A Framework for QoS Adaptive Grid Meta Scheduling

Nainwal, Kalash Chandra; Lakshmi, J.; Nandy, S. K.; Narayan, Ranjani; Varadarajan, Keshavan

doi:10.1109/dexa.2005.15

Cited by 9 publications

(5 citation statements)

References 4 publications

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“…To realize seamless execution of long running computations across variability points there is a need for a Meta Scheduler [11] that performs the following functions (a) Discovery (identify a matching service at a variability point), (b) Monitor resources (proactively prepare for transience of resources) (c) Matchmaking engine (decides the best resources that can be used for execution of the task).…”

Section: N/amentioning

confidence: 99%

Functional and architectural adaptation in pervasive computing environments

Narendra

Bellur

Nandy

et al. 2005

Proceedings of the 3rd International Workshop on Middleware for Pervasive and Ad-Hoc Computing - MPAC '05

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Service-oriented computing paradigm encourages the use of dynamic binding of application requirements to the resources needed to fulfill application tasks. Especially in pervasive computing that is characterized by disconnected operation and mobility, the process of using service specifications and dynamic binding becomes critical. For this, adaptation techniques that provide for seamless service composition are needed for user task execution. In this paper we present our approach to enable two kinds of adaptations: functional (due to changes in user requirements) and architectural (due to changes in nonfunctional requirements) in pervasive computing systems.The key features of our approach are to model services as workflows, service handoffs as points of variability, and context detailing for dynamic service provisioning.

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Section: N/amentioning

confidence: 99%

Functional and architectural adaptation in pervasive computing environments

Narendra

Bellur

Nandy

et al. 2005

Proceedings of the 3rd International Workshop on Middleware for Pervasive and Ad-Hoc Computing - MPAC '05

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“…"> QoS‐based metascheduling: the AGMetS framework . AGMetS25 is an early framework for QoS‐based metascheduling. Every application managed by AGMetS has some QoS requirements and constraints on the minimum availability of the set of resources on which it executes.…”

Section: Grid Models and Qos Architecturesmentioning

confidence: 99%

Quality of Service on Grid: architectural and methodological issues

Merlo

Clematis

Corana

et al. 2010

Concurrency and Computation

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SUMMARYThe rapid evolution of Grid Computing and the development of new middleware services make Grid platforms increasingly used not only for best effort scientific jobs but also in industrial and business applications. This has taken to the growing demand of Quality of Service (QoS) support. However, the QoS issue on Grid is quite difficult, as Grid has been originally designed without any QoS support, and it is a complex system. During the previous years some solutions have been proposed to supply QoS for specific classes of applications. This results in a focused and a heterogeneous approach, so that it is hard to evaluate its sufficiency and its robustness with respect to the large spectrum of possible Grid applications. In this context, our contribution concerns three points: first, we analyze the current approach to QoS on Grid as a relationship among QoS features, applications, and architectures; second, we evaluate the QoS requirements of two recent QoS-demanding applications on Grid, namely Massive Multiplayer Online Games and Urgent Computing, comparing these requirements with the support provided by current QoS architectures; and third, we propose an alternative approach to QoS provision on Grid based on the definition of a dedicated QoS-management layer to overcome the limitations of the current methodologies.

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“…AGMeTs [12] is a framework for adaptive metascheduling. When a grid task is submitted to AGMeTS, the resource discovery module retrieves the characteristics of the resources, including their availability.…”

Section: Qos In Grid Computingmentioning

confidence: 99%

“…Many recent proposals [6][17] [12][3] have focused on offering QoS for grids. Of those, only GrADS (Grid Application Development Software) [17] and JSS (Job Submission Service) [3] use agreements to negotiate QoS levels.…”

Section: Introductionmentioning

confidence: 99%

GrAMoS: A Flexible Service for WS-Agreement Monitoring in Grid Environments

Scorsatto

Melo

2008

Lecture Notes in Computer Science

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In this paper, we propose and evaluate GrAMoS, a low-overhead WS-Agreement monitoring service that runs on Globus 4 and is integrated to JSS. To monitor resources and detect agreement violations, GrAMoS uses the stratified random sampling technique and proposes flexible agreement violation actions. The experimental results collected with a prototype of GrAMoS which monitored CPU reservation WS-Agreements show that the overhead incurred is lower than 2.8%, for grid tasks that take longer than 55s.

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A Framework for QoS Adaptive Grid Meta Scheduling

Abstract: Abstract

Cited by 9 publications

References 4 publications

Functional and architectural adaptation in pervasive computing environments

Functional and architectural adaptation in pervasive computing environments

Quality of Service on Grid: architectural and methodological issues

GrAMoS: A Flexible Service for WS-Agreement Monitoring in Grid Environments

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