Yang Zhang, Xing Yang, Yuan Xu et al. · 2024
The load balancing algorithm of streaming media server is facing unique challenges. Different from the traditional Web server, the streaming media server needs to consider the characteristics of real-time, bandwidth occupation and multimedia data transmission. Therefore, the traditional load balancing algorithm is not necessarily suitable for streaming media servers, and needs special design and research. Aiming at the network requests of streaming media server clusters, a multi-dimensional matrix evaluation method based on streaming media characteristics is proposed, the genetic algorithm is improved, and it is applied to the dynamic load balancing algorithm scheduling scenario, and analyzed and simulated. Experimental results show that the algorithm can effectively reduce the average execution time of all requests, speed up the average response time and improve throughput.
Cited by 0
Jianzong Chen, Hanlu Li, Qing Xie et al. · Lecture notes in computer science · 2019
Cited by 3
Armin Eftekhari, Raphael Hauser, Andreas Grammenos · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2019
This paper introduces Memory-limited Online Subspace Estimation Scheme (MOSES) for both estimating the principal components of streaming data and reducing its dimension. More specifically, in various applications such as sensor networks, the data vectors are presented sequentially to a user who has limited storage and processing time available. Applied to such problems, MOSES can provide a running estimate of leading principal components of the data that has arrived so far and also reduce its dimension. MOSES generalises the popular incremental Singular Vale Decomposition (iSVD) to handle thin blocks of data, rather than just vectors. This minor generalisation in part allows us to complement MOSES with a comprehensive statistical analysis, thus providing the first theoretically-sound variant of iSVD, which has been lacking despite the empirical success of this method. This generalisation also enables us to concretely interpret MOSES as an approximate solver for the underlying non-convex optimisation program. We find that MOSES consistently surpasses the state of the art in our numerical experiments with both synthetic and real-world datasets, while being computationally inexpensive.
Cited by 13Open access
Association for Artificial Intelligence 2026, Chao Chen, Longkun Guo et al. · Underline Science Inc. · 2026
Many real-world applications pose challenges in incorporating fairness constraints into the $k$-center clustering problem, where the dataset consists of $m$ demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper addresses the problem in a streaming setting, where data points arrive one by one sequentially in a continuous stream. Leveraging a structure called the $\lambda$-independent center set, we propose a one-pass streaming algorithm that first computes a reserved set of points during the streaming process. Then, for the post-streaming process, we propose an approach for selecting centers from the reserved point set by analyzing all three possible cases, transforming the most complicated one into a specially constrained vertex cover problem in an auxiliary graph. Our algorithm achieves a tight approximation ratio of 5 while consuming $O(k\log n)$ memory. It can also be readily adapted to solve the offline fair $k$-center problem, achieving a 3-approximation ratio that matches the current state of the art. Furthermore, we extend our approach to a semi-structured data stream, where data points from each group arrive in batches. In this setting, we present a 3-approximation algorithm for $m = 2$ and a 4-approximation algorithm for general $m$. Lastly, we conduct extensive experiments to evaluate the performance of our approaches, demonstrating that they outperform existing baselines in both clustering cost and runtime efficiency.
Cited by 0Open access
Andreas Oslandsbotn, Željko Kereta, Valeriya Naumova et al. · Applied Mathematics and Computation · 2022
Cited by 1Open access
V. Friese · EPJ Web of Conferences · 2019
In position-sensitive detectors with segmented readout (pixels or strips), charged particles activate in general several adjacent read-out channels. The first step in the reconstruction of the hit position is thus to identify clusters of active channels associated to one particle crossing the detector. In conventionally triggered systems, where the association of raw data to events is given by a hardware trigger, this is an easy-to-solve problem. It, however, becomes more involved in untriggered, free-streaming read-out systems like the one employed by the CBM experiment. Here, the time coordinate of the single-channel measurement must be taken into account to decider whether neighbouring active channels belong to a cluster. A simple extension of well-known cluster finding algorithms is not satisfactory because of involving increasing combinatorics, which are prohibitive for reconstruction in real-time. In this article, a cluster-finding solution for the Silicon Tracking System of the CBM experiment is presented which avoids any combinatorics or loops over detector channels. Its execution time is thus independent on the size of the data packages (time slices) delivered by the data acquisition, making it suitable for being used in online reconstruction.
Cited by 5Open access
Umesh Kumar, Rohit Pathak, Arun Kumar · 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC) · 2020
In the present era, the transfer of information has increased using the internet, which cannot be reliable because any unauthorized user can tamper the data or obtain the unintended information, especially in healthcare. With the rise in the number of patient's health record, there's an increase in the number of data breaches. Healthcare data is something which sometimes can't be shared with unauthorized people because it stores lots of important personal and sensitive data. However, technologies such as cloud storage stored at any remote location (cloud) which is vulnerable to many cyber-attacks pose an adverse impression on the secrecy and safety of patient's healthcare records and require lots of security for protecting the medical information. So, protecting the confidentiality of the patient's health information is of utmost importance. This work tries to provide a better and more efficient solution for securing all the health care data that are stored on the cloud which protect health care data from unauthorized access from an unknown source.
