Cluster load balancing algorithm based on dynamic consistent hash

2021 ◽  
pp. 1-8
Author(s):  
Xiaoming Jiang ◽  
Ya Yang ◽  
Zhanfang Chen ◽  
Hua Min Yang
Keyword(s):  
Load Balancing ◽  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Superior Performance ◽  
Sudden Increase ◽  
Static Scheduling ◽  
The Face ◽  
Load Balancing Algorithm ◽  
Server Clusters ◽  
Better Than

In server clusters, the static scheduling algorithm has superior performance when the user visits are relatively stable. In the face of sudden increase in user traffic, the dynamic scheduling algorithm has a better load balancing effect than the static scheduling algorithm. However, in the face of complex network environments, the static scheduling algorithm cannot adjust the load according to the performance of the server in real time. The dynamic scheduling algorithm using a single weight to evaluate server performance is unreliable, and load scheduling with reference to the number of connections has uncertainty. In view of this problem, this paper proposes a cluster load balancing algorithm based on dynamic consistent hash based on the study of load balancing based on LVS clusters. By analyzing the performance and load parameters, we divide the request process into in-cycle and out-of-cycle. By setting up the LVS cluster system, the performance weights, load parameters, number of virtual nodes and cycles of the algorithm in this paper are determined experimentally. Finally, the response time and throughput of the algorithm in this paper are compared with the WRR algorithm and WLC algorithm. The results show that the time and throughput of this algorithm are better than WRR algorithm and WLC algorithm.

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).

A PARALLEL SCHEDULING ALGORITHM FOR TREE STRUCTURED INTERCONNECTION NETWORKS

1996 ◽  
Vol 06 (01) ◽  
pp. 45-54 ◽  
Author(s):  
MIN-YOU WU
Keyword(s):  
Load Balancing ◽  
Interconnection Networks ◽  
Dynamic Scheduling ◽  
Scheduling Algorithm ◽  
Tree Topology ◽  
Alternative Strategy ◽  
Parallel Scheduling ◽  
High Quality ◽  
New Approach

Global parallel scheduling is a new approach for runtime load balancing. In parallel scheduling, all processors cooperate together to schedule work. Parallel scheduling accurately balances the load by using global load information. As an alternative strategy to the commonly used dynamic scheduling, it provides a high-quality, low-overhead load balancing. This paper presents a parallel scheduling algorithm for interconnection networks of the tree topology. This algorithm minimizes communications and maximize locality.

scholarly journals Node Availability for Distributed Systems considering processor and RAM utilization for Load Balancing

2010 ◽  
Vol 5 (3) ◽  
pp. 336
Author(s):  
Antonio Menendez Leonel de Cervantes ◽  
Hector Benitez Perez
Keyword(s):  
Distributed Systems ◽  
Load Balancing ◽  
Distributed System ◽  
Dynamic Scheduling ◽  
Decision Criterion ◽  
Dynamic Load Balancing ◽  
Other Information ◽  
Node Availability ◽  
Load Balancing Algorithm

<p>Node-Availability is a new metric that based on processor utilization, free RAM and number of processes queued at a node, compares different workload levels of the nodes participating in a distributed system. Dynamic scheduling and Load-Balancing in distributed systems can be achieved through the Node-Availability metric as decision criterion, even without previously knowing the execution time of the processes, nor other information about them such as process communication requirements.<br /> This paper also presents a case study which shows that the metric is feasible to implement in conjunction with a dynamic Load-Balancing algorithm, obtaining an acceptable performance.</p>

scholarly journals Measuring the Performance on Load Balancing Algorithms

2019 ◽  
pp. 41-49 ◽  
Author(s):  
Saumendu Roy ◽  
Dr. Md. Alam Hossain ◽  
Sujit Kumar Sen ◽  
Nazmul Hossain ◽  
Md. Rashid Al Asif
Keyword(s):  
Cloud Computing ◽  
Reaction Time ◽  
Load Balancing ◽  
Virtual Machines ◽  
Huge Amount ◽  
Heavy Load ◽  
Cloud Server ◽  
Load Balancing Algorithm ◽  
Day By Day ◽  
Better Than

Load balancing is an integrated aspect of the environment in cloud computing. Cloud computing has lately outgoing technology. It has getting exoteric day by day residence widespread chance in close to posterior. Cloud computing is defined as a massively distributed computing example that is moved by an economic scale in which a repertory of abstracted virtualized energetically. The number of clients in cloud computing is increasing exponentially. The huge amount of user requests attempt to entitle the collection for numerous applications. Which alongside with heavy load not far afield off from cloud server. Whenever particular (Virtual Machine) VMs are overloaded then there are no more duties should be addressed to overloaded VM if under loaded VMs are receivable. For optimizing accomplishment and better response or reaction time the load has to be balanced between overloaded VMs (virtual machines). This Paper describes briefly about the load balancing accession and identifies which is better than others (load balancing algorithm).

scholarly journals Resource Allocation in Cloud Computing using Heuristic Load Balancing Algorithm

2020 ◽  
Author(s):  
Anup Shrestha ◽  
Suriayati Chuprat ◽  
Nandini Mukherjee
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Completion Time ◽  
Virtual Machines ◽  
It Services ◽  
Computing Environment ◽  
Load Balancing Algorithm ◽  
Better Than

Cloud computing is becoming more popular, unlike conventional computing, due to its added advantages. This is because it offers utility-based services to its subscribers upon their demand. Furthermore, this computing environment provides IT services to its users where they pay for every use. However, the increasing number of tasks requires virtual machines for them to be accomplished quickly. Load balancing a critical concern in cloud computing due to the massive increase in users' numbers. This paper proposes the best heuristic load balancing algorithm that will schedule a strategy for resource allocation that will minimize make span (completion time) in any technology that involves use cloud computing. The proposed algorithm performs better than other load balancing algorithms.

