Framework to Model User Request Access Patterns in the World Wide Web

In this paper, we present a novel approach to model user request patterns in the World Wide Web. Instead of focusing on the user traffic for web pages, we capture the user interaction at the object level of the web pages. Our framework model consists of three sub-models: one for user file access, one for web pages, and one for storage servers. Web pages are assumed to consist of different types and sizes of objects, which are characterized using several categories: articles, media, and mosaics. The model is implemented with a discrete event simulation and then used to investigate the performance of our system over a variety of parameters in our model. Our performance measure of choice is mean response time and by varying the composition of web pages through our categories, we find that our framework model is able to capture a wide range of conditions that serve as a basis for generating a variety of user request patterns. In addition, we are able to establish a set of parameters that can be used as base cases. One of the goals of this research is for the framework model to be general enough that the parameters can be varied such that it can serve as input for investigating other distributed applications that require the generation of user request access patterns.

An Efficient Hybrid Algorithm for Mining Web Frequent Access Patterns

We propose an efficient hybrid algorithm WDHP in this paper for mining frequent access patterns. WDHP adopts the techniques of DHP to optimize its performance, which is using hash table to filter candidate set and trimming database. Whenever the database is trimmed to a size less than a specified threshold, the algorithm puts the database into main memory by constructing a tree, and finds frequent patterns on the tree. The experiment shows that WDHP outperform algorithm DHP and main memory based algorithm WAP in execution efficiency.

Optimizing Memory Access Efficiency in CUDA Kernel via Data Layout Technique

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing. Despite these advancements, efficiently programming GPUs remains a daunting challenge, often relying on trial-and-error optimization methods. This paper introduces an optimization technique for CUDA programs through a novel Data Layout strategy, aimed at restructuring memory data arrangement to significantly enhance data access locality. Focusing on the dynamic programming algorithm for chained matrix multiplication—a critical operation across various domains including artificial intelligence (AI), high-performance computing (HPC), and the Internet of Things (IoT)—this technique facilitates more localized access. We specifically illustrate the importance of efficient matrix multiplication in these areas, underscoring the technique’s broader applicability and its potential to address some of the most pressing computational challenges in GPU-accelerated applications. Our findings reveal a remarkable reduction in memory consumption and a substantial 50% decrease in execution time for CUDA programs utilizing this technique, thereby setting a new benchmark for optimization in GPU computing.

Uplink Grant-Free Pattern Division Multiple Access (GF-PDMA) for 5G Radio Access

Massive machine type communication(m MTC) is one of the key application scenarios for the fifth generation mobile communication(5 G). Grant-free(GF) transmission can reduce the high signaling overhead in m MTC. Non-orthogonal multiple access(NMA) can support more users for m MTC than orthogonal frequency division multiple access(OFDMA). Applying GF transmission in NMA system becomes an active topic recently. The in-depth study on applying GF transmission in pattern division multiple access(PDMA), a competitive candidate scheme of NMA, is investigated in this paper. The definition, latency and allocation of resource and transmission mechanism for GF-PDMA are discussed in detail. The link-level and system-level evaluations are provided to verify the analysis. The analysis and simulation results demonstrate that the proposed GF-PDMA has lower latency than grant based PDMA(GB-PDMA), possesses strong scalability to confront collision and provides almost 2.15 times gain over GF-OFDMA in terms of supporting the number of active users in the system.

Distributed cache replacement method for geospatial data using spatiotemporal localitybased sequence

Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.