Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm

Saeed, Faizan; Javaid, Nadeem; Zubair, M.; Ismail, Muhammad Ali; Zakria, Muhammad; Ashraf, Muhammad Hassaan; Kamal, Muhammad Babar

doi:10.1007/978-3-319-98557-2_42

Cited by 5 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These administrations are extensively separated into three classifications: Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS). Cloud computing is separated into three portions: application, storage and network [6]. Each section fills an alternate need and offers distinctive items for organizations and people the world over.…”

Section: Fig 2: Cloud Computing Platformmentioning

confidence: 99%

Deep Learning Based Load Balancing Using Multidimensional Queuing Load Optimization Algorithm for Cloud Environment.

2020

IJESRT

View full text Add to dashboard Cite

Cloud computing becoming one of the most advanced and promising technologies in these days for information technology era. It has also helped to reduce the cost of small and medium enterprises based on cloud provider services. Resource scheduling with load balancing is one of the primary and most important goals of the cloud computing scheduling process. Resource scheduling in cloud is a non-deterministic problem and is responsible for assigning tasks to virtual machines (VMs) by a servers or service providers in a way that increases the resource utilization and performance, reduces response time, and keeps the whole system balanced. So in this paper, we presented a model deep learning based resource scheduling and load balancing using multidimensional queuing load optimization (MQLO) algorithm with the concept of for cloud environment Multidimensional Resource Scheduling and Queuing Network (MRSQN) is used to detect the overloaded server and migrate them to VMs. Here, ANN is used as deep learning concept as a classifier that helps to identify the overloaded or under loaded servers or VMs and balanced them based on their basis parameters such as CPU, memory and bandwidth. In particular, the proposed ANN-based MQLO algorithm has improved the response time as well success rate. The simulation results show that the proposed ANN-based MQLO algorithm has improved the response time compared to the existing algorithms in terms of Average Success Rate, Resource Scheduling Efficiency, Energy Consumption and Response Time.

show abstract

Section: Fig 2: Cloud Computing Platformmentioning

confidence: 99%

Deep Learning Based Load Balancing Using Multidimensional Queuing Load Optimization Algorithm for Cloud Environment.

2020

IJESRT

View full text Add to dashboard Cite

show abstract

“…It also managed to reduce the number of context switches which is seldom researched performance parameter. Different operating system scheduling algorithms like First-come first-serve [7], Shortest job first, round-robin, etc were analyzed and evaluated using CloudSim in [8]. A task scheduling algorithm for autonomous and dividable tasks based on genetic algorithm was proposed in [9] taking into account the computation and memory requirements of tasks.…”

Section: Related Workmentioning

confidence: 99%

Load Balancing in Cloud Computing Using Hungarian Algorithm

Bala¹,

Chishti²

2019

IJWMT

View full text Add to dashboard Cite

Cloud computing is a highly popular computing paradigm providing on-demand resources with high reliability and availability. The user requests are fulfilled by providing a virtual machine with the requested configuration. However, with the ever-increasing load on the cloud resources, the need for optimal resource utilization of the cloud resources has become the need of the hour. Load balancing has been identified as one of the possible ways to improve resource utilization in the cloud and the current state-of-the-art algorithms indicate the numerous attempts made to find the approximate solution for this NP-hard problem. In this work, we have focused on evaluating the efficiency of the Hungarian algorithm for load distribution in the cloud and compared its performance with First-come-first-serve (FCFS). The simulations were carried out in CloudSim and show remarkable improvement in various performance parameters. Finish time of a given task schedule was reduced by 41% and average execution time was reduced by 13% in the Hungarian algorithm when compared with FCFS. The simulations were carried out under different workload conditions to validate our results.

show abstract

Energy-Efficient Transmission Scheduling with Guaranteed Data Imputation in MHealth Systems

Gao

Zhao

Xie

et al. 2023

2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS)

View full text Add to dashboard Cite

Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm

Cited by 5 publications

References 12 publications

Deep Learning Based Load Balancing Using Multidimensional Queuing Load Optimization Algorithm for Cloud Environment.

Deep Learning Based Load Balancing Using Multidimensional Queuing Load Optimization Algorithm for Cloud Environment.

Load Balancing in Cloud Computing Using Hungarian Algorithm

Energy-Efficient Transmission Scheduling with Guaranteed Data Imputation in MHealth Systems

Contact Info

Product

Resources

About