Task Priority-Based Cached-Data Prefetching and Eviction Mechanisms for Performance Optimization of Edge Computing Clusters

Ihsan Ullah,Junsu Kim,Muhammad Sajjad Khan,Su Min Kim,Mohammad Faisal,Marc St-Hilaire,Chien-Ming Chen

doi:10.1155/2021/5541974

Abstract

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. 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 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. The proposed TPDS prioritizes the tasks in the queue based on the available cached data in the edge computing nodes. Consequently, it increases the utilization of cached data and reduces the overhead caused by data eviction. The simulation results show that the proposed TPDS can be effective in terms of task scheduling and data locality.

Highlights

Edge computing is a paradigm to extend cloud computing services to those at edge nodes in networks. us, it brings the computing services near to Internet of things (IoT) devices [1]
We extend the idea from our earlier work [11] to utilize the existing preloaded data effectively based on a cost-effective scheduling strategy, named task priority-based data-prefetching scheduler (TPDS), which distributes the tasks to the computing nodes logically. e proposed TPDS tries to match the task in the queue with the cached-data at the computing node
We assume that five tasks arrive in the system as shown in Figure 2. e details of task allocation to the computing node, en ∈ E, are given in Table 1. e task t1 is assigned to the computing node e1 since the cached-data of the node, Ce1∈E, have the data block d8 that is needed for the processing of t1

Summary

Introduction

Edge computing is a paradigm to extend cloud computing services to those at edge nodes in networks. us, it brings the computing services near to Internet of things (IoT) devices [1]. 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. 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

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