SDN based testbeds for evaluating and promoting multipath TCP

Multipath TCP is an experimental transport proto- col with remarkable recent past and non-negligible future poten- tial. It has been standardized recently, however the evaluation studies focus only on a limited set of isolated use-cases and a comprehensive analysis or a feasible path of Internet-wide adoption is still missing. This is mostly because in the current networking practice it is unusual to configure multiple paths between the endpoints of a connection. Therefore, conducting and precisely controlling multipath experiments over the real “inter- net” is a challenging task for some experimenters and impossible for others. In this paper, we invoke SDN technology to make this control possible and exploit large-scale internet testbeds to conduct end-to-end MPTCP experiments. More specifically, we establish a special purpose control and measurement framework on top of two distinct internet testbeds. First, using the OpenFlow support of GÉANT, we build a testbed enabling measurements with real traffic. Second, we design and establish a publicly available large-scale multipath capable measurement framework on top of PlanetLab Europe and show the challenges of such a system. Furthermore, we present measurements results with MPTCP in both testbeds to get insight into its behavior in such not well explored environment

On Comparing the Performance of Dynamic Multi-Network Optimizations

Abstract-With a large variety of wireless access technologies available, multi-homed devices may strongly improve the performance and reliability of communication when using multiple networks simultaneously. A key question for the practical application of multi-path strategies is the granularity at which the traffic streams should be dispersed among the available networks. This level of granularity may be expected to have a major impact on both the efficiency and complexity of practical realizations. Motivated by this, we compare two dynamic strategies that operate at different levels of granularity. The first strategy, which we call network selection, requires little operational complexity and dynamically assigns an arriving application data transfer to the network that delivers the highest expected performance. Our second strategy, which we call traffic-splitting, is of higher complexity and aims to optimally split individual data transfers among the available networks. To this end, we (1) develop quantitative models that describe the performance of both strategies, (2) determine the (near-)optimal algorithms for both strategies, and (3) validate the efficiency and practical usefulness of the algorithms via extensive network simulations and experiments in a real-life testbed environment. These experimental results show that the optimal strategies obtained from the theoretical models lead to extremely well-performing solutions in practical circumstances. Moreover, the results show that the splitting of data transfers, which is easy to embed in the network requiring no information on the number of flows in the system, leads to a much better performance compared to dynamic network selection

Adaptive and robust media streaming over multiple channels with bursty losses

This paper addresses the problem of efficiently delivering a layered media stream from multiple senders to a single receiver, over channels that present correlated packet loss patterns. Using a digital fountain approach, the performance of a distributed streaming system is driven by the probability of receiving a given number of packets on aggregate over the multiple channels. In addition, such a system allows to avoid the need for communication between streaming servers. We devise an optimization problem whose solution provides the optimal number of packets that need to be transmitted per channel, in order to maximize the probability of correct decoding for a given media stream. Our findings indicate that it is in general important to consider both the Packet Loss Ratio (PLR) and Average Burst Length (ABL) in channel selection problems such as multipath routing or rate aggregation on multiple bursty channels. Finally we present a low-complexity algorithm which is able to quickly find a suboptimal yet effective solution to the combinatorial optimization problem

Optimal file splitting for wireless networks with concurrent access

Abstract. The fundamental limits on channel capacity form a barrier to the sustained growth on the use of wireless networks. To cope with this, multi-path communication solutions provide a promising means to improve reliability and boost Quality of Service (QoS) in areas that are covered by a multitude of wireless access networks. Today, little is known about how to effectively exploit this potential. Motivated by this, we consider N parallel communication networks, each of which is modeled as a processor sharing (PS) queue that handles two types of traffic: foreground and background. We consider a foreground traffic stream of files, each of which is split into N fragments according to a fixed splitting rule (α1, . . . , αN ), where P αi = 1 and αi ≥ 0 is the fraction of the file that is directed to network i. Upon completion of transmission of all fragments of a file, it is re-assembled at the receiving end. The background streams use dedicated networks without being split. We study the sojourn time tail behavior of the foreground traffic. For the case of light foreground traffic and regularly varying foreground filesize distributions, we obtain a reduced-load approximation (RLA) for the sojourn times, similar to that of a single PS-queue. An important implication of the RLA is that the tail-optimal splitting rule is simply to choose αi proportional to ci − ρi, where ci is the capacity of network i and ρi is the load offered to network i by the corresponding background stream. This result provides a theoretical foundation for the effectiveness of such a simple splitting rule. Extensive simulations demonstrate that this simple rule indeed performs well, not only with respect to the tail asymptotics, but also with respect to the mean sojourn times. The simulations further support our conjecture that the same splitting rule is also tail-optimal for non-light foreground traffic. Finally, we observe near-insensitivity of the mean sojourn times with respect to the file-size distribution

