A Model-Driven Simulation Study of World-Wide-Web Cache Policies.

Web cache is considered as an efficient mechanism to reduce network traffic and origin servers load. It aims to keep close to users the most popular Web objects. And the performance of a Web cache always depends on the replacement strategy it chooses. In view of these, we are devoted to propose and implement a Web cache based on the LRU replacement algorithm in this paper. To improve the performance of LRU algorithm, we present an expansion to the typical LRU by using a new data storing structure to store caching data and add other factors to evict in-cache objects. We evaluate the performance of this algorithm by doing some experiments. The results show that this algorithm outperforms than typical LRU in selected performance metrics. The Web cache is applied to our Multi-Media Conference System and it improves the performance significantly.

The last decade has seen tremendous growth in usage of the World Wide Web. Web caching is a technology aimed at reducing the transmission of redundant network traffic and improving access to the Web. The key idea in Web caching is to cache frequently- accessed content so that it may be used profitably later. This leads to cost savings, reduction in network traffic, improved access and better content availability. Web Caching and Its Applications gives the reader an understanding of the latest developments in Web caching research. Topics covered include architectural aspects, aspects requiring coordination among caches, aspects related to network traffic, techniques that complement caching, practical aspects, and aspects related to performance. While Web Caching and Its Applications is designed for a professional audience, students will appreciate the exercises for applying the knowledge to solving practical problems related to Web caching and Internet performance. The book includes an exhaustive list of references for further study.

Web caching has been used extensively to enhance content delivery to the clients by minimizing client-observed latency, reducing network bandwidth usage and improving scalability of the network. Caching performance can be improved by designing good replacement policies, prefetching techniques, clustering of web users and proper placement of proxy caches in the network. In this paper, we discuss the various approaches that were designed based on neural networks, genetic algorithms and fuzzy logic to optimize the performance of web caching. The approaches discussed here proved to be more effective in solving the problems as compared to the conventional techniques that were used earlier in this problem domain. Neural networks and evolutionary algorithms can be considered for further exploration in the various issues related to web caching and content delivery.

Due to the increasing gap between CPU and memory speed, cache performance plays an increasingly critical role in determining the overall performance of microprocessor systems. One of the important factors that a affect cache performance is the cache replacement policy. Despite the importance, current analytical cache performance models ignore the impact of cache replacement policies on cache performance. To the best of our knowledge, this paper is the first to propose an analytical model which predicts the performance of cache replacement policies. The input to our model is a simple circular sequence profiling of each application, which requires very little storage overhead. The output of the model is the predicted miss rates of an application under different replacement policies. The model is based on probability theory and utilizes Markov processes to compute each cache access' miss probability. The model realistic assumptions and relies solely on the statistical properties of the application, without relying on heuristics or rules of thumbs. The model's run time is less than 0.1 seconds, much lower than that of trace simulations. We validate the model by comparing the predicted miss rates of seventeen Spec2000 and NAS benchmark applications against miss rates obtained by detailed execution-driven simulations, across a range of different cache sizes, associativities, and four replacement policies, and show that the model is very accurate. The model's average prediction error is 1.41%,and there are only 14 out of 952 validation points in which the prediction errors are larger than 10%.

As the Internet has become a more central aspect for information technology, so have concerns with supplying enough bandwidth and serving web requests to end users in an appropriate time frame. Web caching was introduced in the 1990s to help decrease network traffic, lessen user perceived lag, and reduce loads on origin servers by storing copies of web objects on servers closer to end users as opposed to forwarding all requests to the origin servers. Since web caches have limited space, web caches must effectively decide which objects are worth caching or replacing for other objects. This problem is known as cache replacement. We used neural networks to solve this problem and proposed the Neural Network Proxy Cache Replacement (NNPCR) method. The goal of this research is to implement NNPCR in a real environment like Squid proxy server. In order to do so, we propose an improved strategy of NNPCR referred to as NNPCR-2. We show how the improved model can be trained with up to twelve times more data and gain a 5–10% increase in Correct Classification Ratio (CCR) than NNPCR. We implemented NNPCR-2 in Squid proxy server and compared it with four other cache replacement strategies. In this paper, we use 84 times more data than NNPCR was tested against and present exhaustive test results for NNPCR-2 with different trace files and neural network structures. Our results demonstrate that NNPCR-2 made important, balanced decisions in relation to the hit rate and byte hit rate; the two performance metrics most commonly used to measure the performance of web proxy caches.

