Design and implementation of an efficient load-balancing method for virtual machine cluster based on cloud service

2011 ◽  
Author(s):  
Rui Wang ◽  
Wei Le ◽  
Xuejie Zhang
Keyword(s):  
Load Balancing ◽  
Virtual Machine ◽  
Cloud Service ◽  
Design And Implementation

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 ENHANCED DYNAMIC RESOURCE ALLOCATION SCHEME BASED ON PACKAGE LEVEL ACCESS IN CLOUD COMPUTING

2017 ◽  
Vol 16 (5) ◽  
pp. 6903-6912
Author(s):  
Manpreet Kaur ◽  
Dr. Rajinder Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machine ◽  
Job Scheduling ◽  
Research Work ◽  
Cloud Service ◽  
Current Status ◽  
Cloud Environment ◽  
Single Node ◽  
Load Balancing Algorithm

Cloud computing is distributed computing, storing, sharing and accessing data over the Internet. It provides a pool of shared resources to the users available on the basis of pay as you go service that means users pay only for those services which are used by him according to their access times. This research work deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. We propose an improved load balancing algorithm for job scheduling in the cloud environment using load distribution table in which the current status, current package, VM Capacity and the number of cloudlets submitted to each and every virtual machine will be stored. Submit the job of the user to the datacenter broker. Data center broker will first find the suitable Vm according to the requirements of the cloudlet and will match and find the most suitable Vm according to its availability or the machine with least load in the distribution table. Multiple number of experiments have been conducted by taking different configurations of cloudlets and virtual machine. Various parameters like waiting time, execution time, turnaround time and the usage cost have been computed inside the cloudsim environment to demonstrate the results. The main contributions of the research work is to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

Intelligente Arbeitsvorbereitung in der Cloud/Smarter operations planning and scheduling in the cloud - Cloud application reduces setup and non-productive times by the use of virtual machine tools

2016 ◽  
Vol 106 (01-02) ◽  
pp. 77-82
Author(s):  
G. Rehage ◽  
F. Isenberg ◽  
R. Reisch ◽  
J. Weber ◽  
B. Jurke ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Machine Tools ◽  
Distributed Simulation ◽  
Cloud Service ◽  
Planning And Scheduling ◽  
Operations Planning ◽  
Manufacturing Operations ◽  
Cloud Application ◽  
Cognitive Information ◽  
Selection Of

Auf dem Weg zu Industrie 4.0 wird die Arbeitsvorbereitung zunehmend von kognitiver Informationstechnik unterstützt. Der Beitrag präsentiert die bisherigen Ergebnisse des Forschungsprojekts „Intelligente Arbeitsvorbereitung auf Basis virtueller Werkzeugmaschinen“. Projektziel ist eine Cloud-Dienstleistungsplattform zur Reduzierung der Rüst- und Nebenzeiten durch eine intelligente Planung. Hierzu zählen unter anderem die Auswahl und Validierung alternativer Maschinen sowie die automatische Optimierung der Einrichtungsparameter durch verteilte Simulationen.   On the way to industry 4.0, the operations planning and scheduling will be aided by cognitive information systems. This contribution presents the previous findings of a research project called “Smart operations planning and scheduling on the basis of virtual machine tools” (translated from German). The aim of the project is the development of a cloud service for the smart planning of manufacturing operations; that will reduce the setup and non-productive times of machine tools. This is achieved by the automatic selection of alternative CNC machines, as well as the optimization of setup parameters via distributed simulation.

scholarly journals Energy Efficient Resource Allocation in Cloud Computing

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2071-2074
Keyword(s):  
Virtual Machine ◽  
Service Provider ◽  
Physical System ◽  
Virtual Machines ◽  
Cloud Service ◽  
Cloud Environment ◽  
Cloud Service Provider ◽  
Host System ◽  
Physical Systems ◽  
Single Host

Cloud service provider in cloud environment will provide or provision resource based on demand from the user. The cloud service provider (CSP) will provide resources as and when required or demanded by the user for execution of the job on the cloud environment. The CSP will perform this in a static and dynamic manner. The CSP should also consider various other factors in order to provide the resources to the user, the prime among that will be the Service Level Agreement (SLA), which is normally signed by the user and cloud service provider during the inception phase of service. There are many algorithm which are used in order to allocate resources to the user in cloud environment. The algorithm which is proposed will be used to reduce the amount of energy utilized in performing various job execution in cloud environment. Here the energy utilized for execution of various jobs are taken into account by increasing the number of virtual machines that are used on a single physical host system. There is no thumb rule to calculate the number of virtual machines to be executed on a single host. The same can be derived by calculating the amount of space, speed required along with the time to execute the job on a virtual machine. Based up on this we can derive the number of Virtual machine on a single host system. There can be 10 virtual machines on a single system or even 20 number of virtual machines on single physical system. But if the same is calculated by the equation then the result will be exactly matching with the threshold capacity of the physical system[1]. If more number of physical systems are used to execute fewer virtual machines on each then the amount of energy consumed will be very high. So in order to reduce the energy consumption , the algorithm can be used will not only will help to calculate the number of virtual machines on single physical system , but also will help to reduce the energy as less number of physical systems will be in need[2].

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