A Scalable And Adaptable Allocation Framework For Heterogeneous Resources In A Large Cluster Environment

Finding an appropriate resource to host the next application to be deployed in a Cloud environment can be a nontrivial task. To deliver the appropriate level of service, the functional requirements of the application must be met. Ideally, this process involves filtering the best resource from a number of possible candidates while simultaneously satisfying multiple objectives. If timely responses to resource requests are to be maintained, the sophistication of the filtering mechanism and size of the search space have to be carefully balanced. The quality of the solution will thus not readily scale with growth in cloud resources and filtering complexity. This limitation is becoming more evident with the emergence of hyperscale clouds and the increased complexity needed to accommodate the growing heterogeneity in resources. Moreover, meeting nonfunctional requirements, reflecting the Cloud Service Provider's business objects, is also becoming increasingly critical as service utilization and energy efficiency in a typical cloud deployment are extremely low. This paper proposes a re-examination of the resource allocation problem by proposing a framework to support distributed resource allocation decisions and that can be dynamically populated with strategies to reflect the ever-growing number of diverse objectives as they become evident in the evolving cloud infrastructure.