Virtual Machine Allocation In Clouds Research Articles

Hybrid approach for virtual machine allocation in cloud computing

In this manuscript, a Combined Approach of Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm and Adaptive β-Hill Climbing Algorithm for Virtual Machine Allocation in Cloud Computing (BA-VMA-CC) is proposed. Generalized Backtracking Regularized Adaptive Matching Pursuit Algorithm (GBRAMP) is used for Virtual Machine (VM) Migration process and Adaptive β-Hill Climbing Algorithm is used to Virtual Machine Placement. These two tasks are essential elements of VM allocation. GBRAMP is used to minimize cost and energy for both cloud service providers and users with help of migration process and to save time and energy. Adaptive β-Hill Climbing Algorithm (AβHCA) is employed for maximizing efficiency, minimizing power consumption and resource wastage. By Combining both GBRAMPA-AβHCA VM is optimally allocated in PM with high efficiency by minimizing cost and energy consumptions. The proposed BA-VMA-CC is implemented in MATLAB platform. The performance of proposed method attains 23.84 %, 28.94 %, 33.94 % lower energy consumption, 28.94 %, 34.95 %, 25.36 % lower CPU utilization is analyzed with existing methods, such as sine cosine with ant lion optimization for VM allocation in Cloud Computing (SCA-ALO-VMA-CC), hybrid distinct multiple object whale optimization and multi-verse optimization for VM allocation in Cloud Computing (DMOWOA-MVO-VMA-CC) and Cuckoo search optimization algorithm and particle swarm optimization algorithm (CSO-PSO-VMA-CC) respectively.

Strategy-proof mechanism for time-varying batch virtual machine allocation in clouds

Strategy-proof mechanism for time-varying batch virtual machine allocation in clouds

Artificial neural network-based virtual machine allocation in cloud computing

The expeditious development in information technology visualizes that facilities for computing are one of the prime utilities of life after the other main essential utilities i.e., water, gas, and electricity. With the help of virtualization, cloud computing helps in transparently sharing of data and services between the cloud users and provides access to more than thousands of computers as a single instance. The virtual machine allocation is a challenging task in virtualization which is regulated as a crucial part of the virtual machine (VM) migration. It is done to discover the best assignment of VMs on physical machines (PMs) because it has explicit effects on resource utilization, energy efficiency, the performance of running applications, etc. So, there is a need to design an effective method for the VM placement problem. The research paper presents a virtual machine allocation technique implementing Enhanced-Modified Best Fit Decreasing (E-MBFD) Algorithm. The outcome of the proposed technique is the cross-validation of allocated virtual machines on physical machines using Artificial Neural Network (ANN). Also, the proposed technique is implemented with the merits of finding the false allocations which happen due to inefficient utilization of resources and it helps in re-allocation of these VMs. The empirical result shows that the E-MBFD technique outperforms in terms of minimized power consumption and a lesser number of service-level agreement (SLA) violations in contrast with the conventional technique.

Hybrid optimization algorithm for task scheduling and virtual machine allocation in cloud computing

In the Internet era, cloud computing is evolved as the efficient distributed platform in the recent years. But the major issue related to the cloud platform is task scheduling. Allocating the suitable VM to the tasks is a challenging task in cloud computing. Many algorithms are proposed to optimize the scheduling process in the cloud environment. The existing algorithms have their own drawbacks. This paper proposed the hybrid model which uses the hierarchical process to prioritize the task before submitting to the scheduler. The Bandwidth-aware divisible task (BAT) scheduling model is modified by adding the Bar system model to develop the hybrid optimization mechanism. The Minimum overload and minimum lease policy is employed for applying the pre-emption in the data center to reduce the overload of the virtual machine. The performance of the proposed hybrid model is evaluated using different parameters. The simulation results prove the efficiency of the hybrid model.

