{"id":298,"date":"2019-11-25T18:42:41","date_gmt":"2019-11-25T18:42:41","guid":{"rendered":"https:\/\/groups.cs.umass.edu\/ramesh\/?page_id=298"},"modified":"2022-12-08T01:30:23","modified_gmt":"2022-12-08T01:30:23","slug":"publications","status":"publish","type":"page","link":"https:\/\/groups.cs.umass.edu\/ramesh\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Copyright notice for published papers: <\/strong>This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright.<\/p>\n

Copyright notice for presentations and other work:<\/strong> All of my presentations, talks, and images\u00a0 on this page are available with Creative Commons Attribution ShareAlike 4.0 license.<\/p>\n

<\/a>
<\/div><\/div><\/form>
125 entries<\/span> «<\/a> ‹<\/a> 1 of 7 ›<\/a> »<\/a> <\/div><\/div>
1.<\/td>

Ahmad, Sohaib; Guan, Hui; Friedman, Brian D.; Williams, Thomas; Sitaraman, Ramesh K.; Woo, Thomas<\/p>

Proteus: A High-Throughput Inference-Serving System with Accuracy Scaling<\/a> Conference<\/span> <\/p>

In 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1 (ASPLOS \u201924), <\/span>2024<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

2.<\/td>

Chen, Jiayi; Sharma, Nihal; Khan, Tarannum; Liu, Shu; Chang, Brian; Akella, Aditya; Shakkottai, Sanjay; Sitaraman, Ramesh K.<\/p>

Darwin: Flexible Learning-based CDN Caching<\/a> Conference<\/span> <\/p>

ACM SIGCOMM Conference, <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

3.<\/td>

Maji, Diptyaroop; Pfaff, Ben; R, Vipin P; Sreenivasan, Rajagopal; Firoiu, Victor; Iyer, Sreeram; Josephson, Colleen; Pan, Zhelong; Sitaraman, Ramesh K.<\/p>

Bringing Carbon Awareness to Multi-cloud Application Delivery<\/a> Conference<\/span> <\/p>

In 2nd Workshop on Sustainable Computer Systems (HotCarbon \u201923), <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

4.<\/td>

Maghakian, Jessica; Lee, Russell; Hajiesmaili, Mohammad; Li, Jian; Sitaraman, Ramesh; Liu, Zhenhua<\/p>

Applied Online Algorithms with Heterogeneous Predictors<\/a> Conference<\/span> <\/p>

Proceedings of the 40th International Conference on Machine Learning (ICML), <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

5.<\/td>

Maji, Diptyaroop; Shenoy, Prashant; Sitaraman, Ramesh K.<\/p>

Multi-day Forecasting of Electric Grid Carbon Intensity using Machine Learning<\/a> Journal Article<\/span> <\/p>

In: <\/span>ACM SIGENERGY Energy Informatics Review, <\/span>vol. 3 , <\/span>iss. 2, <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

6.<\/td>

Kumar, Dhruv; Ahmad, Sohaib; Chandra, Abhishek; Sitaraman, Ramesh K.<\/p>

AggFirstJoin: Optimizing Geo-Distributed Joins using Aggregation-Based Transformations<\/a> Conference<\/span> <\/p>

IEEE\/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid), <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

7.<\/td>

Anirudh Sabnis and, Tareq Si Salem; Neglia, Giovanni; Garetto, Michele; Leonardi, Emilio; Sitaraman, Ramesh K.<\/p>

GRADES: Gradient Descent for Similarity Caching<\/a> Journal Article<\/span> <\/p>

In: <\/span>IEEE\/ACM TRANSACTIONS ON NETWORKING, <\/span>vol. 31, <\/span>no. 1, <\/span>2023<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

8.<\/td>

Vats, Shivi; Park, Jounsup; Nahrstedt, Klara; Zink, Michael; Sitaraman, Ramesh; Hellwagner, Hermann<\/p>

Semantic-Aware View Prediction for 360-Degree Videos at the 5G Edge<\/a> Conference<\/span> <\/p>

2022<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

9.<\/td>

Maji, Diptyaroop; Shenoy, Prashant; Sitaraman, Ramesh K<\/p>

CarbonCast: Multi-Day Forecasting of Grid Carbon Intensity<\/a> Conference<\/span> <\/p>

ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '22), <\/span>2022<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

10.<\/td>

Sabnis, Anirudh; Sitaraman, Ramesh K.<\/p>

JEDI: Model-driven trace generation for cache simulations<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>22nd ACM Internet Measurement Conference (IMC '22), <\/span>2022<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@inproceedings{SabnisS22,
\r\ntitle = {JEDI: Model-driven trace generation for cache simulations},
\r\nauthor = {Anirudh Sabnis and Ramesh K. Sitaraman},
\r\nurl = {https:\/\/groups.cs.umass.edu\/ramesh\/wp-content\/uploads\/sites\/3\/2022\/11\/JEDI.pdf},
\r\nyear  = {2022},
\r\ndate = {2022-10-25},
\r\nurldate = {2022-10-25},
\r\nbooktitle = {22nd ACM Internet Measurement Conference (IMC '22)},
\r\nabstract = {A major obstacle for caching research is the increasing diffi- culty of obtaining original traces from production caching systems. Original traces are voluminous and also may contain private and proprietary information, and hence not generally made available to the public. The lack of original traces hampers our ability to evaluate new cache designs and provides the rationale for JEDI, our new synthetic trace generation tool. JEDI generates a synthetic trace that is \u201csimilar\u201d to the original trace collected from a production cache, in particular, the two traces have similar object-level properties and produce similar hit rates in a cache simulation. JEDI uses a novel traffic model called Popularity-Size Footprint Descriptor (pFD) that concisely captures key properties of the original trace and uses the pFD to generate the synthetic trace. We show that the synthetic traces produced by JEDI can be used to accurately simulate a wide range of cache admission and eviction algorithms and the hit rates obtained from these simulations correspond closely to those obtained from simulations that use the original traces. JEDI will be provided to the public as open-source, along with a library of pFD\u2019s computed from traffic classes hosted on Akamai\u2019s production CDN. This will allow researchers to produce realistic synthetic traces for their own caching research.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
A major obstacle for caching research is the increasing diffi- culty of obtaining original traces from production caching systems. Original traces are voluminous and also may contain private and proprietary information, and hence not generally made available to the public. The lack of original traces hampers our ability to evaluate new cache designs and provides the rationale for JEDI, our new synthetic trace generation tool. JEDI generates a synthetic trace that is \u201csimilar\u201d to the original trace collected from a production cache, in particular, the two traces have similar object-level properties and produce similar hit rates in a cache simulation. JEDI uses a novel traffic model called Popularity-Size Footprint Descriptor (pFD) that concisely captures key properties of the original trace and uses the pFD to generate the synthetic trace. We show that the synthetic traces produced by JEDI can be used to accurately simulate a wide range of cache admission and eviction algorithms and the hit rates obtained from these simulations correspond closely to those obtained from simulations that use the original traces. JEDI will be provided to the public as open-source, along with a library of pFD\u2019s computed from traffic classes hosted on Akamai\u2019s production CDN. This will allow researchers to produce realistic synthetic traces for their own caching research.<\/div>
Close<\/a><\/p><\/div>
11.<\/td>

