This chapter presents: (i) a layered peer-to-peer Cloud provisioning
architecture; (ii) a summary of the current state-of-the-art in Cloud
provisioning with particular emphasis on service discovery and load-balancing;
(iii) a classification of the existing peer-to-peer network management model
with focus on extending the DHTs for indexing and managing complex provisioning
information; and (iv) the design and implementation of novel, extensible
software fabric (Cloud peer) that combines public/private clouds, overlay
networking and structured peer-to-peer indexing techniques for supporting
s
We present the design and development of a data stream system that captures
data uncertainty from data collection to query processing to final result
generation. Our system focuses on data that is naturally modeled as continuous
random variables. For such data, our system employs an approach grounded in
probability and statistical theory to capture data uncertainty and integrates
this approach into high-volume stream processing. The first component of our
system captures uncertainty of raw data streams from sensing devices.