Towards An Implementation of A Modified Static Load Balancing Algorithm To Minimize Execution Time

2019 ◽  
Vol 12 (1) ◽  
pp. 69-74
Author(s):  
Hioual Ouided ◽  
Laskri Mhamed Tayeb ◽  
Hemam Sofiane Mounine ◽  
Hioual Ouassila ◽  
Maifi Lyes
Keyword(s):  
Load Balancing ◽  
Failure Probability ◽  
Execution Time ◽  
Static Load ◽  
Optimal Algorithm ◽  
Scheduling Algorithm ◽  
Static Load Balancing ◽  
Load Balancing Algorithm ◽  
Two Parameters ◽  
The Impact

Purpose: The aim of this article is to discuss the impact of static load balancing over a set of heterogeneous processors, where tasks are independent and unitary in static environments, by showing how to distribute task in order to optimize both the average response time and the degree of the resources used. Methods: Implementation of a modified scheduling algorithm, the latter is based on two parameters which are the execution time and the failure probability. The algorithm is based on the results of an optimal algorithm that already exists, with only one parameter that is execution time. Results: The obtained results show that the modified scheduling algorithm gives us the good results. Conclusion: The modified algorithm assumes that the processor has smallest execute time. So, the failure probability increases because of it’s frequently use. The results obtained by testing this proposed algorithm are better than the optimal algorithm.

Mobility-aware balanced scheduling algorithm in mobile Grid based on mobile agent

2014 ◽  
Vol 29 (4) ◽  
pp. 409-432 ◽  
Author(s):  
Jonghyuk Lee ◽  
Sungjin Choi ◽  
Taeweon Suh ◽  
Heonchang Yu
Keyword(s):  
Load Balancing ◽  
Mobile Devices ◽  
Scheduling Algorithm ◽  
Superior Performance ◽  
Battery Life ◽  
Location Awareness ◽  
Message Delivery ◽  
Mobile Grid ◽  
Fixed Grid ◽  
Grid Resources

AbstractThe emerging Grid is extending the scope of resources to mobile devices and sensors that are connected through loosely connected networks. Nowadays, the number of mobile device users is increasing dramatically and the mobile devices provide various capabilities such as location awareness that are not normally incorporated in fixed Grid resources. Nevertheless, mobile devices exhibit inferior characteristics such as poor performance, limited battery life, and unreliable communication, compared with fixed Grid resources. Especially, the intermittent disconnection from network owing to users’ movements adversely affects performance, and this characteristic makes it inefficient and troublesome to adopt the synchronous message delivery in mobile Grid. This paper presents a mobile Grid system architecture based on mobile agents that support the location management and the asynchronous message delivery in a multi-domain proxy environment. We propose a novel balanced scheduling algorithm that takes users’ mobility into account in scheduling. We analyzed users mobility patterns to quantitatively measure the resource availability, which is classified into three types: full availability, partial availability, and unavailability. We also propose an adaptive load-balancing technique by classifying mobile devices into nine groups depending on availability and by utilizing adaptability based on the multi-level feedback queue to handle the job type change. The experimental results show that our scheduling algorithm provides a superior performance in terms of execution times to the one without considering mobility and adaptive load-balancing.

scholarly journals GWS—A Collaborative Load-Balancing Algorithm for Internet-of-Things

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2479 ◽  
Author(s):  
Hongyu Xiao ◽  
Zhenjiang Zhang ◽  
Zhangbing Zhou
Keyword(s):  
Load Balancing ◽  
Scheduling Algorithm ◽  
Fog Computing ◽  
Nash Bargaining Solution ◽  
Nash Bargaining ◽  
Bargaining Solution ◽  
Time Sharing ◽  
Work Stealing ◽  
The Game Theory ◽  
Load Balancing Algorithm

This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is based on the Nash bargaining solution (NBS) for a cooperative game between FCNs. Pareto optimality is achieved through the collaborative working of FCNs to improve the performance of every FCN. Lastly the simulation results demonstrate that the game-theory based work-stealing algorithm (GWS) outperforms the classical work-stealing algorithm (CWS).

Research of Load Balancing Scheduling under Grid Computing

2014 ◽  
Vol 577 ◽  
pp. 935-938
Author(s):  
Cheng Yu Cai ◽  
Yuan Sheng Lou
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
The Other ◽  
Task Scheduling Algorithm ◽  
Better Than

In order to make up for the shortage of Min-Min in load balancing, a new task scheduling algorithm T-Max-Int Under the grid computing has been proposed in this paper. In T-Max-Int, the Loss Degree of Max-Int has been brought into Min-Min. T was in the form of percentage, which represents the proportion of selected tasks that have loss degree in the total tasks. Then, experiments of T have been taken to make Makespan the minimum. Finally, T-Max-Int, Max-Min, Min-Min were compared, which proved that T-Max-Min is better than the other two algorithms in aspects of Makespan and load balancing.

scholarly journals A binary Bird Swarm Optimization based load balancing algorithm for cloud computing environment

2021 ◽  
Vol 11 (1) ◽  
pp. 146-160
Author(s):  
Kaushik Mishra ◽  
Santosh Kumar Majhi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Service Providers ◽  
Scheduling Algorithm ◽  
Simulation Environment ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Np Hard Problem ◽  
Load Balancing Algorithm

Abstract Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.

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