A Load Balancing Policy for Virtual Desktop Service

2012 ◽  
Vol 263-266 ◽  
pp. 1564-1567
Author(s):  
Sung Hoon Son
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Load Balance ◽  
Large Scale ◽  
Virtual Machines ◽  
Migration Policy ◽  
Virtual Machine Migration ◽  
Physical Machine ◽  
Virtual Desktop

In this paper, a virtual machine migration policy for large scale virtual desktop service is proposed. Usually a virtual desktop service is composed of several physical machines, each of which is running several virtual machines. Sometimes virtual machine should be relocated to other physical machine when load balance over the system lost. In this situation, a management server must answer two questions: who should be relocated and where is the destination host? The proposed migration policy in this paper is three kinds. We suggest the best policy from the viewpoint of user and system. By experiments, we show that our policy reduce user connect time and increase the number of concurrent virtual machines.

scholarly journals A Decentralized Virtual Machine Migration Approach of Data Centers for Cloud Computing

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaoying Wang ◽  
Xiaojing Liu ◽  
Lihua Fan ◽  
Xuhan Jia
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Virtual Machines ◽  
Quality Of Services ◽  
Virtual Machine Migration ◽  
Power Models ◽  
Key Steps

As cloud computing offers services to lots of users worldwide, pervasive applications from customers are hosted by large-scale data centers. Upon such platforms, virtualization technology is employed to multiplex the underlying physical resources. Since the incoming loads of different application vary significantly, it is important and critical to manage the placement and resource allocation schemes of the virtual machines (VMs) in order to guarantee the quality of services. In this paper, we propose a decentralized virtual machine migration approach inside the data centers for cloud computing environments. The system models and power models are defined and described first. Then, we present the key steps of the decentralized mechanism, including the establishment of load vectors, load information collection, VM selection, and destination determination. A two-threshold decentralized migration algorithm is implemented to further save the energy consumption as well as keeping the quality of services. By examining the effect of our approach by performance evaluation experiments, the thresholds and other factors are analyzed and discussed. The results illustrate that the proposed approach can efficiently balance the loads across different physical nodes and also can lead to less power consumption of the entire system holistically.

Virtual Machine Migration Based on Trust Measurement of Computer Node

2014 ◽  
Vol 536-537 ◽  
pp. 678-682
Author(s):  
Zhi Hong Liang ◽  
Zhi Qiang Liang ◽  
Yi Ming Tan ◽  
Xue Cheng Lv
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machine Migration ◽  
Performance Constraints ◽  
Computing Platform ◽  
Physical Machine ◽  
Cloud Computing Platform ◽  
Trust Measurement ◽  
Computer Node

Currently, the study of virtual machine migration in cloud computing platform which usually did not consider the trustworthiness of target physical machine. For this, the paper proposes a trusted virtual machine migration with performance constraints algorithm (TVM2PC). The trustworthiness of target physical machine includes direct trustworthiness and indirect trustworthiness. By this method, a virtual machine will be migrated to a trusted physical machine. A large of experiment shows that the proposed method can give a better result than the existing method in load balancing and trustworthiness.

scholarly journals Static Load Balancing Technique for Geographically Partitioned Public Cloud

2019 ◽  
Vol 20 (2) ◽  
pp. 299-316
Author(s):  
Mandeep Kaur ◽  
Rajni Mohana
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Public Cloud ◽  
Virtual Machine Migration ◽  
First Come First Serve ◽  
Decentralized Load Balancing ◽  
Load Balancer ◽  
Day By Day ◽  
Selection Of

