An elephant herd grey wolf optimization (EHGWO) algorithm for load balancing in cloud

2020 ◽  
Vol 16 (3) ◽  
pp. 259-277
Author(s):  
Pooja Arora ◽  
Anurag Dixit
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
System Performance ◽  
Virtual Machines ◽  
Fitness Function ◽  
Optimization Technique ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Content Type ◽  
Computing Model

Purpose The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. However, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing (LB) method for allocating tasks to virtual machines (VMs) without influencing system performance. This paper aims to develop a technique for LB in cloud using optimization algorithms. Design/methodology/approach This paper proposes a hybrid optimization technique, named elephant herding-based grey wolf optimizer (EHGWO), in the cloud computing model for LB by determining the optimal VMs for executing the reallocated tasks. The proposed EHGWO is derived by incorporating elephant herding optimization (EHO) in grey wolf optimizer (GWO) such that the tasks are allocated to the VM by eliminating the tasks from overloaded VM by maintaining the system performance. Here, the load of physical machine (PM), capacity and load of VM is computed for deciding whether the LB has to be done or not. Moreover, two pick factors, namely, task pick factor (TPF) and VM pick factor (VPF), are considered for choosing the tasks for reallocating them from overloaded VM to underloaded VM. The proposed EHGWO decides the task to be allocated in the VM based on the newly derived fitness functions. Findings The minimum load and makespan obtained in the existing methods, constraint measure based LB (CMLB), fractional dragonfly based LB algorithm (FDLA), EHO, GWO and proposed EHGWO for the maximum number of VMs is illustrated. The proposed EHGWO attained minimum makespan with value 814,264 ns and minimum load with value 0.0221, respectively. Meanwhile, the makespan values attained by existing CMLB, FDLA, EHO, GWO, are 318,6896 ns, 230,9140 ns, 1,804,851 ns and 1,073,863 ns, respectively. The minimum load values computed by existing methods, CMLB, FDLA, EHO, GWO, are 0.0587, 0.026, 0.0248 and 0.0234. On the other hand, the proposed EHGWO with minimum load value is 0.0221. Hence, the proposed EHGWO attains maximum performance as compared to the existing technique. Originality/value This paper illustrates the proposed LB algorithm using EHGWO in a cloud computing model using two pitch factors, named TPF and VPF. For initiating LB, the tasks assigned to the overloaded VM are reallocated to under loaded VMs. Here, the proposed LB algorithm adapts capacity and loads for the reallocation. Based on TPF and VPF, the tasks are reallocated from VMs using the proposed EHGWO. The proposed EHGWO is developed by integrating EHO and GWO algorithm using a new fitness function formulated by load of VM, migration cost, load of VM, capacity of VM and makespan. The proposed EHGWO is analyzed based on load and makespan.

scholarly journals The Hybrid Optimization Algorithm for Load Balancing in Cloud

2019 ◽  
Vol 8 (5S3) ◽  
pp. 67-71 ◽  
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Direction ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Network Access ◽  
Cloud Infrastructure ◽  
Shared Resources

The advancements in the cloud computing has gained the attention of several researchers to provide on-demand network access to users with shared resources. Cloud computing is important a research direction that can provide platforms and softwares to clients using internet. But, handling huge number of tasks in cloud infrastructure is a complicated task. Thus, it needs a load balancing method for allocating tasks to Virtual Machines (VMs) without influencing system performance. This paper proposes a load balancing technique, named Elephant Herd Grey Wolf Optimization (EHGWO) for balancing the loads. The proposed EHGWO is designed by integrating Elephant Herding Optimization (EHO) in Grey Wolf Optimizer (GWO) for selecting the optimal VMs for reallocation based on newly devised fitness function. The proposed load balancing technique considers different parameters of VMs and PMs for selecting the tasks to initiate the reallocation for load balancing. Here, two pick factors, named Task Pick Factor (TPF) and VM Pick Factor (VPF), are considered for allocating the tasks to balance the loads.

