178,259 research outputs found
Network Kriging
Network service providers and customers are often concerned with aggregate
performance measures that span multiple network paths. Unfortunately, forming
such network-wide measures can be difficult, due to the issues of scale
involved. In particular, the number of paths grows too rapidly with the number
of endpoints to make exhaustive measurement practical. As a result, it is of
interest to explore the feasibility of methods that dramatically reduce the
number of paths measured in such situations while maintaining acceptable
accuracy.
We cast the problem as one of statistical prediction--in the spirit of the
so-called `kriging' problem in spatial statistics--and show that end-to-end
network properties may be accurately predicted in many cases using a
surprisingly small set of carefully chosen paths. More precisely, we formulate
a general framework for the prediction problem, propose a class of linear
predictors for standard quantities of interest (e.g., averages, totals,
differences) and show that linear algebraic methods of subset selection may be
used to effectively choose which paths to measure. We characterize the
performance of the resulting methods, both analytically and numerically. The
success of our methods derives from the low effective rank of routing matrices
as encountered in practice, which appears to be a new observation in its own
right with potentially broad implications on network measurement generally.Comment: 16 pages, 9 figures, single-space
Practical issues for the implementation of survivability and recovery techniques in optical networks
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Quality of service assurance for the next generation Internet
The provisioning for multimedia applications has been of increasing interest among researchers and Internet Service Providers. Through the migration from resource-based to service-driven networks, it has become evident that the Internet model should be enhanced to provide support for a variety of differentiated services that match applications and customer requirements, and not stay limited under the flat best-effort service that is currently provided.
In this paper, we describe and critically appraise the major achievements of the efforts to introduce Quality of Service (QoS) assurance and provisioning within the Internet model. We then propose a research path for the creation of a network services management architecture,
through which we can move towards a QoS-enabled network environment, offering support for a variety of different services, based on traffic characteristics and user expectations
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