Cooperative announcement-based caching for video-on-demand streaming

Recently, video-on-demand (VoD) streaming services like Netflix and Hulu have gained a lot of popularity. This has led to a strong increase in bandwidth capacity requirements in the network. To reduce this network load, the design of appropriate caching strategies is of utmost importance. Based on the fact that, typically, a video stream is temporally segmented into smaller chunks that can be accessed and decoded independently, cache replacement strategies have been developed that take advantage of this temporal structure in the video. In this paper, two caching strategies are proposed that additionally take advantage of the phenomenon of binge watching, where users stream multiple consecutive episodes of the same series, reported by recent user behavior studies to become the everyday behavior. Taking into account this information allows us to predict future segment requests, even before the video playout has started. Two strategies are proposed, both with a different level of coordination between the caches in the network. Using a VoD request trace based on binge watching user characteristics, the presented algorithms have been thoroughly evaluated in multiple network topologies with different characteristics, showing their general applicability. It was shown that in a realistic scenario, the proposed election-based caching strategy can outperform the state-of-the-art by 20% in terms of cache hit ratio while using 4% less network bandwidth

On Resource Pooling and Separation for LRU Caching

Caching systems using the Least Recently Used (LRU) principle have now become ubiquitous. A fundamental question for these systems is whether the cache space should be pooled together or divided to serve multiple flows of data item requests in order to minimize the miss probabilities. In this paper, we show that there is no straight yes or no answer to this question, depending on complex combinations of critical factors, including, e.g., request rates, overlapped data items across different request flows, data item popularities and their sizes. Specifically, we characterize the asymptotic miss probabilities for multiple competing request flows under resource pooling and separation for LRU caching when the cache size is large. Analytically, we show that it is asymptotically optimal to jointly serve multiple flows if their data item sizes and popularity distributions are similar and their arrival rates do not differ significantly; the self-organizing property of LRU caching automatically optimizes the resource allocation among them asymptotically. Otherwise, separating these flows could be better, e.g., when data sizes vary significantly. We also quantify critical points beyond which resource pooling is better than separation for each of the flows when the overlapped data items exceed certain levels. Technically, we generalize existing results on the asymptotic miss probability of LRU caching for a broad class of heavy-tailed distributions and extend them to multiple competing flows with varying data item sizes, which also validates the Che approximation under certain conditions. These results provide new insights on improving the performance of caching systems

Cooperative Cashing? An Economic Analysis of Document Duplication in Cooperative Web Caching

Cooperative caching is a popular mechanism to allow an array of distributed caches to cooperate and serve each others\u27 Web requests. Controlling duplication of documents across cooperating caches is a challenging problem faced by cache managers. In this paper, we study the economics of document duplication in strategic and nonstrategic settings. We have three primary findings. First, we find that the optimum level of duplication at a cache is nondecreasing in intercache latency, cache size, and extent of request locality. Second, in situations in which cache peering spans organizations, we find that the interaction between caches is a game of strategic substitutes wherein a cache employs lesser resources towards eliminating duplicate documents when the other caches employs more resources towards eliminating duplicate documents at that cache. Thus, a significant challenge will be to simultaneously induce multiple caches to contribute more resources towards reducing duplicate documents in the system. Finally, centralized decision making, which as expected provides improvements in average latency over a decentralized setup, can entail highly asymmetric duplication levels at the caches. This in turn can benefit one set of users at the expense of the other, and thus will be challenging to implement

Web Proxy Cache Replacement Policies Using Decision Tree (DT) Machine Learning Technique for Enhanced Performance of Web Proxy

Web cache is a mechanism for the temporary storage (caching) of web documents, such as HTML pages and images, to reduce bandwidth usage, server load, and perceived lag. A web cache stores the copies of documents passing through it and any subsequent requests may be satisfied from the cache if certain conditions are met. In this paper, Decision Tree (DT ) a machine learning technique has been used to increase the performance of traditional Web proxy caching policies such as SIZE, and Hybrid. Decision Tree (DT) is used and integrated with traditional Web proxy caching techniques to form better caching approaches known as DT - SIZE and DT - Hybrid. The proposed approaches are evaluated by trace - driven simulation and compared with traditional Web proxy caching techniques. Experimental results have revealed that the proposed DT - SIZE and DT - Hybrid significantly increased Pure Hit - Ratio, Byte Hit - Ratio and reduced the latency when compared with SIZE and Hybrid

