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1st International Conference on AIML-Applications for Engineering and Technology, ICAET 2025

作者： Jadhav, Rohini Jadhav, Veena Pawar, Prajakta Kannaujia, Abhishek Kumar, Abhishek Gohil, Jayesh College of Engineering Department of Information Technology Pune India College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Lavale Pune India Veritas Principal Software Engineering India

ISBN: (纸本)9798350355611

The proactive caching technique known as 'predictive caching' attempts to improve file system performance by anticipating and pre-fetching data that is likely to be requested in the future. Conventional caching strategies, such LRU (Least Recently Used) and LFU (Least Frequently Used), base their decision on which material to keep in the cache on historical access patterns. Although these methods work well in many situations, they could not completely take advantage of the temporal and geographical proximity of file system access patterns, which could result in less-than-ideal cache use. Utilizing machine learning algorithms, statistical analysis, or heuristics, predictive caching makes predictions about future access patterns based on past data, system status, file properties, or user activity. So we propose a predictive caching technique which reduce access latency and boost system performance by pre-fetching and storing pertinent data ahead of time in anticipation of incoming data accesses. © 2025 IEEE.

关键词： Dynamic Data File Access Pattern predictive caching Random Forest Classifier

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predictive caching via Learning Temporal Distribution of Content Requests

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IEEE COMMUNICATIONS LETTERS 2019年第12期23卷 2335-2339页

作者： Song, Hoon-Geun Chae, Seong Ho Shin, Won-Yong Jeon, Sang-Woon Hanyang Univ Dept Elect Elect Control & Instrumentat Engn Ansan 15588 South Korea Korea Polytech Univ Dept Elect Engn Shihung 15073 South Korea Yonsei Univ Dept Computat Sci & Engn Seoul 03722 South Korea Hanyang Univ Dept Mil Informat Engn Ansan 15588 South Korea

In this letter, dynamic content placement of a local cache server that can store a subset of content objects in its cache memory is studied. Contrary to the conventional model in which content placement is optimized based on the time-invariant popularity distribution of content objects, we consider a general time-varying popularity distribution and such a probabilistic distribution is unknown for content placement. A novel learning method for predicting the temporal distribution of future content requests is presented, which utilizes the request histories of content objects whose lifespans are expired. Then we introduce the so-called predictive caching strategy in which content placement is periodically updated based on the estimated future content requests for each update period. Numerical evaluation is performed using real-world datasets reflecting the inherent nature of temporal dynamics, demonstrating that the proposed predictive caching outperforms the conventional online caching strategies.

关键词： Servers Microcell networks Wireless communication Libraries Estimation Cache memory Base stations Cache networks online learning predictive caching small cell networks time-varying popularity distribution

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predictive caching in Non-Stationary Environments: A Time Series Prediction and Survival Analysis Approach

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2024年 5卷 5377-5391页

作者： Rostampoor, Javane Adve, Raviraj S. Afana, Ali Ahmed, Yahia A. Eldemerdash Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 1A1 Canada Ericsson Canada Ottawa ON K2K 2V6 Canada

This paper introduces an innovative predictive caching strategy tailored to a real-world dataset, specifically the Facebook video dataset. Making caching decisions for the dataset is challenging due to its dynamic nature, where users' content requests vary over time without fitting into any known models. Traditional caching strategies, which often rely on a constant pool of files, do not suit this dataset as content is requested by users, and then its popularity fades over time;furthermore, the list of available content changes. We propose a two-stage predictive caching strategy. Initially, it forecasts the number of user requests using content features and historical request data, achieved through training a long short-term memory (LSTM) network. Then, we employ our proposed extended Cox proportional hazard (E-CPH) model to predict the survival probability of content. This facilitates proactive content caching. caching new content is made possible by the timely eviction of content unlikely to be requested again. To incorporate the predicted content popularity and its life cycle into the caching decision, we introduce a partially observable Markov decision process (POMDP)-based caching strategy. Here, the survival probability of content contributes to the belief state of the associated content which leads to our believed predicted reward - a cache hit. The caching algorithm then stores the files based on their predicted believed reward taking into account both the popularity and survival probability predictions. Simulation results validate the efficacy of our proposed predictive caching method in enhancing the cache hit rate compared to conventional recurrent neural network (RNN)-based caching and policy-based caching approaches, such as least frequently used caching and its variants.

