Virtual Machine Consolidation Algorithm Research Articles

Adaptive selection of dynamic VM consolidation algorithm using neural network for cloud resource management

Cloud resource management becomes more important with the increasing usage of cloud resources. With various cloud options available, cloud provider may have different priority in managing the resource through resource scheduling and provisioning. Dynamic VM (Virtual Machine) consolidation algorithm is one of the techniques which can be used to reduce energy consumption through VM migration. Higher VM migration may lead to lower energy consumption and higher SLA violation. Although previous research has successfully decreased energy consumption and SLA violation, cloud providers may need to manage trade-offs between energy and SLA violation through availability of priority in the system. This paper proposes neural network-based adaptive selection of VM consolidation algorithms which adaptively chooses appropriate algorithm according to cloud provider’s goal priority and environment parameters. Dataset generation and performance evaluation using simulations on real-world PlanetLab VMs workload trace showed that adaptive selector produced better average performance score than independent methods on various evaluation priority.

Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing

Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient schedu ling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization.

Energy-efficient virtual machine consolidation algorithm in cloud data centers

Cloud data centers consume a multitude of power leading to the problem of high energy consumption. In order to solve this problem, an energy-efficient virtual machine (VM) consolidation algorithm named PVDE (prediction-based VM deployment algorithm for energy efficiency) is presented. The proposed algorithm uses linear weighted method to predict the load of a host and classifies the hosts in the data center, based on the predicted host load, into four classes for the purpose of VMs migration. We also propose four types of VM selection algorithms for the purpose of determining potential VMs to be migrated. We performed extensive performance analysis of the proposed algorithms. Experimental results show that, in contrast to other energy-saving algorithms, the algorithm proposed in this work significantly reduces the energy consumption and maintains low service level agreement (SLA) violations.

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

Task Classification Based Energy-Aware Consolidation in Clouds

We consider a cloud data center, in which the service provider supplies virtual machines (VMs) on hosts or physical machines (PMs) to its subscribers for computation in an on-demand fashion. For the cloud data center, we propose a task consolidation algorithm based on task classification (i.e., computation-intensive and data-intensive) and resource utilization (e.g., CPU and RAM). Furthermore, we design a VM consolidation algorithm to balance task execution time and energy consumption without violating a predefined service level agreement (SLA). Unlike the existing research on VM consolidation or scheduling that applies none or single threshold schemes, we focus on a double threshold (upper and lower) scheme, which is used for VM consolidation. More specifically, when a host operates with resource utilization below the lower threshold, all the VMs on the host will be scheduled to be migrated to other hosts and then the host will be powered down, while when a host operates with resource utilization above the upper threshold, a VM will be migrated to avoid using 100% of resource utilization. Based on experimental performance evaluations with real-world traces, we prove that our task classification based energy-aware consolidation algorithm (TCEA) achieves a significant energy reduction without incurring predefined SLA violations.

I Q R based Approach for Energy Efficient Dynamic VM Consolidation for Green Cloud Data Centers

the advent of cloud computing in the arena of IT field energy consumption and service level agreement (SLA) violation emerge as a major problem, which reduces the profit of cloud service providers (CSP) and affect the cloud customers by fencing the reusability and scalability of the cloud data center services. This problem needs to be eradicate for the efficient resource provisioning in cloud data center. To satisfy the customer need virtual machine (VM) migration technique is required to balance the load of entire data center. Therefore we need to transfer the virtual machine of the overloaded host to the light weighted host using virtual machine migration technique. Due to frequent load balancing of cloud data center enormous amount of energy consumption takes place. This enhances the overall energy cost and degrades the performance of cloud data center. This paper proposes an Energy Efficient Dynamic VM Consolidation algorithm for reducing energy consumption. Keywordscomputing, Cloud service provider (CSP), Energy consumption, SLA violation, Load balancing, VM migration.

Improving consolidation of virtual machine based on virtual switching overhead estimation

In virtualized data centers, live virtual machine (VM) migration can increase energy efficiency by consolidating VMs on fewer servers. This problem is usually considered a Bin Packing Problem with server capacity constraints, such as CPU, memory and network bandwidth. In order to minimize the communication traffic within the data center network, existing research works used correlation-based strategy to consolidate VMs onto servers, which means that VMs with inter-traffic are consolidated as closely as possible, e.g. within a server or a rack. However, this strategy increases the traffic load of virtual switches on servers, and it causes a certain number of CPU cycles of servers to move traffic through virtual switches. A lack of consideration for the virtual switching overhead may increase the risk that VMs are not allocated enough resources, and consequently reduce VMs׳ performance. In this work, we conduct experiments to estimate the virtual switching overhead on server CPU resource, and based on the experiment results, we propose a virtual-switching-aware VM consolidation algorithm to address this problem. Experiments on representative data center workloads show that the overhead can occupy 10–30% of server׳s CPU resources. Additionally, our algorithm shows a much lower server capacity violation probability as compared with the baseline algorithm.

A Novel Dynamic Virtual Machine Consolidation Algorithm Based on Correlation Coefficient Analysis and Bin Packing

Virt ual Machine Consolidation (VMC) is known as a crucial method to improve system resource utilization and service level agreement in a datacenter. In this paper, we propose a novel algorithm named Segmentation Iteration Correlation Combination (SICC) expressly for the VMC. In this algorithm we integrate the methods of statistic regression modeling, Pearson correlation coefficient analysis and off-line Bin packing to establish a new process strategy to achieve excellent higher one dimensional resource utilization of a datacenter. The SICC operates based on an innovative two-stage strategy: the first stage is to divide all the Virtual Machines (VM) into several groups and reduce the peak-mean difference value of the VM resource utilization inside each VM group as much as possible by the Correlation Coefficient Serial analysis and one kind of improved VM dynamic complementary consolidation algorithm, derived from the algorithm of Iterative Correlation Match Algorithm (ICMA). When the difference of peak-mean value is small enough, we can take advantage of the Bin Packing methods in the second stage to improve resource utilization on account of the reasonable VM consolidation order. The numerical simulation indicates that the algorithm feature 3 % to 20% performance improvement in resource utilization to ICMA algorithm and approximate 50% performance improvement in resource utilization to First Fit Decreasing (FFD) with the same dynamic initial conditions.