19,980 research outputs found
Low-complexity medium access control protocols for QoS support in third-generation radio access networks
One approach to maximizing the efficiency of
medium access control (MAC) on the uplink in a future wideband
code-division multiple-access (WCDMA)-based third-generation
radio access network, and hence maximize spectral efficiency,
is to employ a low-complexity distributed scheduling control
approach. The maximization of spectral efficiency in third-generation
radio access networks is complicated by the need to
provide bandwidth-on-demand to diverse services characterized
by diverse quality of service (QoS) requirements in an interference
limited environment. However, the ability to exploit the full
potential of resource allocation algorithms in third-generation
radio access networks has been limited by the absence of a metric
that captures the two-dimensional radio resource requirement,
in terms of power and bandwidth, in the third-generation radio
access network environment, where different users may have
different signal-to-interference ratio requirements. This paper
presents a novel resource metric as a solution to this fundamental
problem. Also, a novel deadline-driven backoff procedure has
been presented as the backoff scheme of the proposed distributed
scheduling MAC protocols to enable the efficient support of
services with QoS imposed delay constraints without the need
for centralized scheduling. The main conclusion is that low-complexity
distributed scheduling control strategies using overload
avoidance/overload detection can be designed using the proposed
resource metric to give near optimal performance and thus maintain
a high spectral efficiency in third-generation radio access
networks and that importantly overload detection is superior to
overload avoidance
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
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