scholarly journals Optimized Virtual Machine Placement with Traffic-Aware Balancing in Data Center Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Chen ◽  
Xiaofeng Gao ◽  
Guihai Chen
Keyword(s):  
Virtual Machine ◽  
Heuristic Algorithm ◽  
Data Center ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Np Hard ◽  
Data Center Networks ◽  
Placement Problem ◽  
Placement Algorithm ◽  
Communication Locality

Virtualization has been an efficient method to fully utilize computing resources such as servers. The way of placing virtual machines (VMs) among a large pool of servers greatly affects the performance of data center networks (DCNs). As network resources have become a main bottleneck of the performance of DCNs, we concentrate on VM placement with Traffic-Aware Balancing to evenly utilize the links in DCNs. In this paper, we first proposed a Virtual Machine Placement Problem with Traffic-Aware Balancing (VMPPTB) and then proved it to be NP-hard and designed a Longest Processing Time Based Placement algorithm (LPTBP algorithm) to solve it. To take advantage of the communication locality, we proposed Locality-Aware Virtual Machine Placement Problem with Traffic-Aware Balancing (LVMPPTB), which is a multiobjective optimization problem of simultaneously minimizing the maximum number of VM partitions of requests and minimizing the maximum bandwidth occupancy on uplinks of Top of Rack (ToR) switches. We also proved it to be NP-hard and designed a heuristic algorithm (Least-Load First Based Placement algorithm, LLBP algorithm) to solve it. Through extensive simulations, the proposed heuristic algorithm is proven to significantly balance the bandwidth occupancy on uplinks of ToR switches, while keeping the number of VM partitions of each request small enough.

Virtual Machine Placement in IaaS Cloud

2017 ◽  
pp. 130-158
Author(s):  
Prateek Khandelwal ◽  
Gaurav Somani
Keyword(s):  
Customer Satisfaction ◽  
Virtual Machine ◽  
Data Center ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Attractive Alternative ◽  
Operational Cost ◽  
Trade Offs ◽  
Crucial Component

A crucial component of providing services over virtual machines to users is how the provider places those virtual machines on physical servers. While one strategy can offer an increased performance for the virtual machine, and hence customer satisfaction, another can offer increased savings for the cloud operator. Both have their trade-offs. Also, with increasing costs of electricity, and given the fact that the major component of the operational cost of a data center is that of powering it, green strategies also offer an attractive alternative. In this chapter, the authors will look into what kind of different placement strategies have been developed, and the kind of advantages they purport to offer.

A Multi-Objective Fuzzy Ant Colony Optimization Algorithm for Virtual Machine Placement

2016 ◽  
Vol 5 (4) ◽  
pp. 165-191 ◽  
Author(s):  
Boominathan Perumal ◽  
Aramudhan M.
Keyword(s):  
Ant Colony Optimization ◽  
Virtual Machine ◽  
Bin Packing ◽  
Virtual Machines ◽  
Transition Probability ◽  
Virtual Machine Placement ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Placement Problem ◽  
Multi Objective

In cloud computing, the most important challenge is to enforce proper utilization of physical resources. To accomplish the mentioned challenge, the cloud providers need to take care of optimal mapping of virtual machines to a set of physical machines. In this paper, the authors address the mapping problem as a multi-objective virtual machine placement problem (VMP) and propose to apply multi-objective fuzzy ant colony optimization (F-ACO) technique for optimal placing of virtual machines in the physical servers. VMP-F-ACO is a combination of fuzzy logic and ACO, where we use fuzzy transition probability rule to simulate the behaviour of the ants and the authors apply the same for virtual machine placement problem. The results of fuzzy ACO techniques are compared with five variants of classical ACO, three bin packing heuristics and two evolutionary algorithms. The results show that the fuzzy ACO techniques are better than the other optimization and heuristic techniques considered.

scholarly journals A Hybrid Technique for Server Consolidation in Cloud Computing Environment

2020 ◽  
Vol 20 (1) ◽  
pp. 36-52
Author(s):  
C. Vijaya ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Hybrid Technique ◽  
Computing Environment ◽  
Placement Problem ◽  
Cloud Computing Environment ◽  
Server Consolidation ◽  
Memory Utilization

AbstractThe goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

scholarly journals Balanced and Energy Efficient Multi Core-Aware Virtual Machine Placement Algorithm Using Multi-Dimensional Resource Space Partition Model

2021 ◽  
Author(s):  
Nagadevi ◽  
Kasmir Raja
Keyword(s):  
Resource Allocation ◽  
Power Consumption ◽  
Virtual Machine ◽  
Data Center ◽  
Energy Efficient ◽  
Virtual Machine Placement ◽  
Partition Model ◽  
Space Partition ◽  
Resource Space ◽  
Placement Algorithm

Optimal resource management is required in a data center to allocate the resources to users in a balanced manner. Balanced resource allocation is one of the key challenges in the data center. The multi-dimensional resources of a data center must be allocated in a balanced manner in all the dimensions of physical machines. The unbalanced resource allocation leads to unused residual resource fragments. The unused residual resource fragments leads to resource wastage. If the multi-dimensional data center resources are allocated in a balanced manner, the resource wastage does not occur. Also, the balanced allocation improves the power consumption. The balanced resource allocation reduces the resource wastage as well as reduces the power consumption. In this paper, we have designed a Balanced Energy Efficient Multi-Core Aware Virtual Machine Placement algorithm (MCA-BEE-VMP) using multi-dimensional resource space partition model to balance the resources like CPU and memory and also to reduce the power consumption. We used Google Cloud Jobs (GoCJ) dataset for the simulation. In our simulation of MCA-BEE-VMP using Cloud Sim simulation tool we have achieved balanced CPU and memory resources allocation in two dimensions of a physical machine. The resource wastage and power consumption is improved and the simulation results were analyzed.

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