![]() ![]() ![]() Sending all the data and tasks to the cloud for processing makes the core network congested and yields a huge load to the cloud servers. In general, the cloud contains distributed computing resources and processes the data using a group of servers in parallel and distributed way. However, since the enormous number of IoT devices generates a high volume of data, transmitting them to the cloud yields high computational processing. Putting resources at the edge of the network enables achieving low latency processing. Thus, it brings the computing services near to Internet of things (IoT) devices. IntroductionĮdge computing is a paradigm to extend cloud computing services to those at edge nodes in networks. The simulation results show that the proposed TPDS can be effective in terms of task scheduling and data locality. Consequently, it increases the utilization of cached data and reduces the overhead caused by data eviction. The proposed TPDS prioritizes the tasks in the queue based on the available cached data in the edge computing nodes. The proposed scheme named task priority-based data-prefetching scheduler (TPDS) tries to improve the data locality through available cached and prefetching data for offloading tasks to the edge computing nodes. Due to low computing power and small data storage at the edge nodes, the task must be assigned to the computing nodes, where their associated data is available, to reduce overheads caused by data transmissions in the network. The rapid evolution of the Internet of Things (IoT) and the development of cloud computing have endorsed a new computing paradigm called edge computing, which brings the computing resources to the edge of the network. ![]()
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March 2023
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