scholarly journals An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

2021 ◽  
Vol 11 (21) ◽  
pp. 9940
Author(s):  
Jack Marquez ◽  
Oscar H. Mondragon ◽  
Juan D. Gonzalez
Keyword(s):  
Resource Allocation ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Service Providers ◽  
Cloud Service ◽  
Data Placement ◽  
Memory Access ◽  
Heterogeneous Hardware ◽  
Machine Allocation ◽  
Virtual Machine Allocation

Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

scholarly journals A Novel Energy Efficient Resource Management System in Cloud Computing Environment.

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1030
Author(s):  
S. K. Sonkar ◽  
M. U.Kharat
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Resource Management ◽  
Virtual Machine ◽  
Energy Efficient ◽  
Service Providers ◽  
Optimization Techniques ◽  
Cloud Provider ◽  
Efficient Resource ◽  
Machine Allocation

Primary target of cloud provider is to provide the maximum resource utilization and increase the revenue by reducing energy consumption and operative cost. In the service providers point of view, resource allocation, resource sharing, migration of resources on demand, memory management, storage management, load balancing, energy efficient resource usage, computational complexity handling in virtualization are some of the major tasks that has to be dealt with. The major issue focused in this paper is to reduce the energy consumption problem and management of computation capacity utilization.  For the same, an energy efficient resource management method is proposed to grip the resource scheduling and to minimize the energy utilized by the cloud datacenters for the computational work. Here a novel resource allocation mechanism is proposed, based on the optimization techniques. Also a novel dynamic virtual machine (VM) allocation method is suggested to help dynamic virtual machine allocation and job rescheduling to improve the consolidation of resources to execute the jobs. Experimental results indicated that proposed strategy outperforms as compared to the existing systems.  

scholarly journals Improved Virtual Machine Allocation Strategy using Particle Swarm Optimization Algorithm

2018 ◽  
Vol 7 (2.7) ◽  
pp. 813
Author(s):  
B Thirumala Rao ◽  
K Nandavardhini ◽  
K Navya ◽  
G Krishna Venkata Sunil
Keyword(s):  
Virtual Machine ◽  
Virtual Machines ◽  
Particle Swarm Optimization Algorithm ◽  
Critical Issue ◽  
Allocation Strategy ◽  
Swarm Optimization ◽  
Machine Allocation ◽  
Virtual Machine Allocation ◽  
Cloud Condition ◽  
Cross System

Virtual machine position (VMP) is a critical issue in choosing most appropriate arrangement of physical machines (PMs) for an arrangement of virtual machines (VMs) in distributed computing condition. These days information concentrated applications for handling huge information are being facilitated in the cloud. Since the cloud condition gives virtualized assets to calculation, and information concentrated applications require correspondence between the registering hubs, the situation of Virtual Machines (VMs) and area of information influence the general calculation time. The essential target is to decrease cross system activity and transmission capacity use, by setting required number of VMs and information in Physical Machines (PMs) which are physically nearer. This paper exhibits and assesses by a meta-heuristic calculation in view of Parallel Computing and Optimization (PCO) which select an arrangement of adjoining PMs for setting information and VMs . In the wake of choosing the PMs, the information are duplicated to the capacity gadgets of the PMs and the required number of VMs are begun on the PMs based on their VM allotment limits. Recreation comes about demonstrate that this determination diminishes the whole of separations amongst VMs and henceforth lessens the activity fruition time.

scholarly journals A dynamic programming approach to manage virtual machines allocation in cloud computing

2018 ◽  
Vol 7 (4.6) ◽  
pp. 128
Author(s):  
Abdellah Ouammou ◽  
Mohamed Hanini ◽  
Abdelghani Ben Tahar ◽  
Said El Kafhali
Keyword(s):  
Cloud Computing ◽  
Dynamic Programming ◽  
Resource Management ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Stochastic Modelling ◽  
Programming Approach ◽  
Conflicting Objectives ◽  
Machine Allocation ◽  
Virtual Machine Allocation

As a result of the dynamic nature of Virtual Machine allocation in cloud computing, it is not easy to manage system resources or choose the best configuration based solely on human experience.  In this work, we used stochastic modelling instead of comprehensive experiments to evaluate the best resource management of the system. In such complex systems, choosing the best decision is a challenge, for this reason we have designed a heuristic algorithm, specifically, dynamic programming as a resource management and programming tool that finds a way that attempts to satisfy the conflicting objectives of high performance and low power consumption. As a scenario for using this algorithm, we addressed the problem of virtual machine allocation, a subset of physical machines is designated as "reserve", and the reserves are actives when the number of jobs in the system is sufficiently high. The question is how to decide when to activate the reserves. The simulation results demonstrated the benefit of using our framework to identify the policy for consolidation or for a low energy consumption and in order to have a good quality of service in the system

Virtual Machine Allocation in Cloud Computing Environment

2013 ◽  
Vol 3 (2) ◽  
pp. 47-60 ◽  
Author(s):  
Absalom E. Ezugwu ◽  
Seyed M. Buhari ◽  
Sahalu B. Junaidu
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Turnaround Time ◽  
Cloud Infrastructure ◽  
Private Cloud ◽  
Mapping Algorithm ◽  
Machine Allocation ◽  
Virtual Machine Allocation

