Cost optimized Hybrid Genetic-Gravitational Search Algorithm for load scheduling in Cloud Computing

Chaudhary, Divya; Kumar, Bijendra

doi:10.1016/j.asoc.2019.105627

Cited by 59 publications

(25 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Chaudhary and Kumar [11] implemented a new load scheduling algorithm; Hybrid Genetic Gravitational Search Algorithm (HG-GSA) to decrease the power of computation cost. The developed algorithm finds the optimal position of the particle in the search space that was used to calculate the force.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

Kodli¹,

Terdal²

2021

IJIES

View full text Add to dashboard Cite

In recent decades, task scheduling and load balancing in the cloud is a growing research area, due to the vast amount of data stored in the server highly increases the load. In order to address this concern, Hybrid Max-Min Genetic Algorithm (HMMGA) is proposed for task scheduling and load balancing in the cloud environment. At first, the load is evaluated for every Virtual Machine (VM), if the load is high, then HMMGA is used for balancing the load. HMMGA selects the best VMs to assign the tasks and migrates the over-loaded VMs tasks to the under-loaded VMs. HMMGA significantly avoids the imbalanced workload performance in the cloud environment. In this research paper, the proposed HMMGA performance is compared to Max-Min algorithm, Low time complexity and low cost binary Particle Swarm Optimizer (IBPSO-LBS) and PSO with Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm to examine the efficacy of HMMGA. From the experimental simulation, the result shows that HMMGA averagely delivers 1.63 and 3.88 seconds less make span compared to the Max-Min and TOPSIS-PSO algorithm for five VMs. In addition, HMMGA averagely enhances 10% to 40% of resource utilization than the MaxMin and TOPSIS-PSO algorithm. In another experiment, the HMMGA approximately showed 1.7 to 25.99 seconds less average waiting time compared to the Max-Min and IBPSO-LBS.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

Kodli¹,

Terdal²

2021

IJIES

View full text Add to dashboard Cite

show abstract

“…Based on the customer demands, like enhanced wear resistant, corrosion resistant, hardness, hot hardness, etc., different methods need to be selected. Mohammed et al [17] proved that, modified genetic algorithm improves search accuracy and has better global convergence than other related algorithms.…”

Section: Related Workmentioning

confidence: 99%

Resource Scheduling using Cloud in Chemical & Electro Chemical Coating Application

Brintha¹,

Jappes²

2019

IJEAT

View full text Add to dashboard Cite

Due to the advancements and use of computer based technologies in manufacturing sectors, there has been a drastic change in how manufacturing industries perform their business. Manufacturing tied up with cloud computing technologies can help the customers, suppliers and manufacturers in several ways and hence can maximize profit in production lines. However, even though there are several policies for scheduling workflows in production, effective mapping of tasks with resources is always a challenging issue. If scheduling policies are inappropriate, it will have a negative impact on cost, time and therefore may affect the overall performance of the workflow. This work proposes a Multi-Objective Genetic Algorithm (MOGA) based scheduling model to schedule resources related to chemical and electro chemical coating. The major objective of this work is to reduce makespan, improve resource utilization and also minimize the overall cost of the workflow. This information obtained from the workflow can be used for better decision making when several tasks has to be done in parallel by migrating the tasks to the site of resource availability. The computational results also shows that MOGA performs well in makespan minimization, resource utilization and cost minimization because of its convergence speed and robustness. The analysis results prove that, MOGA can be optimal for estimating the path of where the work can be done such that the makespan and cost is minimized.

show abstract

“…In this paper CPU/FPGA heterogeneous architecture scheduling problem is studied and the author proposed two genetic algorithm based approaches to overcome this issue which are the MPSoC and makespan (minimize the schedule length) [ 27 ]. The process of economic optimization is proposed in this paper using genetic algorithm [ 28 , 29 ]. In order to reduce the cost optimization issues hybrid genetic-gravitational search algorithm (HG-GSA) is developed.…”

Section: Introductionmentioning

confidence: 99%

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Ajmi

Helali

Lorenz

et al. 2021

Sensors

View full text Add to dashboard Cite

Nowadays due to smart environment creation there is a rapid growth in wireless sensor network (WSN) technology real time applications. The most critical resource in in WSN is battery power. One of the familiar methods which mainly concentrate in increasing the power factor in WSN is clustering. In this research work, a novel concept for clustering is introduced which is multi weight chicken swarm based genetic algorithm for energy efficient clustering (MWCSGA). It mainly consists of six sections. They are system model, chicken swarm optimization, genetic algorithm, CCSO-GA cluster head selection, multi weight clustering model, inter cluster, and intra cluster communication. In the performance evaluation the proposed model is compared with few earlier methods such as Genetic Algorithm-Based Energy-Efficient Adaptive Clustering Protocol For Wireless Sensor Networks (GA-LEACH), Low energy adaptive Clustering hierarchy approach for WSN (MW-LEACH) and Chicken Swarm Optimization based Genetic Algorithm (CSOGA). During the comparison it is proved that our proposed method performed well in terms of energy efficiency, end to end delay, packet drop, packet delivery ratio and network throughput.

show abstract

Cost optimized Hybrid Genetic-Gravitational Search Algorithm for load scheduling in Cloud Computing

Cited by 59 publications

References 17 publications

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

Hybrid Max-Min Genetic Algorithm for Load Balancing and Task Scheduling in Cloud Environment

Resource Scheduling using Cloud in Chemical & Electro Chemical Coating Application

MWCSGA—Multi Weight Chicken Swarm Based Genetic Algorithm for Energy Efficient Clustered Wireless Sensor Network

Contact Info

Product

Resources

About