Virtual Machine Packing Research Articles

Virtual Machine Placement Using Energy Efficient Particle Swarm Optimization in Cloud Datacenter

AbstractEfficient resource allocation through Virtual machine placement in a cloud datacenter is an ever-growing demand. Different Virtual Machine optimization techniques are constructed for different optimization problems. Particle Swam Optimization (PSO) Algorithm is one of the optimization techniques to solve the multidimensional virtual machine placement problem. In the algorithm being proposed we use the combination of Modified First Fit Decreasing Algorithm (MFFD) with Particle Swarm Optimization Algorithm, used to solve the best Virtual Machine packing in active Physical Machines to reduce energy consumption; we first screen all Physical Machines for possible accommodation in each Physical Machine and then the Modified Particle Swam Optimization (MPSO) Algorithm is used to get the best fit solution.. In our paper, we discuss how to improve the efficiency of Particle Swarm Intelligence by adapting the efficient mechanism being proposed. The obtained result shows that the proposed algorithm provides an optimized solution compared to the existing algorithms.

Page-sharing-based virtual machine packing with multi-resource constraints to reduce network traffic in migration for clouds

Virtual machine (VM) packing plays an important role in improving resource utilization in cloud data centers. Recently, memory content similarity among VM instances has been used to speed up multiple VM migration in large clouds. Based on this, many VM packing algorithms have been proposed, which only considered the memory capacity of physical machines (PMs) as the resource constraint. However, in practice the results of such algorithms are not feasible, because thy may not satisfy the constraints of multiple resources (e.g., CPU of the PMs). Besides, the granularities of memory sharing in existing studies are very coarse, and they cannot fully leverage the benefits of memory content similarity which mainly appears at memory page level. In this paper, we study the page-sharing-based VM packing that considers constraints in multiple resources. Given a set of VM instances that share a large number of common memory pages, we pack them into the minimum number of PMs, subject to the constraints in the multiple resources on the PMs. This problem is solved in two steps. First, we pack the maximum number of VMs into a given PM, and then propose an approximation algorithm. The approximation ratio is better than that of the existing algorithm. Then, based on this approximation algorithm, we propose a heuristic algorithm to solve the general problem. Experimental results show that our heuristic algorithm outperforms existing approaches with at most 25% less required PMs and at most 40% less memory page transferring.

Sharing-Aware Online Virtual Machine Packing in Heterogeneous Resource Clouds

One of the key problems that cloud providers need to efficiently solve when offering on-demand virtual machine (VM) instances to a large number of users is the VM Packing problem, a variant of Bin Packing. The VM Packing problem requires determining the assignment of user requested VM instances to physical servers such that the number of physical servers is minimized. In this paper, we consider a more general variant of the VM Packing problem, called the Sharing-Aware VM Packing problem, that has the same objective as the standard VM Packing problem, but allows the VM instances collocated on the same physical server to share memory pages, thus reducing the amount of cloud resources required to satisfy the users’ demand. Our main contributions consist of designing several online algorithms for solving the Sharing-Aware VM Packing problem, and performing an extensive set of experiments to compare their performance against that of several existing sharing-oblivious online algorithms. For small problem instances, we also compare the performance of the proposed online algorithms against the optimal solution obtained by solving the offline variant of the Sharing-Aware VM Packing problem (i.e., the version of the problem that assumes that the set of VM requests are known a priori). The experimental results show that our proposed sharing-aware online algorithms activate a smaller average number of physical servers relative to the sharing-oblivious algorithms, directly reduce the amount of required memory, and thus, require fewer physical servers to instantiate the VM instances requested by users.

Novel Resource Allocation Algorithm for Energy-Efficient Cloud Computing in Heterogeneous Environment

Energy efficiency is one of the most important design considerations for a cloud data center. Recent approaches to the energy-efficient resource management for data centers usually model the problem as a bin packing problem with the goal of minimizing the number of physical machines (PMs) employed. However, minimizing the number of PMs may not necessarily minimize the energy consumption in a heterogeneous cloud environment. To address the problem, this paper models the resource allocation problem in a heterogeneous cloud data center as a constraint satisfaction problem (CSP). By solving this constraint satisfaction problem, an optimal resource allocation scheme, which includes a virtual machine provision algorithm and a virtual machine packing algorithm, is designed to minimize the energy consumption in a virtualized heterogeneous cloud data center. Performance studies show that this proposed new scheme outperforms the existing bin-packing based approaches in terms of energy consumption in heterogeneous cloud data centers.