Abstract
This paper undertakes the challenge of server selection problem in Erlang-loss system (ELS). We propose a novel approach to the server selection problem in the ELS taking into account probabilistic modelling to reflect a practical scenario when user arrivals vary over time. The proposed framework is divided into three stages, including i) developing a new method for server selection based on the M/M/n/n queuing model with probabilistic arrivals; ii) combining server allocation results with further research on utility-maximising server selection to optimise system performance; and iii) designing a heuristic approach to efficiently solve the developed optimisation problem. Simulation results show that by using this framework, Internet Service Providers (ISPs) can significantly improve QoS for better revenue with optimal server allocation in their data centre networks.
Highlights
A variety of technologies [1] have emerged to provide enhanced services, a tremendously increasing number of users causes heavy congestion when many users simultaneously access the limited network resources, e.g. shared databases or file servers
A lower number of servers are required with probabilistic modelling for i.n.d. arrivals when compared to the i.i.d. case and a higher utility per server (UpS) is shown to achieve for all cases over the employment of all servers
We have presented a novel utilitymaximising server selection for an Erlang-Loss System (ELS), i.e. M/M/n/n queuing model
Summary
A variety of technologies [1] have emerged to provide enhanced services, a tremendously increasing number of users causes heavy congestion when many users simultaneously access the limited network resources, e.g. shared databases or file servers. The distribution of Internet services across the network allows us to place the servers geographically closer to the end-users, which improves the QoS and enhances the service scalability by sharing the load among several server farms. This raises a principal issue of how to direct clients to the most appropriate service locations. We combine server selection results with further research on the utility-based solution to optimise system performance.
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