A Two Stage Heuristic Algorithm for Solving the Server Consolidation Problem with Item-Item and Bin-Item Incompatibility Constraints

Fuzzy Bio-Inspired Hybrid Techniques for Server Consolidation and Virtual Machine Placement in Cloud Environment

“…The process of mapping of a set of Virtual Machines to a set of Physical Machines (VM-PM mapping) is called virtual machine placement and the process of finding 53 optimal placement solution is considered here as virtual machine placement problem. In literature, G u p t a et al [2] introduced a two stage heuristic approach to solve server consolidation problem focusing on bin-item incompatibility constraints. Some of the heuristic techniques widely used for consolidation problem are the First Fit Decreasing (FFD) [3], Best Fit [4], Best Fit Decreasing [5] and other heuristics [6,7].…”

A Graph Theoretic Approach for Minimizing Storage Space using Bin Packing Heuristics

“…The problem is to minimize the number of bins. ii) Server Consolidation [5] is an approach to the efficient usage of computer server resources in order to reduce the total number of servers or server locations that an organization requires. In this case, existing servers can be treated as items, resource utilizations are item sizes, bins are destination servers and the bin capacity is the utilization threshold of the destination servers.…”

A Novel Configuration Method of Virtual Machine Fitness

“…Gupta pointed out that in the process of virtual machine resource scheduling, in cloud computing the topology structure of each computing terminal has an important influence. According to this, he constructed a scheduling algorithm for the virtual machine resource topology [8]. According to Bhutani, the virtual machine scheduling problem in cloud computing is essentially a resource optimization problem.…”

“…In the genetic algorithm, we need to based on the probability of each chromosome was selected in the all round to select the best chromosome from each chromosome. For this purpose, we first calculate the probability of that i D is selected in the k-th iteration, as shown in the formula (8).…”