On the Intrinsic Locality Properties of Web Reference Streams

There has been considerable work done in the study of Web reference streams: sequences of requests for Web objects. In particular, many studies have looked at the locality properties of such streams, because of the impact of locality on the design and performance of caching and prefetching systems. However, a general framework for understanding why reference streams exhibit given locality properties has not yet emerged. In this work we take a first step in this direction, based on viewing the Web as a set of reference streams that are transformed by Web components (clients, servers, and intermediaries). We propose a graph-based framework for describing this collection of streams and components. We identify three basic stream transformations that occur at nodes of the graph: aggregation, disaggregation and filtering, and we show how these transformations can be used to abstract the effects of different Web components on their associated reference streams. This view allows a structured approach to the analysis of why reference streams show given properties at different points in the Web. Applying this approach to the study of locality requires good metrics for locality. These metrics must meet three criteria: 1) they must accurately capture temporal locality; 2) they must be independent of trace artifacts such as trace length; and 3) they must not involve manual procedures or model-based assumptions. We describe two metrics meeting these criteria that each capture a different kind of temporal locality in reference streams. The popularity component of temporal locality is captured by entropy, while the correlation component is captured by interreference coefficient of variation. We argue that these metrics are more natural and more useful than previously proposed metrics for temporal locality. We use this framework to analyze a diverse set of Web reference traces. We find that this framework can shed light on how and why locality properties vary across different locations in the Web topology. For example, we find that filtering and aggregation have opposing effects on the popularity component of the temporal locality, which helps to explain why multilevel caching can be effective in the Web. Furthermore, we find that all transformations tend to diminish the correlation component of temporal locality, which has implications for the utility of different cache replacement policies at different points in the Web.National Science Foundation (ANI-9986397, ANI-0095988); CNPq-Brazi

Study and optimization of the memory management in Memcached

Over the years the Internet has become more popular than ever and web applications like Facebook and Twitter are gaining more users. This results in generation of more and more data by the users which has to be efficiently managed, because access speed is an important factor nowadays, a user will not wait no more than three seconds for a web page to load before abandoning the site. In-memory key-value stores like Memcached and Redis are used to speed up web applications by speeding up access to the data by decreasing the number of accesses to the slower data storage’s. The first implementation of Memcached, in the LiveJournal’s website, showed that by using 28 instances of Memcached on ten unique hosts, caching the most popular 30GB of data can achieve a hit rate around 92%, reducing the number of accesses to the database and reducing the response time considerably. Not all objects in cache take the same time to recompute, so this research is going to study and present a new cost aware memory management that is easy to integrate in a key-value store, with this approach being implemented in Memcached. The new memory management and cache will give some priority to key-value pairs that take longer to be recomputed. Instead of replacing Memcached’s replacement structure and its policy, we simply add a new segment in each structure that is capable of storing the more costly key-value pairs. Apart from this new segment in each replacement structure, we created a new dynamic cost-aware rebalancing policy in Memcached, giving more memory to store more costly key-value pairs. With the implementations of our approaches, we were able to offer a prototype that can be used to research the cost on the caching systems performance. In addition, we were able to improve in certain scenarios the access latency of the user and the total recomputation cost of the key-value stored in the system

Check before storing: what is the performance price of content integrity verification in LRU caching?

In some network and application scenarios, it is useful to cache content in network nodes on the fly, at line rate. Resilience of in-network caches can be improved by guaranteeing that all content therein stored is valid. Digital signatures could be indeed used to verify content integrity and provenance. However, their operation may be much slower than the line rate, thus limiting caching of cryptographically verified objects to a small subset of the forwarded ones. How this affects caching performance? To answer such a question, we devise a simple analytical approach which permits to assess performance of an LRU caching strategy storing a randomly sampled subset of requests. A key feature of our model is the ability to handle traffic beyond the traditional Independent Reference Model, thus permitting us to understand how performance vary in different temporal locality conditions. Results, also verified on real world traces, show that content integrity verification does not necessarily bring about a performance penalty; rather, in some specific (but practical) conditions, performance may even improve