While web proxy caching is a widely deployed technique, the performance of a proxy cache is limited by the local storage. Some studies have addressed this limitation by using the residual resources of clients via a p2p method and have achieved a very high hit rate. However, these approaches treat web objects as homogeneous objects and there is no consideration of various web characteristics. Consequently, the byte hit rate of the system is limited, external bandwidth is wasted, and perceived user latency is increased. The present paper suggests an efficient p2p based web caching technique that manages objects with different policies so as to exploit the characteristics of web objects, such as size and temporal locality. Small objects are stored alone whereas large objects are stored by dividing them into numerous small blocks, which are distributed in clients. On a proxy cache, header blocks of large objects take the place of objects themselves and smaller objects are cached. This technique increases the hit rate. Unlike a web cache, which evicts large objects as soon as possible in the case where clients fulfill the role of backup storage, large objects are given higher priority than small objects in the proposed approach. This maximizes the effect of hits for large objects and thereby increases the byte hit rate. Furthermore, we construct simple latency models for various p2p based web caching systems and analyze the effects of the proposed policies on these systems. We then examine the performances of the efficient policies via a trace driven simulation. The results demonstrate that the proposed techniques effectively enhance web cache performance, including hit rate, byte hit rate, and response time.

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The paper presents the cache performance analysis of the Chronological Garbage Collection algorithm used in the LVM system. The LVM is a new Logic Virtual Machine for Prolog. It adopts one stack policy for all dynamic memory requirements and cooperates with an efficient garbage collection algorithm, Chronological Garbage Collection to recuperate space, not as deliberate garbage collection operation but as a natural activity of the LVM engine to gather useful objects. This algorithm takes advantages of the traditional copying, mark-compact, generational, and incremental garbage collection schemes. In order to determine the improvement of cache performance under our garbage collection algorithm, we developed an emulator to do the trace driven cache simulation. Direct mapped cache and set-associative cache with different cache sizes, block sizes and set associativities are simulated and measured. The objectives of this simulation are to verify and validate our experimental results, and to find important factors which influence the performance of the CGC algorithm.

SUMMARY Peer-to-peer (P2P) web caching has been studied recently as it can exploit local caches of peers for web caching without additional infrastructure. However, dynamic join/leave behaviors or local caching strategies of the peers due to their autonomy in a P2P network may limit the performance of P2P web caching. To overcome these limitations, we propose an effective directory-based P2P web caching system under dynamic participation of peers. We introduce the object lifetime in a P2P network considering the lifetimes of both an object in the local cache of a peer and a peer who owns the object, and utilize this object lifetime information for neighbor selection and storage management in the P2P web caching framework. For the neighbor selection, the proposed system utilizes the object lifetimes in selecting accurately a neighbor who would still retain the requested object and still remain in the P2P network. To improve the storage management, the proposed system uses efficiently the entire cache storage of the P2P network in such a way that the object is stored selectively in the local cache of the peer who requested it, considering the object lifetime. The trace-driven simulation results show that the proposed system has higher accuracy and fewer redirection failures than the conventional directory-based P2P web caching system in the feasible P2P network.

Edge caching is emerging as the most promising solution to reduce the content retrieval delay and relieve the huge burden on the backhaul links in the ultra-dense networks by proactive caching popular contents in the small base station (SBS). However, constraint cache resource of individual SBSs significantly throttles the performance of edge caching. In this paper, we propose a device-to-device (D2D) assisted cooperative edge caching (DCEC) policy for millimeter (mmWave) dense networks, which cooperatively utilizes the cache resource of users and SBSs in proximity. In the proposed DCEC policy, a content can be cached in either users' devices or SBSs according to the content popularity, and a user can retrieve the requested content from neighboring users via D2D links or the neighboring SBSs via cellular links to efficiently exploit the cache diversity. Unlike existing cooperative caching policies in the lower frequency bands that require complex interference management techniques to suppress interference, we take advantage of directional antenna in mmWave systems to ensure high transmission rate whereas mitigating interference footprint. Taking the practical directional antenna model and the network density into consideration, we derive closed-form expressions of the backhaul offloading performance and content retrieval delay based on the stochastic information of network topology. In addition, analytical results indicate that, with the increase of the network density, the content retrieval delay via D2D links increases significantly while that via cellular links increases slightly. Comprehensive simulations validate our theoretical analysis and demonstrate that the proposed policy can achieve higher performance in offloading the backhaul traffic and reducing the content retrieval delay compared with the state-of-the-art most popular caching policy.

