Review of Quality of Service Based Techniques in Cloud Computing
01 Jan 2019-pp 255-265
TL;DR: This paper is presenting main existing research work of different QoS management techniques in cloud computing and also compare them with their strength and weakness and will be helpful for new researchers in this field to get exposure of quality of service based management techniques.
Abstract: Nowadays cloud computing is an emerging technology for enhancing our daily life. cloud computing provides the facility to pay for use. There are some important service providers in cloud computing such as IBM, Google, Amazon, Microsoft, etc., these service providers give the different services to the users. Cloud providers offer services to the users based on the expected quality requirements, So it is a big challenge of cloud to provide enhanced Quality of Service (QoS) to their users. In this paper, we are presenting main existing research work of different QoS management techniques in cloud computing and also compare them with their strength and weakness. This review will be helpful for new researchers in this field to get exposure of quality of service based management techniques.
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01 Jan 2021
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TL;DR: This paper first examines the basic problem of QoS routing, namely, finding a path that satisfies multiple constraints, and its implications on routing metric selection, and presents three path computation algorithms for source routing and for hop-by-hop routing.
Abstract: Several new architectures have been developed for supporting multimedia applications such as digital video and audio. However, quality-of-service (QoS) routing is an important element that is still missing from these architectures. In this paper, we consider a number of issues in QoS routing. We first examine the basic problem of QoS routing, namely, finding a path that satisfies multiple constraints, and its implications on routing metric selection, and then present three path computation algorithms for source routing and for hop-by-hop routing.
1,769 citations
05 Dec 2011
TL;DR: A framework and a mechanism, which measure the quality and prioritize Cloud services, which will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).
Abstract: With the growth of Cloud Computing, more and more companies are offering different cloud services. From the customer's point of view, it is always difficult to decide whose services they should use, based on users' requirements. Currently there is no software framework which can automatically index cloud providers based on their needs. In this work, we propose a framework and a mechanism, which measure the quality and prioritize Cloud services. Such framework can make significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their Quality of Services (QoS).
337 citations
TL;DR: This paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers, and shows that the experimental results show that the approaches outperform other competing approaches.
Abstract: Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.
292 citations
TL;DR: An extensive evaluation study is conducted to analyse which solution suits best in which scenario to maximize SaaS [email protected]?s profit and shows that the proposed algorithms provide substantial improvement over reference ones across all ranges of variation in QoS parameters.
229 citations
TL;DR: A survey of the state of the art of QoS modeling approaches suitable for cloud systems is provided, which review and classify their early application to some decision-making problems arising in cloud QoS management.
Abstract: Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.
225 citations