MULS: A General Framework of Providing Multilevel Service Quality in Sequential Data Broadcasting

Abstract

In recent years, data broadcasting has become a promising technique to design a mobile information system with power conservation, high scalability, and high bandwidth utilization. In many applications, the query issued by a mobile client corresponds to multiple items that should be accessed in a sequential order. In this paper, we study the scheduling approach in such a sequential data broadcasting environment. Explicitly, we propose a general framework referred to as MULS (standing for MUltiLevel Service) for an information system. There are two primary stages in MULS: online scheduling (OLS) and optimization procedure. In the first stage, we propose an OLS algorithm to allocate the data items into multiple channels. As for the second stage, we devise an optimization procedure, called Sampling with Controlled Iteration (SCI), to enhance the quality of broadcast programs generated by algorithm OLS. Procedure SCI is able to strike a compromise between effectiveness and efficiency by tuning the control parameters. According to the experimental results, we show that algorithm OLS with procedure SCI outperforms the approaches in prior works prominently in both effectiveness (that is, the average access time of mobile users) and efficiency (that is, the complexity of the scheduling algorithm). Therefore, by cooperating algorithm OLS with procedure SCI, the proposed MULS framework is able to generate broadcast programs with the flexibility of providing different service qualities under different requirements of effectiveness and efficiency: in the dynamic environment in which the access patterns and information contents change rapidly, the parameters used in SCI will perform OLS with satisfactory service quality. As for the static environment in which the query profile and the database are updated infrequently, larger values of parameters are helpful to generate an optimized broadcast program, indicating the advantageous feature of MULS.

Exploiting replication on dependent data allocation for ordered queries over multiple broadcast channels

“…As shown in [26], algorithm MULS is of the best performance among all other broadcast program generation algorithms for ordered queries. The experimental results are shown in the following subsections.…”

“…The effect of index items is not considered in algorithm BPGR and its competitors [23,26] since all these algorithms focus on data allocation, and the effect of index items will be considered in the algorithm used in index allocation phase. It is true that there is a trade-off between average access time and average tuning time, and it is the index allocation algorithm's responsibility to strike a balance between them.…”

“…Similar to [43], the number of channels and data items are set to six and 2,000, respectively. The channel bandwidth is set to 80 kb/s and the data size is set to 8 kb [26]. By default, we consider ideal broadcast channels by setting data error rate to zero.…”

“…By default, we consider ideal broadcast channels by setting data error rate to zero. In addition, the average query length and query number are set to 10 and 100, respectively [26]. The simulation executes for 12 h. For better readability, Table 4 shows the system parameters in our experiments.…”

“…1 [23,26]. The system periodically broadcasts data items according to a predetermined broadcast program.…”