The growing popularity of the World Wide Web is placing tremendous demands on the Internet. The world wide can be considered as a large distributed information system that provides access to shared data objects. As one of the most popular applications currently running on the Internet, the World Wide Web is of an exponential growth in size, which results in network congestion and server overloading. A key strategy for scaling the Internet to meet these increasing demands is to cache data near clients and thus improve access latency and reduce network and server load. Although web cache can reduce the load on the server significantly, network congestion, and the latency of access, it brings a question of cache consistency. That is, a stale cache might return to a client. Thereby, all web caches must try to keep cached objects up to date with the original copies of those objects to avoid returning stale pages to users. In this paper, we first describe an existing web cache consistency technique which has been used in web caching system. Later, we propose a strong cache consistency that is be suitable for web appellations. Compared with previous strong consistency, our proposed algorithm is more efficient.

Compression is widely used to reduce data transmission costs, but its impact on the performance of network entities such as Web caches has not yet been studied. We evaluate the impact of compression on cache performance when the cache compresses content and the client decompresses it. We further determine the performance bounds by benchmarking the best-case (server compression-client decompression) and the worst-case (cache compression and decompression) scenarios. Our results show that caching compressed content significantly improves the cache's hit ratio, reduces user latency and increases cache throughput. With a compression ratio of 1.69:1, we see a 43% increase in hit ratio, a 12% decrease in average end-to-end latency and a 29% latency reduction for cache hits. We also find that cache hit ratio is critical to average end-to-end latency. Even in the worst-case, the cost of compressing and decompressing content is outweighed by the benefits at a reasonable compression ratio of 1.58:1.

It was recently proved by Jelenković and Radovanović (2004) that the least-recently-used (LRU) caching policy, in the presence of semi-Markov-modulated requests that have a generalized Zipf's law popularity distribution, is asymptotically insensitive to the correlation in the request process. However, since the previous result is asymptotic, it remains unclear how small the cache size can become while still retaining the preceding insensitivity property. In this paper, assuming that requests are generated by a nearly completely decomposable Markov-modulated process, we characterize the critical cache size below which the dependency of requests dominates the cache performance. This critical cache size is small relative to the dynamics of the modulating process, and in fact is sublinear with respect to the sojourn times of the modulated chain that determines the dependence structure.

The topic of cache performance has been well studied in recent years. Compiler optimizations exist and optimizations have been done for many problems. Much of this work has focused on dense linear algebra problems. At first glance, the Floyd--Warshall algorithm appears to fall into this category. In this paper, we begin by applying two standard cache-friendly optimizations to the Floyd--Warshall algorithm and show limited performance improvements. We then discuss the unidirectional space time representation (USTR). We show analytically that the USTR can be used to reduce the amount of processor-memory traffic by a factor of O(√C), where C is the cache size, for a large class of algorithms. Since the USTR leads to a tiled implementation, we develop a tile size selection heuristic to intelligently narrow the search space for the tile size that minimizes total execution time. Using the USTR, we develop a cache-friendly implementation of the Floyd--Warshall algorithm. We show experimentally that this implementation minimizes the level-1 and level-2 cache misses and TLB misses and, therefore, exhibits the best overall performance. Using this implementation, we show a 2x improvement in performance over the best compiler optimized implementation on three different architectures. Finally, we show analytically that our implementation of the Floyd--Warshall algorithm is asymptotically optimal with respect to processor-memory traffic. We show experimental results for the Pentium III, Alpha, and MIPS R12000 machines using problem sizes between 1024 and 2048 vertices. We demonstrate improved cache performance using the Simplescalar simulator.

Commercial image serving systems, such as Flickr and Facebook, rely on large image caches to avoid the retrieval of requested images from the costly backend image store, as much as possible. Such systems serve the same image in different resolutions and, thus, in different sizes to different clients, depending on the properties of the clients' devices. The requested resolutions of images can be cached individually, as in the traditional caches, reducing the backend workload. However, a potentially better approach is to store relatively high-resolution images in the cache and resize them during the retrieval to obtain lower-resolution images. Having this kind of on-the-fly image resizing capability enables image serving systems to deploy more sophisticated caching policies and improve their serving performance further. In this paper, we formalize the static caching problem in image serving systems which provide on-the-fly image resizing functionality in their edge caches or regional caches. We propose two gain-based caching policies that construct a static, fixed-capacity cache to reduce the average serving time of images. The basic idea in the proposed policies is to identify the best resolution(s) of images to be cached so that the average serving time for future image retrieval requests is reduced. We conduct extensive experiments using real-life data access logs obtained from Flickr. We show that one of the proposed caching policies reduces the average response time of the service by up to 4.2% with respect to the best-performing baseline that mainly relies on the access frequency information to make the caching decisions. This improvement implies about 25% reduction in cache size under similar serving time constraints.