454 research outputs found
Distributed Collaborative Monitoring in Software Defined Networks
We propose a Distributed and Collaborative Monitoring system, DCM, with the
following properties. First, DCM allow switches to collaboratively achieve flow
monitoring tasks and balance measurement load. Second, DCM is able to perform
per-flow monitoring, by which different groups of flows are monitored using
different actions. Third, DCM is a memory-efficient solution for switch data
plane and guarantees system scalability. DCM uses a novel two-stage Bloom
filters to represent monitoring rules using small memory space. It utilizes the
centralized SDN control to install, update, and reconstruct the two-stage Bloom
filters in the switch data plane. We study how DCM performs two representative
monitoring tasks, namely flow size counting and packet sampling, and evaluate
its performance. Experiments using real data center and ISP traffic data on
real network topologies show that DCM achieves highest measurement accuracy
among existing solutions given the same memory budget of switches
Optimal Elephant Flow Detection
Monitoring the traffic volumes of elephant flows, including the total byte
count per flow, is a fundamental capability for online network measurements. We
present an asymptotically optimal algorithm for solving this problem in terms
of both space and time complexity. This improves on previous approaches, which
can only count the number of packets in constant time. We evaluate our work on
real packet traces, demonstrating an up to X2.5 speedup compared to the best
alternative.Comment: Accepted to IEEE INFOCOM 201
Firewalls Policies Based on Software Defined Networking: A survey
Software-Defined Networking (SDN) introduces granularity, visibility and flexibility to networking, which separates the control-logic from networking devices. SDN programmatically modifies the functionality and behaviour of network devices. It separates control plane and data plane, and thus provides centralized control. Though SDN provides better performance but there are some security issues that need to be taken care of. This includes firewalls, monitoring applications, IDS(Intrusion detection systems) etc. Therefore, this research work reviews the related approaches which have been proposed by identifying their firewall scope, their practicability, their advantages and drawbacks related with SDN. This paper describes the firewall policies as the forth new security challenges.Keywords: Software defined networking, Architecture, OpenFlow, Firewalls, Anomaly detectio
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Accurate and Resource-Efficient Monitoring for Future Networks
Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery
Tracking Normalized Network Traffic Entropy to Detect DDoS Attacks in P4
Distributed Denial-of-Service (DDoS) attacks represent a persistent threat to
modern telecommunications networks: detecting and counteracting them is still a
crucial unresolved challenge for network operators. DDoS attack detection is
usually carried out in one or more central nodes that collect significant
amounts of monitoring data from networking devices, potentially creating issues
related to network overload or delay in detection. The dawn of programmable
data planes in Software-Defined Networks can help mitigate this issue, opening
the door to the detection of DDoS attacks directly in the data plane of the
switches. However, the most widely-adopted data plane programming language,
namely P4, lacks supporting many arithmetic operations, therefore, some of the
advanced network monitoring functionalities needed for DDoS detection cannot be
straightforwardly implemented in P4. This work overcomes such a limitation and
presents two novel strategies for flow cardinality and for normalized network
traffic entropy estimation that only use P4-supported operations and guarantee
a low relative error. Additionally, based on these contributions, we propose a
DDoS detection strategy relying on variations of the normalized network traffic
entropy. Results show that it has comparable or higher detection accuracy than
state-of-the-art solutions, yet being simpler and entirely executed in the data
plane.Comment: Accepted by TDSC on 24/09/202
- …