Abstract
An attractive advantage of mobile networks is that their users can gain easy access to different services. In some cases, equivalent services could be fulfilled by different providers, which brings the question of how to rationally select the best provider among all possibilities. In this paper, we investigate an answer to this question from both quality-of-service (QoS) and energy perspectives by formulating an optimisation problem. We illustrate the theoretical results with examples from experimental measurements of the resulting energy and performance.
Highlights
An attractive advantage of mobile networks is that their users can gain easy access to different services
When the user decides to transfer some fraction α of its jobs to the remote cluster, and assuming that the RC has another load of jobs arriving at rate λ, we obtain that the net average response time perceived by the users who emanate from the local cluster (LC) is: WU = αWR (FR, λ + αλL ) + (1 − α)WL (FL, (1 − α)λL )
Under the assumption that each of the two clusters shares its load among its processors, that the RC processors are f times faster than the LC processors, and that all job arrival traffic is Poisson, we can use the well known Pollaczek–Khintchine formula [4] to estimate the average response time per job as a function of the load dispatching policy characterised by the fraction α of jobs that are sent to the RC
Summary
The power consumption of mobile services will depend on the load. Will depend on load because a more heavily loaded computational or communication resource will quite naturally increase response times Such issues are somewhat more complex, because the server clusters hosting the services may turn off some of the resources under lighter loads, so that when load is higher power consumption will obviously increase, QoS can improve. In this paper we discuss the much broader question: suppose that a mobile community could access services from both a local server within the operator provider (the “local server”) and from remote service providers (“remote server”), what fraction of their workload should they send remotely if they wish to optimise both QoS and energy consumption. We formulate the optimisation problem, describe its solution and present some numerical examples
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