Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Hasan, Mohammed Zaki; Al‐Rizzo, Hussain M.

doi:10.1002/cpe.5442

Cited by 28 publications

(13 citation statements)

References 41 publications

(126 reference statements)

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“…An experiment was carried out on real Hadoop cluster environments with real time dataset gathered and Bit brains task. Hasan et al [11] proposed a task scheduling approach in terms of CPSO method for solving the problems of resource management and RA in heterogeneous and homogeneous IoT CC. The aim is to fulfill the Makespan by carrying out optimum task scheduling when taking into account various policies of incoming tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Saraereh¹,

Ali²

2022

Computers, Materials &Amp; Continua

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Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes the energy utilization of the cloud infrastructure and rises the income of service providers by the minimization of the processing time of the user job. With this motivation, this paper presents an intelligent Chaotic Artificial Immune Optimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabled cloud environment. The proposed CAIOA-RS algorithm solves the issue of resource allocation in the IoT enabled cloud environment. It also satisfies the makespan by carrying out the optimum task scheduling process with the distinct strategies of incoming tasks. The design of CAIOA-RS technique incorporates the concept of chaotic maps into the conventional AIOA to enhance its performance. A series of experiments were carried out on the CloudSim platform. The simulation results demonstrate that the CAIOA-RS technique indicates that the proposed model outperforms the original version, as well as other heuristics and metaheuristics.

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Section: Literature Reviewmentioning

confidence: 99%

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Saraereh¹,

Ali²

2022

Computers, Materials &Amp; Continua

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“…Therefore, dynamic models with parameters driven by real-time data and hyper-connected logistics mechanism have been considered in recent scheduling research. Kwak et al (2014) and Hasan and Al-Rizzo (2020) have proposed logistics scheduling models considering the real-time data of resource conditions, context information, and coordination policies of incoming tasks. Chen (2020) further developed a logistics pipeline scheduling system docking with an intelligent interactive database, which synchronizes real-time data of all connected online equipment and environmental and personnel information.…”

Section: Cloud-based Scheduling In Smart Logisticsmentioning

confidence: 99%

Operations management of smart logistics: A literature review and future research

Feng

2021

Front. Eng. Manag.

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The global collaboration and integration of online and offline channels have brought new challenges to the logistics industry. Thus, smart logistics has become a promising solution for handling the increasing complexity and volume of logistics operations. Technologies, such as the Internet of Things, information communication technology, and artificial intelligence, enable more efficient functions into logistics operations. However, they also change the narrative of logistics management. Scholars in the areas of engineering, logistics, transportation, and management are attracted by this revolution. Operations management research on smart logistics mainly concerns the application of underlying technologies, business logic, operation framework, related management system, and optimization problems under specific scenarios. To explore these studies, the related literature has been systematically reviewed in this work. On the basis of the research gaps and the needs of industrial practices, future research directions in this field are also proposed.

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“…Meta-heuristic algorithms are the improvement of heuristic algorithms. The idea of meta-heuristic scheduling algorithms is to simulate natural behavior, such as the Genetic Algorithm (GA), 30,31 Particle Swarm Optimization (PSO), [32][33][34] Simulated Annealing (SA), 35 Artificial Bee Colony (ABC), 36 and differential evolution (DE). 37 Meta-heuristic algorithms are processes that combine the results gained from the previous search with the random search results to generate new search results in the global scope.…”

Section: Related Workmentioning

confidence: 99%

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

Liu

Chen

et al. 2021

Concurrency and Computation

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Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy-conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy-conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption.Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy-conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real-world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy-saving and makespan reducing.

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Task scheduling in Internet of Things cloud environment using a robust particle swarm optimization

Cited by 28 publications

References 41 publications

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

Operations management of smart logistics: A literature review and future research

Scheduling energy‐conscious tasks in distributed heterogeneous computing systems

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