scholarly journals A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

2019 ◽  
Vol 2019 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Karunakaran V
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Cloud Infrastructure ◽  
Solution Quality ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
Selection For ◽  
Stochastic Development ◽  
Data Centres

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

Genetic-Based Task Scheduling Algorithm with Dynamic Virtual Machine Generation in Cloud Computing

2021 ◽  
pp. 165-174
Author(s):  
Ahmed A. A. Gad-Elrab ◽  
Tamer A.A. Alzohairy ◽  
Kamal R. Raslan ◽  
Farouk A. Emara
Keyword(s):  
Cloud Computing ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Heuristic Methods ◽  
Flexible Scheduling ◽  
Np Completeness ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
User Tasks ◽  
Available Resources

Recently, cloud computing has become the most common platform in the computing world. scheduling is one of the most important mechanism for managing cloud resources. Scheduling mechanism is a mechanism for scheduling user tasks among datacenters, host and virtual machines (VMs) and is an NP completeness problem. Most of existing mechanisms are heuristic and meta-heuristic methods, developed to address a part of scheduling problem and did not consider the dynamic creation of VMs by taking into account the required resources for a user task and the capabilities of a set of available hosts. To deal with this dynamic behavior, this paper introduces a new mechanism that uses a genetic algorithm (GA) for establishing a flexible scheduling mechanism that can adapt the dynamic number of VMs based on the required resources by user tasks and the available resources of hosts. Simulation results show that the proposed algorithm can distribute any number of user tasks on the available resources and it achieves better performance than existing algorithms in terms of response time, makespan, FlowTime, throughput, and resource utilization.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

2018 ◽  
pp. 96-101
Author(s):  
Shailendra Raghuvanshi ◽  
Priyanka Dubey
Keyword(s):  
Load Balancing ◽  
Task Scheduling ◽  
Waiting Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cloud Environment ◽  
Task Scheduling Algorithm ◽  
Machine Utilization ◽  
Independent Tasks ◽  
Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

Task scheduling algorithm based on improved genetic algorithm in cloud computing environment

2020 ◽  
Vol 13 ◽  
Author(s):  
Ge Weiqing ◽  
Cui Yanru
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Fitness Functions ◽  
Task Scheduling Algorithm ◽  
Simulation Results

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.

Using Value-Based Approach for Managing Cloud-Based Services

2017 ◽  
pp. 33-60
Author(s):  
Salah Eddin Murad ◽  
Salah Dowaji
Keyword(s):  
Response Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Software As A Service ◽  
Business Service ◽  
Cloud Infrastructure ◽  
Current Strategy ◽  
Target Value ◽  
Different Types ◽  
And Control

Software-as-a-Service (SaaS) providers are influenced by a variety of characteristics and capabilities of the available cloud infrastructure resources (IaaS). As a result, the decision made by business service owners to lease and use certain resources is an important one in order to achieve the planned outcome. This chapter uses value based approach to manage the SaaS service provided to the customers. Based on our approach, customer satisfaction is modeled not only based on the response time, but also based on the allotted budget. Using our model, the application owner is able to direct and control the decision of renting cloud resources as per the current strategy. This strategy is led by a set of defined key performance indicators. In addition, we present a scheduling algorithm that can bid for different types of virtual machines to achieve the target value. Furthermore, we proposed the required Ontology to semantically discover the needed IaaS resources. We conduct extensive simulations using different types of Amazon EC2 instances with dynamic prices.

scholarly journals The development of the system for arc nordugrid based grid-computing organization using virtual environments of the docker platform

2021 ◽  
Vol 6 (2 (114)) ◽  
pp. 117-124
Author(s):  
Olga Prila ◽  
Volodymyr Kazymyr ◽  
Volodymyr Bazylevych ◽  
Oleksandr Sysa
Keyword(s):  
Virtual Environments ◽  
Virtual Machines ◽  
Distributed Storage ◽  
Scheduling Algorithm ◽  
Simulation Models ◽  
Dynamic Programming Method ◽  
Cloud Infrastructure ◽  
Grid Environment ◽  
Urgent Task ◽  
Virtual Images

