Title:
Toward A Method of Grouping Server Data Fragments for Improving Scalability in Intermittently Synchronized Databases
Toward A Method of Grouping Server Data Fragments for Improving Scalability in Intermittently Synchronized Databases
Author(s)
Yee, Wai Gen
Donahoo, Michael J.
Navathe, Shamkant B.
Donahoo, Michael J.
Navathe, Shamkant B.
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Abstract
We consider the class of mobile computing applications with periodically
connected clients. These clients wish to share data; however, due to the
expense of mobile communication, they only connect periodically -- and not
necessarily synchronously -- to a common network. Traditionally, a
continuously-connected server, containing an aggregate of client data,
facilitates sharing amongst clients by allowing the clients to upload local
updates and download updates submitted by other clients. The server computes
and transmits these updates on a client-by-client basis; consequently, the
complexity of these operations is on the order of the number of clients,
limiting scalability. Recent research proposes exploiting client data
overlap by grouping updates according to how the data is shared amongst
clients (data-centric) instead of on a client-by-client basis
(client-centric). Each client downloads updates for the relevant set of
groups. By grouping, update operation distribution is computed only once
per group, irrespective of the number of clients downloading a particular
group's updates. Additionally, we may gain bandwidth scalability by
employing broadcast delivery since, unlike the case in the per-client
approach, multiple clients may be interested in a group's updates. Clearly,
group composition directly affects the scalability of this approach. Given a
relative cost of resources such as server processing, bandwidth, and storage
space, we focus on developing a group derivation approach that significantly
improves the scalability of the resources. We construct a formal
specification of this problem and discuss the intractability of an optimal
solution. Based on observations from the specification, we derive
a heuristically based approach and evaluate its efficacy with respect to the
client-centric approach. We run experiments on an implemented system that
demonstrates that as the amount of overlap increases between client
subscriptions, the data-centric approach with groups generated by our
heuristic-based algorithm yields significant cost reduction when compared to
the traditional client-centric approach.
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Date Issued
1999
Extent
268689 bytes
Resource Type
Text
Resource Subtype
Technical Report