ECF: An MPTCP path scheduler to manage heterogeneous paths

© 2017 ACM. Multi-Path TCP (MPTCP) is a new standardized transport protocol that enables devices to utilize multiple network interfaces. The default MPTCP path scheduler prioritizes paths with the smallest round trip time (RTT). In this work, we examine whether the default MPTCP path scheduler can provide applications the ideal aggregate bandwidth, i.e., the sum of available bandwidths of all paths. Our experimental results show that heterogeneous paths cause under-utilization of the fast path, resulting in undesirable application behaviors such as lower video streaming quality than can be obtained using the available aggregate bandwidth. To solve this problem, we propose and implement a new MPTCP path scheduler, ECF (Earliest Completion First), that utilizes all relevant information about a path, not just RTT. Our results show that ECF consistently utilizes all available paths more efficiently than other approaches under path heterogeneity, particularly for streaming video

Minimizing Detection Probability Routing in Ad Hoc Networks Using Directional Antennas

In a hostile environment, it is important for a transmitter to make its wireless transmission invisible to adversaries because an adversary can detect the transmitter if the received power at its antennas is strong enough. This paper defines a detection probability model to compute the level of a transmitter being detected by a detection system at arbitrary location around the transmitter. Our study proves that the probability of detecting a directional antenna is much lower than that of detecting an omnidirectional antenna if both the directional and omnidirectional antennas provide the same Effective Isotropic Radiated Power (EIRP) in the direction of the receiver. We propose a Minimizing Detection Probability (MinDP) routing algorithm to find a secure routing path in ad hoc networks where nodes employ directional antennas to transmit data to decrease the probability of being detected by adversaries. Our study shows that the MinDP routing algorithm can reduce the total detection probability of deliveries from the source to the destination by over 74%.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Hierarchical Learning Algorithms for Multi-scale Expert Problems

In this paper, we study the multi-scale expert problem, where the rewards of different experts vary in different reward ranges. The performance of existing algorithms for the multi-scale expert problem degrades linearly proportional to the maximum reward range of any expert or the best expert and does not capture the non-uniform heterogeneity in the reward ranges among experts. In this work, we propose learning algorithms that construct a hierarchical tree structure based on the heterogeneity of the reward range of experts and then determine differentiated learning rates based on the reward upper bounds and cumulative empirical feedback over time. We then characterize the regret of the proposed algorithms as a function of non-uniform reward ranges and show that their regrets outperform prior algorithms when the rewards of experts exhibit non-uniform heterogeneity in different ranges. Last, our numerical experiments verify our algorithms' efficiency compared to previous algorithms

Burst reduction properties of rate-based flow control schemes : downstream queue behavior

In this paper we considerer rate-based flow control throttles feeding a sequence of single server infinite capacity queues. Specifically, we consider two types of throttles, the token bank and the leaky bucket. We show that the cell waiting times at the downstream queues are increasing functions of the token buffer capacity. These results are established when the rate-based throttles have finite capacity data buffers as well as infinite capacity buffers. In the case that the data buffer has finite capacity, we require that the sum of the capacities of the data buffer and token buffer be a constant. Last, we establish similar results for the process of number of losses at the last downstream queue in the case that the waiting buffer has finite capacity

Use coupled LSTM networks to solve constrained optimization problems

Gradient-based iterative algorithms have been widely used to solve optimization problems, including resource sharing and network management. When system parameters change, it requires a new solution independent of the previous parameter settings from the iterative methods. Therefore, we propose a learning approach that can quickly produce optimal solutions over a range of system parameters for constrained optimization problems. Two Coupled Long Short-Term Memory networks (CLSTMs) are proposed to find the optimal solution. The advantages of this framework include: (1) near-optimal solution for a given problem instance can be obtained in few iterations during the inference, (2) enhanced robustness as the CLSTMs can be trained using system parameters with distributions different from those used during inference to generate solutions. In this work, we analyze the relationship between minimizing the loss functions and solving the original constrained optimization problem for certain parameter settings. Extensive numerical experiments using datasets from Alibaba reveal that the solutions to a set of nonconvex optimization problems obtained by the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, where the number of iterations and CPU time consumption are reduced by 81% and 33%, respectively, when compared with the gradient descent with momentum