Charu C. Aggarwal

A permutation-based pyramid broadcasting scheme for video-on-demand systems

Periodic broadcasting can be used to support near video on demand for popular videos. For a given bandwidth allocation, pyramid broadcasting schemes substantially reduce the viewer latency (waiting) time as compared with conventional broadcasting schemes. Nevertheless, such pyramid schemes typically have substantial storage requirements at the client end, and this results in set top boxes needing disks with high transfer rate capabilities. We present a permutation based pyramid scheme in which the storage requirements and disk transfer rates are greatly reduced, and yet the viewer latency is smaller as well. Under the proposed approach, each video is partitioned into contiguous segments of geometrically increasing sizes and each segment is further divided into blocks, where a block is the basic unit of transmission. As in the original pyramid scheme, frequencies of transmission for the different segments of a video vary in a manner inversely proportional to their size. Instead of transmitting the block in each segment in sequential order, the proposed scheme transmits these blocks in a prespecified cyclic permutation to save on storage requirements in the client end. Performance analyses are provided to quantify the benefits of the new scheme.

On optimal batching policies for video-on-demand storage servers

In a video-on-demand environment, batching of video requests is often used to reduce I/O demand and improve throughput. Since viewers may defect if they experience long waits, a good video scheduling policy needs to consider not only the batch size but also the viewer defection probabilities and wait times. Two conventional scheduling policies for batching are first-come-first-served (FCFS) and maximum queue length (MOL). Neither of these policies lead to entirely satisfactory results. MQL tends to be too aggressive in scheduling popular videos by only considering the queue length to maximize batch size, while FCFS has the opposite effect. We introduce the notion of factored queue length and propose a batching policy that schedules the video with the maximum factored queue length. We refer to this as the MFQ policy. The factored queue length is obtained by weighting each video queue length with a factor which is biased against the more popular videos. An optimization problem is formulated to solve the best weighting factors for the various videos. A simulation is developed to compare the proposed MFQ policy with FCFS and MQL. Our study shows that MFQ yields excellent empirical results in terms of standard performance measures such as average latency time, defection rates and fairness.

Optimized Crossover for the Independent Set Problem

We propose a knowledge-based crossover mechanism for genetic algorithms that exploits the structure of the solution rather than its coding. More generally, we suggest broad guidelines for constructing the knowledge-based crossover mechanisms. This technique uses an optimized crossover mechanism, in which the one of the two children is constructed in such a way as to have the best objective function value from the feasible set of children, while the other is constructed so as to maintain the diversity of the search space. We implement our approach on a classical combinatorial problem, called the independent set problem. The resulting genetic algorithm dominates all other genetic algorithms for the problem and yields one of the best heuristics for the independent set problem in terms of robustness and time performance. The primary purpose of this paper is to demonstrate the power of knowledge based mechanisms in genetic algorithms.