Work within next generation networks considers additional network convergence possibilities and the integration of new services to the web. This trend responds to the ongoing growth of end-user demand for services that can be delivered anytime, anywhere, on any web-capable device, and of traffic generated by new applications, e.g., the Internet of Things. To support the massive traffic generated by the enormous user base and number of devices with reliability and high quality, web services run from redundant servers. As new servers need to be regularly deployed at different geographical locations, energy costs have become a source of major concern for operators. We propose a cost aware method for routing web requests across replicated and distributed servers that can exploit the spatial and temporal variations of both electricity prices and the server network. The method relies on a learning automaton that makes per-request decisions, which can be computed much faster than regular global optimization methods. Using simulation and testbed measurements, we show the cost reductions that are achievable with minimal impact on performance compared to standard web routing algorithms.
Keywords: QoS; autonomous systems; datacenter; energy; learning automata; web.