Decentralized Grid Scheduling with Evolutionary Fuzzy Systems

Fölling, Alexander; Grimme, Christian; Lepping, Joachim; Papaspyrou, Alexander

doi:10.1007/978-3-642-04633-9_2

Cited by 14 publications

(22 citation statements)

References 12 publications

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“…Grimme et al [20] analyze the prospects of collaborative job sharing and compare their results to the non-cooperative scenario of the same machines. Recent work of Fölling et al [16,17] proposes a fuzzy-based, evolutionary-optimized exchange policy for a fully decentralized scenario, which shows robustness even in changing environments and automatically adapts to the current local load.…”

Section: Capacity Planning In Modern DCI Environmentsmentioning

confidence: 99%

Infrastructure Federation Through Virtualized Delegation of Resources and Services

et al. 2011

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Infrastructure federation is becoming an increasingly important issue for modernDistributed Computing Infrastructures (DCIs): Dynamic elasticity of quasi-static Grid environments, incorporation of special-purpose resources into commoditized Cloud infrastructures, crosscommunity collaboration for increasingly diverging areas of modern e-Science, and Cloud Bursting pose major challenges on the technical level for many resource and middleware providers. Especially with respect to increasing costs of operating data centers, the intelligent yet automated and secure sharing of resources is a key factor for success. With the D-Grid Scheduler Interoperability (DGSI) project within the German D-Grid Initiative, we provide a strategic technology for the automatically negotiated, SLAsecured, dynamically provisioned federation of resources and services for Grid-and Cloud-type infrastructures. This goal is achieved by complementing current DCI schedulers with the ability to federate infrastructure for the temporary leasing of resources and rechanneling of workloads. In this work, we describe the overall architecture and SLA-secured negotiation protocols within DGSI and depict an advanced mechanism for resource delegation through means of dynamically provisioned, virtualized middleware. Through this methodology, we provide the technological foundation for intelligent capacity planning and workload management in a cross-infrastructure fashion. 356 G. Birkenheuer et al.

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Section: Capacity Planning In Modern DCI Environmentsmentioning

confidence: 99%

Infrastructure Federation Through Virtualized Delegation of Resources and Services

et al. 2011

Self Cite

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“…While fully decentralized cooperative grid solutions bear advantages over their centralized counterparts, interoperability of the diverse systems involved is often hindered by infrastructural or organizational problems, such as lack of standardization [16]. As discussed in [17], to alleviate these issues, collaborative scheduling solutions should avoid enforcing control over local resources by establishing a clear separation between global and local resource management.…”

Section: Related Workmentioning

confidence: 99%

ARiA: A Protocol for Dynamic Fully Distributed Grid Meta-scheduling

Brocco

Malatras

Huang

et al. 2010

2010 IEEE 30th International Conference on Distributed Computing Systems

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Critical to the successful deployment of grid systems is their ability to guarantee efficient meta-scheduling, namely optimal allocation of jobs across a pool of sites with diverse local scheduling policies. The centralized nature of current meta-scheduling solutions is not well suited for the envisioned increasing scale and dynamicity of next-generation grids, the success of which relies on the development of fully distributed, flexible and autonomic systems tailored to very large sets of highly volatile and heterogeneous resources. In this respect, we introduce a fully distributed grid meta-scheduling scheme that effectively addresses the concerns regarding the scalability and adaptability of future grid systems. Our approach employs a lightweight protocol, called ARiA, that is based on peer-to-peer communication between grid nodes, and makes use of dynamic rescheduling to consider and adapt to changes in the availability of resources. Extensive evaluation by means of an in depth simulation study highlighted the effectiveness of the proposed solution in improving the overall performance in terms of job completion time and load-balancing.

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“…Specifically, a meta-scheduler could select available resources from multiple clouds taking into account appropriate Service Level Agreements (SLAs), operating conditions (e.g. cost, availability) and performance criteria [4]. This requires resources from multiple clouds to be orchestrated in such a way that tasks are efficiently executed.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to other efforts, as described in [2] and [4], we propose a more inclusive design that provides task federation through a decentralized meta-scheduling solution. Each cloud infrastructure may have their own local scheduler which may not have information about resources in other clouds.…”

Section: Introductionmentioning

confidence: 99%

An Inter-Cloud Meta-Scheduling (ICMS) Simulation Framework: Architecture and Evaluation

Sotiriadis

Bessis

Anjum

et al. 2018

IEEE Trans. Serv. Comput.

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Abstract-Inter-cloud is an approach that facilitates scalable resource provisioning across multiple cloud infrastructures. In this paper, we focus on the performance optimization of Infrastructure as a Service (IaaS) using the meta-scheduling paradigm to achieve an improved job scheduling across multiple clouds. We propose a novel inter-cloud job scheduling framework and implement policies to optimize performance of participating clouds. The framework, named as Inter-Cloud Meta-Scheduling (ICMS), is based on a novel message exchange mechanism to allow optimization of job scheduling metrics. The resulting system offers improved flexibility, robustness and decentralization. We implemented a toolkit named "Simulating the InterCloud" (SimIC) to perform the design and implementation of different inter-cloud entities and policies in the ICMS framework. An experimental analysis is produced and an improved performance is observed for a number of parameters such as job execution, makespan, and turnaround times. The results highlight that the overall performance of individual clouds is improved when these are brought together under the proposed ICMS framework.

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Decentralized Grid Scheduling with Evolutionary Fuzzy Systems

Cited by 14 publications

References 12 publications

Infrastructure Federation Through Virtualized Delegation of Resources and Services

Infrastructure Federation Through Virtualized Delegation of Resources and Services

ARiA: A Protocol for Dynamic Fully Distributed Grid Meta-scheduling

An Inter-Cloud Meta-Scheduling (ICMS) Simulation Framework: Architecture and Evaluation

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