Iterative Refinement Approach for QOS-Aware Service Configuration

Nogueira, Luís; Pinho, Luís Miguel

doi:10.1007/978-0-387-39362-9_17

Cited by 4 publications

(4 citation statements)

References 18 publications

(19 reference statements)

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“…The degree of acceptability of proposed level of service and stability period is determined by measuring the distance between user's preferred values and the values proposed by a service provider [6].…”

Section: Expressing Desired Quality and Stabilitymentioning

confidence: 99%

“…Given the spectrum of user's acceptable QoS levels a service provider formulates the best instantaneous SLA it can offer. The local QoS optimisation (re)computes the set of QoS levels for all local tasks, including the new requested one, finding a feasible set of service configurations that maximise users' satisfaction [6].…”

Section: Supporting Qos Adaptation and Stabilitymentioning

confidence: 99%

“…The promise of service includes a stability period ∆ t , indicating that during a specific interval of time the promised level of service will be assured, either by the service proposal formulation algorithm [6] that may imply service degradation of existing tasks in order to accommodate a new service and by the QoS reconfiguration algorithm proposed in Section 4 that tries to upgrade the current SLA of previously degraded tasks.…”

Section: Supporting Qos Adaptation and Stabilitymentioning

confidence: 99%

“…We base our approach on a general form of QoS contract between service providers and users, achieved by negotiation and dictated by users' preferences [6]. Service providers allocate resources to each new set of tasks, achieving the best possible instantaneous QoS, establishing an initial Service Level Agreement (SLA) for the new task.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Dynamic Adaptation of Stability Periods for Service Level Agreements

Nogueira

Pinho

2006

12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'06)

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A QoS adaptation to dynamically changing system conditions that takes into consideration the user's constraints on the stability of service provisioning is presented. The goal is to allow the system to make QoS adaptation decisions in response to fluctuations in task traffic flow, under the control of the user. We pay special attention to the case where monitoring the stability period and resource load variation of Service Level Agreements for different types of services is used to dynamically adapt future stability periods, according to a feedback control scheme. System's adaptation behaviour can be configured according to a desired confidence level on future resource usage. The viability of the proposed approach is validated by preliminary experiments. Dynamic Adaptation of Stability Periods for Service Level Agreements

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Section: Expressing Desired Quality and Stabilitymentioning

confidence: 99%

Section: Supporting Qos Adaptation and Stabilitymentioning

confidence: 99%

Section: Supporting Qos Adaptation and Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Dynamic Adaptation of Stability Periods for Service Level Agreements

Nogueira

Pinho

2006

12th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA'06)

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Self-organising Clusters in Edge Computing

Nogueira

Coelho

2019

Intelligent Systems Applications in Software Engineering

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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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Iterative Refinement Approach for QOS-Aware Service Configuration

Cited by 4 publications

References 18 publications

Dynamic Adaptation of Stability Periods for Service Level Agreements

Dynamic Adaptation of Stability Periods for Service Level Agreements

Self-organising Clusters in Edge Computing

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

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