Effective technique for allocating servers to support cloud using GPS and GIS

Cloud must be provided services to customers in all circumstances with high quality because it associated with market´s applications that cannot be delayed, postponed for another time, etc because that will lead to considerable material losses. Under the cloud there is group of servers (data centers) may work as distributed system (like Google Apps engine, Amazon, etc) these servers can support each other rapidly and effective cost at peak time, failure, backup, etc to offer continuously services to clients. A big problem appears when there is not idle server in the same data center, which has source server need the support, in this case need to decide any server from outside the data center site will allocate to that source server (which needed the support) in cloud rapidly with effective cost. This paper proposed a new technique for allocating servers to support cloud, i.e. determine the available servers which relatively have a higher idle (not busy) to support source servers using queue model at the same time employs GIS and GPS techniques via algorithm of Haversine equation to select the idle server which closer to satisfy lowest cost and reach the optimal throughput (performance). Implement and applying this proposed technique shows may not allocated the server which has the highest rate idle but may allocate the server with lowest rate idle to support the source server, and that will not cause a big difference/changing because all those idle servers in cloud are supercomputers with higher resources, reaching the aim of this new technique means an effective cost with optimal throughput when select the idle server (lowest busy relatively), which is closer to source server.