Abstract
Several schedulability analyses have been proposed for a variety of parallel task systems with real-time constraints. However, these analyses are mostly restricted to global scheduling policies. The problem with global scheduling is that it adds uncertainty to the lower-level timing analysis which on multicore systems are heavily context-dependent. As parallel tasks typically exhibit intense communication and concurrency among their sequential computational units, this problem is further exacerbated. This paper considers instead the schedulability of partitioned parallel tasks. More precisely, we present a response time analysis for sporadic DAG tasks atop multiprocessors under partitioned fixed-priority scheduling. We assume the partitioning to be given. We show that a partitioned DAG task can be modeled as a set of self-suspending tasks. We then propose an algorithm to traverse a DAG and characterize such worst-case scheduling scenario. With minor modifications, any state-of-the-art technique for sporadic self-suspending tasks can thus be used to derived the worstcase response time of a partitioned DAG task. Experiments show that the proposed approach significantly tightens the worst-case response time of partitioned parallel tasks comparatively to the state-of-the-art when the most accurate technique is chosen.

Abstract
Flit-level preemptions via virtual channels have been proposed as one viable method to implement priority-preemptive arbitration policies in NoC routers, and integrate NoCs in the hard real-time domain. In recent years, researchers have explored several aspects of priority-preemptive NoCs, such as different arbitration techniques, different priority assignment methods (where applicable) and different workload mapping approaches, all with the common objective to use interconnect mediums more efficiently. Yet, the impact of different routing techniques on such a model is still an unexplored topic. Motivated by this reality, in this work we study the effects of routing flexibility on wormhole-switched priority-preemptive NoCs.

Abstract

Abstract
Several schedulability analyses have been proposed for a variety of parallel task systems with real-Time constraints. However, these analyses are mostly restricted to global scheduling policies. The problem with global scheduling is that it adds uncertainty to the lower-level timing analysis which on multicore systems are heavily context-dependent. As parallel tasks typically exhibit intense communication and concurrency among their sequential computational units, this problem is further exacerbated. This paper considers instead the schedulability of partitioned parallel tasks. More precisely, we present a response time analysis for sporadic DAG tasks atop multiprocessors under partitioned fixed-priority scheduling. We assume the partitioning to be given. We show that a partitioned DAG task can be modeled as a set of self-suspending tasks. We then propose an algorithm to traverse a DAG and characterize such worst-case scheduling scenario. With minor modifications, any state-of-The-Art technique for sporadic self-suspending tasks can thus be used to derived the worst-case response time of a partitioned DAG task. Experiments show that the proposed approach significantly tightens the worst-case response time of partitioned parallel tasks comparatively to the state-of-The-Art when the most accurate technique is chosen. © 2016 IEEE.

Abstract

Abstract
Wireless sensor networks show great potential to successfully address the timeliness and energy-efficiency requirements of different cyber-physical system applications. Generally, these requirements span several layers of the stack and demand an on-line mechanism capable of efficiently tuning several parameters, in order to better support highly dynamic traffic characteristics. This work presents a cross-layer QoS management framework for ZigBee cluster-tree networks. The proposed framework carries out an on-line control of a set of parameters ranging from the MAC sub-layer to the network layer, improving the successful transmission probability and minimizing the memory requirements and queuing delays through an efficient bandwidth allocation at the network clusters. Through extensive simulations in a real datacenter monitoring application scenario, we show that the proposed framework improves the successful transmission probability by 10%, and reduces the end-to-end delay by 94%.