A Decentralized Replica Placement Algorithm for Edge Computing

As the devices that make up the Internet become more powerful, algorithms that orchestrate cloud systems are on the verge of putting more responsibility for computation and storage on these devices. In our current age of Big Data, dissemination and storage of data across end cloud devices is becoming a prominent problem subject to this expansion. In this paper, we propose a distributed data dissemination approach that relies on dynamic creation/replacement/removal of replicas guided by continuous monitoring of data requests coming from edge nodes of the underlying network. Our algorithm exploits geographical locality of data during the dissemination process due to the plenitude of common data requests that stem from the clients within a close proximity. Our results using both real-world and synthetic data demonstrate that a decentralized replica placement approach provides significant cost benefits compared to client side caching that is widely used in traditional distributed systems.