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Optimal resource allocation in cloud systems is NP-hard due to the involvement of several conflicting objectives and unpredictable cloud traffic. To improve user satisfaction and resource utilization while minimizing end-user cost, the joint allocation of cloud resources is inevitable. In this work, we model end-user cost in cloud as the optimization objective using bandwidth and compute allocation as the decision variables. To solve the joint Virtual Machine Placement (VMP) problem we propose a single point, greedy, software-defined network (SDN)-based solution that minimizes end-user cost by making certain changes to the fat-tree datacenter architecture. Mathematically, we show that the overall objective function is convex hence solving it using a weighted-sum greedy approach will induce solutions that are Pareto-optimal. Experimental evaluations confirm up to 15% reduction in the response time and up to 14 …