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  • Open Access

    ARTICLE

    A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

    Kaili Shao1, Hui Fu1, Ying Song2, Bo Wang3,*

    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 >

  • Open Access

    ARTICLE

    Hyper-Heuristic Task Scheduling Algorithm Based on Reinforcement Learning in Cloud Computing

    Lei Yin1, Chang Sun2, Ming Gao3, Yadong Fang4, Ming Li1, Fengyu Zhou1,*

    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 >

  • Open Access

    ARTICLE

    An OP-TEE Energy-Efficient Task Scheduling Approach Based on Mobile Application Characteristics

    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 >

  • Open Access

    ARTICLE

    Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

    Jixun Gao1,2, Bingyi Hu2, Jialei Liu3,4,*, 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 >

  • Open Access

    ARTICLE

    Many-Objective Optimization-Based Task Scheduling in Hybrid Cloud Environments

    Mengkai Zhao1, Zhixia Zhang2, Tian Fan1, Wanwan Guo1, Zhihua Cui1,*

    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 >

  • Open Access

    ARTICLE

    Edge Computing Task Scheduling with Joint Blockchain and Task Caching in Industrial Internet

    Yanping Chen1,2,3, Xuyang Bai1,2,3,*, Xiaomin Jin1,2,3, Zhongmin Wang1,2,3, 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 >

  • Open Access

    ARTICLE

    TRS Scheduling for Improved QoS Performance in Cloud System

    G. John Samuel Babu1, M. Baskar2,*

    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 >

  • Open Access

    ARTICLE

    A Broker-Based Task-Scheduling Mechanism Using Replication Approach for Cloud Systems

    Abdulelah Alwabel*

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2217-2232, 2023, DOI:10.32604/iasc.2023.033703

    Abstract The reliability and availability of cloud systems have become major concerns of service providers, brokers, and end-users. Therefore, studying fault-tolerance mechanisms in cloud computing attracts intense attention in industry and academia. The task-scheduling mechanisms can improve the fault-tolerance level of cloud systems. A task-scheduling mechanism distributes tasks to a group of instances to be executed. Much work has been undertaken in this direction to improve the overall outcome of cloud computing, such as improving service quality and reducing power consumption. However, little work on task scheduling has studied the problem of lost tasks from the broker’s perspective. Task loss can… More >

  • Open Access

    ARTICLE

    Adaptive Resource Planning for AI Workloads with Variable Real-Time Tasks

    Sunhwa Annie Nam1, Kyungwoon Cho2, Hyokyung Bahn3,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6823-6833, 2023, DOI:10.32604/cmc.2023.035481

    Abstract AI (Artificial Intelligence) workloads are proliferating in modern real-time systems. As the tasks of AI workloads fluctuate over time, resource planning policies used for traditional fixed real-time tasks should be re-examined. In particular, it is difficult to immediately handle changes in real-time tasks without violating the deadline constraints. To cope with this situation, this paper analyzes the task situations of AI workloads and finds the following two observations. First, resource planning for AI workloads is a complicated search problem that requires much time for optimization. Second, although the task set of an AI workload may change over time, the possible… More >

  • Open Access

    ARTICLE

    A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

    Abdulaziz Alhubaishy1,*, Abdulmajeed Aljuhani2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2461-2481, 2023, DOI:10.32604/csse.2023.032217

    Abstract In a cloud environment, consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost. On the other hand, Cloud Service Providers (CSPs) seek to maximize their profits by attracting and serving more consumers based on their resource capabilities. The literature has discussed the problem by considering either consumers’ needs or CSPs’ capabilities. A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task scheduling in a more efficient… More >

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