Optimal Data Placement and Replication Approach for SIoT with Edge

Abstract

Social networks (SNs) are sources with extreme number of users around the world who are all sharing data like images, audio, and video to their friends using IoT devices. This concept is the so-called Social Internet of Things (SIot). The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources, and this task demands an efficient storage procedure. For this kind of large volume of data storage, the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency. The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently. For efficient storage of these data, we use edge server, which is part of the cloud server, in this study. Thus, the data are distributed and stored with quick access, which will reduce the latency with response. The proposed data placement approach learns with machine learning (ML) algorithm called radial basis kernel function assisted with support vector machine (RBF-SVM) to classify the data center for storing the user and friend’s data from the SIoT devices. These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots. The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning (GP) method to meet the individual user’s demand of low latency with minimum costs. This way will keep the SIoT data placement efficient and effective over time. Accordingly, this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach. (i) Rather than storing the user data in a single cloud, this study uses the edge server closest to the SIoT devices for faster access with reduced response time. (ii) The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication. (iii) Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN. The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches. Therefore, our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency, effectiveness, and performance with reduced latency and minimum cost.