Digital Signal Processing Reference
In-Depth Information
4
Decentralized Approach
4.1
Limits of Centralized Approaches and Necessity of a
Decentralized Approach
The centralized approach presented in the previous sections has several limita-
tions:
1.
System and Information Bottlenecks:
Centralized approaches require a central
agent that collects all information, generates optimal order and operating points
per classifier, and distributes and enforces results on all classifiers. This creates a
bottleneck, as well as a single point of failure, and is unlikely to scale well as the
number of classifiers, topologic settings, data rates, and computing infrastructure
grow.
2.
Topology Specificity:
A centralized approach is designed to construct one
topology for each user application of interest. In practice the system may be
shared by multiple such applications—each of which may require the reuse of
different subsets of classifiers. In this case, the centralized algorithm needs to
design multiple orders and configurations that need to be changed dynamically
as application requirements change, and applications come and go.
3.
Resource Constraints:
Currently designed approaches minimize a combination
of processing delay and misclassification penalty. However, in general we also
need to satisfy the resource constraints of the underlying infrastructure. These
may in general lead to distributed non-convex constraints in the optimization,
thereby further increasing the sub-optimality of the solution, and increasing the
complexity of the approach. Hence, we need decentralized approaches to solve
these complex problems.
4.
Synchronization Requirements:
The processing times vary from one classifier
to the other. As a result, transmission from one classifier to another is not
synchronized. Note that this asynchrony is intrinsic to the stream mining system.
Designing one centralized optimization imposes synchronization requirements
among classifiers. As the number of classifiers and the size of the system
increases, such synchronization becomes harder to achieve, and may reduce the
overall efficiency of the system.
5.
Limited Sensitivity to Dynamics:
As an online process, stream mining opti-
mization must involve algorithms which take into account the system's dynamics,
both in terms of the evolving stream characteristics and classifiers' processing
time variations. This time-dependency is yet all the more true in a multi-query
context, because the overall data stream processed is not homogeneous, since it
is the concatenation of chunks of data from each query. Such plurality of queries
can lead to very abrupt changes over small intervals of time: two consecutive
tuples could belong to streams corresponding to two different queries, each with
its specific selectivities, processing time and, most importantly, with its specific
set of classifiers to go through. Centralized algorithms are unable to cope with
such dynamics.
