By Kian-Lee Tan
In our more and more cellular global the power to entry details on call for at any time and position can fulfill people's details wishes in addition to confer on them a aggressive virtue. The emergence of battery-operated, reasonably cheap and transportable desktops comparable to palmtops and PDAs, coupled with the provision and exploitation of instant networks, have made attainable the potential of ubiquitous computing. during the instant networks, moveable equipments turns into an built-in a part of latest disbursed computing environments, and cellular clients could have entry to information saved at info servers positioned on the static element of the community even whereas they're at the move.
normally, info is retrieved following a request-response version. notwithstanding, this version isn't any longer sufficient in a instant computing surroundings. First, the instant channel is unreliable and the bandwidth is low in comparison to the stressed out counterpart. moment, the surroundings is largely uneven with lots of cellular clients gaining access to a small variety of servers. 3rd, battery-operated moveable units can commonly function just for a little while as a result of brief battery lifespan. hence, consumers are anticipated to be disconnected as a rule.
to beat those boundaries, there was a proliferation of analysis efforts on designing info supply mechanisms to aid instant computing extra successfully. Data Dissemination in WirelessComputing Environments specializes in such mechanisms. the aim is to supply a radical and entire overview of modern advances on energy-efficient facts supply protocols, effective instant channel bandwidth usage, trustworthy broadcasting and cache invalidation concepts for consumers with lengthy disconnection time. along with surveying latest equipment, this ebook additionally compares and evaluates a few of the extra promising schemes.
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Additional info for Data Dissemination in Wireless Computing Environments
10, picks the object with maximum (T – R(Ij))2di/ai value as the next object to be broadcast. We note that every object in a partition has the same broadcast frequency. There is no guarantee that the program is cyclic. In [ 119], the bucketing algorithm is compared against the online algorithm. As expected, it performs worse. In fact, the bucketing algorithm performs poorly when there is only one partition, but its performance improves with increasingly larger number of partitions. Input: access probabilities, q1, .
Scheduling Overhead. The overhead incurred by the scheduling algorithm is an important consideration. This is especially so since an algorithm with high overhead is not likely to scale. There are two types of scheduling overhead. The first concerns the processing of a new request when it arrives. For example, upon arrival, certain data structures may have to be updated, the request may have to be inserted into a queue, and so on. The second deals with selecting the next object to broadcast. If the decision incurs excessive overhead, the server may not be able to support a large number of requests.
Interestingly, none of the non-flat broadcast programs can outperform the flat broadcast program (provided keys are sorted) for range queries. Since objects in non-flat broadcast programs are not sorted, the entirety of the “cycle” has to be examined. One straightforward approach, called NBP, works as follows [114, 115]: (a) generate a coalesced broadcast program using the broadcast disks technique, (b) sort the objects within every segment, (c) provide an additional range indicator at the beginning of each segment.