VM Economics for Java Cloud Computing: An Adaptive and Resource-Aware Java Runtime with Quality-of-Execution

  • Authors:
  • Jose Simão;Luis Veiga

  • Affiliations:
  • -;-

  • Venue:
  • CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
  • Year:
  • 2012

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Abstract

Resource management in Cloud Computing has been dominated by system-level virtual machines to enable the management of resources using a coarse grained approach, largely in a manner independent from the applications running on these infrastructures. However, in such environments, although different types of applications can be running, the resources are delivered equally to each one, missing the opportunity to manage the available resources in a more efficient and application driven way. So, as more applications target managed runtimes, high level virtualization is a relevant abstraction layer that has not been properly explored to enhance resource usage, control, and effectiveness. We propose a VM economics model to manage cloud infrastructures, governed by a quality-of-execution (QoE) metric and implemented by an extended virtual machine. The Adaptive and Resource-Aware Java Virtual Machine (ARA-JVM) is a cluster-enabled virtual execution environment with the ability to monitor base mechanisms (e.g. thread cheduling, garbage collection, memory or network consumptions) to assess application's performance and reconfigure these mechanisms in runtime according to previously defined resource allocation policies. Reconfiguration is driven by incremental gains in quality-of-execution (QoE), used by the VM economics model to balance relative resource savings and perceived performance degradation. Our work in progress, aims to allow cloud providers to exchange resource slices among virtual machines, continually addressing where those resources are required, while being able to determine where the reduction will be more economically effective, i.e., will contribute in lesser extent to performance degradation.