Measuring the Impact of Adversarial Errors on Packet Scheduling Strategies

In this paper we explore the problem of achieving efficient packet transmission over unreliable links with worst case occurrence of errors. In such a setup, even an omniscient offline scheduling strategy cannot achieve stability of the packet queue, nor is it able to use up all the available bandwidth. Hence, an important first step is to identify an appropriate metric for measuring the efficiency of scheduling strategies in such a setting. To this end, we propose a relative throughput metric which corresponds to the long term competitive ratio of the algorithm with respect to the optimal. We then explore the impact of the error detection mechanism and feedback delay on our measure. We compare instantaneous error feedback with deferred error feedback, that requires a faulty packet to be fully received in order to detect the error. We propose algorithms for worst-case adversarial and stochastic packet arrival models, and formally analyze their performance. The relative throughput achieved by these algorithms is shown to be close to optimal by deriving lower bounds on the relative throughput of the algorithms and almost matching upper bounds for any algorithm in the considered settings. Our collection of results demonstrate the potential of using instantaneous feedback to improve the performance of communication systems in adverse environments

On Packet Scheduling with Adversarial Jamming and Speedup

In Packet Scheduling with Adversarial Jamming packets of arbitrary sizes arrive over time to be transmitted over a channel in which instantaneous jamming errors occur at times chosen by the adversary and not known to the algorithm. The transmission taking place at the time of jamming is corrupt, and the algorithm learns this fact immediately. An online algorithm maximizes the total size of packets it successfully transmits and the goal is to develop an algorithm with the lowest possible asymptotic competitive ratio, where the additive constant may depend on packet sizes. Our main contribution is a universal algorithm that works for any speedup and packet sizes and, unlike previous algorithms for the problem, it does not need to know these properties in advance. We show that this algorithm guarantees 1-competitiveness with speedup 4, making it the first known algorithm to maintain 1-competitiveness with a moderate speedup in the general setting of arbitrary packet sizes. We also prove a lower bound of $\phi+1\approx 2.618$ on the speedup of any 1-competitive deterministic algorithm, showing that our algorithm is close to the optimum. Additionally, we formulate a general framework for analyzing our algorithm locally and use it to show upper bounds on its competitive ratio for speedups in $[1,4)$ and for several special cases, recovering some previously known results, each of which had a dedicated proof. In particular, our algorithm is 3-competitive without speedup, matching both the (worst-case) performance of the algorithm by Jurdzinski et al. and the lower bound by Anta et al.Comment: Appeared in Proc. of the 15th Workshop on Approximation and Online Algorithms (WAOA 2017

QF-MAC: Adaptive, Local Channel Hopping for Interference Avoidance in Wireless Meshes

The throughput efficiency of a wireless mesh network with potentially malicious external or internal interference can be significantly improved by equipping routers with multi-radio access over multiple channels. For reliably mitigating the effect of interference, frequency diversity (e.g., channel hopping) and time diversity (e.g., carrier sense multiple access) are conventionally leveraged to schedule communication channels. However, multi-radio scheduling over a limited set of channels to minimize the effect of interference and maximize network performance in the presence of concurrent network flows remains a challenging problem. The state-of-the-practice in channel scheduling of multi-radios reveals not only gaps in achieving network capacity but also significant communication overhead. This paper proposes an adaptive channel hopping algorithm for multi-radio communication, QuickFire MAC (QF-MAC), that assigns per-node, per-flow ``local'' channel hopping sequences, using only one-hop neighborhood coordination. QF-MAC achieves a substantial enhancement of throughput and latency with low control overhead. QF-MAC also achieves robustness against network dynamics, i.e., mobility and external interference, and selective jamming attacker where a global channel hopping sequence (e.g., TSCH) fails to sustain the communication performance. Our simulation results quantify the performance gains of QF-MAC in terms of goodput, latency, reliability, communication overhead, and jamming tolerance, both in the presence and absence of mobility, across diverse configurations of network densities, sizes, and concurrent flows

Dynamic Packet Scheduling in Wireless Networks

We consider protocols that serve communication requests arising over time in a wireless network that is subject to interference. Unlike previous approaches, we take the geometry of the network and power control into account, both allowing to increase the network's performance significantly. We introduce a stochastic and an adversarial model to bound the packet injection. Although taken as the primary motivation, this approach is not only suitable for models based on the signal-to-interference-plus-noise ratio (SINR). It also covers virtually all other common interference models, for example the multiple-access channel, the radio-network model, the protocol model, and distance-2 matching. Packet-routing networks allowing each edge or each node to transmit or receive one packet at a time can be modeled as well. Starting from algorithms for the respective scheduling problem with static transmission requests, we build distributed stable protocols. This is more involved than in previous, similar approaches because the algorithms we consider do not necessarily scale linearly when scaling the input instance. We can guarantee a throughput that is as large as the one of the original static algorithm. In particular, for SINR models the competitive ratios of the protocol in comparison to optimal ones in the respective model are between constant and O(log^2 m) for a network of size m.Comment: 23 page

On packet scheduling with adversarial jamming and speedup

In Packet Scheduling with Adversarial Jamming, packets of arbitrary sizes arrive over time to be transmitted over a channel in which instantaneous jamming errors occur at times chosen by the adversary and not known to the algorithm. The transmission taking place at the time of jamming is corrupt, and the algorithm learns this fact immediately. An online algorithm maximizes the total size of packets it successfully transmits and the goal is to develop an algorithm with the lowest possible asymptotic competitive ratio, where the additive constant may depend on packet sizes. Our main contribution is a universal algorithm that works for any speedup and packet sizes and, unlike previous algorithms for the problem, it does not need to know these parameters in advance. We show that this algorithm guarantees 1-competitiveness with speedup 4, making it the first known algorithm to maintain 1-competitiveness with a moderate speedup in the general setting of arbitrary packet sizes. We also prove a lower bound of ϕ+1≈2.618 on the speedup of any 1-competitive deterministic algorithm, showing that our algorithm is close to the optimum. Additionally, we formulate a general framework for analyzing our algorithm locally and use it to show upper bounds on its competitive ratio for speedups in [1, 4) and for several special cases, recovering some previously known results, each of which had a dedicated proof. In particular, our algorithm is 3-competitive without speedup, matching both the (worst-case) performance of the algorithm by Jurdzinski et al. (Proceedings of the 12th workshop on approximation and online algorithms (WAOA), LNCS 8952, pp 193–206, 2015. http://doi.org/10.1007/978-3-319-18263-6_17) and the lower bound by Anta et al. (J Sched 19(2):135–152, 2016. http://doi.org/10.1007/s10951-015-0451-z)