QoS Management of Web Services

Zheng, Zibin; Lyu, Michael R.

doi:10.1007/978-3-642-34207-3

Cited by 22 publications

(11 citation statements)

References 67 publications

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“…Prior work on QoS of web services mainly focuses on performance, reliability, scalability, capacity, robustness, exception handling, accuracy, integrity, accessibility, availability, interoperability, security, and network-related QoS requirements [2,[17][18][19]. Bartalos and Blake [20] propose the integration of energy efficiency as a separate concern of QoS.…”

Section: Related Workmentioning

confidence: 99%

“…Web services are the forerunners to implementing service-oriented architecture (SOA) [1]. Therefore, they are widely employed for building loosely coupled distributed systems, such as e-commerce, e-government, automotive system, and multimedia services [2]. Meanwhile, web services have attracted many studies researching on it.…”

Section: Introductionmentioning

confidence: 99%

“…Meanwhile, web services have attracted many studies researching on it. Some studies concentrate on enabling mobile hosting of web services [3], some studies focus on the composition and discovery of web services [4][5][6], some studies concern the quality of service (QoS) of web services [2,7], and so on.…”

Section: Introductionmentioning

confidence: 99%

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Power consumption prediction of web services for energy-efficient service selection

Liu

Jiang

Cui

et al. 2015

Pers Ubiquit Comput

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Service-oriented computing (SOC) is a popular software paradigm that is widely employed in IT industry. SOC uses ''services'' as the unit of functionality of a software application. The massive wave of SOC applications involves considerable energy consumption of servers, which should not be ignored in large-scale computing environment. When a service requirement can be answered by several web services, the energy consumption for each service to reply to the service request may be different. When this happens, web service selection (WSS) is often required to choose appropriate services to maximize global energy efficiency of SOC applications. Accordingly, this paper proposes a Virtual Power Meter Supported Power Consumption Prediction method for WSS (VPMSPCP). VPMSPCP facilitates choosing appropriate services to minimize wasteful electrical energy from the overall environment of SOC applications. According to our empirical proof, there is a correlation between the power consumption of a service and the status of the server where this service resides. We take advantage of this discovery to develop VPMSPCP by combining a ridge regression model with a well-known web service power modeling method. There are mainly two steps to establish VPMSPCP. First, we develop a virtual power meter (VPM) for each server. VPM is used to estimate the average power of a server under a certain status. Second, we apply the VPM to develop VPMSPCP which estimates power consumption of a web service according to the current status of the corresponding servers. Experiments show that VPMSPCP performs well in improving energy saving in WSS.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power consumption prediction of web services for energy-efficient service selection

Liu

Jiang

Cui

et al. 2015

Pers Ubiquit Comput

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“…denotes that the Web service does not have been packaged. These data are set according to [2]. Given the service plan, the candidate services for each task in the service plan, the detailed information of each service, and the platforms, the object is to select one service from the candidate services for each task in the service plan and to select one platform for the new mashup in order to maximize the cost performance of the generated mashup.…”

Section: Motivation Scenariomentioning

confidence: 99%

“…For QoS, we should consider both service execution time and parameter transmission time, and try to save the parameter transmission time. As for cost, due to service packages, the traditional greedy algorithm [2,3] that selects the services with lowest price is not suitable any longer.…”

Section: Introductionmentioning

confidence: 99%

Cost Performance Driven Service Mashup: A Developer Perspective

Deng

Taheri

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the QoS (quality of service) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, GA4MC (genetic algorithm for mashup creation), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

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Mobile Service Provisioning

Deng

Yin

2020

Mobile Service Computing

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QoS Management of Web Services

Cited by 22 publications

References 67 publications

Power consumption prediction of web services for energy-efficient service selection

Power consumption prediction of web services for energy-efficient service selection

Cost Performance Driven Service Mashup: A Developer Perspective

Mobile Service Provisioning

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