Scheduling Jobs in the Cloud Using On-Demand and Reserved Instances

Abstract

Abstract. Deploying applications in leased cloud infrastructure is increasingly considered by a variety of business and service integrators. However, the challenge of selecting the leasing strategy -larger or faster instances? on-demand or reserved instances? etc.-and to configure the leasing strategy with appropriate scheduling policies is still daunting for the (potential) cloud user. In this work, we investigate leasing strategies and their policies from a broker's perspective. We propose, CoH, a family of Cloud-based, online Hybrid scheduling policies that minimizes rental cost by making use of both on-demand and reserved instances. We formulate the resource provisioning and job allocation policies as Integer Programming problems. As the policies need to be executed online, we limit the time to explore the optimal solution of the integer program, and compare the obtained solution with various heuristics-based policies; then automatically pick the best one. We show, via simulation and using multiple real-world traces, that the hybrid leasing policy can obtain significantly lower cost than typical heuristics-based policies.

IaaS reserved contract procurement optimisation with load prediction

“…Another limitation is that the workload traces are shaped by the limited resources available in the cluster or grid. We found traces that do not exhibit these limitations at Quantcast, 3 an advertising company that tracks page views on a large number of websites. Quantcast's page view reports are available for free on their website.…”

“…Shen et al [3] use Integer Programming to develop a cloudbased online hybrid scheduling policy for both on-demand and reserved instances, given the future workload distribution. The derivation of this predicted distribution however is not discussed.…”

Business-Driven Long-Term Capacity Planning for SaaS Applications

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Massivizing online games using cloud computing: A vision

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