Overcoming Large Data Transfer Bottlenecks in RESTful Service Orchestrations

As REST (Representational State Transfer)-ful services are closely coupled to the HTTP (Hypertext Transfer Protocol), which eventually sits above the connection-based TCP (Transmission Control Protocol), it is common for RESTful services to experience latency and transfer inefficiencies especially in situations requiring the services to transfer large-scale data (i.e. above gigabytes of data) in RESTful workflows. Such inefficiencies are undesirable and impractical, and are compounded for RESTful service orchestrations in data-intensive industries such as Big Data analytics, cloud computing and life sciences. In this paper, we propose a non-invasive novel technique, Fast-Optimised-REST (FOREST), which enables RESTful services to overcome the traditional bottlenecks experienced during transfer of large sets of data. The initial experimental results show promise and demonstrated very significant reductions of up to 80% from original REST-ful data transfer times for extremely large data sets.