Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing

Doulamis, Nikolaos; Λίτκε, Αντώνιος; Panagakis, Athanasios; Varvarigou, Theodora; Varvarigos, Emmanouel

doi:10.1016/j.comcom.2005.11.013

Cited by 44 publications

(26 citation statements)

References 19 publications

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“…Game theoretic algorithms have been used for a few aspects of cloud computing; e.g., application placement [13]- [15], task allocation [16] and data replication [17]. In [13]- [15], greedy algorithms seek equilibria in application placement problems with respect to multiple performance objectives.…”

Section: Related Workmentioning

confidence: 99%

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Cielo: An Evolutionary Game Theoretic Framework for Virtual Machine Placement in Clouds

Ren

Suzuki

Vasilakos

et al. 2014

2014 International Conference on Future Internet of Things and Cloud

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Abstract-This paper studies an evolutionary game theoretic framework for adaptive and stable application deployment in clouds. The framework, called Cielo, aids cloud operators to adapt the resource allocation to applications and their locations to the operational conditions in a cloud (e.g., workload and resource availability) with respect to multiple conflicting objectives (e.g., response time and power consumption). Moreover, Cielo theoretically guarantees that each application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given operational conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies. Cielo outperforms well-known existing heuristics.

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Section: Related Workmentioning

confidence: 99%

“…In [13]- [15], greedy algorithms seek equilibria in application placement problems with respect to multiple performance objectives. This means they do not attain the stability to reach equilibria as Cielo does.…”

Section: Related Workmentioning

confidence: 99%

Cielo: An Evolutionary Game Theoretic Framework for Virtual Machine Placement in Clouds

Ren

Suzuki

Vasilakos

et al. 2014

2014 International Conference on Future Internet of Things and Cloud

View full text Add to dashboard Cite

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“…Several game theoretic approaches have been proposed for adaptive application placement in clouds [7,16,17]. They formulate equilibria in application placement and use greedy algorithms to seek equilibrium solutions under multiple performance objectives.…”

Section: Related Workmentioning

confidence: 99%

“…In contrast, Nuage employs an evolutionary game theoretic approach and theoretically guarantees its stability in finding adaptive strategies for resource allocation as well as application placement under multiple performance objectives. Nuage also considers multi-tier application architecture, while [7,16,17] do not . [18,19] and [20] seek the optimal placement of a single application and multi-server applications, respectively, under a single performance objective.…”

Section: Related Workmentioning

confidence: 99%

An evolutionary game theoretic approach to adaptive and stable application deployment in clouds

Lee

Suzuki

Vasilakos

et al. 2010

Proceedings of the 2nd Workshop on Bio-Inspired Algorithms for Distributed Systems

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This paper studies an evolutionary game theoretic mechanism for adaptive and stable application deployment in cloud computing environments. The proposed mechanism, called Nuage, allows applications to adapt their locations and resource allocation to the environmental conditions in a cloud (e.g., workload and resource availability) with respect to given performance objectives such as response time. Moreover, Nuage theoretically guarantees that every application performs an evolutionarily stable deployment strategy, which is an equilibrium solution under given environmental conditions. Simulation results verify this theoretical analysis; applications seek equilibria to perform adaptive and evolutionarily stable deployment strategies.

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“…Sin embargo, G-QoSM estaba limitado por un conjunto predefinido de posibles configuraciones y por la precariedad de los sistemas de monitorización en los que se apoyaba para conocer el estado de los recursos. Varios años después, Doulamis y colaboradores [29] presentaron un enfoque diferente que consiste en utilizar un modelo no lineal de la complejidad computacional de una aplicación para predecir la carga que supone cada trabajo antes de que sea enviado al Grid. Esta predicción es utilizada luego para seleccionar el recurso que, por sus características, tenga la mayor probabilidad de completar el trabajo en el menor tiempo posible.…”

Section: Estado Del Arteunclassified

Técnicas de monitorización y diferenciación de servicios para la asignación de recursos en entornos de computación Grid, en base a indicadores de nivel de servicio

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Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing

Cited by 44 publications

References 19 publications

Cielo: An Evolutionary Game Theoretic Framework for Virtual Machine Placement in Clouds

Cielo: An Evolutionary Game Theoretic Framework for Virtual Machine Placement in Clouds

An evolutionary game theoretic approach to adaptive and stable application deployment in clouds

Técnicas de monitorización y diferenciación de servicios para la asignación de recursos en entornos de computación Grid, en base a indicadores de nivel de servicio

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