Hamayun Khan^1,*, Irfan Ud Din², Arshad Ali³, Mohammad Husain³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2097-2113, 2023, DOI:10.32604/cmc.2023.032999 - 22 September 2022

Abstract Minimizing the energy consumption to increase the life span and performance of multiprocessor system on chip (MPSoC) has become an integral chip design issue for multiprocessor systems. The performance measurement of computational systems is changing with the advancement in technology. Due to shrinking and smaller chip size power densities on-chip are increasing rapidly that increasing chip temperature in multi-core embedded technologies. The operating speed of the device decreases when power consumption reaches a threshold that causes a delay in complementary metal oxide semiconductor (CMOS) circuits because high on-chip temperature adversely affects the life span of… More >

Atif Ishaq Khan^*, Syed Asad Raza Kazmi, Awais Qasim

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1183-1197, 2023, DOI:10.32604/cmc.2023.032691 - 22 September 2022

Abstract A self-adaptive resource provisioning on demand is a critical factor in cloud computing. The selection of accurate amount of resources at run time is not easy due to dynamic nature of requests. Therefore, a self-adaptive strategy of resources is required to deal with dynamic nature of requests based on run time change in workload. In this paper we proposed a Cloud-based Adaptive Resource Scheduling Strategy (CARSS) Framework that formally addresses these issues and is more expressive than traditional approaches. The decision making in CARSS is based on more than one factors. The MAPE-K based framework More >

Ahmed Y. Hamed¹, M. Kh. Elnahary^1,*, Faisal S. Alsubaei², Hamdy H. El-Sayed¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2133-2148, 2023, DOI:10.32604/cmc.2023.032215 - 22 September 2022

Abstract Cloud computing has taken over the high-performance distributed computing area, and it currently provides on-demand services and resource polling over the web. As a result of constantly changing user service demand, the task scheduling problem has emerged as a critical analytical topic in cloud computing. The primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence restrictions. Assignments and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor system. The diverse processes inside the heuristic-based task scheduling method will result in varying… More >

Hamayun Khan^1,*, Irfan Ud din², Arshad Ali³, Sami Alshmrany³

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 965-981, 2023, DOI:10.32604/cmc.2023.031223 - 22 September 2022

Abstract Increasing the life span and efficiency of Multiprocessor System on Chip (MPSoC) by reducing power and energy utilization has become a critical chip design challenge for multiprocessor systems. With the advancement of technology, the performance management of central processing unit (CPU) is changing. Power densities and thermal effects are quickly increasing in multi-core embedded technologies due to shrinking of chip size. When energy consumption reaches a threshold that creates a delay in complementary metal oxide semiconductor (CMOS) circuits and reduces the speed by 10%–15% because excessive on-chip temperature shortens the chip’s life cycle. In this… More >

Zheng Zhang, Juan Chen^*, Qing Guo

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1529-1563, 2023, DOI:10.32604/cmes.2022.021451 - 20 September 2022

Abstract Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms More > Graphic Abstract

Liang Luo¹, Xingmei Li¹, Kaijiang Yang¹, Mengyang Wei¹, Jiong Chen¹, Junqian Yang¹, Liang Yao^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1565-1595, 2023, DOI:10.32604/cmes.2022.021198 - 20 September 2022

Abstract The emergence of power dispatching automation systems has greatly improved the efficiency of power industry operations and promoted the rapid development of the power industry. However, with the convergence and increase in power data flow, the data dispatching network and the main station dispatching automation system have encountered substantial pressure. Therefore, the method of online data resolution and rapid problem identification of dispatching automation systems has been widely investigated. In this paper, we perform a comprehensive review of automated dispatching of massive dispatching data from the perspective of intelligent identification, discuss unresolved research issues and More >

Shaoxuan Yun, Ying Chen^*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 1815-1835, 2023, DOI:10.32604/cmes.2022.022797 - 20 September 2022

Abstract Nowadays, with the widespread application of the Internet of Things (IoT), mobile devices are renovating our lives. The data generated by mobile devices has reached a massive level. The traditional centralized processing is not suitable for processing the data due to limited computing power and transmission load. Mobile Edge Computing (MEC) has been proposed to solve these problems. Because of limited computation ability and battery capacity, tasks can be executed in the MEC server. However, how to schedule those tasks becomes a challenge, and is the main topic of this piece. In this paper, we More >

Qinhui Liu¹, Nengjian Wang^1,*, Jiang Li¹, Tongtong Ma², Fapeng Li¹, Zhijie Gao¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2073-2091, 2023, DOI:10.32604/cmes.2022.021433 - 20 September 2022

Abstract As a typical transportation tool in the intelligent manufacturing system, Automatic Guided Vehicle (AGV) plays an indispensable role in the automatic production process of the workshop. Therefore, integrating AGV resources into production scheduling has become a research hotspot. For the scheduling problem of the flexible job shop adopting segmented AGV, a dual-resource scheduling optimization mathematical model of machine tools and AGVs is established by minimizing the maximum completion time as the objective function, and an improved genetic algorithm is designed to solve the problem in this study. The algorithm designs a two-layer coding method based More > Graphic Abstract

Qi Zhou^1,2, Jinghua Li^1,3, Ruipu Dong^1,*, Qinghua Zhou^3,*, Boxin Yang³

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1263-1281, 2023, DOI:10.32604/cmes.2022.020744 - 31 August 2022

Abstract Offshore engineering construction projects are large and complex, having the characteristics of multiple execution modes and multiple resource constraints. Their complex internal scheduling processes can be regarded as resourceconstrained project scheduling problems (RCPSPs). To solve RCPSP problems in offshore engineering construction more rapidly, a hybrid genetic algorithm was established. To solve the defects of genetic algorithms, which easily fall into the local optimal solution, a local search operation was added to a genetic algorithm to defend the offspring after crossover/mutation. Then, an elitist strategy and adaptive operators were adopted to protect the generated optimal solutions, More >

Bao Rong Chang¹, Hsiu-Fen Tsai^2,*, Yu-Chieh Lin¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128 - 31 August 2022

Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >