Creating Elasticity with Enhanced Weighted Optimization Load Balancing Algorithm in Cloud Computing

Tyagi, Neha; Rana, Ajay; Kansal, Vineet

doi:10.1109/aicai.2019.8701375

Cited by 27 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equation (6)shows the total execution time, which includes the time spent waiting for each task. In an efficient cloud environment, the total response time will be low.…”

Section: Comparative Analysismentioning

confidence: 99%

See 1 more Smart Citation

DL‐DC: Deep learning‐based deadline constrained load balancing technique

Champla,

Dhandapani,

Velmurugan

2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryLoad balancing is a crucial feature of cloud computing that evenly distributes workload between servers, network interfaces, and hard drives. Because of dynamic computing over the internet, cloud computing suffers from request overloading. To address this challenge, this paper proposes a new deep learning‐based deadline constrained (DL‐DC) load balancing technique. The proposed DL‐DC technique will improve resource utilization, reduce cost, latency, and response time, as well as balance load between servers and improve reliability. The proposed DL‐DC technique will direct traffic to a load balancer, which will forward the load with a deadline to a deep Inception ResNet. This network considers some parameters, such as sticky session, content‐based and instance health check and efficiently predicts the schedule for the task. Finally, a predicted task schedule has been derived using the DL‐DC model which is used to distribute the task to the virtual machine. The proposed DL‐DC load balancing algorithm is compared with other existing algorithms such as QMPSO, DQTS, and ACSO in terms of cost, makespan, response time, transmission time and task migration. The proposed method achieves up to 21.08% low response time, 27.3% decrease in make span, 25.5% decrease in task migration, and 38.9% decrease in cost respectively.

show abstract

“…Equation (6)shows the total execution time, which includes the time spent waiting for each task. In an efficient cloud environment, the total response time will be low.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Virtual machines (VMs) are the processing units in the physical machine that calculate and allocate cloud resources in CC. The CC connects large VMs by allocating resources in a non‐preemptive and preemptive manner 6,7 . When scheduling work on VMs, it is possible that some VMs will be overloaded while others will be underloaded 8 .…”

Section: Introductionmentioning

confidence: 99%

DL‐DC: Deep learning‐based deadline constrained load balancing technique

Champla,

Dhandapani,

Velmurugan

2023

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Among researchers, distributed computing is one of the prominent technologies for large scale computing issues. Furthermore, recent advances in big data and communication enabled new paradigms of distributed computing such as cloud computing [1] and ad hoc networks. The characteristic of the mobile ad hoc networks (MANETs) [2] is to provide robust and efficient operations in wireless networks by comprising routing among mobile nodes.…”

Section: Introductionmentioning

confidence: 99%

OCSR: Overlapped Cluster-Based Scalable Routing Approach for Vehicular Ad Hoc Networks (VANETs)

Pandey

Kansal

Swaroop

2022

Wireless Communications and Mobile Computing

Self Cite

View full text Add to dashboard Cite

Vehicular ad hoc networks (VANETs) are eminent class of mobile ad hoc networks due to their applications. However, mobility management and network scalability are still addressable problems in VANETs. In the current paper, a hierarchical approach has been designed for handling significantly large VANETs by providing better mobility management. The formation of multiple overlapped clusters from large VANETs using k-means algorithm is major characteristics of this approach. Additionally, an addressing architecture has been introduced using two data registers. The derived algorithm allows preparing an appropriate route between source and destination vehicles. Correctness and performance of the approach have been discussed.

show abstract

“…The cloud based data prediction and scheduling system requires the dynamic update of data according to the user load, and it requires certain period of time for processing the tasks frequently. For this purpose, there are several techniques [9][10][11] are developed in the conventional works, which are related to the concepts of clustering, optimization, and classification. In these mechanisms, the sequence of patterns are used to categorize the relevant tasks with respect to the load.…”

mentioning

confidence: 99%

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

Laxman

Lathigara

Aluvalu

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Load balancing and task scheduling in cloud have gained a significant attention by many researchers, due to the increased demand of computing resources and services. For this purpose, there are various load balancing methodologies are developed in the existing works, which are mainly focusing on allocating the tasks to Virtual Machines (VMs) based on their priority, order of tasks, and execution time.Still, it facing the major difficulties in finding the best tasks for allocation, because the sequence of patterns are normally used to categorize the relevant tasks with respect to the load. Thus, this research work intends to develop an intelligent group of mechanisms for efficiently allocating the tasks to the VMs by finding the best tasks with respect to the scheduling parameters. Initially, the user tasks are given to the load balancer unit, where the Probabilistic Gray Wolf Optimization (PGWO) technique is used to find the best fitness value for selecting the tasks. Then, the Adaptive Vector Searching (AVS) methodology is utilized to cluster the group of tasks for efficiently allocating the tasks with improved Quality of Service (QoS). Finally, the Recursive Data Acquisition (RDA) based scheduler unit can allocate the clustered tasks to the appropriate VMs in the cloud system by analyzing the properties of storage capacity, balancing load of VM, CPU usage, memory consumption, and execution time of tasks. During evaluation, the performance of the proposed load balancing model is validated by using various measures. Then, the obtained results are compared with some state-of-the-art models for proving the betterment of the proposed scheme.

show abstract

Creating Elasticity with Enhanced Weighted Optimization Load Balancing Algorithm in Cloud Computing

Cited by 27 publications

References 6 publications

DL‐DC: Deep learning‐based deadline constrained load balancing technique

DL‐DC: Deep learning‐based deadline constrained load balancing technique

OCSR: Overlapped Cluster-Based Scalable Routing Approach for Vehicular Ad Hoc Networks (VANETs)

PGWO‐AVS‐RDA: An intelligent optimization and clustering based load balancing model in cloud

Contact Info

Product

Resources

About