11,644 research outputs found
Segment Routing: a Comprehensive Survey of Research Activities, Standardization Efforts and Implementation Results
Fixed and mobile telecom operators, enterprise network operators and cloud
providers strive to face the challenging demands coming from the evolution of
IP networks (e.g. huge bandwidth requirements, integration of billions of
devices and millions of services in the cloud). Proposed in the early 2010s,
Segment Routing (SR) architecture helps face these challenging demands, and it
is currently being adopted and deployed. SR architecture is based on the
concept of source routing and has interesting scalability properties, as it
dramatically reduces the amount of state information to be configured in the
core nodes to support complex services. SR architecture was first implemented
with the MPLS dataplane and then, quite recently, with the IPv6 dataplane
(SRv6). IPv6 SR architecture (SRv6) has been extended from the simple steering
of packets across nodes to a general network programming approach, making it
very suitable for use cases such as Service Function Chaining and Network
Function Virtualization. In this paper we present a tutorial and a
comprehensive survey on SR technology, analyzing standardization efforts,
patents, research activities and implementation results. We start with an
introduction on the motivations for Segment Routing and an overview of its
evolution and standardization. Then, we provide a tutorial on Segment Routing
technology, with a focus on the novel SRv6 solution. We discuss the
standardization efforts and the patents providing details on the most important
documents and mentioning other ongoing activities. We then thoroughly analyze
research activities according to a taxonomy. We have identified 8 main
categories during our analysis of the current state of play: Monitoring,
Traffic Engineering, Failure Recovery, Centrally Controlled Architectures, Path
Encoding, Network Programming, Performance Evaluation and Miscellaneous...Comment: SUBMITTED TO IEEE COMMUNICATIONS SURVEYS & TUTORIAL
Dynamic Arrival Rate Estimation for Campus Mobility on Demand Network Graphs
Mobility On Demand (MOD) systems are revolutionizing transportation in urban
settings by improving vehicle utilization and reducing parking congestion. A
key factor in the success of an MOD system is the ability to measure and
respond to real-time customer arrival data. Real time traffic arrival rate data
is traditionally difficult to obtain due to the need to install fixed sensors
throughout the MOD network. This paper presents a framework for measuring
pedestrian traffic arrival rates using sensors onboard the vehicles that make
up the MOD fleet. A novel distributed fusion algorithm is presented which
combines onboard LIDAR and camera sensor measurements to detect trajectories of
pedestrians with a 90% detection hit rate with 1.5 false positives per minute.
A novel moving observer method is introduced to estimate pedestrian arrival
rates from pedestrian trajectories collected from mobile sensors. The moving
observer method is evaluated in both simulation and hardware and is shown to
achieve arrival rate estimates comparable to those that would be obtained with
multiple stationary sensors.Comment: Appears in 2016 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS).
http://ieeexplore.ieee.org/abstract/document/7759357
Detecting Flow Anomalies in Distributed Systems
Deep within the networks of distributed systems, one often finds anomalies
that affect their efficiency and performance. These anomalies are difficult to
detect because the distributed systems may not have sufficient sensors to
monitor the flow of traffic within the interconnected nodes of the networks.
Without early detection and making corrections, these anomalies may aggravate
over time and could possibly cause disastrous outcomes in the system in the
unforeseeable future. Using only coarse-grained information from the two end
points of network flows, we propose a network transmission model and a
localization algorithm, to detect the location of anomalies and rank them using
a proposed metric within distributed systems. We evaluate our approach on
passengers' records of an urbanized city's public transportation system and
correlate our findings with passengers' postings on social media microblogs.
Our experiments show that the metric derived using our localization algorithm
gives a better ranking of anomalies as compared to standard deviation measures
from statistical models. Our case studies also demonstrate that transportation
events reported in social media microblogs matches the locations of our detect
anomalies, suggesting that our algorithm performs well in locating the
anomalies within distributed systems
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
Traffic matrix estimation on a large IP backbone: a comparison on real data
This paper considers the problem of estimating the point-to-point
traffic matrix in an operational IP backbone. Contrary to previous studies, that have used a partial traffic matrix or demands estimated from aggregated Netflow traces, we use a unique data set of complete traffic matrices from a global IP network measured over five-minute intervals. This allows us to do an accurate data analysis on the time-scale of typical link-load measurements and enables us to make a balanced evaluation of different traffic matrix estimation techniques. We describe the data collection infrastructure, present spatial and temporal demand distributions, investigate the stability of fan-out factors, and analyze the mean-variance relationships between demands. We perform a critical evaluation of existing and novel methods for traffic matrix estimation, including recursive fanout estimation, worst-case bounds, regularized estimation techniques, and methods that rely on mean-variance relationships. We discuss the weaknesses and strengths of the various methods, and highlight differences in the results for the European and American subnetworks
- …