scholarly journals Saksham: Resource Aware Block Rearrangement Algorithm for Load Balancing in Hadoop

2020 ◽  
Vol 167 ◽  
pp. 47-56 ◽  
Author(s):  
Ankit Shah ◽  
Mamta Padole
Keyword(s):  
Load Balancing ◽  
Rearrangement Algorithm ◽  
Resource Aware

Resource-Aware Load Balancing of Parallel Applications

2009 ◽  
pp. 12-21 ◽  
Author(s):  
Eric Aubanel
Keyword(s):  
Load Balancing ◽  
Dynamic Network ◽  
Computational Grid ◽  
Parallel Applications ◽  
Computational Grids ◽  
Concurrent Execution ◽  
Processor Performance ◽  
Wide Range ◽  
Tightly Coupled ◽  
Resource Aware

The problem of load balancing parallel applications is particularly challenging on computational grids, since the characteristics of both the application and the platform must be taken into account. This chapter reviews the wide range of solutions that have been proposed. It considers tightly coupled parallel applications that can be described by an undirected graph representing concurrent execution of tasks and communication of tasks, executing on computational grids with static and dynamic network and processor performance. While a rich set of solution techniques have been proposed, there has not been of yet any performance comparisons between them. Such comparisons will require parallel benchmarks and computational grid emulators and simulators.

A Resource-Aware Dynamic Load-Balancing Parallelization Algorithm in a Farmer-Worker Environment

2012 ◽  
pp. 88-104
Author(s):  
M. Leeman
Keyword(s):  
Load Balancing ◽  
Dynamic Load ◽  
Execution Time ◽  
Time Frame ◽  
Parallel Programs ◽  
Time Constraints ◽  
Digital Television ◽  
Television Content ◽  
Content Processing ◽  
Resource Aware

This paper describes an algorithm for dynamically assigning tasks to processing entities in a world where each task has a set of resource or service requirements and each processing entity a set of resources or service capabilities. A task needs to be assigned to a node that offers all required services and the set of tasks is finished within a minimal execution time frame. Dependability and adaptability are inherent to the algorithm so that it accounts for the varying execution time of each task or the failure of a processing node. The algorithm is based on a dependable technique for farmer-worker parallel programs and is enhanced for modeling the time constraints in combination with the required configuration set in a multidimensional resources model. This paper describes how the algorithm is used for dynamically load balancing and parallelizing the nightly tests of a digital television content-processing embedded device.

scholarly journals An Improved Rasa for Load Balancing in Cloud Computing

2021 ◽  
pp. 52-66
Author(s):  
Oyekanmi Ezekiel Olufunminiyi ◽  
Oladoja Ilobekemen Perpetual ◽  
Omotehinwa Temidayo Oluwatosin
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Performance Metrics ◽  
Scheduling Algorithm ◽  
Resource Provisioning ◽  
The Common ◽  
Resource Aware ◽  
Cloud Users ◽  
Better Than

Cloud is specifically known to have difficulty in managing resource usage during task scheduling, this is an innate from distributed computing and virtualization. The common issue in cloud is load balancing management. This issue is more prominent in virtualization technology and it affects cloud providers in term of resource utilization and cost and to the users in term of Quality of Service (QoS). Efficient procedures are therefore necessary to achieve maximum resource utilization at a minimized cost. This study implemented a load balancing scheme called Improved Resource Aware Scheduling Algorithm (I-RASA) for resource provisioning to cloud users on a pay-as-you-go basis using CloudSim 3.0.3 package tool. I-RASA was compared with recent load balancing algorithms and the result shown in performance evaluation section of this paper is better than Max-min and RASA load balancing techniques. However, it sometimes outperforms or on equal balance with Improved Max-Min load balancing technique when using makespan, flow time, throughput, and resource utilization as the performance metrics.

SLA-RALBA: cost-efficient and resource-aware load balancing algorithm for cloud computing

2019 ◽  
Vol 75 (10) ◽  
pp. 6777-6803 ◽  
Author(s):  
Altaf Hussain ◽  
Muhammad Aleem ◽  
Muhammad Azhar Iqbal ◽  
Muhammad Arshad Islam
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Cost Efficient ◽  
Load Balancing Algorithm ◽  
Resource Aware

Resource-aware load balancing model for batch of tasks (BoT) with best fit migration policy on heterogeneous distributed computing systems

2020 ◽  
Vol 16 (2) ◽  
pp. 113-141
Author(s):  
Mahfooz Alam ◽  
Raza Abbas Haidri ◽  
Mohammad Shahid
Keyword(s):  
Distributed Computing ◽  
Load Balancing ◽  
Superior Performance ◽  
Distributed Environment ◽  
Distributed Computing Systems ◽  
Content Type ◽  
Real Model ◽  
Heterogeneous Distributed Computing ◽  
Resource Aware ◽  
Best Fit

Purpose Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks. Design/methodology/approach To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration. Findings The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment. Originality/value Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.

A Resource-Aware Dynamic Load-Balancing Parallelization Algorithm in a Farmer-Worker Environment

2011 ◽  
Vol 2 (1) ◽  
pp. 1-17
Author(s):  
M. Leeman
Keyword(s):  
Load Balancing ◽  
Execution Time ◽  
Time Frame ◽  
Parallel Programs ◽  
Dynamic Load Balancing ◽  
Time Constraints ◽  
Digital Television ◽  
Television Content ◽  
Content Processing ◽  
Resource Aware

This paper describes an algorithm for dynamically assigning tasks to processing entities in a world where each task has a set of resource or service requirements and each processing entity a set of resources or service capabilities. A task needs to be assigned to a node that offers all required services and the set of tasks is finished within a minimal execution time frame. Dependability and adaptability are inherent to the algorithm so that it accounts for the varying execution time of each task or the failure of a processing node. The algorithm is based on a dependable technique for farmer-worker parallel programs and is enhanced for modeling the time constraints in combination with the required configuration set in a multidimensional resources model. This paper describes how the algorithm is used for dynamically load balancing and parallelizing the nightly tests of a digital television content-processing embedded device.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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