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  • Open Access


    A Systematic Literature Review on Task Allocation and Performance Management Techniques in Cloud Data Center

    Nidhika Chauhan1, Navneet Kaur2, Kamaljit Singh Saini2, Sahil Verma3, Abdulatif Alabdulatif4, Ruba Abu Khurma5,7, Maribel Garcia-Arenas6, Pedro A. Castillo6,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 571-608, 2024, DOI:10.32604/csse.2024.042690

    Abstract As cloud computing usage grows, cloud data centers play an increasingly important role. To maximize resource utilization, ensure service quality, and enhance system performance, it is crucial to allocate tasks and manage performance effectively. The purpose of this study is to provide an extensive analysis of task allocation and performance management techniques employed in cloud data centers. The aim is to systematically categorize and organize previous research by identifying the cloud computing methodologies, categories, and gaps. A literature review was conducted, which included the analysis of 463 task allocations and 480 performance management papers. The… More >

  • Open Access


    Mobile Crowdsourcing Task Allocation Based on Dynamic Self-Attention GANs

    Kai Wei1, Song Yu2, Qingxian Pan1,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 607-622, 2024, DOI:10.32604/cmc.2024.048240

    Abstract Crowdsourcing technology is widely recognized for its effectiveness in task scheduling and resource allocation. While traditional methods for task allocation can help reduce costs and improve efficiency, they may encounter challenges when dealing with abnormal data flow nodes, leading to decreased allocation accuracy and efficiency. To address these issues, this study proposes a novel two-part invalid detection task allocation framework. In the first step, an anomaly detection model is developed using a dynamic self-attentive GAN to identify anomalous data. Compared to the baseline method, the model achieves an approximately 4% increase in the F1 value More >

  • Open Access


    Optimizing Service Stipulation Uncertainty with Deep Reinforcement Learning for Internet Vehicle Systems

    Zulqar Nain1, B. Shahana2, Shehzad Ashraf Chaudhry3, P. Viswanathan4, M.S. Mekala1, Sung Won Kim1,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5705-5721, 2023, DOI:10.32604/cmc.2023.033194

    Abstract Fog computing brings computational services near the network edge to meet the latency constraints of cyber-physical System (CPS) applications. Edge devices enable limited computational capacity and energy availability that hamper end user performance. We designed a novel performance measurement index to gauge a device’s resource capacity. This examination addresses the offloading mechanism issues, where the end user (EU) offloads a part of its workload to a nearby edge server (ES). Sometimes, the ES further offloads the workload to another ES or cloud server to achieve reliable performance because of limited resources (such as storage and… More >

  • Open Access


    Task Allocation Approach for Minimizing Make-Span in Wireless Sensor Actor Networks

    Mohammad Reza Okhovvat1, Mohammad Taghi Kheirabadi1,*, Ali Nodehi1, Morteza Okhovvat2

    Computer Systems Science and Engineering, Vol.39, No.2, pp. 165-178, 2021, DOI:10.32604/csse.2021.05468

    Abstract Wireless Sensor Actor Networks (WSANs) have contributed to the development of pervasive computing wherein time consideration to perform the tasks of pervasive applications is necessary. Hence, time constraint is one of the major challenges of WSANs. In this paper, we propose an analytical approach based on queuing theory to minimize the total time taken for completion of tasks, i.e., make-span, in WSANs with hybrid architecture. The best allocation rates of tasks to actor nodes are figured out through solving inequities and qualities resulting from a steady state analysis of the proposed model. Applying the calculated… More >

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