Cited by 4
Dong‐Hwan Kim, Jung Keun Kim, Tae‐Gyu Chang · 2005
An adaptive chunk algorithm is newly devised and a collaborative streaming system is designed for a smoothed multimedia streaming service under time varying traffic conditions. An LMS based prediction filter is used to compensate the effect of time varying background traffic of a wide area network. The performance of the algorithm is tested and its successful operation is verified with MPEG-2 video files.
Cited by 0
Longkun Guo, Zeyu Lin, Chaoqi Jia et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 2026
Many real-world applications call for incorporating fairness constraints into the k-center clustering problem, where the dataset is partitioned into m demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper addresses the problem in a streaming setting, where data points arrive sequentially in a continuous stream. Leveraging a structure called the λ-independent center set, we propose a one-pass streaming algorithm that first computes a reserved set of points during the streaming process. In the post-streaming process, we then select centers from the reserved point set by analyzing three possible cases and transforming the most complex one into a specially constrained vertex-cover problem on an auxiliary graph. Our algorithm achieves an approximation ratio of 5 + ? and memory complexity O(k log ?), where ? is the aspect ratio and ? > 0 is any small constant. Furthermore, we extend our approach to semi-structured data streams, where data points arrive in groups. In this setting, we present a (3 + ?)-approximation algorithm for m = 2, which can be readily adapted to solve the offline fair k-center problem, achieving an approximation ratio of 3 that matches the current state of the art. Lastly, we conduct extensive experiments to evaluate the performance of our approaches, demonstrating that they outperform existing baselines in both clustering cost and runtime efficiency.
Cited by 0Open access
Qiao Jie-hua · 2013
To overcome the shortcoming of low precision and low speed in the network retransmitting of streaming media,by analyzing the header of RTP protocol,an optimization integrating with the RTP-protocol-based analysis of traffic characteristics,threshold judgments and buffer reclaim is proposed.And the RTP Streaming automatic detection recognition and fast retransmit is implemented under the Linux/Netfilter framework.Experimental results show that comparing to the traditional RTP streaming recognition algorithm,RTP streaming recognition precision and fault tolerance are improved,while the delay of RTP protocol retransmitting is reduced significantly,and improve the efficiency of RTP streaming detection recognition is improved significantly.
Cited by 0
Aashi Jindal, Prashant Gupta, Debarka Sengupta et al. · arXiv (Cornell University) · 2020
We propose Enhash, a fast ensemble learner that detects \textit{concept drift} in a data stream. A stream may consist of abrupt, gradual, virtual, or recurring events, or a mixture of various types of drift. Enhash employs projection hash to insert an incoming sample. We show empirically that the proposed method has competitive performance to existing ensemble learners in much lesser time. Also, Enhash has moderate resource requirements. Experiments relevant to performance comparison were performed on 6 artificial and 4 real data sets consisting of various types of drifts.
Cited by 0Open access
Chang–Hong Fu, Yui‐Lam Chan, Wan-Chi Siu · 2004
Reverse playback is the most common video cassette recording (VCR) functions in many digital video players. However, the predictive processing techniques employed in MPEG severely complicate the reverse-play operation. In this paper, we propose a compressed-domain approach for the efficient implementation of an MPEG video streaming system to provide the reverse-play operation over a network. This allows the minimum requirement on the network bandwidth and decoder complexity. In the proposed video streaming server, a macroblock-based algorithm is used to adaptively select the necessary macroblocks, manipulate some of the macroblocks in VLC-domain and send the processed macroblocks to the client machine. Experimental results show that, as compared to the conventional system, the new streaming system reduces the required network bandwidth and the decoder complexity significantly.
Cited by 2
Jun Yang, Shaokun Jia, Chenglin Zhang et al. · Contemporary Mathematics · 2025
Online Streaming Feature Selection (OSFS) is a feature selection method to identify relevant features in real-time from high-dimensional, continuously generated data streams. Traditional methods require the pre-setting of static parameters and exhibit insufficient robustness. Therefore, these methods may fail to adapt effectively to evolving feature sets and are prone to noise interference. To overcome these limitations, this paper proposes an online streaming feature selection algorithm based on Granular Ball-Online Streaming Feature Selection (GB-OSFS). GB-OSFS adopts a granular ball approach in OSFS, shifting the focus from individual sample points to granular balls for calculating feature dependencies. Furthermore, we select granular balls with a purity of 1 as the evaluation metric for feature importance. As a result, this approach addresses the issues of traditional streaming feature selection methods being prone to noise interference and the necessity of pre-setting various hyperparameters. To validate the efficacy of GB-OSFS, a series of comprehensive empirical tests were conducted across ten datasets. The findings indicate that GB-OSFS surpasses six other OSFS algorithms in terms of prediction accuracy and robustness.