Performance analysis of ACO‐based improved virtual machine allocation in cloud for IoT‐enabled healthcare

SummaryThe Internet of Things (IoT)–enabled healthcare environment irregularly requires the resources from the Cloud to handle massive amounts of data, which impacts the response times of the Cloud. A typical healthcare application scenario could be to monitor the heart‐patients who require the highest response times, immediate attention of all the stakeholders, and very quick decisions. This paper replaces the default First‐Come‐First‐Serve (FCFS) Virtual Machine (VM) allocation scheme of the Cloud computing in CloudSim with the implementation of Ant Colony Optimization (ACO) by the efficient tuning of parameters. An IoT‐enabled healthcare environment is designed, which irregularly requires the resources from the Cloud to handle massive amounts of data to assess the impact of the response times of the Cloud. Several experiments have been carried out to assess the optimal VM allocation using ACO by varying different parameters like the variation of the number of ants, the strength of pheromone, the number of VMs, the number of hosts, the strength of computing power of processing elements, the number of users, and the size of the workloads. Experimental results indicate that the ACO optimally utilizes the resources in a Cloud when compared to the default FCFS strategy. The results indicate that on an average, the ACO gives 25% to 30% improved response times than FCFS in the given healthcare and Cloud scenarios.

Global host allocation policy for virtual machine in cloud computing

In world of cloud computing, allocation of virtual machine are on bases of available hardware and software in server of data centers. Most of allocation policy depends on utilization of hardware and software without affecting SLA and Quality of Service as main concern. Every allocation policy maintains some parameter for provide service in extensive level by keep allocation of virtual machine between upper and lower threshold. But dynamic allocation of virtual machine may have deviation in host workload. To deal with this situation a methodology was adopted utilization threshold that provide absolute median deviation for setting up threshold. This methodology has a major impact on dynamic virtual machine allocation in cloud. The dynamic workload on a host occurs due change in VM requesting resources. Even we get effective utilization of virtual machine through median absolute policy still is some resource unallocated in host. For deal with this situation we introduce a Global cloud methodology, main role is to collect all unallocated resource from different resource and provide access to new VM request for increase resource utilization. In this paper we consider Median absolute deviation with global host establishment for better resource utilization and reduce energy consumption.

SGAM: strategy‐proof group buying‐based auction mechanism for virtual machine allocation in clouds

SummaryWe study the cloud resource auction problem where users can bid for resource bundle containing heterogeneous types of virtual machines, and providers allocate virtual machines to their users through group price model. Compared with fixed price model, which is not always the best approach for trading resources as its economically inefficient and inflexible nature, the group price model possess the better flexibility and monetary benefits for auction participants (e.g., cloud providers and users). The proposed auction mechanism strategy‐proof group buying‐based auction mechanism, which formulates the problem of virtual machine allocation in clouds as a combinatorial auction problem, and holds some important property such as individual rationality, ex‐post budget balance, and truthfulness, meanwhile guaranteeing efficiency in both the provider's revenue and system efficiency. Extensive simulation results show that the proposed mechanism yields the allocation efficiency and computational tractability compared with the mechanism with fixed price model. Copyright © 2015 John Wiley & Sons, Ltd.

Combinatorial auction-based allocation of virtual machine instances in clouds

Most of the current cloud computing providers allocate virtual machine instances to their users through fixed-price allocation mechanisms. We argue that combinatorial auction-based allocation mechanisms are especially efficient over the fixed-price mechanisms since the virtual machine instances are assigned to users having the highest valuation. We formulate the problem of virtual machine allocation in clouds as a combinatorial auction problem and propose two mechanisms to solve it. The proposed mechanisms are extensions of two existing combinatorial auction mechanisms. We perform extensive simulation experiments to compare the two proposed combinatorial auction-based mechanisms with the currently used fixed-price allocation mechanism. Our experiments reveal that the combinatorial auction-based mechanisms can significantly improve the allocation efficiency while generating higher revenue for the cloud providers.