Abstract
Recent years have witnessed a growth in the Internet of Things (IoT) applications and devices; however, these devices are unable to meet the increased computational resource needs of the applications they host. Edge servers can provide sufficient computing resources. However, when the number of connected devices is large, the task processing efficiency decreases due to limited computing resources. Therefore, an edge collaboration scheme that utilizes other computing nodes to increase the efficiency of task processing and improve the quality of experience (QoE) was proposed. However, existing edge server collaboration schemes have low QoE because they do not consider other edge servers’ computing resources or communication time. In this paper, we propose a resource prediction-based edge collaboration scheme for improving QoE. We estimate computing resource usage based on the tasks received from the devices. According to the predicted computing resources, the edge server probabilistically collaborates with other edge servers. The proposed scheme is based on the delay model, and uses the greedy algorithm. It allocates computing resources to the task considering the computation and buffering time. Experimental results show that the proposed scheme achieves a high QoE compared with existing schemes because of the high success rate and low completion time.
Highlights
Smart Internet of Things (IoT) devices are becoming increasingly popular and play an increasingly important role in every aspect of our daily lives [1]
We propose an edge collaboration and computing resource allocation algorithm for improving the quality of experience (QoE) in terms of task completion time and task success rate
As computing resource usage is expected to increase, we reduce the number of tasks assigned to edge servers to improve QoE
Summary
Smart Internet of Things (IoT) devices are becoming increasingly popular and play an increasingly important role in every aspect of our daily lives [1]. When network resource is not considered, the edge server selects the collaboration target based on the task processing time. When the number of processing tasks in the edge server is small, sufficient computing resources are allocated, and the completion time to meet the deadline is low. Even if the average completion time of tasks is the same, an edge server that can improve the average success rate is not selected as a collaboration target. The proposed scheme probabilistically determines collaboration based on computing resource prediction when the edge server receives a task. Computing resource allocation is determined based on the number of tasks to be processed by the edge server according to the trade-off between computation and buffering time.
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