scholarly journals Performance Evaluation of Meta-Heuristic Algorithms for Task Scheduling in Cloud Environment

2021 ◽  
pp. 87-93
Author(s):  
Jai Bhagwan ◽  
Sanjeev Kumar
Keyword(s):  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Minimum Cost ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Positive Role ◽  
Current Time ◽  
Heuristic Technique ◽  
Software Services ◽  
Cost Factors

Cloud Computing is one of the important fields in the current time of technological era. Here, the resources are available virtually for users according to pay-per-usage. Many industries are providing cloud services nowadays as pay for usage which reduces the computing cost drastically. The updated software services, hardware services can be provided to the user at a minimum cost. The target of the industries and scientists is to reduce the computing cost by various technologies. Resource management or task scheduling may also play a positive role in this regard. There are various virtual machine management algorithms available that can be tested and enhanced for research and benefit of the society. In this paper, three famous Max-Min, Ant Colony Optimization, and Particle Swarm Optimization algorithms have been used for experiments. After simulation results, it is found that the PSO algorithm is performing well for makes pan and cost factors. Further, a new algorithm can be proposed or a meta-heuristic technique can be enhanced or modified for getting better results.

scholarly journals A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 441 ◽  
Author(s):  
Qurat-ul-ain Mastoi ◽  
Teh Ying Wah ◽  
Ram Gopal Raj ◽  
Abdullah Lakhan
Keyword(s):  
Health Care ◽  
Task Scheduling ◽  
Minimum Cost ◽  
Cloud Services ◽  
Cloud System ◽  
Scheduling Problems ◽  
Healthcare Applications ◽  
Algorithm Framework ◽  
Internet Of Medical Things ◽  
Cost Efficient

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

Genetic Algorithm-Enabled Particle Swarm Optimization (PSOGA)-Based Task Scheduling in Cloud Computing Environment

2018 ◽  
Vol 17 (04) ◽  
pp. 1237-1267 ◽  
Author(s):  
Mohit Agarwal ◽  
Gur Mauj Saran Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Hybrid Techniques ◽  
Heuristic Technique

Task scheduling is one of the most difficult problems which is associated with cloud computing. Due to its nature, as it belongs to nondeterministic polynomial time (NP)-hard class of problem. Various heuristic as well as meta-heuristic approaches have been used to find the optimal solution. Task scheduling basically deals with the allocation of the task to the most efficient machine for optimal utilization of the computing resources and results in better makespan. As per literature, various meta-heuristic algorithms like genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO) and their other hybrid techniques have been applied. Through this paper, we are presenting a novel meta-heuristic technique — genetic algorithm enabled particle swarm optimization (PSOGA), a hybrid version of PSO and GA algorithm. PSOGA uses the diversification property of PSO and intensification property of the GA. The proposed algorithm shows its supremacy over other techniques which are taken into consideration by presenting less makespan time in majority of the cases which leads up to 22.2% improvement in performance of the system and also establishes that proposed PSOGA algorithm converges faster than the others.

Task Scheduling Using an Adaptive PSO Algorithm in Cloud Computing Environment

2018 ◽  
pp. 419-426
Author(s):  
B. Sivaramakrishna ◽  
T. V. Rao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Electricity Generation ◽  
Heuristic Algorithms ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Adaptive Pso

Now-a-days energy planners are aiming to increase the use of renewable energy sources and nuclear to meet the electricity generation. But till now coal-based power plants are the major source of electricity generation. The problem of task scheduling is one of the most important steps in taking advantage of the cloud computing environment. Various experiments show that although it is almost impossible to have an optimal solution, it seems that there is a more optimal solution using heuristic algorithms. This work compares three heuristic approaches to scheduling cloud environment tasks. These approaches are the PSO algorithm, the ACO, and the adaptive PSO algorithm for efficient task scheduling. The goal of all three of these algorithms is to generate an optimal schedule to minimize task completion time.

A novel approach using modified filtering algorithm (MFA) for effective completion of cloud tasks

2020 ◽  
Vol 39 (6) ◽  
pp. 8409-8417
Author(s):  
Sudan Jha ◽  
Deepak Prashar ◽  
Ahmed A. Elngar
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Ideal Solution ◽  
Swarm Optimization ◽  
Filtering Algorithm ◽  
Novel Approach ◽  
Task Accomplishment

In today’s era, cloud computing has played a major role in providing various services and capabilities to a number of researchers around the globe. One of the major problems we face in cloud is to identify the various constraints related with the delay in the Task accomplishment as well as the enhanced approach to execute the task with high throughput. Many studies have shown that it is almost difficult to create an ideal solution but it seems feasible to provide a sub-optimal solution utilizing heuristic algorithms. In this paper, compared to previously used particle swarm optimization (PSO), heuristic approaches, and improved PSO algorithm for efficient task scheduling, we propose “Modified Filtering Algorithm” for task scheduling on cloud setting. Comparing all these three algorithms, we strive to build an optimum schedule to reduce the completion period of execution of activities.