Virtual machine allocation problem is one of the challenges in cloud computing environments, especially for the private cloud design. In this environment, each virtual machine is mapped unto the physical host in accordance with the available resource on the host machine. Specifically, quantifying the performance of scheduling and allocation policy on a Cloud infrastructure for different application and service models under varying performance metrics and system requirement is an extremely challenging and difficult problem to resolve. In this paper, the authors present a Virtual Computing Laboratory framework model using the concept of private cloud by extending the open source IaaS solution Eucalyptus. A rule based mapping algorithm for Virtual Machines (VMs) which is formulated based on the principles of set theoretic is also presented. The algorithmic design is projected towards being able to automatically adapt the mapping between VMs and physical hosts’ resources. The paper, similarly presents a theoretical study and derivations of some performance evaluation metrics for the chosen mapping policies, these includes determining the context switching, waiting time, turnaround time, and response time for the proposed mapping algorithm.

scholarly journals Energy efficiency in virtual machines allocation for cloud data centers with lottery algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 546
Author(s):  
Mehran Tarahomi ◽  
Mohammad Izadi
Keyword(s):  
Virtual Machine ◽  
Data Centers ◽  
Virtual Machines ◽  
Complex Issue ◽  
New Approach ◽  
Cloud Data ◽  
Time Period ◽  
Cloud Data Centers ◽  
Machine Allocation ◽  
Virtual Machine Allocation

<p>Energy usage of data centers is a challenging and complex issue because computing applications and data are growing so quickly that increasingly larger servers and disks are needed to process them fast enough within the required time period. In the past few years, many approaches to virtual machine placement have been proposed. This study proposes a new approach for virtual machine allocation to physical hosts. Either minimizes the physical hosts and avoids the SLA violation. The proposed method in comparison to the other algorithms achieves better results.</p>

A Secure Virtual Machine Allocation Strategy Against Co-Resident Attacks

2019 ◽  
Vol 23 (5) ◽  
pp. 898-908
Author(s):  
Hefei Jia ◽  
Xu Liu ◽  
Xiaoqiang Di ◽  
Hui Qi ◽  
Binbin Cai ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Service Providers ◽  
Cloud Service ◽  
Allocation Strategy ◽  
Side Channels ◽  
Vm Allocation ◽  
Private Data ◽  
Physical Resources ◽  
Balancing Energy ◽  
Machine Allocation

In the area of network development, especially cloud computing, security has been a long-standing issue. In order to better utilize physical resources, cloud service providers usually allocate different tenants on the same physical machine, i.e., physical resources such as CPU, memory, and network devices are shared among multiple tenants on the same host. Virtual machine (VM) co-resident attack, a serious threat in this sharing methodology, includes malicious tenants who tend to steal private data. Currently, most solutions focus on how to eliminate known specific side channels, but they have little effect on unknown side channels. Compared to eliminating side channels, developing a VM allocation strategy is an effective countermeasure against VM co-resident attack as it reduces the probability of VM co-residency, but research on this topic is still in its infancy. In this study, firstly, a novel, efficient, and secure VM allocation strategy named Against VM Co-resident attack based on Multi-objective Optimization Best Fit Decreasing (AC-MOBFD) is proposed, which simultaneously optimizes load balancing, energy consumption, and host resource utilization during VM placement. Subsequently, security of the proposed allocation strategy is measured using two metrics – VM attack efficiency and VM attack coverage. Extensive experiments on simulated and real cloud platforms, CloudSim and OpenStack, respectively, demonstrate that using our strategy, the attack efficiency of VM co-residency is reduced by 37.3% and VM coverage rate is reduced by 24.4% when compared to existing strategies. Finally, we compare the number of co-resident hosts with that of hosts in a real cloud platform. Experimental results show that the deviation is below 9.4%, which validates the feasibility and effectiveness of the presented strategy.

Distance Aware VM allocation process to minimize energy consumption in cloud computing

2019 ◽  
Vol 12 ◽  
Author(s):  
Gurpreet Singh ◽  
Manish Mahajan ◽  
Rajni Mohana
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Physical Machine ◽  
Allocation Process

BACKGROUND: Cloud computing is considered as an on-demand service resource with the applications towards data center on pay per user basis. For allocating the resources appropriately for the satisfaction of user needs, an effective and reliable resource allocation method is required. Because of the enhanced user demand, the allocation of resources has now considered as a complex and challenging task when a physical machine is overloaded, Virtual Machines share its load by utilizing the physical machine resources. Previous studies lack in energy consumption and time management while keeping the Virtual Machine at the different server in turned on state. AIM AND OBJECTIVE: The main aim of this research work is to propose an effective resource allocation scheme for allocating the Virtual Machine from an ad hoc sub server with Virtual Machines. EXECUTION MODEL: The execution of the research has been carried out into two sections, initially, the location of Virtual Machines and Physical Machine with the server has been taken place and subsequently, the cross-validation of allocation is addressed. For the sorting of Virtual Machines, Modified Best Fit Decreasing algorithm is used and Multi-Machine Job Scheduling is used while the placement process of jobs to an appropriate host. Artificial Neural Network as a classifier, has allocated jobs to the hosts. Measures, viz. Service Level Agreement violation and energy consumption are considered and fruitful results have been obtained with a 37.7 of reduction in energy consumption and 15% improvement in Service Level Agreement violation.

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