838 research outputs found
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
Dynamic bandwidth allocation in ATM networks
Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes
Mining Butterflies in Streaming Graphs
This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection.
sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals
Renegotiation based dynamic bandwidth allocation for selfsimilar VBR traffic
The provision of QoS to applications traffic depends heavily on how different traffic types are categorized and classified, and how the prioritization of these applications are managed. Bandwidth is the most scarce network resource. Therefore, there is a need for a method or system that distributes an available bandwidth in a network among different applications in such a way that each class or type of traffic receives their constraint QoS requirements.
In this dissertation, a new renegotiation based dynamic resource allocation method for variable bit rate (VBR) traffic is presented. First, pros and cons of available off-line methods that are used to estimate selfsimilarity level (represented by Hurst parameter) of a VBR traffic trace are empirically investigated, and criteria to select measurement parameters for online resource management are developed. It is shown that wavelet analysis based methods are the strongest tools in estimation of Hurst parameter with their low computational complexities, compared to the variance-time method and R/S pox plot. Therefore, a temporal energy distribution of a traffic data arrival counting process among different frequency sub-bands is considered as a traffic descriptor, and then a robust traffic rate predictor is developed by using the Haar wavelet analysis. The empirical results show that the new on-line dynamic bandwidth allocation scheme for VBR traffic is superior to traditional dynamic bandwidth allocation methods that are based on adaptive algorithms such as Least Mean Square, Recursive Least Square, and Mean Square Error etc. in terms of high utilization and low queuing delay. Also a method is developed to minimize the number of bandwidth renegotiations to decrease signaling costs on traffic schedulers (e.g. WFQ) and networks (e.g. ATM). It is also quantified that the introduced renegotiation based bandwidth management scheme decreases heavytailedness of queue size distributions, which is an inherent impact of traffic self similarity.
The new design increases the achieved utilization levels in the literature, provisions given queue size constraints and minimizes the number of renegotiations simultaneously. This renegotiation -based design is online and practically embeddable into QoS management blocks, edge routers and Digital Subscriber Lines Access Multiplexers (DSLAM) and rate adaptive DSL modems
System Support for Bandwidth Management and Content Adaptation in Internet Applications
This paper describes the implementation and evaluation of an operating system
module, the Congestion Manager (CM), which provides integrated network flow
management and exports a convenient programming interface that allows
applications to be notified of, and adapt to, changing network conditions. We
describe the API by which applications interface with the CM, and the
architectural considerations that factored into the design. To evaluate the
architecture and API, we describe our implementations of TCP; a streaming
layered audio/video application; and an interactive audio application using the
CM, and show that they achieve adaptive behavior without incurring much
end-system overhead. All flows including TCP benefit from the sharing of
congestion information, and applications are able to incorporate new
functionality such as congestion control and adaptive behavior.Comment: 14 pages, appeared in OSDI 200
ALBUS: a Probabilistic Monitoring Algorithm to Counter Burst-Flood Attacks
Modern DDoS defense systems rely on probabilistic monitoring algorithms to
identify flows that exceed a volume threshold and should thus be penalized.
Commonly, classic sketch algorithms are considered sufficiently accurate for
usage in DDoS defense. However, as we show in this paper, these algorithms
achieve poor detection accuracy under burst-flood attacks, i.e., volumetric
DDoS attacks composed of a swarm of medium-rate sub-second traffic bursts.
Under this challenging attack pattern, traditional sketch algorithms can only
detect a high share of the attack bursts by incurring a large number of false
positives.
In this paper, we present ALBUS, a probabilistic monitoring algorithm that
overcomes the inherent limitations of previous schemes: ALBUS is highly
effective at detecting large bursts while reporting no legitimate flows, and
therefore improves on prior work regarding both recall and precision. Besides
improving accuracy, ALBUS scales to high traffic rates, which we demonstrate
with an FPGA implementation, and is suitable for programmable switches, which
we showcase with a P4 implementation.Comment: Accepted at the 42nd International Symposium on Reliable Distributed
Systems (SRDS 2023
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