Queuing Theoretic and Evolutionary Deployment Optimization with Probabilistic SLAs for Service Oriented Clouds

Wada, Hiroshi; Suzuki, Junichi; Oba, Katsuya

doi:10.1109/services-i.2009.59

Cited by 19 publications

(9 citation statements)

References 11 publications

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“…지금까지 SOA 환경에서 응용의 성능 분석 방안 에 대해서 다수의 연구가 수행되어 왔다 (Gao et al, 2005;Kim, 2013aKim, , 2013bLiu, Gorton and Zhu, 2007;Teixeira et al, 2009;Wada et al, 2009;Yuan and Ji, 2007). Brebner et al(2009) (Gao et al, 2005;Kim, 2013b;Liu et al, 2007), 다수 서비스 인스턴스 프 로비저닝을 고려하였으나 우선순위를 고려하지 않 거나 최적해를 찾는 알고리즘에 대한 논의가 없었 다 (Kim, 2013a;Liu et al, 2007).…”

Section: 관련 연구unclassified

Service Deployment and Priority Optimization for Multiple Service-Oriented Applications in the Cloud

Kim¹,

Keum²,

Bae³

2014

Journal of the Korea society of IT services

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This paper considers service deployment and priority optimization for multiple service-oriented applications sharing reusable services, which are deployed as multiple instances in the cloud. In order to handle variations in the workloads of the multiple applications, service instances of the individual reusable services are dynamically provisioned in the cloud. Also service priorities for each application in a particular reusable service are dynamically adjusted. In this paper, we propose an analytic performance model, based on a queueing network model, to predict the expected sojourn times of multiple service-oriented applications, given the number of service instances and priority disciplines in individual reusable services. We also propose a simple heuristic algorithm to search an optimal number of service instances in the cloud and service priority disciplines for each application in individual reusable services. A numerical example is also presented to demonstrate the applicability of the proposed performance model and algorithm to the proposed optimal decision problem.

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Section: 관련 연구unclassified

Service Deployment and Priority Optimization for Multiple Service-Oriented Applications in the Cloud

Kim¹,

Keum²,

Bae³

2014

Journal of the Korea society of IT services

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“…Holland [7] was the first to introduce and propose those simulations and genetic algorithms (GA) in different IT systems. Afterwards, numerous papers and studies followed that used the genetic algorithm when optimizing different systems in the IT World, especially in GRID, scheduling, cloud and similar ones [8][9][10][11][12]. In time, many version and formats of the genetic algorithm emerged.…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Multi-objective Optimizations during Parallel Processing in a Dynamic Heterogeneous Cloud Environment

Kjamilji

2014

2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks

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In the quest of fulfilling the rapidly increasing needs for processing power, while avoiding the expensive supercomputers, scientists look for cheaper parallel processing alternatives and technologies such as those found on GRID, clusters and similar ones. With the emergence of cloud, it was a matter of time when this will be done on cloud as well. However, cloud has its unique nature. On one side, its servers are hybrid in terms of their SOA as well as the maximum guaranteed processing power they offer. On the other side the Internet (the medium by which the cloud services are offered by default) is often unpredictable and dynamic. Furthermore, during our parallel processing, simultaneously we want to optimize the time and price, goals often in contradiction to each other. Three algorithms are studied and compared with the optimal solution, including our proposed cloud adopted feedback algorithm, which outperformed the others.

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“…This paper describes a set of extensions to the authors' prior work [3]. A key extension is a new objective reduction method that addresses the issue of high dimensionality of optimization objectives.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary deployment optimization for service‐oriented clouds

et al. 2011

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SUMMARYThis paper focuses on service deployment optimization in cloud computing environments. In a cloud, an application is assumed to consist of multiple services. Each service in an application can be deployed as one or more service instances. Different service instances operate at different quality of service (QoS) levels depending on the amount of computing resources assigned to them. In order to satisfy given performance requirements, i.e., service level agreements (SLAs), each application is required to optimize its deployment configuration such as the number of service instances, the amount of computing resources to assign and the locations of service instances. Since this problem is NP-hard and often faces trade-offs among conflicting QoS objectives in SLAs, existing optimization methods often fail to solve it. E 3 -R is a multiobjective genetic algorithm that seeks a set of Pareto-optimal deployment configurations that satisfy SLAs and exhibit the trade-offs among conflicting QoS objectives. By leveraging queueing theory, E 3 -R estimates the performance of an application and aids defining SLAs in a probabilistic manner. Moreover, E 3 -R automatically reduces the number of QoS objectives and improves the quality of solutions further. Experimental studies demonstrate that E 3 -R efficiently obtains quality deployment configurations that satisfy given SLAs.

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Queuing Theoretic and Evolutionary Deployment Optimization with Probabilistic SLAs for Service Oriented Clouds

Cited by 19 publications

References 11 publications

Service Deployment and Priority Optimization for Multiple Service-Oriented Applications in the Cloud

Service Deployment and Priority Optimization for Multiple Service-Oriented Applications in the Cloud

Multi-objective Optimizations during Parallel Processing in a Dynamic Heterogeneous Cloud Environment

Evolutionary deployment optimization for service‐oriented clouds

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