Using Dedicated and Opportunistic Networks in Synergy for a Cost-effective Distributed Stream Processing Platform

This paper presents a case for exploiting the synergy of dedicated and opportunistic network resources in a distributed hosting platform for data stream processing applications. Our previous studies have demonstrated the benefits of combining dedicated reliable resources with opportunistic resources in case of high-throughput computing applications, where timely allocation of the processing units is the primary concern. Since distributed stream processing applications demand large volume of data transmission between the processing sites at a consistent rate, adequate control over the network resources is important here to assure a steady flow of processing. In this paper, we propose a system model for the hybrid hosting platform where stream processing servers installed at distributed sites are interconnected with a combination of dedicated links and public Internet. Decentralized algorithms have been developed for allocation of the two classes of network resources among the competing tasks with an objective towards higher task throughput and better utilization of expensive dedicated resources. Results from extensive simulation study show that with proper management, systems exploiting the synergy of dedicated and opportunistic resources yield considerably higher task throughput and thus, higher return on investment over the systems solely using expensive dedicated resources.Comment: 9 page

A horizontally-scalable multiprocessing platform based on Node.js

This paper presents a scalable web-based platform called Node Scala which allows to split and handle requests on a parallel distributed system according to pre-defined use cases. We applied this platform to a client application that visualizes climate data stored in a NoSQL database MongoDB. The design of Node Scala leads to efficient usage of available computing resources in addition to allowing the system to scale simply by adding new workers. Performance evaluation of Node Scala demonstrated a gain of up to 74 % compared to the state-of-the-art techniques.Comment: 8 pages, 7 figures. Accepted for publication as a conference paper for the 13th IEEE International Symposium on Parallel and Distributed Processing with Applications (IEEE ISPA-15