The recent years have been characterised by the production of a huge amount of data. As matter of fact, human being, overall, is generating an unprecedented amount of data flowing in multiple and heterogeneous sources, ranging from scientific devices, social network, business transactions, etc. Data that is usually represented as a graph, which, due to its size, is often infeasible to process on a single machine.
The direct consequence is the need for exploiting parallel and distributed computing frameworks to face this problem. This paper proposes TELOS, an high-level approach for large graphs processing. Our approach takes inspiration from overlay networks, that is a widely adopted approach for information dissemination, aggregation and computing orchestration in highly distributed systems.
TELOS consists of a programming framework supporting the definition of computations on graphs as a composition of overlays, each devoted to a specific aim. The framework is implemented on the top of Apache Spark. A set of experimental results is presented to give a clear evidence of the effectiveness of our approach.