A Dynamic Web Service Selection Strategy with QoS Global Optimization Based on Multi-objective Genetic Algorithm

Liu, Shulei; Liu, Yunxiang; Jing, Ning; Tang, Guifen; Tang, Yu

doi:10.1007/11590354_10

Cited by 33 publications

(28 citation statements)

References 4 publications

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“…Some studies only considered sequential composition structure [25]- [28], and some studies did not consider the global quality of service (QoS) constraints [25], [27]- [30]. Only single solutions were given by other studies [29], [31], [32].…”

Section: Related Workmentioning

confidence: 99%

Multi-objective Ant Colony System for Data-Intensive Service Provision

Wang¹,

Shen²,

Luo³

2014

2014 Second International Conference on Advanced Cloud and Big Data

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Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective dataintensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multiobjective genetic algorithm with respect to five performance metrics Abstract-Data-intensive services have become one of the most challenging applications in cloud computing. The classical service composition problem will face new challenges as the services and correspondent data grow. A typical environment is the large scale scientific project AMS, which we are processing huge amount of data streams. In this paper, we will resolve service composition problem by considering the multi-objective data-intensive features. We propose to apply ant colony optimization algorithms and implemented them with simulated workflows in different scenarios. To evaluate the proposed algorithm, we compared it with a multi-objective genetic algorithm with respect to five performance metrics.

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Section: Related Workmentioning

confidence: 99%

Multi-objective Ant Colony System for Data-Intensive Service Provision

Wang¹,

Shen²,

Luo³

2014

2014 Second International Conference on Advanced Cloud and Big Data

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“…Genetic algorithms have recently been leveraged for solving QoS-aware service composition problems [13,14,15,16]. While some are solving single-objective optimization problems [14], other approaches deal with multi-objective problems.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Multi-Objective Optimization Framework for Selecting Distributed Deployments in a Heterogeneous Environment

Vinek

Beran

Schikuta

2011

Procedia Computer Science

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In distributed systems, where several deployments of a specific service exist, it is a crucial task to select and combine concrete deployments to build an executable workflow. Non-functional properties such as performance and availability are taken into account in such selection processes that are designed to reach certain objectives while meeting constraints. In this paper, a concrete data-intensive application scenario from a High-Energy Physics experiment comprising a deployment selection challenge is introduced. A generic model for distributed systems is presented based on which a formal model representing the individual components of the system is derived. The optimization problem is approached both from the angle of the user and the angle of the system provider. Moreover the dynamic aspects of the underlying system are taken into account. This results in a dynamic multi-objective optimization problem for which an explicit memory-based genetic algorithm is proposed.

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“…Let pr be the offered price value, the price aggregation is formulated based on (12) for the sequence pattern as in Observation 2. For the parallel pattern as in Observation 3, the price aggregation is formulated based on (13). Meanwhile, (14) is applied for parallel pattern as in Observation 4.…”

Section: Objective Functionsmentioning

confidence: 99%

“…Meanwhile, consistency check for candidates for c results in only c 7 as the consistent candidate. Obviously c 7 is selected for activity c. Once all activities have been assigned with a candidate, the aggregation of price is computed as in (13).…”

Section: Case Studymentioning

confidence: 99%

“…In [8] [12], they used Simple Additive Weighting (SAW) technique to find the best candidate in the same set for the local selection. Furthermore, in [8] [11] [13], they transformed the problem into global optimization for the global selection. Meanwhile the work by [12] has suggested a pattern-wise selection as a graph-based approach to apply for the global selection.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Service Selection for Service Composition with Time Constraints

Ismail

Yan

Shen

2009

2009 Australian Software Engineering Conference

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Service selection is a kind of planning approach that evaluates and selects from multiple services to form a composite plan. In service selection, we found one additional issue that has not been investigated yet namely time constraints consistency among composite services. This issue is significant because there are potential time constraints involved that might cause inconsistency between selected services although they can offer aggregated QoS that satisfy global requirements. Furthermore, there might be some unintended waiting time to be considered in the QoS aggregation. Thus, this paper contributes to the analysis on the problem caused by the time constraints and proposes general selection approach to tackle these issues. The approach comprises two major functionalities; (i) exploration of the process model based on patterns, (ii) evaluation of candidates based on time constraints and objective functions.

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A Dynamic Web Service Selection Strategy with QoS Global Optimization Based on Multi-objective Genetic Algorithm

Cited by 33 publications

References 4 publications

Multi-objective Ant Colony System for Data-Intensive Service Provision

Multi-objective Ant Colony System for Data-Intensive Service Provision

A Dynamic Multi-Objective Optimization Framework for Selecting Distributed Deployments in a Heterogeneous Environment

Dynamic Service Selection for Service Composition with Time Constraints

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