Intelligent Replica Selection in Edge and IoT Environments Using Artificial Neural Networks

Abstract

Cloud, edge and Internet of Things (IoT) technologies have emerged to overcome the challenges involved in sharing computational resources and information services. Within generic cloud systems, two models have been identified as having widespread applicability: computation clouds and data clouds. A data cloud is cloud computing that aims to manage, unify and operate multiple data workloads. Many current applications generate datasets consisting of petabytes (PB) of information. Managing large datasets is a complex issuel; in particular, datasets associated with many applications can be distributed widely in geographical terms, particularly in IoT systems. Edge and IoT systems are facing new challenges with increased complexity, making scalability an important issue that will affect the performance of the system. Data replication services are widely accepted techniques to improve availability and fault tolerance, and to improve the data access time. Current replication services, however, often exhibit an increase in response time, reflecting the problems associated with the ever-increasing size of databases. This paper proposes a prediction model to predict replica locations using the files’ access profile, which feeds the neural networks with the access and location behavior (file profile) to minimize the overhead of transferring large volumes of data, which slows down the system and requires careful management. This new model has shown high accuracy and low overheads. The result shows a significant improvement in total task execution time using the proposed model for locating files by 16.34% and 30.45%; in addition, the results show bandwidth improvement by 24.7% and 49.4% compared to the user profile prediction model and replica service model without prediction, respectively. Consequently, the proposed algorithm can improve data access speed, reduce data access latency and decrease bandwidth consumption.

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

“…In [ 41 ] the authors proposed a fog resource selection algorithm (FResS) which enables automated fog selection and allocation, the performance data of each fog are stored in standard format as execution logs. For an incoming job to be executed, these logs will be used for predicting its run-time to have real time estimate for best fog selection.In [ 42 ] the authors proposed a neural network prediction model to predict replica locations using the files’ access profile. The run-time prediction model is meant to generate file location predictions for incoming tasks using historical executions.…”