OSIA: Out-of-order scheduling for in-order arriving in concurrent multi-path transfer.

One major problem of concurrent multi-path transfer (CMT) scheme in multi-homed mobile networks is that the utilization of different paths with diverse delays may cause packet reordering among packets of the same ?ow. In the case of TCP-like, the reordering exacerbates the problem by bringing more timeouts and unnecessary retransmissions, which eventually degrades the throughput of connections considerably. To address this issue, we ?rst propose an Out-of-order Scheduling for In-order Arriving (OSIA), which exploits the sending time discrepancy to preserve the in-order packet arrival. Then, we formulate the optimal traf?c scheduling as a constrained optimization problem and derive its closedform solution by our proposed progressive water-?lling solution. We also present an implementation to enforce the optimal scheduling scheme using cascaded leaky buckets with multiple faucets, which provides simple guidelines on maximizing the utilization of aggregate bandwidth while decreasing the probability of triggering 3 dupACKs. Compared with previous work, the proposed scheme has lower computation complexity and can also provide the possibility for dynamic network adaptability and ?ner-grain load balancing. Simulation results show that our scheme signi?cantly alleviates reordering and enhances transmission performance

Optimal job splitting in parallel processor sharing queues

The main barrier to the sustained growth of wireless communications is the Shannon limit that applies to the channel capacity. A promising means to realize high-capacity enhancements is the use of multi-path communication solutions to improve reliability and network performance in areas that are covered by a multitude of overlapping wireless access networks. Despite the enormous potential for capacity enhancements offered by multi-path communication techniques, little is known about how to effectively exploit this. Motivated by this, we study a model where jobs are split and downloaded over N multiple parallel networks, each of which is modeled as a processor sharing (PS) queue. Each job is fragmented, according to a fixed splitting rule α=

OSCAR: A Collaborative Bandwidth Aggregation System

The exponential increase in mobile data demand, coupled with growing user expectation to be connected in all places at all times, have introduced novel challenges for researchers to address. Fortunately, the wide spread deployment of various network technologies and the increased adoption of multi-interface enabled devices have enabled researchers to develop solutions for those challenges. Such solutions aim to exploit available interfaces on such devices in both solitary and collaborative forms. These solutions, however, have faced a steep deployment barrier. In this paper, we present OSCAR, a multi-objective, incentive-based, collaborative, and deployable bandwidth aggregation system. We present the OSCAR architecture that does not introduce any intermediate hardware nor require changes to current applications or legacy servers. The OSCAR architecture is designed to automatically estimate the system's context, dynamically schedule various connections and/or packets to different interfaces, be backwards compatible with the current Internet architecture, and provide the user with incentives for collaboration. We also formulate the OSCAR scheduler as a multi-objective, multi-modal scheduler that maximizes system throughput while minimizing energy consumption or financial cost. We evaluate OSCAR via implementation on Linux, as well as via simulation, and compare our results to the current optimal achievable throughput, cost, and energy consumption. Our evaluation shows that, in the throughput maximization mode, we provide up to 150% enhancement in throughput compared to current operating systems, without any changes to legacy servers. Moreover, this performance gain further increases with the availability of connection resume-supporting, or OSCAR-enabled servers, reaching the maximum achievable upper-bound throughput