There has been a surge of interest in the delivery of personalized information to users (e.g., personalized stocks or travel information), particularly as mobile users with limited terminal device capabilities increasingly desire updated and targeted information in real time. When the number of information recipients is large and there is sufficient commonality in their interests, as is often the case, IP multicast is an efficient way of delivering the information. However, IP multicast services do not consider the structure and semantics of the information in the multicast process. We propose the use of Content-Based Multicast (CBM) where extra content filtering is performed at the interior nodes of the IP multicast tree; this will reduce network bandwidth usage and delivery delay, as well as the computation required at the sources and sinks. We evaluate the situations in which CBM is advantageous. The benefits of CBM depend critically upon how well filters are placed at interior nodes of the IP multicast tree and the costs depend upon those introduced by filters themselves. Further, we consider the benefits of allowing the filters to be mobile so as to respond to user mobility or changes in user interests and the corresponding costs of filter mobility. The criterion that we consider is the total network bandwidth utilization. For this criterion, we develop an optimal filter placement algorithm, as well as a heuristic that executes faster than the optimal algorithm. We evaluate the algorithms by means of simulation experiments. Our results indicate that filters can be effective in substantially reducing bandwidth. We also find filter mobility is worthwhile if there is marked large-scale user mobility. We conclude with suggestions for further work. 相似文献
The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.
In this article, we have examined the performance of some useful capability indices using normal and non-normal distributions. The confidence intervals are calculated and mean coverage rates are observed for different capability indices. The effects of symmetry and kurtosis of parent distributions are examined on the mean coverage rates of different capability indices. Moreover, we have investigated the robustness (of confidence interval) using the median and percentile-based indices. We have considered the well-known distributions including normal, gamma, t, Weibull, and chi-squared. For these process scenarios, we have observed that some indices resist disturbance only in symmetry of the parent distribution, some resist the disturbance in symmetry and kurtosis of the distribution, and some indices don’t resist against either type of disturbance. 相似文献