scholarly journals A Load Balancing Algorithm for Mobile Devices in Edge Cloud Computing Environments

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 686
Author(s):  
JongBeom Lim ◽  
DaeWon Lee
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Mobile Devices ◽  
Virtual Machines ◽  
Hot Spot ◽  
Mobile User ◽  
Current Data ◽  
Computing Systems ◽  
Computing Environments ◽  
Load Balancing Algorithm

As current data centers and servers are growing in size by orders of magnitude when needed, load balancing is a great concern in scalable computing systems, including mobile edge cloud computing environments. In mobile edge cloud computing systems, a mobile user can offload its tasks to nearby edge servers to support real-time applications. However, when users are located in a hot spot, several edge servers can be overloaded due to suddenly offloaded tasks from mobile users. In this paper, we present a load balancing algorithm for mobile devices in edge cloud computing environments. The proposed load balancing technique features an efficient complexity by a graph coloring-based implementation based on a genetic algorithm. The aim of the proposed load balancing algorithm is to distribute offloaded tasks to nearby edge servers in an efficient way. Performance results show that the proposed load balancing algorithm outperforms previous techniques and increases the average CPU usage of virtual machines, which indicates a high utilization of edge servers.

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

scholarly journals An Optimal Solution in cloud computing for Load Balancing using Genetic Algorithm

2019 ◽  
Vol 8 (6) ◽  
pp. 2490-2494
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Optimal Solution ◽  
Cloud Services ◽  
Research Trend ◽  
Schedule Length ◽  
Research Gap ◽  
Load Balancing Algorithm

Cloud computing is a research trend which bring various cloud services to the users. Cloud environment face various challenges and issues to provide efficient services. In this paper, a novel Genetic Algorithm based load balancing algorithm has been implemented to balance the load in the network. The literature review has been studied to understand the research gap. More specifically, load balancing technique authenticate the network by enabling Virtual Machines (VM). The proposed technique has been further evaluated using the Schedule Length Runtime (SLR) and Energy consumption (EC) parameters. Overall, the effective results has been obtained such as 46% improvement in consuming the energy and 12 % accuracy for the SLR measurement. In addition, results has been compared with the conventional approaches to validate the outcomes.

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.

scholarly journals Resilience enhancement of container-based cloud load balancing service

2018 ◽  
Author(s):  
Dongsheng Zhang
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Real Time ◽  
Significant Role ◽  
Current Load ◽  
Web Traffic ◽  
Computing Environments ◽  
Load Balancing Algorithm ◽  
Load Balancer ◽  
Computing Infrastructure

Web traffic is highly jittery and unpredictable. Load balancer plays a significant role in mitigating the uncertainty in web environments. With the growing adoption of cloud computing infrastructure, software load balancer becomes more common in recent years. Current load balancer services distribute the network requests based on the number of network connections to the backend servers. However, the load balancing algorithm fails to work when other resources such as CPU or memory in a backend server saturates. We experimented and discussed the resilience evaluation and enhancement of container-based software load balancer services in cloud computing environments. We proposed a pluggable framework that can dynamically adjust the weight assigned to each backend server based on real-time monitoring metrics.

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.

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>

scholarly journals Hybrid Heuristic Load Balancing Algorithm For Resource Allocation In Cloud Computing

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 ◽  
Hybrid Heuristic ◽  
Load Balancing Algorithm ◽  
Better Than

<div> <div> <div> <p>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.</p> </div> </div> </div>

scholarly journals Hybrid Heuristic Load Balancing Algorithm For Resource Allocation In Cloud Computing

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 ◽  
Hybrid Heuristic ◽  
Load Balancing Algorithm ◽  
Better Than

<div> <div> <div> <p>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.</p> </div> </div> </div>

Sign in / Sign up

Export Citation Format

Share Document