Chirag Chandrashekar¹, Pradeep Krishnadoss^1,*, Vijayakumar Kedalu Poornachary¹, Balasundaram Ananthakrishnan^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2593-2616, 2024, DOI:10.32604/cmc.2024.046304

Abstract Cloud computing provides a diverse and adaptable resource pool over the internet, allowing users to tap into various resources as needed. It has been seen as a robust solution to relevant challenges. A significant delay can hamper the performance of IoT-enabled cloud platforms. However, efficient task scheduling can lower the cloud infrastructure’s energy consumption, thus maximizing the service provider’s revenue by decreasing user job processing times. The proposed Modified Chimp-Whale Optimization Algorithm called Modified Chimp-Whale Optimization Algorithm (MCWOA), combines elements of the Chimp Optimization Algorithm (COA) and the Whale Optimization Algorithm (WOA). To enhance MCWOA’s identification precision, the Sobol sequence… More >

Shasha Zhao^1,2,3,*, Huanwen Yan^1,2, Qifeng Lin^1,2, Xiangnan Feng^1,2, He Chen^1,2, Dengyin Zhang^1,2

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1135-1156, 2024, DOI:10.32604/cmc.2024.045660

Abstract Task scheduling plays a key role in effectively managing and allocating computing resources to meet various computing tasks in a cloud computing environment. Short execution time and low load imbalance may be the challenges for some algorithms in resource scheduling scenarios. In this work, the Hierarchical Particle Swarm Optimization-Evolutionary Artificial Bee Colony Algorithm (HPSO-EABC) has been proposed, which hybrids our presented Evolutionary Artificial Bee Colony (EABC), and Hierarchical Particle Swarm Optimization (HPSO) algorithm. The HPSO-EABC algorithm incorporates both the advantages of the HPSO and the EABC algorithm. Comprehensive testing including evaluations of algorithm convergence speed, resource execution time, load balancing,… More >

Laila Almutairi¹, Shabnam Mohamed Aslam^2,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2791-2811, 2023, DOI:10.32604/cmc.2023.040268

Abstract The rapid growth of service-oriented and cloud computing has created large-scale data centres worldwide. Modern data centres’ operating costs mostly come from back-end cloud infrastructure and energy consumption. In cloud computing, extensive communication resources are required. Moreover, cloud applications require more bandwidth to transfer large amounts of data to satisfy end-user requirements. It is also essential that no communication source can cause congestion or bag loss owing to unnecessary switching buffers. This paper proposes a novel Energy and Communication (EC) aware scheduling (EC-scheduler) algorithm for green cloud computing, which optimizes data centre energy consumption and traffic load. The primary goal… More >

Kaili Shao¹, Hui Fu¹, Ying Song², Bo Wang^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454

Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem into binary non-linear programming and… More >

Lei Yin¹, Chang Sun², Ming Gao³, Yadong Fang⁴, Ming Li¹, Fengyu Zhou^1,*

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1587-1608, 2023, DOI:10.32604/iasc.2023.039380

Abstract The solution strategy of the heuristic algorithm is pre-set and has good performance in the conventional cloud resource scheduling process. However, for complex and dynamic cloud service scheduling tasks, due to the difference in service attributes, the solution efficiency of a single strategy is low for such problems. In this paper, we presents a hyper-heuristic algorithm based on reinforcement learning (HHRL) to optimize the completion time of the task sequence. Firstly, In the reward table setting stage of HHRL, we introduce population diversity and integrate maximum time to comprehensively determine the task scheduling and the selection of low-level heuristic strategies.… More >

Hai Wang^*, Xuan Hao, Shuo Ji^*, Jie Zheng, Yuhui Ma, Jianfeng Yang

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1621-1635, 2023, DOI:10.32604/iasc.2023.037898

Abstract Trusted Execution Environment (TEE) is an important part of the security architecture of modern mobile devices, but its secure interaction process brings extra computing burden to mobile devices. This paper takes open portable trusted execution environment (OP-TEE) as the research object and deploys it to Raspberry Pi 3B, designs and implements a benchmark for OP-TEE, and analyzes its program characteristics. Furthermore, the application execution time, energy consumption and energy-delay product (EDP) are taken as the optimization objectives, and the central processing unit (CPU) frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through… More >

Jixun Gao^1,2, Bingyi Hu², Jialei Liu^3,4,*, Huaichen Wang⁵, Quanzhen Huang¹, Yuanyuan Zhao⁶

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1-16, 2023, DOI:10.32604/iasc.2023.036890

Abstract Mobile Edge Computing (MEC) is proposed to solve the needs of Internet of Things (IoT) users for high resource utilization, high reliability and low latency of service requests. However, the backup virtual machine is idle when its primary virtual machine is running normally, which will waste resources. Overbooking the backup virtual machine under the above circumstances can effectively improve resource utilization. First, these virtual machines are deployed into slots randomly, and then some tasks with cooperative relationship are offloaded to virtual machines for processing. Different deployment locations have different resource utilization and average service response time. We want to find… More >

Mengkai Zhao¹, Zhixia Zhang², Tian Fan¹, Wanwan Guo¹, Zhihua Cui^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2425-2450, 2023, DOI:10.32604/cmes.2023.026671

Abstract Due to the security and scalability features of hybrid cloud architecture, it can better meet the diverse requirements of users for cloud services. And a reasonable resource allocation solution is the key to adequately utilize the hybrid cloud. However, most previous studies have not comprehensively optimized the performance of hybrid cloud task scheduling, even ignoring the conflicts between its security privacy features and other requirements. Based on the above problems, a many-objective hybrid cloud task scheduling optimization model (HCTSO) is constructed combining risk rate, resource utilization, total cost, and task completion time. Meanwhile, an opposition-based learning knee point-driven many-objective evolutionary… More >

Yanping Chen^1,2,3, Xuyang Bai^1,2,3,*, Xiaomin Jin^1,2,3, Zhongmin Wang^1,2,3, Fengwei Wang⁴, Li Ling⁴

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2101-2117, 2023, DOI:10.32604/cmc.2023.035530

Abstract Deploying task caching at edge servers has become an effective way to handle compute-intensive and latency-sensitive tasks on the industrial internet. However, how to select the task scheduling location to reduce task delay and cost while ensuring the data security and reliable communication of edge computing remains a challenge. To solve this problem, this paper establishes a task scheduling model with joint blockchain and task caching in the industrial internet and designs a novel blockchain-assisted caching mechanism to enhance system security. In this paper, the task scheduling problem, which couples the task scheduling decision, task caching decision, and blockchain reward,… More >

G. John Samuel Babu¹, M. Baskar^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1547-1559, 2023, DOI:10.32604/cmc.2023.033300

Abstract Numerous methods are analysed in detail to improve task scheduling and data security performance in the cloud environment. The methods involve scheduling according to the factors like makespan, waiting time, cost, deadline, and popularity. However, the methods are inappropriate for achieving higher scheduling performance. Regarding data security, existing methods use various encryption schemes but introduce significant service interruption. This article sketches a practical Real-time Application Centric TRS (Throughput-Resource utilization–Success) Scheduling with Data Security (RATRSDS) model by considering all these issues in task scheduling and data security. The method identifies the required resource and their claim time by receiving the service… More >