B. Lindell

Bio: B. Lindell is an academic researcher. The author has contributed to research in topics: Scheduling (computing) & Integrated services. The author has an hindex of 1, co-authored 1 publications receiving 733 citations.

A distributed resource management architecture that supports advance reservations and co-allocation

Abstract: The realization of end-to-end quality of service (QoS) guarantees in emerging network-based applications requires mechanisms that support first dynamic discovery and then advance or immediate reservation of resources that will often be heterogeneous in type and implementation and independently controlled and administered. We propose the Globus Architecture for Reservation and Allocation (GARA) to address these four issues. GARA treats both reservations and computational elements such as processes, network flows, and memory blocks as first-class entities, allowing them to be created, monitored, and managed independently and uniformly. It simplifies management of heterogeneous resource types by defining uniform mechanisms for computers, networks, disk, memory, and other resources. Layering on these standard mechanisms, GARA enables the construction of application-level co-reservation and co-allocation libraries that applications can use to dynamically assemble collections of resources, guided by both application QoS requirements and the local administration policy of individual resources. We describe a prototype GARA implementation that supports three different resource type-parallel computers, individual CPU under control of the dynamic soft real-time scheduler, and integrated services networks, and provide performance results that quantify the costs of our techniques.

The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration

Abstract: In both e-business and e-science, we often need to integrate services across distributed, heterogeneous, dynamic “virtual organizations” formed from the disparate resources within a single enterprise and/or from external resource sharing and service provider relationships. This integration can be technically challenging because of the need to achieve various qualities of service when running on top of different native platforms. We present an Open Grid Services Architecture that addresses these challenges. Building on concepts and technologies from the Grid and Web services communities, this architecture defines a uniform exposed service semantics (the Grid service); defines standard mechanisms for creating, naming, and discovering transient Grid service instances; provides location transparency and multiple protocol bindings for service instances; and supports integration with underlying native platform facilities. The Open Grid Services Architecture also defines, in terms of Web Services Description Language (WSDL) interfaces and associated conventions, mechanisms required for creating and composing sophisticated distributed systems, including lifetime management, change management, and notification. Service bindings can support reliable invocation, authentication, authorization, and delegation, if required. Our presentation complements an earlier foundational article, “The Anatomy of the Grid,” by describing how Grid mechanisms can implement a service-oriented architecture, explaining how Grid functionality can be incorporated into a Web services framework, and illustrating how our architecture can be applied within commercial computing as a basis for distributed system integration—within and across organizational domains. This is a DRAFT document and continues to be revised. The latest version can be found at http://www.globus.org/research/papers/ogsa.pdf. Please send comments to foster@mcs.anl.gov, carl@isi.edu, jnick@us.ibm.com, tuecke@mcs.anl.gov Physiology of the Grid 2

Cloud Computing and Grid Computing 360-Degree Compared

Abstract: Cloud computing has become another buzzword after Web 2.0. However, there are dozens of different definitions for cloud computing and there seems to be no consensus on what a cloud is. On the other hand, cloud computing is not a completely new concept; it has intricate connection to the relatively new but thirteen-year established grid computing paradigm, and other relevant technologies such as utility computing, cluster computing, and distributed systems in general. This paper strives to compare and contrast cloud computing with grid computing from various angles and give insights into the essential characteristics of both.

Virtual Infrastructure Management in Private and Hybrid Clouds

Abstract: One of the many definitions of "cloud" is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.

System and method of brokering cloud computing resources

Abstract: System, method, and tangible computer-readable storage media are disclosed for providing a brokering service for compute resources The method includes, at a brokering service, polling a group of separately administered compute environments to identify resource capabilities and information, each compute resource environment including the group of managed nodes for processing workload, receiving a request for compute resources at the brokering service system, the request for compute resources being associated with a service level agreement (SLA) and based on the resource capabilities across the group of compute resource environments, selecting compute resources in one or more of the group of compute resource environments The brokering service system receives workload associated with the request and communicates the workload to the selected resources for processing The brokering services system can aggregate resources for multiple cloud service providers and act as an advocate for or a guarantor of the SLA associated with the workload