Evaluation, Analysis and adaptation of web prefetching techniques in current web

Abstract This dissertation is focused on the study of the prefetching technique applied to the World Wide Web. This technique lies in processing (e.g., downloading) a Web request before the user actually makes it. By doing so, the waiting time perceived by the user can be reduced, which is the main goal of the Web prefetching techniques. The study of the state of the art about Web prefetching showed the heterogeneity that exists in its performance evaluation. This heterogeneity is mainly focused on four issues: i) there was no open framework to simulate and evaluate the already proposed prefetching techniques; ii) no uniform selection of the performance indexes to be maximized, or even their definition; iii) no comparative studies of prediction algorithms taking into account the costs and benefits of web prefetching at the same time; and iv) the evaluation of techniques under very different or few significant workloads. During the research work, we have contributed to homogenizing the evaluation of prefetching performance by developing an open simulation framework that reproduces in detail all the aspects that impact on prefetching performance. In addition, prefetching performance metrics have been analyzed in order to clarify their definition and detect the most meaningful from the user's point of view. We also proposed an evaluation methodology to consider the cost and the benefit of prefetching at the same time. Finally, the importance of using current workloads to evaluate prefetching techniques has been highlighted; otherwise wrong conclusions could be achieved. The potential benefits of each web prefetching architecture were analyzed, finding that collaborative predictors could reduce almost all the latency perceived by users. The first step to develop a collaborative predictor is to make predictions at the server, so this thesis is focused on an architecture with a server-located predictor. The environment conditions that can be found in the web are alsDoménech I De Soria, J. (2007). Evaluation, Analysis and adaptation of web prefetching techniques in current web [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1841Palanci

Using Intelligent Prefetching to Reduce the Energy Consumption of a Large-scale Storage System

Many high performance large-scale storage systems will experience significant workload increases as their user base and content availability grow over time. The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) center hosts one such system that has recently undergone a period of rapid growth as its user population grew nearly 400% in just about three years. When administrators of these massive storage systems face the challenge of meeting the demands of an ever increasing number of requests, the easiest solution is to integrate more advanced hardware to existing systems. However, additional investment in hardware may significantly increase the system cost as well as daily power consumption. In this paper, we present evidence that well-selected software level optimization is capable of achieving comparable levels of performance without the cost and power consumption overhead caused by physically expanding the system. Specifically, we develop intelligent prefetching algorithms that are suitable for the unique workloads and user behaviors of the world\u27s largest satellite images distribution system managed by USGS EROS. Our experimental results, derived from real-world traces with over five million requests sent by users around the globe, show that the EROS hybrid storage system could maintain the same performance with over 30% of energy savings by utilizing our proposed prefetching algorithms, compared to the alternative solution of doubling the size of the current FTP server farm

Key factors in web latency savings in an experimental prefetching system

Although Internet service providers and communications companies are continuously offering higher and higher bandwidths, users still complain about the high latency they perceive when downloading pages from the web. Therefore, latency can be considered as the main web performance metric from the user's point of view. Many studies have demonstrated that web prefetching can be an interesting technique to reduce such latency at the expense of slightly increasing the network traffic. In this context, this paper presents an empirical study to investigate the maximum benefits that web users can expect from prefetching techniques in the current web. Unlike previous theoretical studies, this work considers a realistic prefetching architecture using real traces. In this way, the influence of real imple- mentation constraints are considered and analyzed. The results obtained show that web prefetching could improve page latency up to 52% in the studied traces. ©Springer Science+Business Media, LLC 2011De La Ossa Perez, BA.; Sahuquillo Borrás, J.; Pont Sanjuan, A.; Gil Salinas, JA. (2012). Key factors in web latency savings in an experimental prefetching system. Journal of Intelligent Information Systems. 39(1):187-207. doi:10.1007/s10844-011-0188-xS187207391Balamash, A., Krunz, M., & Nain, P. (2007). Performance analysis of a client-side caching/prefetching system for web traffic. Computer Networks, 51(13), 3673–3692.Bestavros, A. (1995). Using speculation to reduce server load and service time on the www. In Proc. of the 4th ACM international conference on information and knowledge management. Baltimore, USA.Bestavros, A., & Cunha, C. (1996). Server-initiated document dissemination for the WWW. In IEEE data engineering bulletin. [Online]. Available: http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.266 . 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Integrating web caching and web prefetching in client-side proxies. IEEE Transactions on Parallel and Distributed Systems, 16(5), 444–455

funcX: A Federated Function Serving Fabric for Science

Exploding data volumes and velocities, new computational methods and platforms, and ubiquitous connectivity demand new approaches to computation in the sciences. These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e.g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available. They also require new design approaches in which monolithic applications can be decomposed into smaller components, that may in turn be executed separately and on the most suitable resources. To address these needs we present funcX---a distributed function as a service (FaaS) platform that enables flexible, scalable, and high performance remote function execution. funcX's endpoint software can transform existing clouds, clusters, and supercomputers into function serving systems, while funcX's cloud-hosted service provides transparent, secure, and reliable function execution across a federated ecosystem of endpoints. We motivate the need for funcX with several scientific case studies, present our prototype design and implementation, show optimizations that deliver throughput in excess of 1 million functions per second, and demonstrate, via experiments on two supercomputers, that funcX can scale to more than more than 130000 concurrent workers.Comment: Accepted to ACM Symposium on High-Performance Parallel and Distributed Computing (HPDC 2020). arXiv admin note: substantial text overlap with arXiv:1908.0490

Optimizing Hypervideo Navigation Using a Markov Decision Process Approach

Interaction with hypermedia documents is a required feature for new sophisticated yet flexible multimedia applications. This paper presents an innovative adaptive technique to stream hypervideo that takes into account user behaviour. The objective is to optimize hypervideo prefetching in order to reduce the latency caused by the network. This technique is based on a model provided by a Markov Decision Process approach. The problem is solved using two methods: classical stochastic dynamic programming algorithms and reinforcement learning. Experimental results under stochastic network conditions are very promising