A machine learning approach for result caching in web search engines

A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement. © 2017 Elsevier Lt

Dağıtım transformatörlerinin metasezgisel algoritmalarla tasarım optimizasyonu

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Dünyadaki pek çok ekonomi, yüksek verimli transformatörlerin kullanımını zorunlu kılan veya teşvik eden enerji verimliliği yönetmelikleri veya teşvik programları kabul etmiştir. Öte yandan, transformatör verimliliğindeki artışlar, transformatör ağırlık ve boyutunda bazen % 50 hatta daha fazla bir artışı gerektirmektedir. Transformatör endüstrisi bu nedenle gerçekten en iyi tasarımları geliştirme uğraşısı ile karşı karşıyadır. Transformatör tasarım optimizasyonu (TDO) problemi, karmaşık ve süreksiz amaç fonksiyonlu ve kısıtlı karma-tamsayılı bir doğrusal olmayan programlama problemidir. TDO'nun amacı, ulusal ve/veya ulusal standartlar ve müşteri şartnameleri uyarıca, mevcut malzemeleri ekonomik olarak kullanarak daha düşük boyut, ağırlık ve maliyet ve daha yüksek işletme performansı elde etmek üzere transformatörün tüm bileşenlerinin niteliklerinin detaylı olarak hesaplanmasıdır. Bu çalışmada TDO probleminin çözümü için beş modern metasezgisel optimizasyon algoritması uygulamasının ayrıntılı karşılaştırmalı analizi üç test vakası üzerinde gösterilmiş ve iki algoritma önerilmiştir önerilen bu algoritmaların, rassal özelliklerine rağmen, garanti edilmiş küresel yakınsama özelliklerine sahip oldukları doğrulanmıştır. Algoritmaların karşılaştırılması için pragmatik bir kıyaslama yöntemi geliştirilmiştir. Literatürde sunulan TDO yöntemleri nadiren üretimde doğrudan uygulanabilir çözümler üretir. tasarım mühendisinin genellikle teorik çözümü pratik olarak uygulanabilir bir hale dönüştürmek için ek çaba harcaması gerekir. Bu problem bu çalışmada ele alınmış ve piyasada mevcut veya üretime uygun boyutlara sahip çözümler üreten bir ayrık transformatör tasarım optimizasyon yöntemi önerilmiştir Ayrıca, amaç fonksiyonu ve kısıt hesaplamalarını azaltmak için basit bir yöntem önerilmiştir. Yöntem, önbellekleme tekniği kullanılarak arama işlemi sırasında yinelenen tasarım vektörleri için hesaplamaların atlanması esasına dayanmaktadır. Performans testleri, teorik TDO için Rekabetçi-Uyarlamalı Diferansiyel Gelişim ve Guguk Kuşu Arama, Pratik TDO için de Guguk Kuşu Arama ve Çiçek Tozlaşma algoritmaları kullanıldığında küresel optimum ve ona çok yakın sonuçlar elde edildiğini göstermiştir.Many economies in the world have adopted energy-efficiency requirements or incentive programs mandating or promoting the use of energy-efficient transformers. On the other hand, increases in transformer efficiency are subject to increases in transformer weight and size, sometimes as much as 50% or even more. The transformer manufacturing industry is therefore faced with the challenge to develop truly optimum designs. Transformer design optimization (TDO) is a mixed-integer nonlinear programming problem having complex and discontinuous objective function and constraints, with the objective of detailed calculation of the characteristics of a transformer based on national and/or international standards and transformer user and two algorithms are proposed, for which it has been verified that they possess guaranteed global convergence properties in spite of their inherent stochastic nature. A pragmatic benchmarking scheme is used for comparison of the algorithms. Transformer design optimization methods presented in the literature rarely yield solutions directly applicable in productionrequirements, using available materials and manufacturing processes, to minimize manufacturing cost or total owning cost, while maximizing operating performance. Detailed comparative analysis of the application of five modern metaheuristic optimization algorithms for the solution of TDO problem are carried out in this study, demonstrated on three test cases the design engineer usually needs to convert the theoretical solution to a practical one. This problem is addressed in this study, and a discrete transformer design optimization method is proposed which yields solutions with commercially available or productionally feasible dimensions Furthermore, a simple method is proposed to reduce the number of objective function and constraint calculations. The method is based on skipping calculations for design vectors recurring during the search process, by the use of caching technique Performance tests showed that global or near-global optimum solutions can be obtained with b6e6rl and CS for TDO, and CS and FPA algorithms for DTDO

Modeling and analyzing device-to-device content distribution in cellular networks

Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized proximity-based communication. To obtain caching gains through D2D, mobile nodes must possess content that other mobiles want. Thus, devising intelligent cache placement techniques are essential for D2D. The goal of this dissertation is to provide randomized spatial models for content distribution in cellular networks by capturing the locality of the content, and additionally, to provide dynamic content placement algorithms exploiting the node configurations. First, a randomized content caching scheme for D2D networks in the cellular context is proposed. Modeling the locations of the devices as a homogeneous Poisson Point Process (PPP), the probability of successful content delivery in the presence of interference and noise is derived. With some idealized modeling aspects, i.e., given that (i) only a fraction of users to be randomly scheduled at a given time, and (ii) the request distribution does not change over time, it has been shown that the performance of caching can be optimized by smoothing out the request distribution, where the smoothness of the caching distribution is mainly determined by the path loss exponent, and holds under Rayleigh, Ricean and Nakagami fading models. Second, to take the randomized caching model a step further, a spatially correlated content caching scenario is contemplated. Inspired by the Matérn hard-core point process of type II, which is a first-order pairwise interaction model, D2D nodes caching the same file are never closer to each other than the exclusion radius. The exclusion radius plays the role of a substitute for caching probability. The optimal exclusion radii that maximize the hit probability can be determined by using the request distribution and cache memory size. Unlike independent content placement, which is oblivious to the geographic locations of the nodes, the new strategy can be effective for proximity-based communication even when the cache size is small. Third, an auction-aided Matérn carrier sense multiple access (CSMA) policy that considers the joint analysis of scheduling and caching is studied. The auction scheme is distributed. Given a cache configuration, i.e., the set of cached files in each user at a given snapshot, each D2D receiver determines the value of its request, by bidding on the set of potential transmitters in its communication range. The values of the receiver bids are reported to the potential transmitter, which computes the cumulated sum of these variables taken on all users in its cell. The potential transmitter then reports the value of the bid sum to other potential transmitters in its contention range. Given the accumulated bids of all potential transmitters, the contention range and the medium access probability, a fraction of the potential transmitters are jointly scheduled, determined by the auction policy, in order to optimize the throughput. Later, a Gibbs sampling-based cache update strategy is proposed to iteratively optimize the hit rate by taking the scheduling scheme into account. In this dissertation, a variety of distributed algorithms for D2D content caching are proposed. Our results indicate that the geographic locality and the network parameters have a significant role in determining and optimizing the placement strategy. Exploiting the user interactions and spatial diversity, and incentivizing cooperation among D2D nodes are crucial in realizing the full potential of caching. Furthermore, from a network point of view, the scheduling and the caching phases are closely linked to each other. Hence, understanding the interaction between these two phases helps develop novel dynamic caching strategies capturing the temporal and spatial locality of the demand.Electrical and Computer Engineerin