Energy-Delay Aware Web Request Routing Using Learning Automata
Velusamy, Gandhimathi 1970-
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The ever-increasing dependency on the Internet in our day-to-day life and the pay-as-you-go model of the cloud computing causes an extensive number of applications to be deployed as web services. The web services are normally deployed on clusters of redundant servers replicated across different geographical locations to provide reliable and better-quality services. Usually, a front-end server receives the requests from clients and distributes to the redundant servers based on load balancing policies. The explosion of web services and the replication causes a massive number of servers to be run from data centers. These servers consume enormous electricity and become a concern for data-center owners with increased electricity bills. The U.S. electricity market possesses spatio-temporal variations in electricity prices. Normally, the requests are served from the nearest servers to the clients. However, this will increase the load on the data centers in more populated areas. Moreover, the electricity rates at the nearest locations may be higher. In this scenario, by making the front-end servers route the requests to the back-end servers based on the electricity prices, the cost of delivering the web services for a data-center owner could be controlled. However, if we try to optimize the energy cost by serving a request from a location where the electricity cost is cheaper, it may increase the delay in receiving the response based on the distance between the server and client, state of the network and the server. In certain applications, the increased latency in receiving the responses may lead to revenue loss if any dissatisfied customer revokes his subscription. So, reducing the energy cost without increasing the latency is a great challenge in web-based service delivery. In this dissertation, we propose a solution to reduce the electricity costs for data-center owners and to serve the requests with reduced latency. We propose an online learning automata based request routing algorithm to be run from the front-end servers to select back-end servers with energy-delay awareness. Our experiments on a cloud testbed with real-time workload have proved with better performance in both electricity cost and delay compared to the existing methods.