Analysis of load balancing in cloud data centers

2019 ◽  
Author(s):  
Sweekriti M. Shetty ◽  
Sudheer Shetty
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Cloud Data ◽  
Cloud Data Centers

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

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).

Cloud Load Balancing Algorithms: A Comparative Assessment

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.

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

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.

scholarly journals Integration of 6LOWPAN and Cloud Data Centers: The Role of Link Capacity between Sensor Networks and Cloud Data Centers

2019 ◽  
Vol 9 (1) ◽  
pp. 2159-2164
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Data Centers ◽  
Virtual Machines ◽  
Poor Response ◽  
Optimal Time ◽  
Available Bandwidth ◽  
Cloud Data ◽  
Cloud Data Centers ◽  
The Impact

Internet of Things (IoT) and Internet of Mobile Things (IoMT) acquired widespread popularity by its ease of deployment and support for innovative applications. The sensed and aggregated data from IoT and IoMT are transferred to Cloud through Internet for analysis, interpretation and decision making. In order to generate timely response and sending back the decisions to the end users or Administrators, it is important to select appropriate cloud data centers which would process and produce responses in a shorter time. Beside several factors that determine the performance of the integrated 6LOWPAN and Cloud Data Centers, we analyze the available bandwidth between various user bases (IoT and IoMT networks) and the cloud data centers. Amidst of various services offered in cloud, problems such as congestion, delay and poor response time arises when the number of user request increases. Load balancing/sharing algorithms are the popularly used techniques to improve the performance of the cloud system. Load refers to the number of user requests (Data) from different types of networks such as IoT and IoMT which are IPv6 compliant. In this paper we investigate the impact of homogeneous and heterogeneous bandwidth between different regions in load balancing algorithms for mapping user requests (Data) to various virtual machines in Cloud. We investigate the influence of bandwidth across different regions in determining the response time for the corresponding data collected from data harvesting networks. We simulated the cloud environment with various bandwidth values between user base and data centers and presented the average response time for individual user bases. We used Cloud- Analyst an open source tool to simulate the proposed work. The obtained results can be used as a reference to map the mass data generated by various networks to appropriate data centers to produce the response in an optimal time.

scholarly journals ENHANCING JOB ALLOCATION USING NBST IN CLOUD ENVIRONMENT: A REVIEW

2017 ◽  
Vol 16 (3) ◽  
pp. 6247-6253
Author(s):  
Ashima Ashima ◽  
Mrs Navjot Jyoti
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
User Satisfaction ◽  
Data Centers ◽  
Virtual Machines ◽  
Efficient Manner ◽  
Equal Distribution ◽  
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 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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