Efficient task scheduling on virtual machine in cloud computing environment

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahfooz Alam ◽  
Mahak ◽  
Raza Abbas Haidri ◽  
Dileep Kumar Yadav
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Virtual Machine ◽  
Task Allocation ◽  
Completion Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Content Type ◽  
Minimum Completion Time

Purpose Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization. Design/methodology/approach Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing. Findings To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time. Originality/value The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

scholarly journals Efficient Docker Container Scheduling using ABC Optimization Technique

2019 ◽  
Vol 8 (9S4) ◽  
pp. 26-32
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
System Performance ◽  
Virtual Machines ◽  
Optimization Technique ◽  
Performance Level ◽  
Major Objective ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Load Location

In recent years the usage of virtualized technology is increasing rapidly. This makes enhancement in the performance efficiency leads to the need of the virtualization machine. This study is developed to enhance the performance level of the docker containers in cloud computing. The work presented in the paper considers the major parameters like availability, load, location, and energy of virtual machines to increase the system performance. The major objective of the work is to analyze and distribute the load of machines equally. The ABC (Artificial or Counterfeit Bee Colony) algorithm is used. For this purpose the ABC algorithm replaces the traditional ACO approach because of its various features such as simplicity, flexibility, and robustness. The output of the proposed work is evaluated in the terms of energy consumption and job completion. The observed values corresponding to these factors prove the proficiency of the suggested ABC algorithm based technique over traditional ACO algorithm based technique.

scholarly journals A Hybrid Approach Based on Grey Wolf and Whale Optimization Algorithms for Solving Cloud Task Scheduling Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Jafar Ababneh
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Task Scheduling ◽  
Virtual Machines ◽  
Hybrid Approach ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Use Of Resources ◽  
Whale Optimization ◽  
Resources Assessment

In the context of cloud computing, one problem that is frequently encountered is task scheduling. This problem has two primary implications, which are the planning of tasks on virtual machines and the attenuation of performance. In order to address the problem of task scheduling in cloud computing, requisite nontraditional optimization attitudes to attain the optima of the problem, the present paper puts forth a hybrid multiple-objective approach called hybrid grey wolf and whale optimization (HGWWO) algorithms, that integrates two algorithms, namely, the grey wolf optimizer (GWO) and the whale optimization algorithm (WOA), with the purpose of conjoining the advantages of each algorithm for minimizing costs, energy consumption, and total execution time needed for task implementation, beside that improving the use of resources. Assessment of the aims of the proposed approach is carried out with the help of the tool known as CloudSim. As pointed out by the results of the experimental work undertaken, the proposed approach has the capability of performing at a superior level by comparison to the original algorithms GWO and WOA on their own with regard to costs, energy consumption, makespan, use of resources, and degree of imbalance.

MOEAGAC: an energy aware model with genetic algorithm for efficient scheduling in cloud computing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Prasadhu Marri ◽  
N.R. Rajalakshmi
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Research Work ◽  
Energy Aware ◽  
Content Type ◽  
Multi Objective ◽  
Proposed Model

PurposeMajority of the research work either concentrated on the optimization of scheduling length and execution cost or energy optimization mechanism. This research aims to propose the optimization of makespan, energy consumption and data transfer time (DTT) by considering the priority tasks. The research work is concentrated on the multi-objective approach based on the genetic algorithm (GA) and energy aware model to increase the efficiency of the task scheduling.Design/methodology/approachCloud computing is the recent advancement of the distributed and cluster computing. Cloud computing offers different services to the clients based on their requirements, and it works on the environment of virtualization. Cloud environment contains the number of data centers which are distributed geographically. Major challenges faced by the cloud environment are energy consumption of the data centers. Proper scheduling mechanism is needed to allocate the tasks to the virtual machines which help in reducing the makespan. This paper concentrated on the minimizing the consumption of energy as well as makespan value by introducing the hybrid algorithm called as multi-objective energy aware genetic algorithm. This algorithm employs the scheduling mechanism by considering the energy consumption of the CPU in the virtual machines. The energy model is developed for picking the task based on the fitness function. The simulation results show the performance of the multi-objective model with respect to makespan, DTT and energy consumption.FindingsThe energy aware model computes the energy based on the voltage and frequency distribution to the CPUs in the virtual machine. The directed acyclic graph is used to represent the task dependencies. The proposed model recorded 5% less makespan compared against the MODPSO and 0.7% less compared against the HEFT algorithms. The proposed model recorded 125 joules energy consumption for 50 VMs when all are in active state.Originality/valueThis paper proposed the multi-objective model based on bio-inspired approach called as genetic algorithm. The GA is combined with the energy aware model for optimizing the consumption of the energy in cloud computing. The GA used priority model for selecting the initial population and used the roulette wheel selection method for parent selection. The energy model is used as fitness function to the GA for selecting the tasks to perform the scheduling.

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