AssessGrid Strategies for Provider Ranking Mechanisms in Risk–Aware Grid Systems

Battré, Dominic; Djemame, Karim; Gourlay, Iain; Hovestadt, Matthias; Kao, Odej; Padgett, James; Voß, Kerstin; Warneke, Daniel

doi:10.1007/978-3-540-85485-2_19

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The AssessGrid project [24], [25] also is intended to empower selection of service provider with risk in a Grid. The project mainly focuses on timely delivery of results and considers risk only as a probability to satisfy this requirement [26], [27], [28].…”

Section: Related Workmentioning

confidence: 99%

Risk-Based Usage Control for Service Oriented Architecture

Krautsevich

Lazouski

Martinelli³

et al. 2010

2010 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing

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In Service Oriented Architecture (SOA) data belonging to a client (data provider) is often processed by a provider (data consumer). During this processing the data can be compromised. A client wants to be sure that its data is used in the least risky way while is under provider's control. The risk level should be low when access to the data is granted and should remain low during the whole interaction and, maybe, some time after. Therefore, a client has to consider closely various providers and decide which one provides the service with the smallest risk. More importantly, the risk has to be constantly recomputed after granting the access to the data, i.e., usage of data must be controlled.In this work we propose a method to empower usage control with a risk-based decision making process for more efficient and flexible control of access to data. Employing this idea we show how to select a service provider using risk, re-evaluate the risk level when some changes have happened and how to improve an infrastructure in order to reduce the risk level.

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Section: Related Workmentioning

confidence: 99%

Risk-Based Usage Control for Service Oriented Architecture

Krautsevich

Lazouski

Martinelli³

et al. 2010

2010 18th Euromicro Conference on Parallel, Distributed and Network-Based Processing

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“…in the path computation scheme. Other works [9,10] focus on management risk mechanisms to assure QoS guarantees in Grid Systems.…”

Section: Related Workmentioning

confidence: 99%

Risk-Aware SLA Negotiation

Lamali¹,

Pouyllau

Cohen

et al. 2014

Proceedings of the 7th International Conference on Performance Evaluation Methodologies and Tools

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In order to assure Quality of Service (QoS) connectivity, Network Service Providers (NSPs) negotiate Service Level Agreements (SLAs). However, a committed SLA might fail to respect its QoS promises. In such a case, the customer is refunded. To maximize their revenues, the NSPs must deal with risks of SLA violations, which are correlated to their network capacities. Due to the complexity of the problem, we first study a system with one NSP provider and give a method to compute its risk-aware optimal strategy using (max, +)-algebras. Using the same method, we study the case where two NSPs collaborate and the case where they compete, and we derive the Price of Anarchy. This method provides optimal negotiation strategies but, when modeling customers' reaction to SLA failure, analytical results do not hold. Hence, we propose a learning framework that chooses the NSP risk-aware optimal strategy under failures capturing the impact of reputation. Finally, by simulation, we observe how the NSP can benefit from such a framework.

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“…Some recent works [1,2] propose management risk and ranking mechanisms to assure QoS in Grid Systems.…”

Section: Related Workmentioning

confidence: 99%

Reputation-Aware Learning for SLA Negotiation

Lamali¹,

Barth²,

Cohen³

2012

NETWORKING 2012 Workshops

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Abstract. Assuring Quality of Service (QoS) over multiple Network Service Providers (NSPs) requires to negotiate QoS contracts as Service Level Agreements (SLAs) between NSPs. The goal of an NSP is to maximize its revenues by selling as much as possible SLAs. However, provisioning too much SLAs might increase the risk of violations of the committed QoS thus impacting the NSP's reputation. In order to determine the appropriate provisioning strategies, we propose to extend existing solutions based on Reinforcement Learning with reputation-awareness so that NSPs maximize their revenues.

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AssessGrid Strategies for Provider Ranking Mechanisms in Risk–Aware Grid Systems

Cited by 5 publications

References 7 publications

Risk-Based Usage Control for Service Oriented Architecture

Risk-Based Usage Control for Service Oriented Architecture

Risk-Aware SLA Negotiation

Reputation-Aware Learning for SLA Negotiation

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