System adaptivity is becoming an important feature
of modern embedded multiprocessor systems. To achieve the
goal of system adaptivity when executing Polyhedral Process
Networks (PPNs) on a generic tiled Network-on-Chip (NoC)
MPSoC platform, we propose an approach to enable the run-time
migration of processes among the available platform resources. In
our approach, process migration is allowed by a middleware layer
which comprises two main components. The first component
concerns the inter-tile data communication between processes.
We develop and evaluate a number of different communication
approaches which implement the semantics of the PPN model
of computation on a generic NoC platform. The presented
communication approaches do not depend on the mapping
of processes and have been implemented on a Network-on-Chip multiprocessor platform prototyped on an FPGA. Their
comparison in terms of the introduced overhead is presented in
two case studies with different communication characteristics.
The second middleware component allows the actual run-time
migration of PPN processes. To this end, we propose and evaluate
a process migration mechanism which leverages the PPN model
of computation to guarantee a predictable and efficient migration
procedure. The efficiency and applicability of the proposed
migration mechanism is shown in a real-life case study.
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.
Modern embedded systems increasingly require adaptive run-time management. The system may adapt the mapping of the applications in order to accommodate the current workload conditions, to balance load for efficient resource utilization, to meet quality of service agreements, to avoid thermal hot-spots and to reduce power consumption. As the possibility of experiencing run-time faults becomes increasingly relevant with deep-sub-micron technology nodes, in the scope of the MADNESS project, we focus particularly on the problem of graceful degradation by dynamic remapping in presence of runtime faults.In this paper, we summarize the major results achieved in the MADNESS project until now regarding the system adaptivity and fault tolerant processing. We report the first results of the integration between platform level and middleware level support for adaptivity and fault tolerance. A case study demonstrates the survival ability of the system via a low-overhead process migration mechanism and a near-optimal online remapping heuristic.
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