关键词： predictive models Long short term memory Time series analysis Forecasting Uncertainty Optimization Correlation predictive caching LSTM networks Cox proportional hazard model POMDP cache hit maximization

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predictive caching for Video on Demand CDNs

Predictive Caching for Video on Demand CDNs

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54th Annual IEEE Global Telecommunications Conference (GLOBECOM)

作者： Carbunar, Bogdan Pearce, Michael Vasudevan, Venu Needham, Michael Florida Int Univ Comp & Informat Sci Miami FL 33199 USA Motorola Solut Schaumburg IL 60661 USA Motorola Mobil Appl Res Ctr Schaumburg IL 60661 USA

ISBN: (纸本)9781424492688

Video on Demand (VoD) services provide a wide range of content options and enable subscribers to select, retrieve and locally consume desired content. In this work we propose caching solutions to improve the scalability of the content distribution networks (CDNs) of existing VoD architectures. We first investigate metrics relevant to this caching framework and subsequently define goals that should be satisfied by an efficient solution. We propose novel techniques for predicting future values of metrics of interest. We use our prediction mechanisms to define the cost imposed on the system (network and caches) by items that are not cached. We use this cost to develop novel caching and static placement strategies. We validate our solutions using log data collected from Motorola equipment from several Comcast VoD deployments.

关键词： Content distribution networks Equations IEEE Communications Society Measurement Motorola equipment Prediction algorithms Servers Streaming media VoD content distribution networks predictive caching scalability static placement strategies video on demand video on demand CDN

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On Effects of Applying predictive caching for State Machines 16

On Effects of Applying Predictive Caching for State Machines

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16th International Conference on Software Technologies (ICSOFT)

作者： Akyuz, James Ryan Perry Ovatman, Tolga Istanbul Tech Univ Dept Comp Engn TR-34469 Istanbul Turkey

ISBN: (纸本)9789897585234

State machines are frequently used in software development, in many different contexts, ranging from modeling control software to distributed applications that operate in cloud environments. We have implemented and experimented on basic execution path-based predictive caching approaches for state machines to show that due to the limited number of paths that can be taken during a state machine run better pre-fetching can be achieved for state machine caches. We have applied our predictive approaches over least frequently used (LFU) and least recently used (LRU) replacement on two different state machine instances run with real-world execution traces.

关键词： State Machines predictive caching Execution Path Prediction

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A Novel predictive-Collaborative-Replacement (PCR) Intelligent caching Scheme for Multi-Access Edge Computing

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IEEE ACCESS 2021年 9卷 37103-37115页

作者： Ugwuanyi, Emeka E. Iqbal, Muddesar Dagiuklas, Tasos London South Bank Univ Div Comp Sci & Informat London SE1 0AA England

Multi-Access Mobile Edge Computing (MEC) is proclaimed as a key technology for reducing service processing delays in 5G networks. One of the use cases in MEC is content caching as a way of bringing resources closer to the end-users. Consequently, both latency and QoE are reduced. However, MEC has a limited storage space compared to the cloud. Therefore, there is a need to effectively manage the cache storage. This article proposes and evaluates a novel scheme (PCR) that combines proactive prediction, collaboration among MECs and replacement algorithm to manage content caching in MEC. Results show that the proposed replacement scheme outperforms conventional baseline content caching algorithms LFU, LRU, MQ, FBR, LFRU. This has been validated with experimental results using a real dataset (MovieLens20M dataset) and comparison with contemporary Long Short Term Memory (LSTM) based caching algorithm.

关键词： Time complexity Prediction algorithms Time-frequency analysis Solid modeling Delays Training Quality of experience Edge intelligence intelligent caching MEC predictive caching LSTM

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ChronoCache: predictive and Adaptive Mid-Tier Query Result caching 20

ChronoCache: Predictive and Adaptive Mid-Tier Query Result C...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Glasbergen, Brad Langendoen, Kyle Abebe, Michael Daudjee, Khuzaima Univ Waterloo Cheriton Sch Comp Sci Waterloo ON Canada

ISBN: (纸本)9781450367356

The performance of data-driven, web-scale client applications is sensitive to access latency. To address this concern, enterprises strive to cache data on edge nodes that are closer to users, thereby avoiding expensive round-trips to remote data centers. However, these geo-distributed approaches are limited to caching static data. In this paper we present ChronoCache, a mid-tier caching system that exploits the presence of geo-distributed edge nodes to cache database query results closer to users. ChronoCache transparently learns and leverages client application access patterns to predictively combine query requests and cache their results ahead of time, thereby reducing costly round-trips to the remote database. We show that ChronoCache reduces query response times by up to 2/3 over prior approaches on multiple representative benchmark workloads.

