A Modified Particle Swarm Optimization for Task Scheduling in Cloud Computing

Sharma, Sanjay Kumar; Kumar, Niraj

doi:10.2139/ssrn.3349598

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…This approach optimizes completion time and operating cost and is tested with 11 data sets with different size. Chaudhary et al [6] presented a modified PSO based scheduling technique to address the long scheduling time and high computation cost issues in a cloud environment. This approach regulates premature convergence and local search capability in particles.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Energy Makespan Optimization Based Task Mapping In Fog Enabled IoT Application

Tripathy,

Sahoo,

Tripathy

et al. 2024

Preprint

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The Internet of Things (IoT) points to billions of connected devices that share data through the Internet. However, the increasing amount of data generated by IoT devices makes remotely placed cloud data centers inefficient for delay-sensitive applications. In this regard, fog computing, which brings computation near the data source, plays a significant role in overcoming the above issue. However, resource constraints in fog demand an effective task-scheduling technique for the enormous amount of data. Many researchers have proposed a variety of heuristic and meta-heuristic approaches for effective scheduling; however, there is still scope for improvement. In this paper, we propose EMAPSO (energy makespan-aware PSO). The makespan and energy minimization simultaneously are presented as a bi-objective optimization problem. The approach also considered the load balancing factor while assigning a task to a VM in a fog/cloud environment. The proposed algorithm EMAPSO is compared to standard PSO, Modified PSO (MPSO), Bird swam optimization (BSO), and the Bee Life Algorithm (BLA). The experimental results show the proposed method outperforms the compared algorithms in terms of resource utilization, makespan, and energy consumption.

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Section: Related Workmentioning

confidence: 99%

An Efficient Energy Makespan Optimization Based Task Mapping In Fog Enabled IoT Application

Tripathy,

Sahoo,

Tripathy

et al. 2024

Preprint

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“…Zhou Wu et al [27] modeled the fitness function based on Execution time and applied modified PSO [28] Even though PSO has gained great importance in different fields at solving the optimization problems, it makes the system to suffer from local optimum problem for high dimensional space. Further, it also has low convergence rate in the iterative process.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Multiple Resource Attributes and Conditional Logic Assisted Task Scheduling in Cloud Computing

2023

IJIES

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From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

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