scholarly journals Modified Active Monitoring Ant Clustering based Load Balancing over Public Clouds

2017 ◽  
Vol 167 (1) ◽  
pp. 29-34 ◽  
Author(s):  
Sonam Raghuwanshi ◽  
Rashmi Nigoti
Keyword(s):  
Load Balancing ◽  
Active Monitoring

scholarly journals Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment

2022 ◽  
Vol 12 (2) ◽  
pp. 2087
Author(s):  
Noha G. Elnagar ◽  
Ghada F. Elkabbany ◽  
Amr A. Al-Awamry ◽  
Mohamed B. Abdelhalim
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Processing Time ◽  
Virtual Machines ◽  
Active Monitoring ◽  
Load Fluctuation ◽  
Load Balancing Algorithm ◽  
And Performance ◽  
Load Balancer

<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>

Fuzzy Dynamic Load Balancing in Virtualized Data Centers of SaaS Cloud Provider

2015 ◽  
Vol 4 (3) ◽  
pp. 50-71 ◽  
Author(s):  
Md. S. Q. Zulkar Nine ◽  
Abul Kalam Azad ◽  
Saad Abdullah ◽  
Rashedur M. Rahman
Keyword(s):  
Load Balancing ◽  
Data Centers ◽  
Cloud Model ◽  
Cloud Provider ◽  
Active Monitoring ◽  
Computing Power ◽  
Novel Approach ◽  
Virtualized Data Centers ◽  
Load Balancer ◽  
Saas Cloud

Cloud computing provides a robust infrastructure that can facilitate computing power as a utility service. All the virtualized services are made available to end users in a pay-as-you-go basis. Serving user requests using distributed network of Virtualized Data Centers is a challenging task as response time increases significantly without a proper load balancing strategy. As the parameters involved in generating load in the Virtualized Data Center has imprecise effect on the overall load of Virtual Machine, a fuzzy load balancing strategy is required. This paper proposes two efficient fuzzy load balancing methods - Fuzzy Active Monitoring Load Balancer (FAM-LB) and Fuzzy Throttled Load Balancer (FT-LB) for the distributed SaaS cloud provider. The authors implemented a cloud model in simulation environment and compared the results of otheir novel approach with the existing techniques. Among them FT-LB has provided better performance compared to other scheduling algorithms.

scholarly journals Minimizing Deadline Misses and Total Run-time with Load Balancing for a Connected Car Systems in Fog Computing

2020 ◽  
Vol 21 (1) ◽  
pp. 73-84
Author(s):  
K Jairam Naik ◽  
D Hanumanth Naik
Keyword(s):  
Load Balancing ◽  
Fog Computing ◽  
Problem Formulation ◽  
Mobility Patterns ◽  
Active Monitoring ◽  
Connected Cars ◽  
Time Required ◽  
Load Balancer ◽  
Optimization Problem Formulation ◽  
Better Than

Cloud computing helps in providing the applications with a few number of resources that are used to unload the tasks. But there are certain applications like coordinated lane change assistance which are helpful in cars that connects to internet has strict time constraints, and it may not be possible to get the job done just by unloading the tasks to the cloud. Fog computing helps in reducing the latency i.e the computation is now done in local fog servers instead of remote datacentres and these fog servers are connected to the nearby distance to clients. To achieve better timing performance in fog computing load balancing in these fog servers is to be performed in an efficient manner.The challenges in the proposed application includes the number of tasks are high, client mobility and heterogeneous nature of fog servers. We use mobility patterns of connected cars and load balancing is done periodically among fog servers. The task model presented here in this paper solves scheduling problem and this is done at the server level and not on the device level. And at last, we present an optimization problem formulation for balancing the load and for reducing the misses in deadline, also the time required for running the task in these cars will be minimized with the help of fog computing. It also performs better than somecommon algorithms such as active monitoring, weighted round robin and throttled load balancer.

Sign in / Sign up

Export Citation Format

Share Document