Cited by 0Open access
Zina Chkirbene, Ridha Hamila, Aiman Erbad et al. · Computer Networks · 2023
Cited by 3
Author(s Wen, Z Yeung, K Lei et al. · 2016
Abstract — Streaming layered video over peer-to-peer (P2P) networks has been recognized as an efficient way to address the receiver heterogeneity problem. The distinctive characteristics of layer encoded video also introduce complexities to data scheduling. In this paper, we propose a new data scheduling algorithm for layered P2P video-on-demand (VoD) streaming networks. Our algorithm consists of two parts: 1) layer adaptation, where peers adaptively adjust the number of subscribed layers to ensure a continuous playback of the highest possible video quality; and 2) piece selection, in which a peer selects a missing data piece to request based on its utility. The piece utility is calculated based on the playback constraint and the layer dependency of a piece. Through extensive packet-level simulations we show that our proposed data scheduling algorithm can effectively enhance the video playback quality. I.
Cited by 0
Saba Fouad Hassan, Rana Fareed Ghani · Baghdad Science Journal · 2019
يُعد بث الفيديو أكثر الوسائط شيوعًا التي يستخدمها الأشخاص على الإنترنت اليوم ويستهلك الكثير من عمليات نقل الإنترنت. يتم استخدام كمية هائلة من استخدام الإنترنت لتدفق الفيديو الذي ينفق ما يقرب من 70٪ من الإنترنت اليوم. ومع ذلك ، توجد قيود على الوسائط التفاعلية ممثلة في زيادة استخدام النطاق الترددي وتأخره ، مثل بث الفيديو المباشر الذي يتطلب الإرسال في الوقت الفعلي. تستخدم تقنيات حوسبة الضباب للتخفيف من حدة هذه المشكلات من خلال توفير استجابة عالية في الوقت الحقيقي وموارد حاسوبية قريبة من العميل عند حدود الشبكة ، والضباب عبارة عن طبقة وسيطة بين السحابة والمستخدم النهائي ، تقترح هذه الورقة خوارزمية Weighted Round Robin (PWRR) ذات الأولوية لجدولة عمليات التدفق في بنية الضباب لإعطاء استباقية لبث طلب فيديو مباشر لتقديم وقت استجابة أقل للغاية وتواصل في الوقت الفعلي. تعرض نتائج تجربة PWRR في البنية المقترحة لتدفق الفيديوعبر حوسبة الضباب ، تقليل وقت الاستجابة ونوعية جيدة لطلبات الفيديو المباشرة التي تم تحقيقها مع تغييرات النطاق الترددي بالاضافة الى تلبية كل طلبات الاخرى للزبائن في نفس الوقت.
Cited by 7Open access
Mustafa Ali Hassoune, Zoulikha Mekkakia Maaza, Sidi‐Mohammed Senouci · Wireless Personal Communications · 2017
Cited by 15
Ishani Sarkar, Guillaume Urvoy-Keller, Dino Lopez Pacheco et al. · HAL (Le Centre pour la Communication Scientifique Directe) · 2021
Live video streaming is gaining momentum and now represents a significant share of Internet traffic. Typical distribution architectures for this type of service rely on CDNs to meet the stringent QoS requirements of live video applications. The video is broken into small chunks, encoded at different quality levels, that the viewer downloads from a CDN server. As CDN-based solutions are costly to operate, hybrid architectures have been proposed, where video viewers are exchanging video chunks between each other (V2V mode), reverting to the CDN server only if the chunk could not be downloaded in V2V mode. Our focus in this work is on the design of the adaptive bit-rate (ABR) algorithm that governs the quality level requested by the viewer. We demonstrate that the ABR algorithm is facing specific challenges, and thus needs to be adapted for the hybrid V2V-CDN case. We introduce MSHLS, a new ABR algorithm that takes into account the difference of performance when downloading from another viewer or a CDN server in a live streaming context where the video buffers are kept small as clients need to remain synchronized when watching the stream. We evaluate MSHLS using controlled and live experiments in a commercial hybrid V2V-CDN system and demonstrate that it outperforms the HLS public reference implementation, designed with a single source of content in mind.
Cited by 0Open access
Rong-zhen BAO, Ming Cai · Journal of Computer Applications · 2011
Cited by 0Open access
Hongbo Huang · The Open Automation and Control Systems Journal · 2015
With the development of wireless communication technology, video communication over the mobile ad hoc networks has been more and more applied.How to ensure the QoS of video communication is an important problem that should be solved quickly.In this paper, we propose a QoS routing algorithm for Video Streaming in MANET.The algorithm introduces multi-constrained QoS routing mechanisms, and optimizes paths through bandwidth, delay, service priorities and congestion degree constraints.The simulation results show that, in medium speed or low-speed MANET where nodes are concentrated, this algorithm can perform well in respects like extending the life of the nodes, reducing packet delay and improving packet delivery fraction.
Cited by 0