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

doi:10.5829/ije.2022.35.02b.20

Cited by 11 publications

(4 citation statements)

References 52 publications

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“…Securing Cloud data by controlling user access and eligibility to restrict usage to authorized users only [38]. Figure 3 shows Topology through the Cloud.…”

Section: The Cp System With Cloud Network Topologymentioning

confidence: 99%

Design of a Printed Circuit Board for Real-time Monitoring and Control of Pipeline’s Cathodic Protection System via IoT and a Cloud Platform

Mekki,

Kessar,

Mouaz

2023

IJE

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The integration of Internet of Things (IoT) and cloud-based cathodic protection (CP) systems is an innovative approach that can lead to improve pipeline protection from corrosion. Using a printed circuit board (PCB) to measure and control current and voltage makes it possible to monitor PC systems in real time. The web interface that is connected to the PCB circuit through IoT technology provides a platform for instant evaluation of the data obtained, thereby enabling early detection of potential problems. One of the key benefits of real-time monitoring is improved data management and security. The data obtained can be stored on a cloud server, making it easier to access and analyze. This eliminates the need for manual inspections, which can be time-consuming and error-prone. Additionally, real-time monitoring can reduce downtime, as problems can be detected and resolved quickly, preventing the need for lengthy manual inspections and maintenance. This innovative approach has tremendous potential for the future of pipeline protection and corrosion control. The developed PCB circuit features a mobile UART that provides program protection, and the interface can control multiple PCB cards and relays independently. The monitoring system can be updated without interrupting data acquisition. The use of open-source software, database hosting, and low-cost PCB development facilitates commercialization. This study could inspire new applications in asset management and monitoring.

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“…Securing Cloud data by controlling user access and eligibility to restrict usage to authorized users only [38]. Figure 3 shows Topology through the Cloud.…”

Section: The Cp System With Cloud Network Topologymentioning

confidence: 99%

Design of a Printed Circuit Board for Real-time Monitoring and Control of Pipeline’s Cathodic Protection System via IoT and a Cloud Platform

Mekki,

Kessar,

Mouaz

2023

IJE

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“…Results of HWACO shown improvement in makespan, cost over compared approaches. In 24 bio inspired task scheduling paradigm which deals with cost saving of resources. It was designed by sea gull optimization technique which adapts to cloud environment.…”

Section: Existing Related Workmentioning

confidence: 99%

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

Mangalampalli,

Karri,

Mohanty

et al. 2023

Sci Rep

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Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.

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“…In the recent decades, there is a growing interest in the design of optimization algorithms and their applications in the fields of sciences and technologies (1)(2)(3)(4). In this way, many optimization techniques have been presented to solve various real-world problems by researchers in the recent years (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

Mahmoodabadi,

Nemati,

Danesh

2024

IJE

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To enhance the performance of meta-heuristic algorithms, the development of new operators and the efficient combination of various optimization techniques are valuable strategies for discovering global optimal solutions. In this research endeavor, we introduce a novel optimization algorithm called PGS (Particle Swarm Optimization-GA-Sliding Surface). PGS combines the strengths of particle swarm optimization (PSO), genetic algorithm (GA), and sliding surface (SS) to tackle both mathematical test functions and real-world optimization problems. To achieve this, we adaptively tune the weighting function and learning coefficients of the PSO algorithm using the sliding mode control's SS relation. The global best particle discovered through the PSO method serves as one of the parents in the GA's crossover operation. This new crossover operator is then probabilistically integrated with an improved particle swarm optimization algorithm, enhancing convergence speed and facilitating escape from local optima. We evaluate the proposed algorithm's performance on both uni-modal and multi-modal mathematical test functions, considering un-rotated and rotated cases, thereby testing its effectiveness and efficiency against other prominent optimization techniques. Furthermore, we successfully implement the PGS algorithm in optimizing the state feedback controller for a nonlinear quadcopter system and determining the cross-section for an inelastic compression member.

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

Cited by 11 publications

References 52 publications

Design of a Printed Circuit Board for Real-time Monitoring and Control of Pipeline’s Cathodic Protection System via IoT and a Cloud Platform

Design of a Printed Circuit Board for Real-time Monitoring and Control of Pipeline’s Cathodic Protection System via IoT and a Cloud Platform

Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment

A New Optimization Algorithm Based on Particle Swarm Optimization Genetic Algorithm and Sliding Surfaces

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