A Fibonacci version of Kraft's inequality applied discrete unimodal search
SIAM Journal on Computing
Energy efficient indexing on air
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Broadcast disks: data management for asymmetric communication environments
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Log-time algorithms for scheduling single and multiple channel data broadcast
MobiCom '97 Proceedings of the 3rd annual ACM/IEEE international conference on Mobile computing and networking
Minimizing service and operation costs of periodic scheduling
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
The data broadcast problem with non-uniform transmission times
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
Polynomial-time approximation scheme for data broadcast
STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Efficient Data Allocation over Multiple Channels at Broadcast Servers
IEEE Transactions on Computers
Multi-Level Multi-Channel Air Cache Designs for Broadcasting in a Mobile Environment
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Optimal Index and Data Allocation in Multiple Broadcast Channels
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Optimal Skewed Data Allocation on Multiple Channels with Flat Broadcast per Channel
IEEE Transactions on Computers
Spatial query processing in road networks for wireless data broadcast
Wireless Networks
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The problem of data broadcasting over multiple channels consists in partitioning data among channels, depending on data popularities, and then cyclically transmitting them over each channel so that the average waiting time of the clients is minimized. Such a problem is known to be polynomially time solvable for uniform length data items, while it is computationally intractable for non-uniform length data items. In this paper, two new heuristics are proposed which exploit a novel characterization of optimal solutions for the special case of two channels and data items of uniform lengths. Sub-optimal solutions for the most general case of an arbitrary number of channels and data items of non-uniform lengths are provided. The first heuristic, called Greedy+, combines the novel characterization with the known greedy approach, while the second heuristic, called Dlinear, combines the same characterization with the dynamic programming technique. Such heuristics have been tested on benchmarks whose popularities are characterized by Zipf distributions, as well as on a wider set of benchmarks. The experimental tests reveal that Dlinear finds optimal solutions almost always, requiring good running times. However, Greedy+ is faster and scales well when changes occur on the input parameters, but provides solutions which are close to the optimum.