Large number of users are shifting to the cloud system for their different kind of needs. Hence the number of applications on public cloud are increasing day by day. Handling public cloud is becoming unmanageable in comparison to other counterparts. Though fog technology has reduced the load on centralized cloud resources to a remarkable extent, still load handled at cloud end is significantly high. Geographic partitioning of public cloud can resolve these issues by adding manageability and efficiency in this situation. Dividing public cloud in smaller partitions opens ways to manage resources and requests in a better way. But, partitioned clouds introduce different ends for submission and operations of tasks and virtual machines. We have tried to handle all these complexities in this paper. Proposed work is focused upon load balancing in the partitioned public cloud by combining centralized and decentralized approaches, assuming the presence of fog layer. A load balancer entity is used for decentralized load balancing at partitions and a controller entity is used for centralized level to balance the overall load at various partitions. In the proposed approach, it is assumed that jobs are segregated first. All the jobs which can be handled locally by fog resources are not forwarded to the cloud layer directly. Those are processed locally by decentralized fog resources. Selection of an appropriate Virtual Machine (VM) for filtered set of job, which are forwarded to cloud environment, is done in three steps. Initially, selecting the partition with a maximum available capacity of resources. Then finding the appropriate node with maximum available resources, within a selected partition. And finally, the VM with minimum execution time for a task is chosen. Results are compared with the results produced in this work with First Come First Serve (FCFS) and Shortest Job First (SJF) algorithms, implemented in same setup i.e. partitioned cloud. This paper compares the Waiting Time, Finish Time and Actual Run Time of tasks using these algorithms. After initial experimentation, it is found that in most of the cases, while comparing above parameters, the proposed approach is producing better results than FCFS algorithm. But results produced by SJF algorithm produce better results. To reduce the number of unhandled jobs, a new load state is introduced which checks load beyond conventional load states. Major objective of this approach is to reduce the need of runtime virtual machine migration and to reduce the wastage of resources, which may be occurring due to predefined values of threshold. The implementation is done using CloudSim.

scholarly journals Load Balancing Model using Elastic Technique in Cloud Computing

2021 ◽  
Vol 10 (4) ◽  
pp. 185-188
Author(s):  
Sovban Nisar ◽  
Deepika Arora
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Service Provider ◽  
Structural Design ◽  
Virtual Machines ◽  
Cloud Service ◽  
Virtual Machine Migration ◽  
Cloud Service Provider

A structural design in which virtual machines are implicated and connect to the cloud service provider is called cloud computing. On the behalf of the users, the virtual machines connect to the cloud service provider. The uncertainties overload the virtual machines. The genetic algorithm is implemented for the migration of virtual machine in the earlier study. The genetic algorithm is low depicts latency within the network is high at the time of virtual machine migration. The genetic algorithm is implemented for virtual machine migration in this study. The proposed algorithm is applied in MATLAB in this work. The obtained results are compared with the results of earlier algorithm. Various parameters like latency, bandwidth consumption, and space utilization are used to analyze the achieved results.

scholarly journals A Review on Different Types of Live VM Migration Methods with Proposed Pre-Copy Approach

2018 ◽  
Vol 7 (4.12) ◽  
pp. 6
Author(s):  
Arvind Kumar Bhatia ◽  
Gursharan Singh
Keyword(s):  
Virtual Machine ◽  
Virtual Machines ◽  
Virtual Machine Migration ◽  
Server Virtualization ◽  
Physical Machine ◽  
Dynamic Movement ◽  
State Transfer ◽  
Different Types ◽  
Migration Method ◽  
Memory Content

Cloud computing is being considered as the future architecture of IT world. Virtualization creates logical resources from physical resources which are allocated with flexibility to applications. Server virtualization is a technique for the division of the physical machine into many Virtual Machines; every Virtual Machine has the capacity of applications execution similar to physical machine. The capability of Virtual Machine migration i.e. dynamic movement of Virtual Machines between physical machines is achieved by virtualization. Migration techniques differ w.r.t order of state transfer. Pre-copy migration method transfers all pages of memory from source to destination while Virtual Machine is executing on source. Post-copy migration is transfer of memory. content after the transfer of process state. Specially, post-copy migration, first copied the process states to the destination machine. Total time of migration, Total pages transferred and Downtime are important parameters considered during live Virtual Machine migration. Many improved live pre copy Virtual Machine migration techniques tries to decrease all the three above mentioned parameters. Proposed approach also tries to minimize all the three performance parameters.  

