A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment

Pradhan, Arabinda; Bisoy, Sukant Kishoro; Das, Amardeep

doi:10.1016/j.jksuci.2021.01.003

Cited by 61 publications

(26 citation statements)

References 31 publications

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“…Cloud computing can speed up the prediction process by utilization of high-speed computing. In the case of the COVID-19 epidemic, a prediction scheme based on the machine learning model could be used in remote cloud nodes for real-time prediction permitting governments and citizens to reply proactively [5].…”

Section: Cloud Computingmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

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Section: Cloud Computingmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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“…PSO is a swarm-based optimization computation procedure through which numbers of particles with various positions and velocities are built to discover solutions [31,32]. Similarly, all agents in HPSOAC algorithm have different weights of load and different velocities of task transfer rate for getting the optimum result.…”

Section: Hpsoac Scheduling Policy For Parameter Updatingmentioning

confidence: 99%

“…To balance the load among VM, HPSOAC scheduling policy finds the best VM, i.e., either under load or ideal VM and then it transfers the extra task by taking the selected action. Task transfer speed can be calculated by using Equation (32). Thus, action selection depends upon current load of VM (VM load ) and task transferred (T tt ) to give maximum reward.…”

Section: Hpsoac Scheduling Policy For Parameter Updatingmentioning

confidence: 99%

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Pradhan

Bisoy

Sain

2022

Wireless Communications and Mobile Computing

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Increasing scale of task in cloud network leads to problem in load balancing and its improvement in parameters. In this paper, we proposed a hybrid scheduling policy which is hybrid of both Particle Swarm Optimization (PSO) algorithm and actor-critic algorithm named as Hybrid Particle Swarm Optimization Actor Critic (HPSOAC) to solve this issue. This hybrid scheduling policy helps to each agent to improve an individual learning as well as learning through exchanging information among other agents. An experiment is carried out by the help of Python simulator with TensorFlow. Outcome shows that our proposed scheduling policy reduces 5.16% and 10.86% in energy consumption, reduces 7.13% and 10.04% in makespan time, and has marginally better resource utilization over Deep Q-network (DQN) and Q-learning based on Modified Particle Swarm Optimization (QMPSO) algorithm, respectively.

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“…Similarly the operation standards of the cloud computing are not alike and the compatible technologies for these standards are non-guaranteed, and hence the data security [6], resource sharing has become the challenges and the area of development in cloud computing. The Particle Swarm Optimization (PSO) [5] algorithm is an heuristic algorithm employed in effective resource scheduling process incorporating the swarm search principle in cloud computing. The PSO algorithm yields optimal solution for the resource allocation objective function by executing the concept of cooperation and competition among the existing particles.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

Tamilarasu

Singaravel²

2022

Preprint

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As the digital era has progressed, so has the volume of digital data, making secure storage of this vast amount of data an increasingly important concern for data administrators. Cloud computing and the ease of storing and accessing data attracted a large number of IT professionals and administrators to migrate from physical infrastructure to cloud infrastructure. Cloud computing provides a wide range of services, but its primary functionality is the efficient distribution of resources among the many people who need to use the cloud. Effective resource allocation, decreased response time, and increased resource availability are all benefits of optimising cloud computing Quality of Service (QoS), which also helps to keep user data safe in the cloud infrastructure. Even though many studies have sought a high quality of service (QoS), no success has been found in combining efficient resource sharing with privacy protection. This paper uses Block chain technology and the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm to overcome the aforementioned challenge of achieving better QoS with high levels of data security. While the Block chain has a reduced parameter, it is highly efficient in protecting cloud network storage data, while the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm is an improvised version of Particle Swarm Optimization (PSO) and is efficient in resource allocation without any restrictions or boundaries in the neighbourhood available resources. Experimentation shows that the proposed meta-heuristic hybrid algorithm outperforms conventional algorithms in terms of Quality of Service.

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A survey on PSO based meta-heuristic scheduling mechanism in cloud computing environment

Cited by 61 publications

References 31 publications

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

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