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نتیجه جستجو - ترافیک مخابراتی

تعداد مقالات یافته شده: 4
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1 Optimized Big Data Management across Multi-Cloud Data Centers: Software-Defined Network-Based Analysis
مدیریت داده های بزرگ بهینه شده در سراسر مراکز داده چند ابری: تحلیل مبتنی بر شبکه نرم افزار تعریف شده-2018
With an exponential increase in smart device users, there is an increase in the bulk amount of data generation from various smart devices, which varies with respect to all the essential Vs used to categorize it as big data. Generally, most service providers, including Google, Amazon, Microsoft and so on, have deployed a large number of geographically distributed data centers to process this huge amount of data generated from various smart devices so that users can get quick response time. For this purpose, Hadoop, and SPARK are widely used by these service providers for processing large datasets. However, less emphasis has been given on the underlying infrastructure (the network through which data flows), which is one of the most important components for successful implementation of any designed solution in this environment. In the worst case, due to heavy network traffic with respect to data migrations across different data centers, the underlying network infrastructure may not be able to transfer data packets from source to destination, resulting in performance degradation. Focusing on all these issues, in this article, we propose a novel SDN-based big data management approach with respect to the optimized network resource consumption such as network bandwidth and data storage units. We analyze various components at both the data and control planes that can enhance the optimized big data analytics across multiple cloud data centers. For example, we analyze the performance of the proposed solution using Bloom-filter-based insertion and deletion of an element in the flow table maintained at the OpenFlow controller, which makes most of the decisions for network traffic classification using the rule-and-action-based mechanism. Using the proposed solution, developers can deploy and analyze real-time traffic behavior for the future big data applications in MCE.
Keywords: Big Data,cloud computing, computer centres, software defined networking, telecommunication traffic
مقاله انگلیسی
2 D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large-Scale Mobile Networks
داده های بزرگ D2D : تحویل محتوا بالا به اشتراک گذاری دستگاه به دستگاه بی سیم درشبکه های بزرگ سیار-2018
Recently the topic of how to effectively offload cellular traffic onto device-to-device sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device sharing application that contains 866 million device-to-device sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.
Keywords: Big Data, data privacy, peer-to-peer computing, radiocommunication, telecommunication traffic
مقاله انگلیسی
3 Characterizing Flow, Application, and User Behavior in Mobile Networks: A Framework for Mobile Big Data
مشخص کردن جریان، برنامه، و رفتار کاربر در شبکه های سیار:یک چارچوب برای داده های بزرگ سیار-2018
The recent explosion of data traffic calls for specialized systems to monitor the status of networks. Traditionally, Internet service providers collect and analyze IP flow data as they present an aggregated view of traffic. In the era of mobile big data, new approaches are required to address new challenges regarding the flow characterization in the next generation wireless networks. In this article, we propose a framework for mobile big data, referred to as FMBD, which provides massive data traffic collection, storage, processing, analysis, and management functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of the mobile big data from flow, application, and user behavior, such as high volume, diversity of applications, and spatio-temporal distribution, our proposed FMBD demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of both operators and individuals. Tested by real mobile big data, FMBD has been operational for more than five years, and can be generalized to other environments with massive data traffic or big data.
Keywords: Big Data, Internet, IP networks, mobile computing, mobile radio, telecommunication traffic
مقاله انگلیسی
4 یک رویکرد نرم افزاری برای بهبود بهره وری انرژی مرکز اطلاعاتی محاسبات ابری و افزایش امنیت از طریق کشف بات نت
سال انتشار: 2016 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 16
این مقاله به ارائه ی نتایج آزمایشی مثبت، پیرامون بهره وری و پتانسیل امنیتی یک رویکرد بهینه و جدید برای یک سیستم توزیع شده ی مدیریتی خودمختار (AMDS) در حال اجرا در محیط محاسباتی ابری می پردازد. نتایج به اثبات طراحی نرم افزاری AMDS پرداخته و پتانسیل آن را به عنوان یک کاربرد صنعتی به منظور استفاده در مراکز اطلاعاتی مدرن نشان می دهد. از یک سو، از نقطه نظر عملکرد عملیاتی می توان گفت که این عملکرد ها نشان می دهند که قابلیت AMDS که مربوط به پیکربندی مجدد خود می شود، در حین اجرا رخ می دهند، ازین رو 14 درصد افزایش بهره وری پیرامون طول عمر اولین آزمایش را می توان مشاهده کرد. از سویی دیگر، این عملکرد ها نشان می دهند که میزان تشخیص بسته ی اطلاعاتی مخرب کل(Botnet) 52 درصد بوده و 1 درصد قابل توجه تنها برای 5000 نمونه داده ی شبکه ای به وسیله ی ماژول نرم افزاری Botnet تعبیه شده در داخل AMDS نمایش می یابد. هر دو آزمایش اجرا شده در داخل یک VMW، محیط ابری را به اجرا در می آورند. هر چند به دلیل معماری انتزاعی AMDS ، این معماری پتانسیل این را دارد که با هر سیستم مدیریتی موجود که API را به نمایش می گذارد، ارتباط برقرار کند.
کليدواژگان: محاسبات ابری | امنیت | سخت افزار | ترافیک مخابراتی | الگوریتم های مکاشفه ای | الگوریتم تقریبی
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