A Middleware Approach to Achieving Fault Tolerance of Kahn Process Networks on Networks on Chips

Kahn process networks (KPNs) is a distributed model of computation used for describing systems where streams of data are transformed by processes executing in sequence or parallel. Autonomous processes communicate through unbounded FIFO channels in absence of a global scheduler. In this work, we propose a task-aware middleware concept that allows adaptivity in KPN implemented over a Network on Chip (NoC). We also list our ideas on the development of a simulation platform as an initial step towards creating fault tolerance strategies for KPNs applications running on NoCs. In doing that, we extend our SACRE (Self-Adaptive Component Run Time Environment) framework by integrating it with an open source NoC simulator, Noxim. We evaluate the overhead that the middleware brings to the the total execution time and to the total amount of data transferred in the NoC. With this work, we also provide a methodology that can help in identifying the requirements and implementing fault tolerance and adaptivity support on real platforms. 1. Introduction Past decade has witnessed a change in the design of powerful processors. It has been realized that running processors at higher and higher frequencies is not sustainable due to unproportional increases in power consumption. This led to the design of multicore chips, usually consisting of multiprocessor symmetric Systems on Chip (MPSoCs), with limited numbers of CPU-L1 cache nodes interconnected by simple bus connections and capable in turn of becoming nodes in larger multiprocessors. However, as the number of components in these systems increases, communication becomes a bottleneck and it hinders the predictability of the metrics of the final system. Networks on Chip (NoCs) [1] emerged as a new communication paradigm to address scalability issues of MPSoCs. Still, achieving goals such as easy parallel programming, good load balancing and ultimate performances, dependability and low-power consumption pose new challenges for such architectures. Technology scaling decreases the yield in manufacturing and thermal effects cause defects at run time. Thus fault tolerance becomes a major concern not only for economical reasons but also for the end user. In addressing these issues, we adopted a component-based approach based on Kahn Process Networks (KPNs) for specifying the applications [2]. KPN is a stream-oriented model of computation based on the idea of organizing an application into streams and computational blocks; streams represent the flow of data, while computational blocks represent operations on a stream of data. KPN