Indiana University-Purdue University Indianapolis (IUPUI) / This thesis demonstrates a novel idea on how components in a distributed real-time & embedded (DRE) system can choose from different data interchange formats at run-time. It also quantitatively evaluates three binary data interchange protocols used in distributed real-time & embedded (DRE) systems: the Common Data Representation (CDR), which collects data "as-is" into a buffer; Binary JSON (BSON), which enables "on the fly" discovery of elements in a message; and FIX Adapted for Streaming (FAST), which is a binary compression algorithm popularly used for data exchange in financial stock market domain. We compare these three data exchange formats to determine if it is possible to minimize the data usage without compromising CPU processing times, data throughput, and data latency. The lack of such a study has made protocols such as CDR popular based on the assumption that collecting data "as-is" will consume less processing time and send with high throughput. We perform the study in the context of an Open Source Architecture for Software Instrumentation of Systems (OASIS). To perform our study, we modified its existing data interchange framework to flexibly and seamlessly integrate either format, and let the components choose a format at run-time. The experiments from our study shows that as data size increases, the throughput of CDR, BSON, and FAST decreases by 96.16%, 97.23%, and 84.41%, respectively. The increase in packaging and un-packaging times are 1985.12% and 1642.28% for FAST, compared to 3158.96% and 2312.50% for CDR, and 5077.98% and 3686.48% for BSON.

The accelerating progress in network speeds and computing power permitted the architectural design paradigm to shift from monolithic applications to microservices. The industry moved from single-core and multi-threads, code-heavy applications, running on giant machines 24/7 to smaller machines, multi-cores single threads where computing power and memory consumption are managed very critically. With the advent of this novel approach to designing systems, traditional multi-tier applications have been broken down into hundreds of microservices that can be easily moved around, start, and stop quickly. In this context, scaling assumed a new meaning, rather than scaling up by adding more resources or computing power, now systems are scaled dynamically by adding more microservices instances. This contribution proposes a theoretical study and a practical experiment to investigate, compare and outline the performance improvements aid by the implementation of Protocol Buffers, Google's language-neutral, binary-based representational data interchange format over traditional text-based serialization formats in a modern, Cloud-Native, REST-based Java Microservice architecture. Findings are presented showing promising results regarding the implementation of Protobuf, with a significant reduction in response time (25.1% faster in the best-case scenario) and smaller payload size (72.28% better in the best-case scenario) when compared to traditional textual serialization formats while literature revealed out-of-the-box mechanisms for message versioning with backward compatibility.