Broadcast Disks: Data Management for Asymmetric Communication Environments

Abstract

This paper proposes the use of repetitive broadcast as a way of augmenting the memory hierarchy of clients in an asymmetric communication environment. We describe a new technique called \Broadcast Disks" for structuring the broadcast in a way that provides improved performance for non-uniformly accessed data. The Broadcast Disk superimposes multiple disks spinning at di erent speeds on a single broadcast channel | in e ect creating an arbitrarily ne-grained memory hierarchy. In addition to proposing and de ning the mechanism, a main result of this work is that exploiting the potential of the broadcast structure requires a re-evaluation of basic cache management policies. We examine several \pure" cache management policies and develop and measure implementable approximations to these policies. These results and others are presented in a set of simulation studies that substantiates the basic idea and develops some of the intuitions required to design a particular broadcast program.

Using Separate Processing for Read-Only Transactions in Mobile Environment

“…Broadcast-based data dissemination [1], [2], [3], [9] becomes a widely accepted approach of communication in mobile environment. A server broadcasts data and clients tune in to the broadcast channel to retrieve their data of interest.…”

Transitive Nearest Neighbor Search in Mobile Environments

“…The former represents the conventional client-server model where a mobile client submits a request, together with its current location, to the server via a dedicated point-to-point channel 1 . After processing the query, the server sends back the answer to the client via the point-to-point channel.…”

“…A mobile client continuously monitors the channel to retrieve the interested data objects in order to answer its queries locally. Different broadcast 1 Current positioning technology (e.g., GPS) is available for clients to obtain their own positions.…”

“…programs based on query access patterns have been proposed [1]. In order to simplify our discussion, we assume a flat broadcast, i.e., each data object is broadcast only once in a broadcast cycle, the duration in which the whole dataset is broadcast once.…”

“…We then address the answer validation issue; a novel algorithm is proposed to enable a client to detect the validity of returned answer as she moves to a new position that is different to the position where she issued the query. In order to simplify the discussion, we assume the issued TNN query is to retrieve a pair of objects (s, r) from two datasets, S and R. A running example is depicted in Figure 4, with S = {s 1 …”