Abstract-The simulation models of wireless networks rapidly increase in complexity to accurately model wireless channel characteristics and the properties of advanced transmission technologies. Such detailed models typically lead to a high computational load per simulation event that accumulates to extensive simulation runtimes. Reducing runtimes through parallelization is challenging since it depends on detecting causally independent events that can execute concurrently. Most existing approaches base this detection on lookaheads derived from channel propagation latency or protocol characteristics. In wireless networks, these lookaheads are typically short, causing the potential for parallelization and the achievable speedup to remain small. This paper presents Horizon, which unlocks a substantial portion of a simulation model's workload for parallelization by going beyond the traditional lookahead. We show how to augment discrete events with durations to identify a much larger horizon of independent simulation events and efficiently schedule them on multi-core systems. Our evaluation shows that this approach can significantly cut down the runtime of simulations, in particular for complex and accurate models of wireless networks.

Abstract-Developing complex technical systems requires a systematic exploration of the given design space in order to identify optimal system configurations. However, studying the effects and interactions of even a small number of system parameters often requires an extensive number of simulation runs. This in turn results in excessive runtime demands which severely hamper thorough design space explorations.In this paper, we present a parallel discrete event simulation scheme that enables cost-and time-efficient execution of large scale parameter studies on GPUs. In order to efficiently accommodate the stream-processing paradigm of GPUs, our parallelization scheme exploits two orthogonal levels of parallelism: External parallelism among the inherently independent simulations of a parameter study and internal parallelism among independent events within each individual simulation of a parameter study. Specifically, we design an event aggregation strategy based on external parallelism that generates workloads suitable for GPUs. In addition, we define a pipelined event execution mechanism based on internal parallelism to hide the transfer latencies between host-and GPU-memory. We analyze the performance characteristics of our parallelization scheme by means of a prototype implementation and show a 25-fold performance improvement over purely CPU-based execution.

Developing an efficient parallel simulation framework for multiprocessor systems is hard. A primary concern is the considerable amount of parallelization overhead imposed on the event handling routines of the simulator. Besides complex event scheduling algorithms, the main sources of overhead are thread synchronization and locking of shared data. Thus, compared to sequential simulation, the overhead of parallelization may easily outweigh its performance benefits.We introduce two efficient event handling schemes based on our parallel-simulation extension Horizon for OMNeT++. First, we present a push-based event handling scheme to minimize the overhead of thread synchronization and locking. Second, we complement this scheme with a novel event scheduling algorithm that significantly reduces the overhead of parallel event scheduling. Lastly, we prove the correctness of the scheduling algorithm. Our evaluation reveals a total reduction of the event handling overhead of up to 16x.

Abstract-Predicting and analyzing runtime performance characteristics is a vital step in the development process of parallel discrete event simulations. For instance, model developers need to identify and eliminate performance bottlenecks within a simulation model in order to derive a model structure that aids parallel execution. Similarly, developers of parallel simulation frameworks require means of assessing the efficiency of the framework. In this paper, we present a performance prediction methodology that computes the best possible performance bound for expanded parallel discrete event simulations in the context of our Horizon simulation framework. The methodology builds upon a linear program which calculates an optimal event execution schedule for a given simulation and a set of CPUs. In order to mitigate the complexity of this NP-complete scheduling problem, we introduce performance optimizations and relaxations of the linear program.

Rapidly changing link conditions make it difficult to accurately estimate the quality of wireless links and predict the fate of future transmissions. In particular bursty links pose a major challenge to online link estimation due to strong fluctuations in their transmission success rates at short time scales. Therefore, the prevalent approach in routing algorithms is to employ a long term link estimator that selects only consistently stable links -PRR > 90% -for packet transmissions. The use of bursty links is thus disregarded although these links provide considerable additional resources for the routing process.Based on significant empirical evidence of over 100,000 transmissions over each link in widely used 802.15.4 and 802.11 testbeds, we propose two metrics, Expected Future Transmissions (EFT) and MAC3, for runtime estimation of bursty wireless links. We introduce the Bursty Link Estimator (BLE) that, based on these two metrics, accurately estimates bursty links in the network rendering them available for packet transmissions.