In order to achieve high performance and throughput, most of the computing applications nowadays are developed in a distributed computing environment. Such an environment is easily provided by a cloud computing platform. The distributed applications running on cloud demand huge network resources for transfer of data along with computing resources (Virtual Machines). Efficient allocation of virtual machines on hosts can help in the reduction of active hosts which lead to a decrease in deployment cost as well an energy consumption of data centers. We emphasize that while allocating VMs to hosts the communications among the VMs (in the case of distributed applications) cannot be ignored altogether. A placement algorithm which takes into account the cost of physical hosts as well as the communication among VMs can significantly reduce i) the cost of network resource usages ii) energy spent in communication iii) time to complete data transfer and thus improve performance. We have given an ILP formulation of this VM placement problem and then proposed a novel heuristic algorithm to achieve a near optimal solution. We have also analyzed the performance improvement of the proposed algorithm over a few well-known algorithms.

Cloud service providers (CSPs) adapt different pricing models for their offered services. Some of the models are suitable for short term requirement while others may be suitable for the cloud service user's (CSU) long term requirement. For example, reservation-based pricing model is appropriate for a CSU's long term demand for resources. Finding the optimal amount of resources to be reserved in advance, to minimize the total cost, needs sufficient research effort. Various algorithms were discussed in the last couple of years to solve the resource reservation problem but most of them are based on integer programming problem (IPP) which is NP in nature. In this paper, we derive some heuristic-based polynomial time algorithms to find some near optimal solution to this problem. We show that the cost for CSU using our approach is comparable to the solution obtained using optimal IPP.