End-to-end quality of service for high-end applications

Foster, Ian; Fidler, Markus; Roy, Alain; Sander, Volker; Winkler, Linda

doi:10.1016/j.comcom.2004.02.014

Cited by 155 publications

(91 citation statements)

References 35 publications

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“…A generic architecture for resource reservation and allocation that supports flow-specific QoS specifications as well as online monitoring and control of both individual resources and heterogeneous resource sets was proposed by Foster et al [31]. The architecture builds on differentiated service mechanisms to enable the coordinated management of distinct flow types, networks, CPUs, and storage systems.…”

Section: Related Workmentioning

confidence: 99%

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

Nogueira

Pinho

2009

Journal of Parallel and Distributed Computing

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The scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users' requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance. This paper proposes a cooperative service execution, allowing resource constrained devices to collectively execute services with their more powerful neighbours, meeting non-functional requirements that otherwise would not be met by an individual execution. Nodes dynamically group themselves into a new coalition, allocating resources to each new service and establishing an initial service configuration which maximises the satisfaction of the QoS constraints associated with the new service and minimises the impact on the global QoS caused by the new service's arrival.However, the increased complexity of open real-time environments may prevent the possibility of computing optimal local and global resource allocations within a useful and bounded time. As such, the QoS optimisation problem is here reformulated as a heuristic-based anytime optimisation problem that can be interrupted at any time and quickly respond to environmental changes. Extensive simulations demonstrate that the proposed anytime algorithms are able to quickly find a good initial service solution and effectively optimise the rate at which the quality of the current solution improves at each iteration of the algorithms, with an overhead that can be considered negligible when compared against the introduced benefits. AbstractThe scarcity and diversity of resources among the devices of heterogeneous computing environments may affect their ability to execute services within the users' requested Quality of Service levels, particularly in open real-time environments where the characteristics of the computational load cannot always be predicted in advance but, nevertheless, response to events still has to be provided within precise timing constraints in order to guarantee a desired level of performance. This paper proposes a cooperative service execution, allowing resource constrained devices to collectively execute services with their more powerful neighbours, meeting non-functional requirements that otherwise would not be met by an individual execution. Nodes dynamically group themselves into a new coalition, allocating resources to each new service and establishing an initial service configuration which maximises the satisfaction of the QoS constraints associated with the new service and minimises the impact on the global QoS caused by the new service's arrival.However, the increased complexity of open real-time environments may prevent the possibility of computing optimal local and global resource allocations within a useful and bounded time. As such, the QoS optimisat...

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Section: Related Workmentioning

confidence: 99%

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

Nogueira

Pinho

2009

Journal of Parallel and Distributed Computing

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“…The problem of network resource reservation [15] has been proposed to be studied within the Grid scope targeting deadline requirements of bulk data transfer requests. An example for a Grid toolkit that supports such mechanisms is Globus with its GARA resource allocation component [16].…”

Section: B Grid Network Reservationsmentioning

confidence: 99%

“…We validate the analytical results and apply the model to plan the network dimensioning. We use Equation (9) and Equation (15) to calculate the analytical results with and without sharing of C r respectively.…”

Section: Rpsmentioning

confidence: 99%

Grid Network Dimensioning by Modeling the Deadline Constrained Bulk Data Transfers

Munir

Primet²,

Welzl

2009

2009 11th IEEE International Conference on High Performance Computing and Communications

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Abstract-Grid applications need to move large amounts of data between distributed resources within deterministic time frames. In most cases it is possible to specify the volume and the deadline in advance. We formally define and analyze a mechanism of network reservations of bulk data transfer requests having opportunistic utilization of residual network capacity and analyze it using an M/M/1/N − RP S queue. We compare the analytical results of our mechanism with the analytical results obtained from an M/M/N/N queue, for the case in which there is no opportunistic sharing of residual capacity. We validate the analytical results through simulations in C++. The analytical model is also validated against ns-2 simulations taking into account network level details (IP and TCP protocols), showing remarkably good coherence even under heavy loads. The model is orders of magnitude faster than simulation, which enables its application for capacity planning of Grid networks (also known as network dimensioning), and to enforce Connection Admission Control (CAC) under the practical hypothesis of a dominating bottleneck on the transfer route.

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“…MPI works very efficiently on supercomputers and clusters. However, if an MPI application is distributed on several remote sites (using for example the MPICH-G2 [6] version) then Y due to the high latency of wide area networks Y the performance turns out to be much worse than it was originally expected [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal

2005

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Computational Grids connect resources and users in a complex way in order to deliver nontrivial qualities of services. According to the current trend various communities build their own Grids and due to the lack of generally accepted standards these Grids are usually not interoperable. As a result, large scale sharing of resources is prevented by the isolation of Grid systems. Similarly, people are isolated, because the collaborative work of Grid users is not supported by current environments. Each user accesses Grids as an individual person without having the possibility of organizing teams that could overcome the difficulties of application development and execution more easily. The paper describes a new workflow-oriented portal concept that solves both problems. It enables the interoperability of various Grids during the execution of workflow applications, and supports users to develop and run their Grid workflows in a collaborative way. The paper also introduces a classification model that can be used to identify workflow-oriented Grid portals based on two general features: Ability to access multiple Grids, and support for collaborative problem solving. Using the approach the different potential portal types are introduced, their unique features are discussed and the portals and Problem Solving Environments (PSE) of our days are classified. The P-GRADE Portal as a Globus-based implementation for the classification model is also presented.

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End-to-end quality of service for high-end applications

Cited by 155 publications

References 35 publications

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

Time-bounded distributed QoS-aware service configuration in heterogeneous cooperative environments

Grid Network Dimensioning by Modeling the Deadline Constrained Bulk Data Transfers

Multi-Grid, Multi-User Workflows in the P-GRADE Grid Portal

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