<Week 3>
Title: PeerCQ: A Decentralized and Self-Configuring Peer-to-Peer Information
Monitoring System
Last three digits of GTID: 430
Peer-to-Peer networks have received widespread deployment and notice primarily due to the advantages
that they offer in the form of improved robustness, scalability and diversity of the data that is available to
the participants of the networks. PeerCQ presents a decentralized system that performs information
monitoring over P2P networks with heterogeneous nodes. The authors mention that most of the P2P
protocols (for both structured and unstructured networks) tend to assume that the participation of all the
nodes in the network is equal. The PeerCQ system uses Continual Queries (CQ) to send out information
monitoring requests to the nodes. The paper makes two main contributions; the first being their approach
to processing CQs and the second is the PeerCQ service partitioning mechanism that takes into account
node heterogeneity and information monitoring characteristics. The paper describes the PeerCQ
architecture and protocol in a detailed manner which gives the reader an insight into how the system
achieves load balancing and good system utilization. Part of the system is the Capability-Sensitive
Service Partitioning which assigns CQs to those nodes in the network that are best suited, in terms of
resource capabilities to perform this task. This is achieved by peer awareness or the knowledge of what
system resources have been allocated by a particular node to processing CQs. The authors also describe
the simulator used for evaluating the service partitioning system and the three sets of experiments used to
verify the load balancing and system utilization properties of the system.
The P2P protocol used by PeerCQ is distributed hash table - based which means that the underlying
network is structured. It would be interesting to see how solving the same problems of load balancing and
utilization can be approached for unstructured networks such as Gnutella. There have been studies such as
described in the paper "Storage load balancing via local interactions among peers in unstructured P2P
networks" which aims to achieve storage load balancing by storing more data with nodes that have a
higher degree (are connected to more peers) thus reducing the average number of hops required to search
for and locate a particular file. They also use an interesting approach to reduce the resulting load on the
well-connected nodes by replicating files in high demand on low-degree nodes which can then share the
load on the high-degree nodes. This approach requires a high degree of localized interactions between
nodes.
The study does an excellent job of explaining the service partitioning mechanism and the P2P protocol in
use and the experimental evaluation is detailed. However, it would have been useful for the reader to
know how the issues addresses by the paper (load balancing & utilization) differ in the cases of
unstructured and structured networks and how, accordingly, their solutions must be different especially
considering that even today unstructured P2P networks are more widespread than structured networks.