A fuzzy adaptive request distribution algorithm for cluster-based Web systems

Borzemski, Leszek; Zatwarnicki, Krzysztof

doi:10.1109/empdp.2003.1183576

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…This is the case of Fuzzy Adaptive Request Distribution (FARD) [8]. This work of Borzemski and Zatwarnicki describes a content-aware load balancing mechanism that estimates the response time of each request in every server of the cluster using a fuzzy estimation mechanism.…”

Section: Non Locality-aware Solutionsmentioning

confidence: 98%

An up-to-date survey in web load balancing

2010

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This survey is an up-to-date state-of-the-art in Web load balancing mechanisms that includes all the possible classifications and focuses on the advantages of using load balancing solutions to increase the performance of the Web system. A general description of the Web load balancing solutions is included and organised by differentiating the OSI protocol stack layer the load balancing is based on. The most important request distributing polices that are proposed in the literature are also included. This article summarises all previous surveys on the Web load balancing subject and updates the state-of-the-art with the most recent load balancing proposals.

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Section: Non Locality-aware Solutionsmentioning

confidence: 98%

An up-to-date survey in web load balancing

2010

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“…Those are the solutions enabling global distributions among server rooms located in different geographical locations e.g. GARD [6] and GARDIB [8] and local approaches like FARD [5] and FNRD strategies [7]. All the systems work in this way to minimize the response time for each HTTP request separately.…”

Section: Related Workmentioning

confidence: 99%

“…The main difference between methods and algorithms presented in the works [5][6][7][8][9][10] and the TLFNRD strategy is that the previously proposed Web switches and brokers make the distribution decision only on one level ( Fig. 2a).…”

Section: Related Workmentioning

confidence: 99%

“…In the previous work [5][6][7][8] a research on intelligent fuzzy-neural distribution methods minimizing service time in cluster-based and cloud-based Web systems has been presented. In the field of cloud systems, an effective HTTP request distribution system using two-layer architecture, in which decisions were made on two independent levels by web switches learning mutual behavior has been proposed [9,10].…”

Section: Introductionmentioning

confidence: 99%

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Two-level fuzzy-neural load distribution strategy in cloud-based web system

Zatwarnicki

2020

J Cloud Comp

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Cloud computing Web systems are today the most important part of the Web. Many companies transfer their services to the cloud in order to avoid infrastructure aging and thus preventing less efficient computing. Distribution of the load is a crucial problem in cloud computing systems. Due to the specifics of network traffic, providing an acceptable time of access to the Web content is not trivial. The utilization of the load distribution with adaptive intelligent distribution strategies can deliver the highest quality of service, short service time and reduce the costs. In the article, a new, two-level, intelligent HTTP request distribution strategy is presented. In the process of designing the architecture of the proposed solution, the results of earlier studies and experiments were taken into account. The proposed decision system contains fuzzy-neural models yielding minimal service times in the Web cloud. The article contains a description of the new solution and the test-bed. In the end, the results of the experiments are discussed and conclusions and presented.

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“…Adaptive learning methods keep learning and updating system parameters in real-time while the system is working. The request distribution strategy of Fuzzy Adaptive Request Distribution (FARD) [24][25][26] used traditional error back propagation algorithm in training the learning system, so as to predict the response time of each back-end node in real-time. Reference [27] used an adaptive learning BPNN to get hold of the relationship between server nodes' percentage of CPU usage, processor clock speed, memory utilization and capacity, network bandwidth utilization, server's NIC bandwidth and their response time.…”

Section: Related Workmentioning

confidence: 99%

Request distribution with pre-learning for distributed SSL reverse proxies

Dong

Yang

Sheng

2016

2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distribu

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As network data security becoming more and more universalized, distributed Secure Sockets Layer (SSL) reverse proxies are often used in Web systems to offload CPU exhausting SSL operations from Web servers and improve the execution performance of the SSL protocol. The distribution strategy of user requests to the SSL reverse proxies is a significant factor affecting the system's performance in processing SSL operations. Aiming at improving the quality of request distribution decisions, this paper proposes a new approach for SSL reverse proxy load estimation, i.e. the family of algorithms called Load Estimation with Pre-Learning (LEPL), which estimates load using prelearned machine learning models. Using LEPL, high accuracy of load estimation can be achieved, so that better request distribution decisions can be made. Our experimental results show that by using pre-learning, the SSL reverse proxy system's average response time can be shortened by about 30% -50%.

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A fuzzy adaptive request distribution algorithm for cluster-based Web systems

Cited by 13 publications

References 8 publications

An up-to-date survey in web load balancing

An up-to-date survey in web load balancing

Two-level fuzzy-neural load distribution strategy in cloud-based web system

Request distribution with pre-learning for distributed SSL reverse proxies

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