ENHANCING JOB ALLOCATION USING NBST IN CLOUD ENVIRONMENT: A REVIEW

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Internet technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner.Cloud computing is Internet based development and use of computer technology. It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. Users need not have knowledge of, expertise in, or control over the technology infrastructure "in the cloud" that supports them. Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous computing systems. On cloud computing platform, load balancing of the entire system can be dynamically handled by using virtualization technology through which it becomes possible to remap virtual machine and physical resources according to the change in load. However, in order to improve performance, the virtual machines have to fully utilize its resources and services by adapting to computing environment dynamically. The load balancing with proper allocation of resources must be guaranteed in order to improve resource utility. Load balancing is a critical aspect that ensures that all the resources and entities are well balanced such that no resource or entity neither is under loaded nor overloaded. The load balancing algorithms can be static or dynamic. Load balancing in this environment means equal distribution of workload across all the nodes. Load balancing provides a way of achieving the proper utilization of resources and better user satisfaction. Hence, use of an appropriate load balancing algorithm is necessary for selecting the virtual machines or servers. This paper focuses on the load balancing algorithm which distributes the incoming jobs among VMs optimally in cloud data centers. In this paper, we have reviewed several existing load balancing mechanisms and we have tried to address the problems associated with them.

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AN ENHANCED TASK ALLOCATION STRATEGY IN CLOUD ENVIRONMENT

INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY ◽

10.24297/ijct.v16i6.6304 ◽

2017 ◽

Vol 16 (6) ◽

pp. 6953-6961

Author(s):

Kavita Redishettywar ◽

Prof. Rafik Juber Thekiya

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Virtual Machine ◽

Data Centers ◽

Job Scheduling ◽

Virtual Machines ◽

Cloud Environment ◽

Cloud Data ◽

Cloud Data Centers ◽

Load Balancing Algorithm

Cloud computing is a vigorous technology by which a user can get software, application, operating system and hardware as a service without actually possessing it and paying only according to the usage. Cloud Computing is a hot topic of research for the researchers these days. With the rapid growth of Interne technology cloud computing have become main source of computing for small as well big IT companies. In the cloud computing milieu the cloud data centers and the users of the cloud-computing are globally situated, therefore it is a big challenge for cloud data centers to efficiently handle the requests which are coming from millions of users and service them in an efficient manner. Load balancing ensures that no single node will be overloaded and used to distribute workload among multiple nodes. It helps to improve system performance and proper utilization of resources. We propose an improved load balancing algorithm for job scheduling in the cloud environment using K-Means clustering of cloudlets and virtual machines in the cloud environment. All the cloudlets given by the user are divided into 3 clusters depending upon clientâ€™s priority, cost and instruction length of the cloudlet. The virtual machines inside the datacenter hosts are also grouped into multiple clusters depending upon virtual machine capacity in terms of processor, memory, and bandwidth. Sorting is applied at both the ends to reduce the latency. 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. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.

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Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

Applied Sciences ◽

10.3390/app11135849 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5849

Author(s):

Nimra Malik ◽

Muhammad Sardaraz ◽

Muhammad Tahir ◽

Babar Shah ◽

Gohar Ali ◽

...

Keyword(s):

Cloud Computing ◽

Energy Consumption ◽

Load Balancing ◽

Resource Utilization ◽

Data Centers ◽

Virtual Machines ◽

Cloud Data ◽

Tasks Scheduling ◽

Benchmark Datasets ◽

Cloud Data Centers

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

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Analysis of Heterogeneous Device Characteristics in Round Robin Based Load Balancing Algorithm with Closest Data Center as Service Broker Policy in Cloud

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8273.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1627-1630

Keyword(s):

Load Balancing ◽

Response Time ◽

Data Center ◽

Data Centers ◽

Virtual Machines ◽

Round Robin ◽

Cloud Data ◽

Cloud Data Centers ◽

Service Broker ◽

Load Balancing Algorithm

Load balancing is an important aspect in cloud to share load among different virtual machines running on various physical nodes. The user response time which is an important performance metric is being highly influenced by the efficient load balancing algorithm for cloud data centers. Virtual machines which are part of the cloud data centers consist of various types of physical devices. The user response time is affected significantly by the capacity of physical devices that exist as part of the data centers. Several load balancing algorithms exist in the literature to allocate task effectively on various virtual machines running in data centers. We investigate the performance of round robin based load balancing algorithm with closest data center as service broker policy in cloud data centers. We have performed a simulation with data centers that consist of devices with different physical characteristics such as memory, storage, bandwidth, processor speed and scheduling policy using Round Robin load balancing algorithm with closest data centers as service broker policy. We present the merits of heterogeneous device characteristics in reducing the user response time and the data center request service time. We used Cloud Analyst, an open source simulation tool for cloud computing environment

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Multidimensional Resource Scheduling in Cloud Data Centers

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1513 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 1513-1516

Author(s):

Hai Na Song ◽

Xiao Qing Zhang ◽

Zhong Tang He

Keyword(s):

Cloud Computing ◽

Data Centers ◽

Bin Packing ◽

Virtual Machines ◽

Three Dimensional ◽

Resource Scheduling ◽

Computing Environment ◽

Cloud Computing Environment ◽

Cloud Data ◽

Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

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Energy-Efficient Virtualized Scheduling and Load Balancing Algorithm in Cloud Data Centers

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2021070103 ◽

2021 ◽

Vol 11 (3) ◽

pp. 34-48

Author(s):

J. K. Jeevitha ◽

Athisha G.

