Wai Gen Yee

Efficient data allocation over multiple channels at broadcast servers

Broadcast is a scalable way of disseminating data because broadcasting an item satisfies all outstanding client requests for it. However, because the transmission medium is shared, individual requests may have high response times. In this paper, we show how to minimize the average response time given multiple broadcast channels by optimally partitioning data among them. We also offer an approximation algorithm that is less complex than the optimal and show that its performance is near-optimal for a wide range of parameters. Finally, we briefly discuss the extensibility of our work with two simple, yet seldom researched extensions, namely, handling varying sized items and generating single channel schedules.

Scaling replica maintenance in intermittently synchronized mobile databases

To avoid the high cost of continuous connectivity, a class of mobile applications employs replicas of shared data that are periodically updated. Updates to these replicas are typically performed on a client-by-client basis--that is, the server individually computes and transmits updates to each client--limiting scalability. By basing updates on replica groups (instead of clients), however, update generation complexity is no longer bound by client population size. Clients then download updates of pertinent groups. Proper group design reduces redundancies in server processing, disk usage and bandwidth usage, and dimininishes the tie between the complexity of updating replicas and the size of the client population. In this paper, we expand on previous work done on group design, include a detailed I/O cost model for update generation, and propose a heuristic-based greedy algorithm for group computation. Experimental results with an adapted commercial replication system demonstrate a significant increase in overall scalability over the client-centric approach.

Bridging the Gap between Response Time and Energy-Efficiency in Broadcast Schedule Design

In this paper, we propose techniques for scheduling data broadcasts that are favorable in terms of both response and tuning time. In other words, these techniques ensure that a typical data request will be quickly satisfied and its reception will require a low client-side energy expenditure. By generating broadcast schedules based on Acharya et al.s broadcast disk paradigm, we bridge the gap between these two mutually exclusive bodies of work-response time and energy expenditure. We prove the utility of our approach analytically and via experiments. Our analysis of optimal scheduling is presented under a variety of assumptions about size and popularity of data items, making our results generalizable to a range of applications.

A framework for designing update objects to improve server scalability in intermittently synchronized databases

We consider the class of mobile computing applications in which clients naturally operate on shared data without a connection to the server. When appropriate, a server connection is made in order to exchange updates. The update server computes and retransmits these updates on a clientby-client basis; consequently, the complexity of these operations is on the order of the number of clients, limiting scalability. We recently proposed exploiting overlap in client data subscriptions by organizing updates to these subscriptions into groups (with less overlap) instead of on a clientby-client basis. By grouping updates, update processing is performed only once per group, irrespective of the number of clients. Additionally, we may gain bandwidth scalability by employing broadcast delivery since, unlike in the case of the per-client approach, multiple clients may be interested in a groups updates. In this work, we model the operations of such database systems and introduce a framework for evaluation of group design. Since such fragmentation algorithms are computationally intractable, a heuristic graphbased group generation algorithm is speci ed. Performance results executed on a prototype developed using commercially available software are presented.

Fast data access on multiple broadcast channels

We give an overview on techniques for quickly finding items that are broadcast on multiple data channels. Previous works assume that throughput increases with the number of data channels because the total available bandwidth increases. This assumption, however, rests on the clients ability read data as soon as it is broadcast on any channel. Without this ability, the benefit of additional channels is compromised as the search space for data also increases. We describe the consequences of increasing the number of broadcast channels on search. We then offer candidate search techniques and show their effects on three metrics: search time, tuning time, and hop count.