Task scheduling algorithm based on reliability perception in cloud computing

2020 ◽  
Vol 13 ◽  
Author(s):  
Kuang Yuejuan ◽  
Luo Zhuojun ◽  
Ouyang Weihao
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Scheduling Algorithm ◽  
Resource Scheduling ◽  
Pso Algorithm ◽  
Cloud Services ◽  
Task Scheduling Algorithm ◽  
Cloud Resource ◽  
Communication Links ◽  
The Impact

Background: In order to obtain reliable cloud resources, reduce the impact of resource node faults in cloud computing environment and reduce the fault time perceived by the application layer, a task scheduling model based on reliability perception is proposed. Methods: The model combines the two-parameter weibull distribution and analyzes various interaction relations between parallel tasks to describe the local characteristics of the failure rules of resource nodes and communication links in different periods.The model is added into the particle swarm optimization (pso) algorithm, and an adaptive inertial weighted pso resource scheduling algorithm based on reliability perception is obtained. Results: Simulation results show that when A increases to 0.3, the average scheduling length of the task increases rapidly.When it is 0.4-0.6, the growth rate is relatively slow.When greater than 0.8, the average scheduling length increases sharply.It can be seen that the r-pso algorithm proposed in this paper can accurately estimate the relevant parameters of cloud resource failure rule, and the generated resource scheduling scheme has better fitness, and the optimization effect is more significant with the increase of the number of tasks. Conclusion: With only a small amount of time added, the reliability of cloud services is greatly improved.

scholarly journals Strengthening the PSO algorithm with a new technique inspired by the golf game and solving the complex engineering problem

2021 ◽  
Author(s):  
Serkan Dereli ◽  
Raşit Köker
Keyword(s):  
Pso Algorithm ◽  
Engineering Problem ◽  
Golf Ball ◽  
New Technique ◽  
Successful Implementation ◽  
Robot Arm ◽  
Bee Colony ◽  
Scientific World ◽  
Heuristic Technique ◽  
A New Technique

AbstractThis study has been inspired by golf ball movements during the game to improve particle swarm optimization. Because, all movements from the first to the last move of the golf ball are the moves made by the player to win the game. Winning this game is also a result of successful implementation of the desired moves. Therefore, the movements of the golf ball are also an optimization, and this has a meaning in the scientific world. In this sense, the movements of the particles in the PSO algorithm have been associated with the movements of the golf ball in the game. Thus, the velocities of the particles have converted to parabolically descending structure as they approach the target. Based on this feature, this meta-heuristic technique is called RDV (random descending velocity) IW PSO. In this way, the result obtained is improved thousands of times with very small movements. For the application of the proposed new technique, the inverse kinematics calculation of the 7-joint robot arm has been performed and the obtained results have been compared with the traditional PSO, some IW techniques, artificial bee colony, firefly algorithm and quantum PSO.

Evaluation of the trust values among human resources in the enterprise cloud using an optimization algorithm and fuzzy logic

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lantian Li ◽  
Bahareh Pahlevanzadeh
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Human Life ◽  
Cloud Services ◽  
Cloud Provider ◽  
Suggested Approach ◽  
Content Type ◽  
Firefly Optimization ◽  
Security Costs ◽  
Firefly Optimization Algorithm

PurposeCloud eases information processing, but it holds numerous risks, including hacking and confidentiality problems. It puts businesses at risk in terms of data security and compliance. This paper aims to maximize the covered human resource (HR) vulnerabilities and minimize the security costs in the enterprise cloud using a fuzzy-based method and firefly optimization algorithm.Design/methodology/approachCloud computing provides a platform to improve the quality and availability of IT resources. It changes the way people communicate and conduct their businesses. However, some security concerns continue to derail the expansion of cloud-based systems into all parts of human life. Enterprise cloud security is a vital component in ensuring the long-term stability of cloud technology by instilling trust. In this paper, a fuzzy-based method and firefly optimization algorithm are suggested for optimizing HR vulnerabilities while mitigating security expenses in organizational cloud environments. MATLAB is employed as a simulation tool to assess the efficiency of the suggested recommendation algorithm. The suggested approach is based on the firefly algorithm (FA) since it is swift and reduces randomization throughout the lookup for an optimal solution, resulting in improved performance.FindingsThe fuzzy-based method and FA unveil better performance than existing met heuristic algorithms. Using a simulation, all the results are verified. The study findings showed that this method could simulate complex and dynamic security problems in cloud services.Practical implicationsThe findings may be utilized to assist the cloud provider or tenant of the cloud infrastructure system in taking appropriate risk mitigation steps.Originality/valueUsing a fuzzy-based method and FA to maximize the covered HR vulnerabilities and minimize the security costs in the enterprise cloud is the main novelty of this paper.

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