Supporting quality of service in HTTP servers

Pandey, Raju; Barnes, J. Fritz; Olsson, Ronald A.

doi:10.1145/277697.277742

Cited by 59 publications

(31 citation statements)

References 19 publications

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“…Another class of approaches uses the threshold methods to support state change detection without the need for statistical test [13]- [15]. These solutions are not comparable to the deterministic and probabilistic models because their performance depends on the choice of the threshold value(s).…”

Section: Related Workmentioning

confidence: 99%

Runtime state change detector of computer system resources under non stationary conditions

Casolari

Colajanni

Presti

2009

2009 IEEE International Symposium on Modeling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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Abstract-All runtime management decisions in computer and information systems require immediate detection of relevant changes in the state of their resources. This is accomplished by continuously monitoring the performance/utilization of key system resources and by using appropriate statistical tests to detect the occurance of significant state changes. Unfortunately, the complexity of today systems and applications and the unpredictability of user request patterns result in highly variable and non stationary time series which are difficult to analyze. As a consequence, present solutions for detecting state changes at runtime are affected by excessive time delays or false positives. We propose a novel "agile" runtime detector that solves the delay vs. false positive tradeoff: it is able to detect the relevant state changes as fast as the best reactive models with the lowest percentages of false positives. All evaluations carried out for a large set of scenarios confirm the efficacy and robustness of the proposed model.

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Section: Related Workmentioning

confidence: 99%

Runtime state change detector of computer system resources under non stationary conditions

Casolari

Colajanni

Presti

2009

2009 IEEE International Symposium on Modeling, Analysis &Amp; Simulation of Computer and Telecommunication Systems

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“…Many results show that simple strategies such as controlling the number of processes can improve the response time of high-priority requests while not penalizing the system throughput [2]. To enforce SLA constraints, Pandey et al [24] examine selective allocation of server resources by assigning different priorities to the page requests. In [1] a control-based approach is proposed for Web service differentiation.…”

Section: Related Workmentioning

confidence: 99%

A Cluster-Based Web System Providing Differentiated and Guaranteed Services

et al. 2004

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Abstract. In a world where many users rely on the Web for up-to-date personal and business information and transactions, it is fundamental to build Web systems that allow service providers to differentiate user expectations with multi-class Service Level Agreements (SLAs). In this paper we focus on the server components of the Web, by implementing QoS principles in a Web-server cluster that is, an architecture composed by multiple servers and one front-end node called Web switch. We first propose a methodology to determine a set of confident SLAs in a real Web cluster for multiple classes of users and services. We then decide to implement at the Web switch level all mechanisms that transform a best-effort Web cluster into a QoS-enhanced system. We also compare three QoS-aware policies through experimental results in a real test-bed system. We show that the policy implementing all QoS principles allows a Web content provider to guarantee the contractual SLA targets also in severe load conditions. Other algorithms lacking some QoS principles cannot be used for respecting SLA constraints although they provide acceptable performance for some load and system conditions.

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“…Recently, Web Quality of Service (QoS) becomes a critical issue in various Web services and has been studied in many ways [2,3,4,5,6,7,8]. With the term QoS, we refer to non-functional requirements, such as performance or availability requirements.…”

Section: Introductionmentioning

confidence: 99%

Approaches to Support Differentiated Quality of Web Service

Ryu

Hong

2001

Quality of Future Internet Services

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Abstract. The exponential rise in the number of Web users has inspired the creation of a diversity of Web applications. Hence, Web Quality of Service (QoS) is an increasingly critical issue in Web services, such as e-commerce, Web hosting, etc. In the future, improved QoS will be linked to a fee for service. Customers expect their requests to be served with a quality proportional to the amount charged to their accounts. Because most Web servers currently process requests on a first-come, first-serve basis, they do not provide differentiated QoS. This paper presents two approaches to implement differentiated quality of Web service. In the user-level approach, the Web server is modified to include a classification process, priority queues and a scheduler. However, with this approach, it is difficult to achieve portability. In this paper, a new, portable user-level approach is presented. In the kernel-level approach, a real-time scheduler to support prioritized user requests has been added to the operating system kernel. Prototype implementations for two approaches have been developed and their performances are evaluated.

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Supporting quality of service in HTTP servers

Cited by 59 publications

References 19 publications

Runtime state change detector of computer system resources under non stationary conditions

Runtime state change detector of computer system resources under non stationary conditions

A Cluster-Based Web System Providing Differentiated and Guaranteed Services

Approaches to Support Differentiated Quality of Web Service

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