Load balancing in virtualized environments using virtual machine migration: A comprehensive survey

2021 ◽  
Vol 25 (2) ◽  
pp. 279-290
Author(s):  
Suresh Chandra Moharana ◽  
Bishwabara Panda ◽  
Manoj Kumar Mishra ◽  
Bhabani Shankar Prasad Mishra ◽  
Amulya Ratna Swain ◽  
...  
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Virtual Machine Migration ◽  
Load Imbalance ◽  
Resource Requirements ◽  
Comprehensive Survey ◽  
Computing Environments ◽  
Cloud Datacenter ◽  
Virtual Resources

Virtualization is a core and requisite technology in Cloud Computing that provisions scalable virtual resources for execution of varied applications. It enables the cloud datacenter resources to be multiplexed within numerous virtual computing environments recognized as virtual machines. These virtual machines consolidates varied applications with diversified resource requirements. It prompts to increase in load imbalance level leading to reduced performance and SLA violations. In order to achieve load balancing across virtual machines varied approaches are presented in literature and virtual machine migration based load balancing is a popular move in this direction. In this work, recent literature on different migration based load balancing schemes are reviewed. The objective of the work is highlight the features, advantages and shortcomings of the considered literature. Alongside that, the effort is conferred to provide an analytical view over different perspectives which will motivate the research in this area.

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.

scholarly journals PeerFlow: Secure Load Balancing in Tor

2017 ◽  
Vol 2017 (2) ◽  
pp. 74-94 ◽  
Author(s):  
Aaron Johnson ◽  
Rob Jansen ◽  
Nicholas Hopper ◽  
Aaron Segal ◽  
Paul Syverson
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Large Scale ◽  
Scanning System ◽  
Large Scale Network ◽  
Improve Accuracy ◽  
Scale Network ◽  
Network Simulations ◽  
Improved Design ◽  
Speed And Accuracy

Abstract We present PeerFlow, a system to securely load balance client traffic in Tor. Security in Tor requires that no adversary handle too much traffic. However, Tor relays are run by volunteers who cannot be trusted to report the relay bandwidths, which Tor clients use for load balancing. We show that existing methods to determine the bandwidths of Tor relays allow an adversary with little bandwidth to attack large amounts of client traffic. These methods include Tor’s current bandwidth-scanning system, TorFlow, and the peer-measurement system EigenSpeed. We present an improved design called PeerFlow that uses a peer-measurement process both to limit an adversary’s ability to increase his measured bandwidth and to improve accuracy. We show our system to be secure, fast, and efficient. We implement PeerFlow in Tor and demonstrate its speed and accuracy in large-scale network simulations.

Advances in Dynamic Virtual Machine Management for Cloud Data Centers

2017 ◽  
pp. 91-114
Author(s):  
Rashmi Rai ◽  
G. Sahoo
Keyword(s):  
Energy Consumption ◽  
Virtual Machine ◽  
Large Scale ◽  
Data Centers ◽  
Economic Modeling ◽  
User Preferences ◽  
Virtual Machine Migration ◽  
Carbon Footprints ◽  
Cloud Data ◽  
Computing Services

The ever-rising demand for computing services and the humongous amount of data generated everyday has led to the mushrooming of power craving data centers across the globe. These large-scale data centers consume huge amount of power and emit considerable amount of CO2.There have been significant work towards reducing energy consumption and carbon footprints using several heuristics for dynamic virtual machine consolidation problem. Here we have tried to solve this problem a bit differently by making use of utility functions, which are widely used in economic modeling for representing user preferences. Our approach also uses Meta heuristic genetic algorithm and the fitness is evaluated with the utility function to consolidate virtual machine migration within cloud environment. The initial results as compared with existing state of art shows marginal but significant improvement in energy consumption as well as overall SLA violations.

scholarly journals Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

2020 ◽  
Vol 10 (7) ◽  
pp. 2323
Author(s):  
T. Renugadevi ◽  
K. Geetha ◽  
K. Muthukumar ◽  
Zong Woo Geem
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Power Consumption ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Resource Provisioning ◽  
Total Power

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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