5,363 research outputs found
Towards Provably Invisible Network Flow Fingerprints
Network traffic analysis reveals important information even when messages are
encrypted. We consider active traffic analysis via flow fingerprinting by
invisibly embedding information into packet timings of flows. In particular,
assume Alice wishes to embed fingerprints into flows of a set of network input
links, whose packet timings are modeled by Poisson processes, without being
detected by a watchful adversary Willie. Bob, who receives the set of
fingerprinted flows after they pass through the network modeled as a collection
of independent and parallel queues, wishes to extract Alice's embedded
fingerprints to infer the connection between input and output links of the
network. We consider two scenarios: 1) Alice embeds fingerprints in all of the
flows; 2) Alice embeds fingerprints in each flow independently with probability
. Assuming that the flow rates are equal, we calculate the maximum number of
flows in which Alice can invisibly embed fingerprints while having those
fingerprints successfully decoded by Bob. Then, we extend the construction and
analysis to the case where flow rates are distinct, and discuss the extension
of the network model
ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads.
Temporal dependence in workloads creates peak congestion that can make service unavailable and reduce system performance. To improve system performability under conditions of temporal dependence, a server should quickly process bursts of requests that may need large service demands. In this paper, we propose and evaluateASIdE, an Autocorrelation-based SIze Estimation, that selectively delays requests which contribute to the workload temporal dependence. ASIdE implicitly approximates the shortest job first (SJF) scheduling policy but without any prior knowledge of job service times. Extensive experiments show that (1) ASIdE achieves good service time estimates from the temporal dependence structure of the workload to implicitly approximate the behavior of SJF; and (2) ASIdE successfully counteracts peak congestion in the workload and improves system performability under a wide variety of settings. Specifically, we show that system capacity under ASIdE is largely increased compared to the first-come first-served (FCFS) scheduling policy and is highly-competitive with SJF. © 2012 IEEE
Decentralised control of material or traffic flows in networks using phase-synchronisation
We present a self-organising, decentralised control method for material flows
in networks. The concept applies to networks where time sharing mechanisms
between conflicting flows in nodes are required and where a coordination of
these local switches on a system-wide level can improve the performance. We
show that, under certain assumptions, the control of nodes can be mapped to a
network of phase-oscillators.
By synchronising these oscillators, the desired global coordination is
achieved. We illustrate the method in the example of traffic signal control for
road networks. The proposed concept is flexible, adaptive, robust and
decentralised. It can be transferred to other queuing networks such as
production systems. Our control approach makes use of simple synchronisation
principles found in various biological systems in order to obtain collective
behaviour from local interactions
On the Catalyzing Effect of Randomness on the Per-Flow Throughput in Wireless Networks
This paper investigates the throughput capacity of a flow crossing a
multi-hop wireless network, whose geometry is characterized by general
randomness laws including Uniform, Poisson, Heavy-Tailed distributions for both
the nodes' densities and the number of hops. The key contribution is to
demonstrate \textit{how} the \textit{per-flow throughput} depends on the
distribution of 1) the number of nodes inside hops' interference sets, 2)
the number of hops , and 3) the degree of spatial correlations. The
randomness in both 's and is advantageous, i.e., it can yield larger
scalings (as large as ) than in non-random settings. An interesting
consequence is that the per-flow capacity can exhibit the opposite behavior to
the network capacity, which was shown to suffer from a logarithmic decrease in
the presence of randomness. In turn, spatial correlations along the end-to-end
path are detrimental by a logarithmic term
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