The study of modern frameworks and means of using virtualization in a grid environment confirmed the relevance of the task of automated configuration of the environment for performing tasks in a grid environment. Setting up a task execution environment using virtualization requires the implementation of appropriate algorithms for scheduling tasks and distributed storage of images of virtual environments in a grid environment. Existing cloud infrastructure solutions to optimize the process of deploying virtual machines on computing resources do not have integration with the Arc Nordugrid middleware, which is widely used in grid infrastructures. An urgent task is to develop tools for scheduling tasks and placing images of virtual machines on the resources of the grid environment, taking into account the use of virtualization tools. The results of the implementation of services of the framework are presented that allow to design and perform computational tasks in a grid environment based on ARC Nordugrid using the virtual environment of the Docker platform. The presented results of the implementation of services for scheduling tasks in a grid environment using a virtual computing environment are based on the use of a scheduling algorithm based on the dynamic programming method. Evaluations of the effectiveness of the solutions developed on the basis of a complex of simulation models showed that the use of the proposed algorithm for scheduling and replicating virtual images in a grid environment can reduce the execution time of a computational task by 88 %. Such estimates need further refinement; it is predicted that planning efficiency will increase over time with an increase in the number of running tasks due to the redistribution of the storage of virtual images

scholarly journals AP: Hybrid Task Scheduling Algorithm for Cloud

2020 ◽  
Vol 34 (4) ◽  
pp. 479-485
Author(s):  
Bhupesh Kumar Dewangan ◽  
Anurag Jain ◽  
Tanupriya Choudhury
Keyword(s):  
Web Service ◽  
Load Distribution ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Optimization Techniques ◽  
Resource Optimization ◽  
Convergence Time ◽  
Hybrid Optimization Algorithm ◽  
Task Scheduling Algorithm

Resource optimization is cost effective process in cloud. The efficiency of load balancing completely depends on how the infrastructure is utilizing. As per the current study, the resource optimization techniques are very costly and taking more convergence time to execute the task and load distribution among different virtual machines (VM). The objective of this paper is to develop a hybrid optimization algorithm to find the best virtual machine based on their fitness values and schedule different task to the fittest VM so that each task should get complete on time, and system can utilize the VM as well. The proposed algorithm is hybrid version of genetic (GA), ant-colony (Aco), and particle-swarm (Pso) algorithms, which is implemented and tested in amazon web service and compared with existing algorithms based on VM utilization, completion time, and cost. The proposed hybrid system genetic-aco-pso based algorithm (GAP) perform utmost while comparing with the existing systems.

scholarly journals Task Scheduling Algorithm Based on Bacterial Foraging Optimization (BFO) in Cloud Computing

2018 ◽  
Vol 7 (1) ◽  
pp. 16-19
Author(s):  
Anupama Gupta ◽  
Kulveer Kaur ◽  
Rajvir Kaur
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Virtual Machine ◽  
Execution Time ◽  
Virtual Machines ◽  
Scheduling Algorithm ◽  
Bacterial Foraging Optimization ◽  
Task Scheduling Algorithm ◽  
Steady Rate ◽  
Time And Energy

Cloud computing is the architecture in which cloudlets are executed by the virtual machines. The most applicable virtual machines are selected on the basis of execution time and failure rate. Due to virtual machine overloading, the execution time and energy consumption is increased at steady rate. In this paper, BFO technique is applied in which weight of each virtual machine is calculated and the virtual machine which has the maximum weight is selected on which cloudlet will be migrated. The performance of proposed algorithm is tested by implementing it in CloudSim and analyzing it in terms of execution time, energy consumption.

The Map-Reduce Parallelism Framework for Task Scheduling in Grid Computing

2011 ◽  
Vol 216 ◽  
pp. 111-115 ◽  
Author(s):  
Yun Xia Pei ◽  
Yue Zhang
Keyword(s):  
Human Resources ◽  
Grid Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Optimization Procedure ◽  
Map Reduce ◽  
Research Issues ◽  
Job Management ◽  
Task Scheduling Algorithm ◽  
Simulation Results

As a rapid developing infrastructure, the grid can share widely distributed computing, storage, data and human resources. In order to improve the usability and QoS of the grid, the job management in the grid is very important, and becomes one of the key research issues in grid computing. Map-Reduce provide an efficient and easy-to-use framework for parallelizing the global optimization procedure. The simulation results show the usefulness and effectiveness of our task scheduling algorithm.

Temperature-Aware Scheduling Algorithm for Multi-Core System

2014 ◽  
Vol 536-537 ◽  
pp. 703-707
Author(s):  
Qi Zhang ◽  
You Lin Ruan ◽  
Feng Gao
Keyword(s):  
Scheduling Algorithm ◽  
Uniform Temperature ◽  
Energy Balancing ◽  
Core System ◽  
Task Scheduling Algorithm ◽  
Multicore System ◽  
Utilization Ratio ◽  
Simulation Results ◽  
And Performance ◽  
Core Simulation

High temperature will affect reliability and performance of multicore system. In this paper, we propose a temperature-aware task scheduling algorithm for real-time multi-core systems, which combines the DVFS and energy balancing by analyzing workload information and multicore utilization. At first, calculate average utilization ratio of tasks. Secondly, balancing strategy according to the workload is proposed for uniform temperature distribution on the cores. Finally, adapt the HR-2 and DVFS to scheduling tasks in each core. Simulation results show that the proposed scheduling algorithm obtains a better effect in temperature and energy-saving than other algorithms.

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