Showing papers by "David Irwin published in 2006"

Ensemble-level Power Management for Dense Blade Servers

Abstract: One of the key challenges for high-density servers (e.g., blades) is the increased costs in addressing the power and heat density associated with compaction. Prior approaches have mainly focused on reducing the heat generated at the level of an individual server. In contrast, this work proposes power efficiencies at a larger scale by leveraging statistical properties of concurrent resource usage across a collection of systems ("ensemble"). Specifically, we discuss an implementation of this approach at the blade enclosure level to monitor and manage the power across the individual blades in a chassis. Our approach requires low-cost hardware modifications and relatively simple software support. We evaluate our architecture through both prototyping and simulation. For workloads representing 132 servers from nine different enterprise deployments, we show significant power budget reductions at performances comparable to conventional systems.

Sharing networked resources with brokered leases

Abstract: This paper presents the design and implementation of Shirako, a system for on-demand leasing of shared networked resources. Shirako is a prototype of a service-oriented architecture for resource providers and consumers to negotiate access to resources over time, arbitrated by brokers. It is based on a general lease abstraction: a lease represents a contract for some quantity of a typed resource over an interval of time. Resource types have attributes that define their performance behavior and degree of isolation. Shirako decouples fundamental leasing mechanisms from resource allocation policies and the details of managing a specific resource or service. It offers an extensible interface for custom resource management policies and new resource types. We show how Shirako enables applications to lease groups of resources across multiple autonomous sites, adapt to the dynamics of resource competition and changing load, and guide configuration and deployment. Experiments with the prototype quantify the costs and scalability of the leasing mechanisms, and the impact of lease terms on fidelity and adaptation.

Virtual Machine Hosting for Networked Clusters: Building the Foundations for "Autonomic" Orchestration

Abstract: Virtualization technology offers powerful resource management mechanisms, including performance-isolating resource schedulers, live migration, and suspend/resume. But how should networked virtual computing systems use these mechanisms? A grand challenge is to devise practical policies to drive these mechanisms in a self-managing or .autonomic . system, without relying on human operators. This paper explores architectural and algorithmic issues for resource management policy and orchestration in Shirako, a system for on-demand leasing of shared networked resources in federated clusters. Shirako enables a flexible factoring of resource management functions across the participants in a federated system, to accommodate a range of models of distributed virtual computing. We present extensions to Shirako to provision fine-grained virtual machine .slivers. and drive virtual machine migration. We illustrate the interactions of provisioning and placement/migration policies, and their impact.

Toward a doctrine of containment: grid hosting with adaptive resource control

Abstract: Grid computing environments need secure resource control and predictable service quality in order to be sustainable. We propose a grid hosting model in which independent, self-contained grid deployments run within isolated containers on shared resource provider sites. Sites and hosted grids interact via an underlying resource control plane to manage a dynamic binding of computational resources to containers. We present a prototype grid hosting system, in which a set of independent Globus grids share a network of cluster sites. Each grid instance runs a coordinator that leases and configures cluster resources for its grid on demand. Experiments demonstrate adaptive provisioning of cluster resources and contrast job-level and container-level resource management in the context of two grid application managers.