Heuristic Approaches for QoS-Based Service Selection

Comes, Diana Elena; Baraki, Harun; Reichle, Roland; Zapf, Michael; Geihs, Kurt

doi:10.1007/978-3-642-17358-5_30

Cited by 22 publications

(19 citation statements)

References 7 publications

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“…The QoSWSC problem can be stated as finding the 39 bindings that meet all the QoS constraints and maxi-40 mize or minimize certain user-defined optimization cri- 41 teria, e.g. minimize cost.…”

mentioning

confidence: 99%

QoS-aware web services composition using GRASP with Path Relinking

Parejo

Segura

Fernández

et al. 2014

Expert Systems with Applications

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In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NPhard optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to 40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when results must be available in 100ms; i.e. it provides compositions with a better QoS, implying cost savings, increased availability and reduced execution times for the end-user.

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mentioning

confidence: 99%

QoS-aware web services composition using GRASP with Path Relinking

Parejo

Segura

Fernández

et al. 2014

Expert Systems with Applications

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“…If a third party rejects the Evolution Request or if it is not available anymore, the Evolution Coordination can start a search for suitable services. To this end, we will adopt our service selection algorithms proposed in [7].…”

Section: Evolution Coordinationmentioning

confidence: 99%

“…In the event of pure Web service selection for business processes, this issue can be solved by our approach in the paper [7] or by other popular approaches like integer linear programming [24] and genetic algorithms [6].…”

Section: Optimization Problemmentioning

confidence: 99%

Service Co-evolution in the Internet of Things

Tran¹,

Baraki²,

Geihs³

2015

EAI Endorsed Transactions on Cloud Systems

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The envisioned Internet of Things (IoT) foresees a future Internet incorporating smart physical objects that offer hosted functionality as IoT services. This service-based integration of IoT will be smarter, easier to communicate with and more valuable for enriching our environment. However, the interfaces and services can be modified due to updates and amendments. Such modifications require adaptations in all participating parties. Therefore, the aim of this research is to present a vision of service co-evolution in IoT. Moreover, we propose a novel agent architecture which supports the evolution by controlling service versions, updating local service instances and enabling the collaboration of agents. In this way, the service co-evolution can make systems more adaptive, efficient and reduce costs to manage maintenance.

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“…Selecting the right concrete services for the composition is known as the service selection problem and is known to be NP-hard [9]. We give a short insight of how we handled this problem in section 3.1 and further details can be found in [4]. In Figure 1 we consider a simple BPEL tree for illustration purposes.…”

Section: Service Composition Representationmentioning

confidence: 99%

“…For brevity reasons we only present a rough sketch of OP T IM P RO in this paper. Further details about the selection algorithms can be found in [4]. OP T IM P RO is an iterative algorithm and improves the variant found on the root node (the objective value of the variant) with each iteration step.…”

Section: Bprules Featuresmentioning

confidence: 99%

BPRules and the BPR-Framework: Comprehensive Support for Managing QoS in Web Service Compositions

Comes

Baraki

Reichle

et al. 2012

Distributed Applications and Interoperable Systems

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Abstract. For a successful collaboration between enterprises, Web services and service compositions need to fulfill certain QoS (Quality of Service) requirements so that they can be trusted by their clients. Thus, the best services have to be chosen for the composition, the performance of the composition needs to be monitored and in case of QoS deviations, appropriate management actions are required. We propose the BPRules language and the BPR-framework that offer novel capabilities and improved flexibility for the management of BPEL processes with regard to QoS concerns. The BPRules language allows to specify the QoS monitoring of BPEL processes and offers a variety of management actions for controlling the process and for the improvement of its QoS behavior. Thereby, the BPR-framework provides the necessary components to perform the QoS monitoring and to execute the management actions. For the selection of high quality services, the BPR-framework comes with efficient selection algorithms, like our OP T IM P RO algorithm. We present the features of BPRules that we consider as indispensable for managing the services' QoS behavior.

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Heuristic Approaches for QoS-Based Service Selection

Cited by 22 publications

References 7 publications

QoS-aware web services composition using GRASP with Path Relinking

QoS-aware web services composition using GRASP with Path Relinking

Service Co-evolution in the Internet of Things

BPRules and the BPR-Framework: Comprehensive Support for Managing QoS in Web Service Compositions

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