scholarly journals A Survey on QoS Requirements Based on Particle Swarm Optimization Scheduling Techniques for Workflow Scheduling in Cloud Computing

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 551 ◽  
Author(s):  
Mazen Farid ◽  
Rohaya Latip ◽  
Masnida Hussin ◽  
Nor Asilah Wati Abdul Hamid
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Innovative Technology ◽  
Utilization Rate ◽  
Future Research ◽  
Workflow Model ◽  
Swarm Optimization

Cloud computing is an innovative technology that deploys networks of servers, located in wide remote areas, for performing operations on a large amount of data. In cloud computing, a workflow model is used to represent different scientific and web applications. One of the main issues in this context is scheduling large workflows of tasks with scientific standards on the heterogeneous cloud environment. Other issues are particular to public cloud computing. These include the need for the user to be satisfied with the quality of service (QoS) parameters, such as scalability and reliability, as well as maximize the end-users resource utilization rate. This paper surveys scheduling algorithms based on particle swarm optimization (PSO). This is aimed at assisting users to decide on the most suitable QoS consideration for large workflows in infrastructure as a service (IaaS) cloud applications and mapping tasks to resources. Besides, the scheduling schemes are categorized according to the variant of the PSO algorithm implemented. Their objectives, characteristics, limitations and testing tools have also been highlighted. Finally, further directions for future research are identified.

A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing

2021 ◽  
pp. 504-526
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Mitrabinda Ray ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.

A Novel Meta-Heuristic Approach for Load Balancing in Cloud Computing

2018 ◽  
Vol 8 (1) ◽  
pp. 29-49 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Mitrabinda Ray ◽  
Subasish Mohapatra
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Virtual Machines ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Swarm Optimization

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue in cloud computing. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better quality of service. Hence it is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a metaheuristic load balancing algorithm using Particle Swarm Optimization (MPSO) has been proposed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization. Performance comparisons are made with Genetic Algorithm (GA) and other popular algorithms on different measures like makespan calculation and resource utilization. Different cloud configurations are considered with varying Virtual Machines (VMs) and Cloudlets to analyze the efficiency of proposed algorithm. The proposed approach performs better than existing schemes.

MPSO

2017 ◽  
Vol 8 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Subhadarshini Mohanty ◽  
Prashanta Kumar Patra ◽  
Subasish Mohapatra ◽  
Mitrabinda Ray
Keyword(s):  
Particle Swarm Optimization ◽  
Load Balancing ◽  
Resource Utilization ◽  
Web Applications ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Critical Issue ◽  
Cloud Environment ◽  
Swarm Optimization ◽  
Load Balancing Algorithm

Cloud computing is gaining more popularity due to its advantages over conventional computing. It offers utility based services to subscribers on demand basis. Cloud hosts a variety of web applications and provides services on the pay-per-use basis. As the users are increasing in the cloud system, the load balancing has become a critical issue. Scheduling workloads in the cloud environment among various nodes are essential to achieving a better Quality of Service (QOS). It is a prominent area of research as well as challenging to allocate the resources with changeable capacities and functionality. In this paper, a load balancing algorithm using Multi Particle Swarm Optimization (MPSO) has been developed by utilizing the benefits of particle swarm optimization (PSO) algorithm. Proposed approach aims to minimize the task overhead and maximize the resource utilization in a homogenous cloud environment. Performance comparisons are made with Genetic Algorithm (GA), Multi GA, PSO and other popular algorithms on different measures like makespan calculation and resource utilization.

Adaptive Privacy Preservation Approach for Big Data Publishing in Cloud using K-Anonymization

2020 ◽  
Vol 13 ◽  
Author(s):  
Suman Madan ◽  
Puneet Goswami
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Classification Accuracy ◽  
Privacy Preservation ◽  
Fitness Function ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Information Loss ◽  
Swarm Optimization

Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

Study on Discrete Particle Swarm Optimization Algorithm

2012 ◽  
Vol 220-223 ◽  
pp. 1787-1794
Author(s):  
Bei Zhan Wang ◽  
Xiang Deng ◽  
Wei Chuan Ye ◽  
Hai Fang Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Future Research ◽  
Continuous Variables ◽  
Swarm Optimization ◽  
Operation Rules ◽  
New Type ◽  
Discrete Pso ◽  
Searching Method

The particle swarm optimization (PSO) algorithm is a new type global searching method, which mostly focus on the continuous variables and little on discrete variables. The discrete forms and discretized methods have received more attention in recent years. This paper introduces the basic principles and mechanisms of PSO algorithm firstly, then points out the process of PSO algorithm and depict the operation rules of discrete PSO algorithm. Various improvements and applications of discrete PSO algorithms are reviewed. The mechanisms and characteristics of two different discretized strategies are presented. Some development trends and future research directions about discrete PSO are proposed.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

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