The nodes at the edge level retain low processing power and limited resources and, thus, they cannot handle such heavy and complicated tasks. Data generated by IoT devices can be processed by middle layer devices such as IoT nodes and base stations. Consequently, there are resource limitations in a cloud computing layer to incline many researchers toward the computation at edge devices.

This increases the frequency of communication exchanges and causes a long latency to the end-users. Thousands of IoT devices are connected to the cloud which can yield a heavy load to the core network and the cloud system. All these data and tasks are generated by IoT devices which are connected to the cloud by a middle layer, i.e., IoT edge nodes. In cloud computing systems, complicated tasks and data are collected to the cloud for computing processes. In other words, a cost-effective task scheduler is needed to assign the tasks closer to the data on a cluster node and bring the resources near to computation nodes while improving the overall system performance. To distribute the tasks resourcefully and efficiently to the edge nodes based on task-associated data, an efficient task scheduling strategy is required. Due to low computing power and limited data storage, the edge nodes are clustered to perform computation and the huge tasks are distributed to the edge nodes. To minimize the workload of the core network and the cloud, novel paradigms such as edge computing and fog computing are developed to bring computational resources to the edge of the network and offer services near to each IoT device as shown in Figure 1. 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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