SNAP: Stateful Network-Wide Abstractions for Packet Processing

Early programming languages for software-defined networking (SDN) were built on top of the simple match-action paradigm offered by OpenFlow 1.0. However, emerging hardware and software switches offer much more sophisticated support for persistent state in the data plane, without involving a central controller. Nevertheless, managing stateful, distributed systems efficiently and correctly is known to be one of the most challenging programming problems. To simplify this new SDN problem, we introduce SNAP. SNAP offers a simpler "centralized" stateful programming model, by allowing programmers to develop programs on top of one big switch rather than many. These programs may contain reads and writes to global, persistent arrays, and as a result, programmers can implement a broad range of applications, from stateful firewalls to fine-grained traffic monitoring. The SNAP compiler relieves programmers of having to worry about how to distribute, place, and optimize access to these stateful arrays by doing it all for them. More specifically, the compiler discovers read/write dependencies between arrays and translates one-big-switch programs into an efficient internal representation based on a novel variant of binary decision diagrams. This internal representation is used to construct a mixed-integer linear program, which jointly optimizes the placement of state and the routing of traffic across the underlying physical topology. We have implemented a prototype compiler and applied it to about 20 SNAP programs over various topologies to demonstrate our techniques' scalability

A control theoretic approach to achieve proportional fairness in 802.11e EDCA WLANs

This paper considers proportional fairness amongst ACs in an EDCA WLAN for provision of distinct QoS requirements and priority parameters. A detailed theoretical analysis is provided to derive the optimal station attempt probability which leads to a proportional fair allocation of station throughputs. The desirable fairness can be achieved using a centralised adaptive control approach. This approach is based on multivariable statespace control theory and uses the Linear Quadratic Integral (LQI) controller to periodically update CWmin till the optimal fair point of operation. Performance evaluation demonstrates that the control approach has high accuracy performance and fast convergence speed for general network scenarios. To our knowledge this might be the first time that a closed-loop control system is designed for EDCA WLANs to achieve proportional fairness

An Optimal Medium Access Control with Partial Observations for Sensor Networks

We consider medium access control (MAC) in multihop sensor networks, where only partial information about the shared medium is available to the transmitter. We model our setting as a queuing problem in which the service rate of a queue is a function of a partially observed Markov chain representing the available bandwidth, and in which the arrivals are controlled based on the partial observations so as to keep the system in a desirable mildly unstable regime. The optimal controller for this problem satisfies a separation property: we first compute a probability measure on the state space of the chain, namely the information state, then use this measure as the new state on which the control decisions are based. We give a formal description of the system considered and of its dynamics, we formalize and solve an optimal control problem, and we show numerical simulations to illustrate with concrete examples properties of the optimal control law. We show how the ergodic behavior of our queuing model is characterized by an invariant measure over all possible information states, and we construct that measure. Our results can be specifically applied for designing efficient and stable algorithms for medium access control in multiple-accessed systems, in particular for sensor networks

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

A Survey of Quality of Service Differentiation Mechanisms for Optical Burst Switching Networks

Cataloged from PDF version of article.This paper presents an overview of Quality of Service (QoS) differentiation mechanisms proposed for Optical Burst Switching (OBS) networks. OBS has been proposed to couple the benefits of both circuit and packet switching for the ‘‘on demand’’ use of capacity in the future optical Internet. In such a case, QoS support imposes some important challenges before this technology is deployed. This paper takes a broader view on QoS, including QoS differentiation not only at the burst but also at the transport levels for OBS networks. A classification of existing QoS differentiation mechanisms for OBS is given and their efficiency and complexity are comparatively discussed. We provide numerical examples on how QoS differentiation with respect to burst loss rate and transport layer throughput can be achieved in OBS networks. © 2009 Elsevier B.V. All rights reserved

Burst switched optical networks supporting legacy and future service types

Focusing on the principles and the paradigm of OBS an overview addressing expectable performance and application issues is presented. Proposals on OBS were published over a decade and the presented techniques spread into many directions. The paper comprises discussions of several challenges that OBS meets, in order to compile the big picture. The OBS principle is presented unrestricted to individual proposals and trends. Merits are openly discussed, considering basic teletraffic theory and common traffic characterisation. A more generic OBS paradigm than usual is impartially discussed and found capable to overcome shortcomings of recent proposals. In conclusion, an OBS that offers different connection types may support most client demands within a sole optical network layer

Congestion window based adaptive burst assembly for TCP traffic in optical burst switching networks

Ankara : The Department of Electrical and Electronics Engineering and the Institute of Engineering and Sciences of Bilkent Univ., 2008.Thesis (Master's) -- Bilkent University, 2008.Includes bibliographical references leaves 51-55.Burst assembly is one of the key factors affecting the TCP performance in Optical Burst Switching (OBS) networks. Timer based burst assembly algorithm generates bursts independent of the rate of TCP flows. When TCP congestion window is small, the fixed-delay burst assembler waits unnecessarily long, which increases the end-to-end delay and decreases the TCP goodput. On the other hand, when TCP congestion window becomes larger, the fixed-delay burst assembler may unnecessarily generate a large number of small-sized bursts, which increases the overhead and decreases the correlation gain, resulting in a reduction in the TCP goodput. Using simulations, we show that the usage of the congestion window (cwnd) size of TCP flows in the burst assembly algorithm consistently improves the TCP goodput (by up to 38.4%) compared with the fixed-delay timer based assembly even when the timer based assembler uses the optimum assembly period threshold value. One limitation of this proposed method is the assumption that the exact value of the congestion window is available at the burst assembler. We then extend the adaptive burstification algorithm such that the burst assembler uses estimated values of the congestion winpassive measurements at the ingress node. It is shown through simulations that even when estimated values are used, TCP goodput can achieve values close to the results obtained by using exact values of the congestion window. dow that are obtained viaÖzsaraç, SeçkinM.S