B. Gomathi¹, S. T. Suganthi^2,*, Karthikeyan Krishnasamy³, J. Bhuvana⁴

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3693-3710, 2021, DOI:10.32604/cmc.2021.018159 - 24 August 2021

Abstract Enterprises have extensively taken on cloud computing environment since it provides on-demand virtualized cloud application resources. The scheduling of the cloud tasks is a well-recognized NP-hard problem. The Task scheduling problem is convoluted while convincing different objectives, which are dispute in nature. In this paper, Multi-Objective Improved Monarch Butterfly Optimization (MOIMBO) algorithm is applied to solve multi-objective task scheduling problems in the cloud in preparation for Pareto optimal solutions. Three different dispute objectives, such as makespan, reliability, and resource utilization, are deliberated for task scheduling problems.The Epsilon-fuzzy dominance sort method is utilized in the multi-objective More >

Ngo Tung Son^1,2,*, Jafreezal Jaafar¹, Izzatdin Abdul Aziz¹, Bui Ngoc Anh², Hoang Duc Binh², Muhammad Umar Aftab³

CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3429-3444, 2021, DOI:10.32604/cmc.2021.017710 - 24 August 2021

Abstract The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional… More >

Jaber Almutairi¹, Mohammad Aldossary^2,*

Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 129-145, 2021, DOI:10.32604/iasc.2021.018480 - 26 July 2021

Abstract With the increasing number of Internet of Things (IoT) devices connected to the internet, a platform is required to support the enormous amount of data they generate. Since cloud computing is far away from the connected IoT devices, applications that require low-latency, real-time interaction and high quality of service (QoS) may suffer network delay in using the Cloud. Consequently, the concept of edge computing has appeared to complement cloud services, working as an intermediate layer with computation capabilities between the Cloud and IoT devices, to overcome these limitations. Although edge computing is a promising enabler… More >

Mohammed Baljon, Shailendra Mishra^*

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 129-144, 2021, DOI:10.32604/cmc.2021.017509 - 04 June 2021

Abstract Energy harvesting (EH) technology in wireless communication is a promising approach to extend the lifetime of future wireless networks. A cross-layer optimal adaptation policy for a point-to-point energy harvesting (EH) wireless communication system with finite buffer constraints over a Rayleigh fading channel based on a Semi-Markov Decision Process (SMDP) is investigated. Most adaptation strategies in the literature are based on channel-dependent adaptation. However, besides considering the channel, the state of the energy capacitor and the data buffer are also involved when proposing a dynamic modulation policy for EH wireless networks. Unlike the channel-dependent policy, which… More >

Donglei Lu¹, Dongjie Zhu^2,*, Yundong Sun³, Haiwen Du³, Xiaofang Li⁴, Rongning Qu⁴, Yansong Wang³, Ning Cao¹, Helen Min Zhou⁵

Computer Systems Science and Engineering, Vol.38, No.3, pp. 339-350, 2021, DOI:10.32604/csse.2021.016683 - 19 May 2021

Abstract In recent years, the Internet of Things technology has developed rapidly, and smart Internet of Things devices have also been widely popularized. A large amount of data is generated every moment. Now we are in the era of big data in the Internet of Things. The rapid growth of massive data has brought great challenges to storage technology, which cannot be well coped with by traditional storage technology. The demand for massive data storage has given birth to cloud storage technology. Load balancing technology plays an important role in improving the performance and resource utilization… More >

Jaber Almutairi¹, Mohammad Aldossary^2,*,*

CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 4143-4160, 2021, DOI:10.32604/cmc.2021.018145 - 06 May 2021

Abstract Recently, the number of Internet of Things (IoT) devices connected to the Internet has increased dramatically as well as the data produced by these devices. This would require offloading IoT tasks to release heavy computation and storage to the resource-rich nodes such as Edge Computing and Cloud Computing. However, different service architecture and offloading strategies have a different impact on the service time performance of IoT applications. Therefore, this paper presents an Edge-Cloud system architecture that supports scheduling offloading tasks of IoT applications in order to minimize the enormous amount of transmitting data in the More >

Chia-Nan Wang¹, Zhao-Hong Cheng^2,*, Nguyen Ky Phuc Phan³, Van Thanh Nguyen⁴

Computer Systems Science and Engineering, Vol.38, No.2, pp. 239-249, 2021, DOI:10.32604/csse.2021.016578 - 23 April 2021

Abstract Production scheduling involves all activities of building production schedules, including coordinating and assigning activities to each person, group of people, or machine and arranging work orders in each workplace. Production scheduling must solve all problems such as minimizing customer wait time, storage costs, and production time; and effectively using the enterprise’s human resources. This paper studies the application of flexible job shop modelling on scheduling a woven labelling process. The labelling process includes several steps which are handled in different work-stations. Each workstation is also comprised of several identical parallel machines. In this study, job… More >

Ehab Nabiel Al-Khanak^1,*, Sai Peck Lee², Saif Ur Rehman Khan³, Navid Behboodian⁴, Osamah Ibrahim Khalaf⁵, Alexander Verbraeck⁶, Hans van Lint¹

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3265-3282, 2021, DOI:10.32604/cmc.2021.015409 - 01 March 2021

Abstract Scientific Workflow Applications (SWFAs) can deliver collaborative tools useful to researchers in executing large and complex scientific processes. Particularly, Scientific Workflow Scheduling (SWFS) accelerates the computational procedures between the available computational resources and the dependent workflow jobs based on the researchers’ requirements. However, cost optimization is one of the SWFS challenges in handling massive and complicated tasks and requires determining an approximate (near-optimal) solution within polynomial computational time. Motivated by this, current work proposes a novel SWFS cost optimization model effective in solving this challenge. The proposed model contains three main stages: (i) scientific workflow… More >

Nabeela Awan¹, Salman Khan², Mohammad Khalid Imam Rahmani³, Muhammad Tahir³, Nur Alam MD^4,*, Ryan Alturki⁵, Ihsan Ullah⁶

CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2449-2462, 2021, DOI:10.32604/cmc.2021.014386 - 05 February 2021

Abstract Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things (IoT). The IoT is the backbone of smart city applications such as smart grids and green energy management. In smart cities, the IoT devices are used for linking power, price, energy, and demand information for smart homes and home energy management (HEM) in the smart grids. In complex smart grid-connected systems, power scheduling and secure dispatch of information are the main research challenge. These challenges can be resolved through various machine learning techniques and data analytics. In this paper,… More >

Zhihong Liu¹, Shuo Zhang^2,*, Yaping Liu², Xiangke Wang¹, Dong Yin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.2, pp. 771-790, 2021, DOI:10.32604/cmes.2021.013244 - 21 January 2021

Abstract MapReduce is a widely used programming model for large-scale data processing. However, it still suffers from the skew problem, which refers to the case in which load is imbalanced among tasks. This problem can cause a small number of tasks to consume much more time than other tasks, thereby prolonging the total job completion time. Existing solutions to this problem commonly predict the loads of tasks and then rebalance the load among them. However, solutions of this kind often incur high performance overhead due to the load prediction and rebalancing. Moreover, existing solutions target the… More >