue sequence preservation) by adapting to changing workload characteristics, variation in bandwidth and available resources. In particular, these opportunistic schedulers use a quadratic response surface model for processing time in concert with a time-of-day dependent bandwidth predictor to increase the throughput and utilization while simultaneously reducing out-of-sequence completions for a document processing workload.