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    Advanced cost-aware Max-Min workflow tasks allocation and scheduling in cloud computing systems
    (Springer, 2024) Raeisi-Varzaneh, Mostafa; Dakkak, Omar; Fazea, Yousef; Kaosar, Mohammed Golam
    Cloud computing has emerged as an efficient distribution platform in modern distributed computing offering scalability and flexibility. Task scheduling is considered as one of the main crucial aspects of cloud computing. The primary purpose of the task scheduling mechanism is to reduce the cost and makespan and determine which virtual machine (VM) needs to be selected to execute the task. It is widely acknowledged as a nondeterministic polynomial-time complete problem, necessitating the development of an efficient solution. This paper presents an innovative approach to task scheduling and allocation within cloud computing systems. Our focus lies on improving both the efficiency and cost-effectiveness of task execution, with a specific emphasis on optimizing makespan and resource utilization. This is achieved through the introduction of an Advanced Max-Min Algorithm, which builds upon traditional methodologies to significantly enhance performance metrics such as makespan, waiting time, and resource utilization. The selection of the Max-Min algorithm is rooted in its ability to strike a balance between task execution time and resource utilization, making it a suitable candidate for addressing the challenges of cloud task scheduling. Furthermore, a key contribution of this work is the integration of a cost-aware algorithm into the scheduling framework. This algorithm enables the effective management of task execution costs, ensuring alignment with user requirements while operating within the constraints of cloud service providers. The proposed method adjusts task allocation based on cost considerations dynamically. Additionally, the presented approach enhances the overall economic efficiency of cloud computing deployments. The findings demonstrate that the proposed Advanced Max-Min Algorithm outperforms the traditional Max-Min, Min-Min, and SJF algorithms with respect to makespan, waiting time, and resource utilization.
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    Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions
    (Ieee-Inst Electrical Electronics Engineers Inc, 2023) Raeisi-Varzaneh, Mostafa; Dakkak, Omar; Habbal, Adib; Kim, Byung-Seo
    An inflection point in the computing industry is occurring with the implementation of the Internet of Things and 5G communications, which has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. The purpose of edge computing is to provide computing, network control, and storage to the network edge to accommodate computationally intense and latency-critical applications at resource-limited endpoints. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server's computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. This paper briefly summarizes the edge computing architecture for information and task processing. Meanwhile, the collaborative scheduling scenarios are examined. Resource scheduling techniques are then discussed and compared based on four collaboration modes. As part of our survey, we present a thorough overview of the various task offloading schemes proposed by researchers for edge computing. Additionally, according to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Finally, edge computing resource scheduling issues, challenges, and future directions have discussed.