关键词： predictive caching prefetching distributed data management

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A novel content classification scheme for web caches

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EVOLVING SYSTEMS 2011年第2期2卷 101-118页

作者： Sajeev, G. P. Sebastian, M. P. Govt Engn Coll Kozhikode WestHill Kozhikode 673005 Kerala India Indian Inst Management Kozhikode IIMK Calicut 673570 Kerala India

Web caches are useful in reducing the user perceived latencies and web traffic congestion. Multi-level classification of web objects in caching is relatively an unexplored area. This paper proposes a novel classification scheme for web cache objects which utilizes a multinomial logistic regression (MLR) technique. The MLR model is trained to classify web objects using the information extracted from web logs. We introduce a novel grading parameter worthiness as a key for the object classification. Simulations are carried out with the datasets generated from real world trace files using the classifier in Least Recently Used-Class Based (LRU-C) and Least Recently Used-Multilevel Classes (LRU-M) cache models. Test results confirm that the proposed model has good online learning and prediction capability and suggest that the proposed approach is applicable to adaptive caching.

关键词： Web caching predictive caching Adaptive caching Content classification Performance Logistic regression

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Dynamic Prefetching of Data Tiles for Interactive Visualization 16

Dynamic Prefetching of Data Tiles for Interactive Visualizat...

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ACM SIGMOD International Conference on Management of Data

作者： Battle, Leilani Chang, Remco Stonebraker, Michael MIT 77 Massachusetts Ave Cambridge MA 02139 USA Tufts Univ Medford MA 02155 USA

ISBN: (纸本)9781450335317

In this paper, we present ForeCache, a general-purpose tool for exploratory browsing of large datasets. ForeCache utilizes a client server architecture, where the user interacts with a lightweight client side interface to browse datasets, and the data to be browsed is retrieved from a DBMS running on a back-end server. We assume a detail-on-demand browsing paradigm, and optimize the back-end support for this paradigm by inserting a separate middleware layer in front of the DBMS. To improve response times, the middleware layer fetches data ahead of the user as she explores a dataset. We consider two different mechanisms for prefetching: (a) learning what to fetch from the user's recent movements, and (b) using data characteristics (e.g., histograms) to find data similar to what the user has viewed in the past. We incorporate these mechanisms into a single prediction engine that adjusts its prediction strategies over time, based on changes in the user's behavior. We evaluated our prediction engine with a user study, and found that our dynamic prefetching strategy provides: (1) significant improvements in overall latency when compared with non-prefetching systems (430% improvement);and (2) substantial improvements in both prediction accuracy (25% improvement) and latency (88% improvement) relative to existing prefetching techniques.

关键词： predictive caching visual exploration data exploration array browsing

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Cache & Carry: Seamless content consumption in an intermittently connected world

Cache & Carry: Seamless content consumption in an intermitte...

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4th IEEE Consumer Communications and Networking Conference

作者： Pearce, M. Chaudhri, R. Almaula, J. Vasudevan, V. Motorola Labs Applicat Res Ctr Schaumburg IL 60196 USA

ISBN: (纸本)9781424406661

Even as mobile devices as well as wireless networks become increasingly capable, newer content-intensive application place increasing demands on the resources of these devices. Some of these applications make conflicting resource demands making it difficult for device platforms to guarantee acceptable user experience. Thus, a resource aware middleware is needed that mobile applications can leverage to manage their content. In this paper we discuss Cache & Carry, a framework for mobile devices that intelligently manages scarce resources on the mobile device across multiple applications. The framework takes into consideration changing mobile device characteristics such as available battery and storage as well as changing network characteristics such as availability of multiple networks, their perceived cost, their available bandwidths etc., to provide an optimal end user experience.

关键词： pervasive computing and contextual systems predictive caching thin client support user mobility and data migration

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