Rule-based peer-to-peer framework for decentralised real-time service oriented architectures

Abstract

Modularity has been a key issue in the design and development of modern embedded RealTime Software Systems (RTS) where modularity enables flexibility with respect to changes in platform, environment, and requirements, as well as reuse. In distributed RTS, similar ideas have led to the adoption of Commercial Off-The-Shelf (COTS) components integrated via Service-Oriented Architecture (SOA) principles and technologies that are already wellestablished in business-oriented information systems. However, current SOA technologies for orchestration, such as Enterprise Service Busses, do not meet strict time-dependent constraints on scalability and latency required by RTS. We present a novel approach to RTS development where the orchestration of real-time processes is decentralised among the services within a fully distributed rule-driven process framework. Our framework wraps around COTS components implementing individual processing steps in a decentralised real-time process. Our execution model incorporates real-time constraints and is configurable through message routing policies distributed as a knowledge base containing rule sets, and therefore dispenses with the need for a central orchestration component which could easily become a bottleneck. Deterministic behaviour can be achieved through the validation of the rule-sets and the use of Modular Performance Analysis (MPA). We analyse the performance of our architecture using the Real-Time Calculus and show by empirical evaluation that our method scales to a typical real-time process application found in a tactical naval combat system context.

Performance Evaluation of Discrete Event Systems with GPenSIM

“…For example, Service-Oriented Architecture [9], Solving Repetitive Production Planning Problems [26], Assembly Line Balancing Problems [27], Gameplay [11], Flexible Manufacturing Systems [12], Service Modeling [13], and Complex Interconnected Manufacturing Systems [10]. With GPenSIM, control of external hardware is also possible, e.g., a logical control system of a marine diesel engine [7], control of Atlantic salmon farming industry processes [28], and real-time control of a humanoid robot [8].…”

“…Being a MATLAB toolbox, GPenSIM gives numerous possibilities for extending the discrete event models, which is not easy (if not impossible) in other software (e.g., Arena). This flexibility offered by GPenSIM is the reason why the Australian research team chose GPenSIM over the other tools, as they quote in their paper [9]. (2) Easy to learn and use: GPenSIM is easy to learn and use.…”

“…This is because the programming language of GPenSIM is MATLAB, which is a very simple Basic language clone. This ease of learning and use is the main reason why some of the users of GPenSIM have chosen it as a tool for the modeling and simulation of various discrete event systems, such as Gameplay analysis, flexible manufacturing systems, motor control, and service modeling (see the references [7,[9][10][11][12][13]). (3) Easy to extend: Being a file-based system, a modeler can easily extend the functions available in GPenSIM.…”

“…However, the General-purpose Petri Net Simulator (GPenSIM) is considered as the ideal tool for working with event graphs [9], and for interacting with other MATLAB toolboxes [10]. The following section describes the functions of GPenSIM for working with SCEG.…”

A Novel Locality Algorithm and Peer-to-Peer Communication Infrastructure for Optimizing Network Performance in Smart Microgrids

“…The absence of such functionality might result in a lack of knowledge about the global status of the microgrid, as well as in a sub-optimal allocation and a poor management of the critical resources [49]. Therefore, a new paradigm based on peer-to-peer (P2P) communications could be a promising solution for improving the use of DERs and the network performance in microgrids [17,[51][52][53]. P2P systems are formed by peers.…”