Event-Driven Quality of Service Prediction

Zeng, Liangzhao; Lingenfelder, Christoph; Liu, Hui; Chang, Henry

doi:10.1007/978-3-540-89652-4_14

Cited by 44 publications

(38 citation statements)

References 14 publications

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“…In reality, SLA violations occur relatively often, leading to providers' losses and customer dissatisfaction. To overcome this issue, it is suggested in [43][44][45] that, based on observations of the actually realised performance, re-composition of the service may be triggered. During the recomposition phase, new concrete service(s) may be chosen for the given workflow.…”

Section: Literature and Related Workmentioning

confidence: 99%

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Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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Abstract. The chapter summarizes activities of COST IC1304 ACROSS European Project corresponding to traffic management for Cloud Federation (CF). In particular, we provide a survey of CF architectures and standardization activities. We present comprehensive multilevel model for traffic management in CF that consists of five levels: Level 5 -Strategies for building CF, Level 4 -Network for CF, Level 3 -Service specification and provision, Level 2 -Service composition and orchestration, and Level 1 -Task service in cloud resources. For each level we propose specific methods and algorithms. The effectiveness of these solutions were verified by simulation and analytical methods. Finally, we also describe specialized simulator for testing CF solution in IoT environment.

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Section: Literature and Related Workmentioning

confidence: 99%

“…Once re-composition phase is over, the (new) composition is used as long as there are no further SLA violations. In particular, the authors of [43][44][45] describe when to trigger such (re-composition) event, and which adaptation actions may be used to improve overall performance.…”

Section: Literature and Related Workmentioning

confidence: 99%

Traffic Management for Cloud Federation

Burakowski

Bęben

Berg

et al. 2018

Lecture Notes in Computer Science

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“…The concept of QoS aggregation has been extended to SLA aggregation by several authors [31], [32]. As an alternative to QoS and SLA aggregation, different authors have proposed to use various machine learning techniques to predict composition QoS from monitored runtime data [6], [7]. This approach is also the one that we use in PREVENT, as explained in Section 3.…”

Section: Related Workmentioning

confidence: 99%

“…If SLOs are violated, agreed upon monetary consequences go into effect. For this reason, providers generally have a strong interest in monitoring SLAs and preventing violations, either by using post mortem analysis and optimization [4], [5], or by runtime prediction of performance problems [6], [7]. We argue that the latter is more powerful, allowing to prevent violations before they have happened by timely application of runtime adaptation actions [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Cost-Based Optimization of Service Compositions

Leitner

Hummer

Dustdar

2013

IEEE Trans. Serv. Comput.

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For providers of composite services preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, in order to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results. Abstract-For providers of composite services preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, in order to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results.

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“…Additionally, this work does not take estimates into account, and relatively little technical information about their implementation is publicly available. A second related approach to QoS prediction has been presented recently in [10]. In this paper the focus is on KPI prediction using analysis of event data.…”

Section: Related Workmentioning

confidence: 99%

Runtime Prediction of Service Level Agreement Violations for Composite Services

Leitner

Wetzstein

Rosenberg

et al. 2010

Lecture Notes in Computer Science

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Abstract. SLAs are contractually binding agreements between service providers and consumers, mandating concrete numerical target values which the service needs to achieve. For service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen, while it is still possible to set counteractive measures. We propose an approach for predicting SLA violations at runtime, which uses measured and estimated facts (instance data of the composition or QoS of used services) as input for a prediction model. The prediction model is based on machine learning regression techniques, and trained using historical process instances. We present the basics of our approach, and briefly validate our ideas based on an illustrative example.

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Event-Driven Quality of Service Prediction

Cited by 44 publications

References 14 publications

Traffic Management for Cloud Federation

Traffic Management for Cloud Federation

Cost-Based Optimization of Service Compositions

Runtime Prediction of Service Level Agreement Violations for Composite Services

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