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Next Generation Task Offloading Techniques in Evolving Computing Paradigms: Comparative Analysis, Current Challenges, and Future Research Perspectives
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DOI：10.1007/s11831-023-10021-2.png)
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Abstract
En 中文
Cloud computing being an integral part of today's technical advancements, still faces issues regarding resource allocation, task scheduling, communication latency, etc. To address these challenges, in the recent decade, other computing paradigms like Fog computing, enabling computing nearer to the Internet of Things (IoT) devices; Edge computing aiding in processing minimal tasks in the Edge nodes; Mist computing, enhancing the efficiency of Fog computing; Dew computing enabling the users to carry on their work even if there is no internet connection; Osmotic computing acting as a software-defined layer through which tasks can migrate to and from any other computing paradigms; and Hybrid computing, being a combination of any two or more computing paradigms; have come into the picture. Many researchers have published research articles addressing certain issues considering only two or three of these computing paradigms. However, this article, being a first of its kind, considers all seven computing paradigms and shows how each computing paradigm interacts with each other when used combinedly. Additionally, a novel computing architecture called 6-layered integrated computing architecture has also been proposed combining all the computing paradigms showcasing their arrangement and interaction with each other as well as the users, thereby giving a clear picture of the scenario when it will be implemented practically. For the current systematic literature review, we have selected survey articles which focused on task scheduling, load balancing and resource allocation, and research articles that implemented meta-heuristic or machine learning or hybrid algorithms for addressing the aforementioned challenges in these computing paradigms. Furthermore, some research questions have been formulated and addressed along with delineating some future scopes for the ease of the readers.
Keywords:
LOAD BALANCING ALGORITHM
RESOURCE-ALLOCATION
SCHEDULING OPTIMIZATION
SWARM OPTIMIZATION
ENERGY EFFICIENCY
CLOUD
FOG
EDGE
IOT
PERFORMANCE
Journal
IF:
12.1
Papers:
1.8K
Citations:
1.2W
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