101 research outputs found
Network calculus for parallel processing
In this note, we present preliminary results on the use of "network calculus"
for parallel processing systems, specifically MapReduce
A Study of Non-Neutral Networks with Usage-based Prices
Hahn and Wallsten wrote that network neutrality "usually means that broadband
service providers charge consumers only once for Internet access, do not favor
one content provider over another, and do not charge content providers for
sending information over broadband lines to end users." In this paper we study
the implications of non-neutral behaviors under a simple model of linear
demand-response to usage-based prices. We take into account advertising
revenues and consider both cooperative and non-cooperative scenarios. In
particular, we model the impact of side-payments between service and content
providers. We also consider the effect of service discrimination by access
providers, as well as an extension of our model to non-monopolistic content
providers
The generating functions of stirling numbers of the second kind derived probabilistically
Stirling numbers of the second kind, S(n, r), denote the number of partitions of a finite set of size n into r disjoint nonempty subsets. The aim of this short article is to shed some light on the generating functions of these numbers by deriving them probabilistically. We do this by linking them to Markov chains related to the classical coupon collector problem; coupons are collected in discrete time (ordinary generating function) or in continuous time (exponential generating function). We also review the shortest possible combinatorial derivations of these generating functions
Computable bounds in fork-join queueing systems
In a Fork-Join (FJ) queueing system an upstream fork station splits incoming jobs into N tasks to be further processed by N parallel servers, each with its own queue; the response time of one job is determined, at a downstream join station, by the maximum of the corresponding tasks' response times. This queueing system is useful to the modelling of multi-service systems subject to synchronization constraints, such as MapReduce clusters or multipath routing. Despite their apparent simplicity, FJ systems are hard to analyze.
This paper provides the first computable stochastic bounds on the waiting and response time distributions in FJ systems. We consider four practical scenarios by combining 1a) renewal and 1b) non-renewal arrivals, and 2a) non-blocking and 2b) blocking servers. In the case of non blocking servers we prove that delays scale as O(logN), a law which is known for first moments under renewal input only. In the case of blocking servers, we prove that the same factor of log N dictates the stability region of the system. Simulation results indicate that our bounds are tight, especially at high utilizations, in all four scenarios. A remarkable insight gained from our results is that, at moderate to high utilizations, multipath routing 'makes sense' from a queueing perspective for two paths only, i.e., response times drop the most when N = 2; the technical explanation is that the resequencing (delay) price starts to quickly dominate the tempting gain due to multipath transmissions
Training set cleansing of backdoor poisoning by self-supervised representation learning
A backdoor or Trojan attack is an important type of data poisoning attack
against deep neural network (DNN) classifiers, wherein the training dataset is
poisoned with a small number of samples that each possess the backdoor pattern
(usually a pattern that is either imperceptible or innocuous) and which are
mislabeled to the attacker's target class. When trained on a backdoor-poisoned
dataset, a DNN behaves normally on most benign test samples but makes incorrect
predictions to the target class when the test sample has the backdoor pattern
incorporated (i.e., contains a backdoor trigger). Here we focus on image
classification tasks and show that supervised training may build stronger
association between the backdoor pattern and the associated target class than
that between normal features and the true class of origin. By contrast,
self-supervised representation learning ignores the labels of samples and
learns a feature embedding based on images' semantic content. %We thus propose
to use unsupervised representation learning to avoid emphasising
backdoor-poisoned training samples and learn a similar feature embedding for
samples of the same class. Using a feature embedding found by self-supervised
representation learning, a data cleansing method, which combines sample
filtering and re-labeling, is developed. Experiments on CIFAR-10 benchmark
datasets show that our method achieves state-of-the-art performance in
mitigating backdoor attacks
VNF placement optimization at the edge and cloud
Network Function Virtualization (NFV) has revolutionized the way network services are offered to end users. Individual network functions are decoupled from expensive and dedicated middleboxes and are now provided as software-based virtualized entities called Virtualized Network Functions (VNFs). NFV is often complemented with the Cloud Computing paradigm to provide networking functions t
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