Keyword(s):

Quality Of Service ◽

Load Balancing ◽

Energy Efficient ◽

Data Centers ◽

Scheduling Algorithm ◽

Cloud Data ◽

The Third ◽

Cloud Data Centers ◽

Load Balancing Algorithm

To scale back the energy consumption, this paper proposed three algorithms: The first one is identifying the load balancing factors and redistribute the load. The second one is finding out the most suitable server to assigning the task to the server, achieved by most efficient first fit algorithm (MEFFA), and the third algorithm is processing the task in the server in an efficient way by energy efficient virtual round robin (EEVRR) scheduling algorithm with FAT tree topology architecture. This EEVRR algorithm improves the quality of service via sending the task scheduling performance and cutting the delay in cloud data centers. It increases the energy efficiency by achieving the quality of service (QOS).

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A multi-objective load balancing algorithm for virtual machine placement in cloud data centers based on machine learning

Computing ◽

10.1007/s00607-020-00813-w ◽

2020 ◽

Vol 102 (9) ◽

pp. 2049-2072 ◽

Cited By ~ 1

Author(s):

Arezoo Ghasemi ◽

Abolfazl Toroghi Haghighat

Keyword(s):

Machine Learning ◽

Load Balancing ◽

Virtual Machine ◽

Data Centers ◽

Virtual Machine Placement ◽

Cloud Data ◽

Multi Objective ◽

Cloud Data Centers ◽

Load Balancing Algorithm

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ENERGY EFFICIENT LOAD BALANCING FOR CLOUD DATA CENTER

Asian Journal of Pharmaceutical and Clinical Research ◽

10.22159/ajpcr.2017.v10s1.19604 ◽

2017 ◽

Vol 10 (13) ◽

pp. 162

Author(s):

Amey Rivankar ◽

Anusooya G

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Energy Efficient ◽

Large Scale ◽

Virtual Machines ◽

Cloud Data Center ◽

Cloud Data ◽

Efficient Resource ◽

Cloud Data Centers ◽

Near Future

Cloud computing is the latest trend in large-scale distributed computing. It provides diverse services on demand to distributive resources such asservers, software, and databases. One of the challenging problems in cloud data centers is to manage the load of different reconfigurable virtual machines over one another. Thus, in the near future of cloud computing field, providing a mechanism for efficient resource management will be very significant. Many load balancing algorithms have been already implemented and executed to manage the resources efficiently and adequately. The objective of this paper is to analyze shortcomings of existing algorithms and implement a new algorithm which will give optimized load balancingresult.

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Cloud Load Balancing Algorithms: A Comparative Assessment

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2019.8282 ◽

2019 ◽

Vol 16 (9) ◽

pp. 3989-3994

Author(s):

Jaspreet Singh ◽

Deepali Gupta ◽

Neha Sharma

Keyword(s):

Cloud Computing ◽

Load Balancing ◽

Data Centers ◽

Research Area ◽

Comparative Assessment ◽

Cloud Services ◽

Cloud Data ◽

Cloud Data Centers ◽

Cloud Users

Nowadays, Cloud computing is developing quickly and customers are requesting more administrations and superior outcomes. In the cloud domain, load balancing has turned into an extremely intriguing and crucial research area. Numbers of algorithms were recommended to give proficient mechanism for distributing the cloud user’s requests for accessing pool cloud resources. Also load balancing in cloud should provide notable functional benefits to cloud users and at the same time should prove out to be eminent for cloud services providers. In this paper, the pre-existing load balancing techniques are explored. The paper intends to provide landscape for classification of distinct load balancing algorithms based upon the several parameters and also address performance assessment bound to various load balancing algorithms. The comparative assessment of various load balancing algorithms will helps in proposing a competent load balancing technique for intensify the performance of cloud data centers.

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An Analysis on ECSE Load Balancing Algorithm with CDC as a Service Broker Policy in Cloud with Heterogeneous Communication and Device Characteristics

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i8274.078919 ◽

2019 ◽

Vol 8 (9) ◽

pp. 1631-1635

Keyword(s):

Load Balancing ◽

Response Time ◽

Data Center ◽

Data Centers ◽

Cloud Data ◽

Heterogeneous Device ◽

Cloud Data Centers ◽

Service Broker ◽

Load Balancing Algorithm ◽

Machine Allocation

Load balancing algorithms and service broker policies plays a crucial role in determining the performance of cloud systems. User response time and data center request servicing time are largely affected by the load balancing algorithms and service broker policies. Several load balancing algorithms and service broker polices exist in the literature to perform the data center allocation and virtual machine allocation for the given set of user requests. In this paper, we investigate the performance of equally spread current execution (ESCE) based load balancing algorithm with closest data center(CDC) service broker policy in a cloud environment that consists of homogeneous and heterogeneous device characteristics in data centers and heterogeneous communication bandwidth that exist between different regions where cloud data centers are deployed. We performed a simulation using CloudAnalyst an open source software with different settings of device characteristics and bandwidth. The user response time and data center request servicing time are found considerably less in heterogeneous environment.

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