[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-133861-en":3,"doc-seo-133861-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},133861,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Reducing IoT Big Data for Efficient Storage and Processing - Edge Storage Optimization","The work addresses efficient management of Internet of Things (IoT) data generated for CDN and cloud storage. It studies the data gathering process that produces big data and introduces a reduction approach that transforms big data into small data without degrading usability or interpretability. The reduction runs at the infrastructure edge using reservoir sampling to generate and maintain synopses. An implementation on the Varnish open source server details key parameters such as synopsis generation frequency and storage behavior.","# Reducing IoT Big Data for Efficient Storage and Processing\n\nEleftheria Katsarou and Stathes HadjiefthymiadesD  \nDepartment of Informatics and Telecommunications,National and Kapodistrian University of Athens,Panepistimioupolis,Athens,Greece  \nContent Distribution Networks,Internet of Things.  \n## Keywords:\n\nAbstract:We focus on the very important problem of managing IoT data.We consider the data gathering process thatyields big data intended for CDN/cloud storage.We aim to reduce big data into small data to efficiently exploitavailable storage without compromising their usability and interpretation.This reduction process is to beperformed at the edge of the infrastructure (IoT edge devices,CDN edge servers)in a computationallyacceptable way.Therefore,we employ reservoir sampling,a method that stochastically samples data andderives synopses that are finally pushed and maintained in the available storage capability.We implementedthe discussed architecture using reverse proxy technologies and in particular the Varnish open source server.We provide details of our implementation and discuss critical parameters like the frequency of synopsisgeneration and CDN/cloud storage  \nproperty of big data is defined by the entry frequencyof data streams in big data systems.The usefulness ofbig data determines its value,as long as its veracity isassured by reliable sources and big data systems.  \n## 1 INTRODUCTION\n\nIn contemporary Internet,Content Delivery Networks(CDN)are the infrastructures intended for optimallydelivering content to users,providing highperformance and availability services.Thearchitecture of such networks has been adapted to thegeographical distribution of servers around the globe.Caching-Replication technology is used to expeditecontent delivery.Nevertheless,the challenges on theWorld Wide Web(WWW)have become even greaterand the content more complex.Nowadays,CDNsneed to cope with multimedia streaming,on-demandvideo,etc.  \nSuch data need to travel through a CDN(or acloud facility),in order to be processed and stored,leading to the required evolution of CDN (further tothe multimedia,dynamic service provision cases).Therefore,new(CDN)features are required to meetthese challenges.Requirements should be highscalability,efficient storage and delivery,andcaching.In the field of scalability demands,whichrefer to IoT technology,integrated architecturesshould be developed.  \nTo cope with these challenges we investigate anarchitecture that turns big data into small data at theedge of the CDN (infrastructure).Our work is basedon solid stochastic sampling techniques like thereservoir sampling.We study the operation of thealgorithm in relation to infinite IoT streams seen /ingested at the CDN.We finally present ourimplementation efforts that port the consideredarchitecture (and associated operational parameters)into the Varnish CDN support server(DYI-do ityourself CDN).  \nMoreover,we live in the age of the Internet ofThings (IoT).The IoT has been promoted as a newtechnology that connects objects,such as sensors,mobile phones,etc.,over the Internet.Smart city,healthcare and transportation are some of the standardservices supported by IoT technology.Such multipledata sources produce data with great heterogeneityvariable speed and quality from different types of datastream,that constitute big data's properties of varietyand variability.Obviously,a huge amount of data,described by the“volume”of big data,is generatedfor delivery,processing,and storage in the context ofIoT infrastructures.Furthermore,the velocity  \nThe paper is structured as follows:In section 2 werefer to the prior work referring to the relative subject.  \n226  \nThe edge computing paradigm is analyzed in section  \n3,where we explain the use of reservoir sampling(algorithm R)in the proposed scheme and we try toquantify the era duration of an event.In section 4 wedescribe a few implementation issues such as the useof Varnish Cache.Section 5 describes the metrics andresults o","cbCaitObvHsuL7cI","https://ap.wps.com/l/cbCaitObvHsuL7cI","pdf",449236,3,1,5,"English","en",105,"# 1 INTRODUCTION\n# 2 RELATED WORK\n# 3 EDGE COMPUTING AND RESERVOIR SAMPLING\n# 4 IMPLEMENTATION ISSUES (VARNISH CACHE)\n# 5 METRICS AND SIMULATION RESULTS\n# 6 CONCLUSION","[{\"question\":\"What problem does the paper aim to solve for IoT data?\",\"answer\":\"It targets efficient management of IoT-generated big data so that storage and processing remain effective while preserving usability and interpretability.\"},{\"question\":\"How does the proposed approach reduce big data size?\",\"answer\":\"It applies reservoir sampling to stochastically sample incoming data streams and derive compact synopses that are pushed and maintained in available storage.\"},{\"question\":\"Where is the reduction performed and why is that important?\",\"answer\":\"The reduction is executed at the edge of the infrastructure (IoT edge devices and CDN edge servers) to remain computationally acceptable while optimizing storage usage.\"}]","Reducing IoT Big Data for Efficient Storage and Processing - Edge Storage Optimization | 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problem does the paper aim to solve for IoT data?","Question",{"text":76,"@type":77},"It targets efficient management of IoT-generated big data so that storage and processing remain effective while preserving usability and interpretability.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed approach reduce big data size?",{"text":81,"@type":77},"It applies reservoir sampling to stochastically sample incoming data streams and derive compact synopses that are pushed and maintained in available storage.",{"name":83,"@type":74,"acceptedAnswer":84},"Where is the reduction performed and why is that important?",{"text":85,"@type":77},"The reduction is executed at the edge of the infrastructure (IoT edge devices and CDN edge servers) to remain computationally acceptable while optimizing storage 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