Allocation and scheduling of applications affect the timing response and system performance, particularly for Network-on-Chip (NoC) based multicore systems executing realtime applications. These systems with multitasking processors provide improved opportunity for parallel application execution. In dynamic scenarios, runtime task allocation improves the system resource utilization and adapts to varying application workload. In this work, we present an efficient hybrid strategy for unified allocation and scheduling of tasks at runtime. By considering multitasking capability of processors, communication cost and task timing characteristics, potential allocation solutions are obtained at design-time. These are adapted for dynamic mapping and scheduling of computation and communication workloads of real-time applications. Simulation results show that the proposed approach achieves 34.2% and 26% average reduction in network latency and communication cost of the allocated applications. Also, the deadline satisfaction of the tasks improves on average by 42.1% while reducing the allocation-time overhead by 32% when compared with existing techniques.

The application workloads in modern multicore platforms are becoming increasingly dynamic. It becomes challenging when multiple applications need to be executed in parallel in such systems. Mapping and scheduling of these applications are critical for system performance and energy consumption, especially in Network-on-Chip– (NoC) based multicore systems. These systems with multitasking processors offer a better opportunity for parallel application execution. Mapping solutions generated at design time may be inappropriate for dynamic workloads. To improve the utilization of the underlying multicore platform and cope with the dynamism of application workload, often task allocation is carried out dynamically. This article presents a hybrid task allocation and scheduling strategy that exploits the design-time results at runtime. By considering the multitasking capability of the processors, communication energy, and timing characteristics of the tasks, different allocation options are obtained at design time. During runtime, based on the availability of the platform resources and application requirements, the design-time allocations are adapted for mapping and scheduling of tasks, which result in improved runtime performance. Experimental results demonstrate that the proposed approach achieves an on average 11.5%, 22.3%, 28.6%, and 34.6% reduction in communication energy consumption as compared to CAM [18], DEAMS [4], TSMM [38], and CPNN [32], respectively, for NoC-based multicore platforms with multitasking processors. Also, the deadline satisfaction of the tasks of allocated applications improves on an average by 32.8% when compared with the state-of-the-art dynamic resource allocation approaches.

In Network-on-Chip (NoC)-based multicore systems, task allocation and scheduling are known to be important problems, as they affect the performance of applications in terms of energy consumption and timing. Advancement of deep submicron technology has made it possible to scale the transistor feature size to the nanometer range, which has enabled multiple processing elements to be integrated onto a single chip. On the flipside, it has made the integrated entities on the chip more susceptible to different faults. Although a significant amount of work has been done in the domain of fault-tolerant mapping and scheduling, existing algorithms either precompute reconfigured mapping solutions at design time while anticipating fault(s) scenarios or adopt a hybrid approach wherein a part of the fault mitigation strategy relies on the design-time solution. The complexity of the problem rises further for real-time dynamic systems where new applications can arrive in the multicore platform at any time instant. For real-time systems, the validity of computation depends both on the correctness of results and on temporal constraint satisfaction. This article presents an improved fault-tolerant dynamic solution to the integrated problem of application mapping and scheduling for NoC-based multicore platforms. The developed algorithm provides a unified mapping and scheduling method for real-time systems focusing on meeting application deadlines and minimizing communication energy. A predictive model has been used to determine the failure-prone cores in the system for which a fault-tolerant resource allocation with task redundancy has been performed. By selectively using a task replication policy, the reliability of the application, executing on a given NoC platform, is improved. A detailed evaluation of the performance of the proposed algorithm has been conducted for both real and synthetic applications. When compared with other fault-tolerant algorithms reported in the literature, performance of the proposed algorithm shows an average reduction of 56.95% in task re-execution time overhead and an average improvement of 31% in communication energy. Further, for time-constrained tasks, deadline satisfaction has also been achieved for most of the test cases by the developed algorithm, whereas the techniques reported in the literature failed to meet deadline in about 45% test cases.