Adaptive grid resource brokering

Othman,; Djemame, Karim; Gourlay,

doi:10.1109/clustr.2003.1253313

Cited by 20 publications

(10 citation statements)

References 12 publications

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“…The authors used NetSolve [1] as a testbed for evaluating the proposed algorithm. Othman et al [16] stress the need for the Grid system's ability to recognize the state of the resources. The authors presented an approach for system adaptation, in which Grid jobs are maintained, using an adaptable Resource Broker.…”

Section: Independent Scheduling Computational Grids Are Parallel In mentioning

confidence: 99%

Computational models and heuristic methods for Grid scheduling problems

Xhafa

Abraham

2010

Future Generation Computer Systems

290

147

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a b s t r a c tIn this paper we survey computational models for Grid scheduling problems and their resolution using heuristic and meta-heuristic approaches. Scheduling problems are at the heart of any Grid-like computational system. Different types of scheduling based on different criteria, such as static versus dynamic environment, multi-objectivity, adaptivity, etc., are identified. Then, heuristic and meta-heuristic methods for scheduling in Grids are presented. The paper reveals the complexity of the scheduling problem in Computational Grids when compared to scheduling in classical parallel and distributed systems and shows the usefulness of heuristic and meta-heuristic approaches for the design of efficient Grid schedulers. We also discuss on requirements for a modular Grid scheduling and its integration with Grid architecture.

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Section: Independent Scheduling Computational Grids Are Parallel In mentioning

confidence: 99%

Computational models and heuristic methods for Grid scheduling problems

Xhafa

Abraham

2010

Future Generation Computer Systems

290

147

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“…24 Resource Broker inspects the stability and resource availability of each host, and adjusts resource allocation for applications. In SymbioticSphere, agents and platforms adapt their populations and locations in a decentralized way.…”

Section: Related Workmentioning

confidence: 99%

Topological Robustness of Biological Systems for Information Networks—Modularity

Eum¹,

Arakawa²,

Murata³

2016

Bio-Inspired Computing and Networking

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Large-scale network systems, such as grid/cloud computing systems, are increasingly expected to be autonomous, scalable, adaptive to dynamic network environments, survivable against partial system failures and simple to implement and maintain. Based on the observation that various biological systems have overcome these requirements, the proposed architecture, SymbioticSphere, applies biological principles and mechanisms to design network systems (i.e., application services and middleware platforms). SymbioticSphere follows key biological principles such as decentralization, evolution, emergence, diversity and symbiosis. Each application service and middleware platform is modeled as a biological entity, analogous to an individual bee in a bee colony, and implements biological mechanisms such as energy exchange, migration, replication, reproduction and death. Each agent/platform possesses behavior policies, as genes, each of which determines when to and how to invoke a particular behavior. Agents and platforms are designed to evolve and adjust their genes (behavior policies) through generations and autonomously improve their scalability, adaptability and survivability. Through this evolution process, agents/platforms strive to satisfy given constraints for quality of service (QoS) such as response time, throughput and workload distribution. This chapter describes the design of SymbioticSphere and evaluates how the biologically-inspired mechanisms in SymbioticSphere impact the autonomy, adaptability, scalability, survivability and simplicity of network systems.

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“…Recognition of existing resources in a grid environment are done dynamically by a broker and the selection and mapping of resources to the appropriate demanding task (Othman et al, 2003;Yokoo et al, 2001;Kant and Grosu, 2004). Grid systems may offer static information about resources such architecture of memory structure, processor speed/frequency, operating system used etc.…”

Section: Resource Allocation and Schedulingmentioning

confidence: 99%

Resource Management in Grid Computing: A Review

Azeez¹,

Haque²

2011

GJSETR

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As technology advances and the popularity and dependence on internet increases by the day, new ways of finding faster and cheaper solutions to computational problems are sought. This has lead to the development of a new concept known as grid computing, which is a type of distributed computing, heterogeneous in nature because it makes an aggregated use of resources distributed over a large geographical area to solve problems usually complex ones on a larger scale. Since it involves using resources from different places, from different ownerships and with different individual qualities, it requires a complex resource management process for its proper functioning. This article provides a brief overview on grid computing and its resource management processes, important factors considered in resource management, comparison of different resource management processes and future outlook of grid computing and its resource management.

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Adaptive grid resource brokering

Cited by 20 publications

References 12 publications

Computational models and heuristic methods for Grid scheduling problems

Computational models and heuristic methods for Grid scheduling problems

Topological Robustness of Biological Systems for Information Networks—Modularity

Resource Management in Grid Computing: A Review

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