Harnessing Virtual Machine Resource Control for Job Management

Virtual machine technology promises important benefits for grid computing and cluster batch job systems, including improved isolation, customizable workspaces, and support for checkpointing and migration. One way to gain these benefits is to “drill holes” in existing batch computing systems; however, we believe these new capabilities warrant a rethinking of the architectures of exist ing systems. We propose separating resource control for VMs into a new foundational layer that focuses narrowly on resource management. We present JAWS, a new batch computing service that is built as a thin-layer above a resource control plane that enables it to share a common pool of networked cluster resources with other cluster services. JAWS executes jobs within isolated virtual machine workspaces. We discuss how exposing resource control allows JAWS to leverage VM-based resource isolation as a means to learn models of application behavior, and use those models to guide scheduling policies for efficient resource