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

Lee, Chonho; Suzuki, Junichi; Vasilakos, Athanasios V.; Yamamoto, Y.; Oba, Katsuya

doi:10.1145/1809018.1809025

Cited by 22 publications

(23 citation statements)

References 16 publications

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“…This means they do not attain the stability to reach equilibria as Cielo does. This paper reports a set of extensions to the authors' prior work [18]. For the problem formulation, this paper considers two extra objectives (bandwidth allocation and power consumption) and an extra parameter in each deployment strategy (bandwidth allocation), all of which are not studied in [18].…”

Section: Related Workmentioning

confidence: 99%

“…This paper reports a set of extensions to the authors' prior work [18]. For the problem formulation, this paper considers two extra objectives (bandwidth allocation and power consumption) and an extra parameter in each deployment strategy (bandwidth allocation), all of which are not studied in [18]. This paper also considers an optimization constraint in CPU allocation and investigates a constraint-handling algorithm (Algorithm 2) while no constraints are assumed in [18].…”

Section: Related Workmentioning

confidence: 99%

“…For the problem formulation, this paper considers two extra objectives (bandwidth allocation and power consumption) and an extra parameter in each deployment strategy (bandwidth allocation), all of which are not studied in [18]. This paper also considers an optimization constraint in CPU allocation and investigates a constraint-handling algorithm (Algorithm 2) while no constraints are assumed in [18]. Moreover, this paper carries out larger-scale simulations with more realistic configurations than [18], which uses only five hosts and simple topologies among them.…”

Section: Related Workmentioning

confidence: 99%

“…This paper also considers an optimization constraint in CPU allocation and investigates a constraint-handling algorithm (Algorithm 2) while no constraints are assumed in [18]. Moreover, this paper carries out larger-scale simulations with more realistic configurations than [18], which uses only five hosts and simple topologies among them.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

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%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

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

Self Cite

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“…Game theoretic algorithms have been used in several aspects of cloud/grid applications; for example, task allocation [11], application placement [12]- [14] and data replication [15]. [11] maintains stability in seeking equilibria; however, it assumes static networks whose conditions (e.g., network traffic) never change over time.…”

Section: Related Workmentioning

confidence: 99%

Self-stabilizable symbiosis for cloud data center applications: A game theoretic perspective

Suzuki

Champrasert

Lee

2012

The 6th International Conference on Soft Computing and Intelligent Systems, and the 13th International Symposium on Advanced In

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Abstract-This paper describes and evaluates a self-stabilizable adaptation framework for data center applications. The design of the proposed architecture, SymbioticSphere, is inspired by key biological principles such as decentralization, natural selection, emergence and symbiosis. In SymbioticSphere, each data center application consists of application services and middleware platforms. Each service and platform is designed as a biological entity, analogous to an individual bee in a bee colony, and implements biological behaviors such as energy exchange, migration, replication and death. SymbioticSphere allows services and platforms to (1) adaptively invoke their behaviors according to dynamic network conditions and (2) autonomously seek stable behavior invocations as equilibria (or symbiosis) between them. A symbiosis between a service and a platform is sought as a Nash equilibrium in an extensive-form game. Simulation results demonstrate that SymbioticSphere allows services and platforms to successfully adapt to dynamic networks in a self-stabilizable manner.

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An adaptive parallel execution strategy for cloud‐based scientific workflows

Oliveira

Ogasawara

Ocaña

et al. 2011

Concurrency and Computation

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Many of the existing large-scale scientific experiments modeled as scientific workflows are computeintensive. Some scientific workflow management systems already explore parallel techniques, such as parameter sweep and data fragmentation, to improve performance. In those systems, computing resources are used to accomplish many computational tasks in high performance environments, such as multiprocessor machines or clusters. Meanwhile, cloud computing provides scalable and elastic resources that can be instantiated on demand during the course of a scientific experiment, without requiring its users to acquire expensive infrastructure or to configure many pieces of software. In fact, because of these advantages some scientists have already adopted the cloud model in their scientific experiments. However, this model also raises many challenges. When scientists are executing scientific workflows that require parallelism, it is hard to decide a priori the amount of resources to use and how long they will be needed because the allocation of these resources is elastic and based on demand. In addition, scientists have to manage new aspects such as initialization of virtual machines and impact of data staging. SciCumulus is a middleware that manages the parallel execution of scientific workflows in cloud environments. In this paper, we introduce an adaptive approach for executing parallel scientific workflows in the cloud. This approach adapts itself according to the availability of resources during workflow execution. It checks the available computational power and dynamically tunes the workflow activity size to achieve better performance. Experimental evaluation showed the benefits of parallelizing scientific workflows using the adaptive approach of SciCumulus, which presented an increase of performance up to 47.1%.

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

Cited by 22 publications

References 16 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

Self-stabilizable symbiosis for cloud data center applications: A game theoretic perspective

An adaptive parallel execution strategy for cloud‐based scientific workflows

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