Kumar, Dhruv; Wolfrath, Joel; Chandra, Abhishek; Sitaraman, Ramesh K.<\/p>

Towards WAN-Aware Join Sampling over Geo-Distributed Data<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>In 5th ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys\u201922), <\/span>2022<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@inproceedings{KumarWCS21,
\r\ntitle = {Towards WAN-Aware Join Sampling over Geo-Distributed Data},
\r\nauthor = {Dhruv Kumar and Joel Wolfrath and Abhishek Chandra and Ramesh K. Sitaraman},
\r\nurl = {https:\/\/groups.cs.umass.edu\/ramesh\/wp-content\/uploads\/sites\/3\/2022\/03\/EdgeSys22_GDSJ_camera_ready.pdf},
\r\nyear  = {2022},
\r\ndate = {2022-04-06},
\r\nurldate = {2022-04-06},
\r\nbooktitle = {In 5th ACM International Workshop on Edge Systems, Analytics and Networking (EdgeSys\u201922)},
\r\njournal = {5th International Workshop on Edge Systems, Analytics and Networking (EdgeSys\u201922)},
\r\nabstract = {Large scale data analytics over geographically distributed data sources is challenging primarily due to the constrained and hetero- geneous resource availability such as the wide area network (WAN) bandwidth. In this work, we look at the problem of generating random samples over joins for geo-distributed data sources. Joins are one of the most fundamental yet expensive operations in data analytics. To reduce the cost of computing joins, existing techniques have looked at efficiently generating a random sample over the join result for centralized environments, where all the data is available in one location. These techniques fail to address the unique chal- lenges posed by geo-distributed environments. To address these challenges, we propose a sampling technique which aims to reduce the WAN traffic and latency, thereby reducing the overall latency for generating samples over joins for geo-distributed data sources. We implement our geo-distributed sampling technique on top of Apache Spark and compare it with existing state-of-the-art sam- pling techniques to identify scenarios where the proposed approach gives significant benefits. Based on this exploration, we provide a detailed outline of additional factors which should be consid- ered when designing a WAN-aware join sampling technique for geo-distributed environments.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Large scale data analytics over geographically distributed data sources is challenging primarily due to the constrained and hetero- geneous resource availability such as the wide area network (WAN) bandwidth. In this work, we look at the problem of generating random samples over joins for geo-distributed data sources. Joins are one of the most fundamental yet expensive operations in data analytics. To reduce the cost of computing joins, existing techniques have looked at efficiently generating a random sample over the join result for centralized environments, where all the data is available in one location. These techniques fail to address the unique chal- lenges posed by geo-distributed environments. To address these challenges, we propose a sampling technique which aims to reduce the WAN traffic and latency, thereby reducing the overall latency for generating samples over joins for geo-distributed data sources. We implement our geo-distributed sampling technique on top of Apache Spark and compare it with existing state-of-the-art sam- pling techniques to identify scenarios where the proposed approach gives significant benefits. Based on this exploration, we provide a detailed outline of additional factors which should be consid- ered when designing a WAN-aware join sampling technique for geo-distributed environments.<\/div>
Close<\/a><\/p><\/div>
12.<\/td>

Yang, Juncheng; Sabnis, Anirudh; Berger, Daniel S.; Rashmi, K. V.; Sitaraman, Ramesh K.<\/p>

C2DN: How to Harness Erasure Codes at the Edge for Efficient Content Delivery<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22), <\/span>pp. 1159\u20131177, <\/span>USENIX Association, <\/span>Renton, WA, <\/span>2022<\/span>, ISBN: 978-1-939133-27-4<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@inproceedings{YangSBRS22,
\r\ntitle = {C2DN: How to Harness Erasure Codes at the Edge for Efficient Content Delivery},
\r\nauthor = {Juncheng Yang and Anirudh Sabnis and Daniel S. Berger and K. V. Rashmi and Ramesh K. Sitaraman},
\r\nurl = {https:\/\/groups.cs.umass.edu\/ramesh\/wp-content\/uploads\/sites\/3\/2022\/03\/nsdi22fall-final146.pdf},
\r\nisbn = {978-1-939133-27-4},
\r\nyear  = {2022},
\r\ndate = {2022-04-01},
\r\nurldate = {2022-04-01},
\r\nbooktitle = {19th USENIX Symposium on Networked Systems Design and Implementation (NSDI 22)},
\r\npages = {1159--1177},
\r\npublisher = {USENIX Association},
\r\naddress = {Renton, WA},
\r\nabstract = {Content Delivery Networks (CDNs) deliver much of the world\u2019s web and video content to users from thousands of clusters deployed at the \u201cedges\u201d of the Internet. Maintain- ing consistent performance in this large distributed system is challenging. Through analysis of month-long logs from over 2000 clusters of a large CDN, we study the patterns of server unavailability. For a CDN with no redundancy, each server unavailability causes a sudden loss in performance as the objects previously cached on that server are not accessible, which leads to a miss ratio spike. The state-of-the-art miti- gation technique used by large CDNs is to replicate objects across multiple servers within a cluster. We find that although replication reduces miss ratio spikes, spikes remain a perfor- mance challenge. We present C2DN, the first CDN design that achieves a lower miss ratio, higher availability, higher resource efficiency, and close-to-perfect write load balancing. The core of our design is to introduce erasure coding into the CDN architecture and use the parity chunks to re-balance the write load across servers. We implement C2DN on top of open-source production software and demonstrate that com- pared to replication-based CDNs, C2DN obtains 11% lower byte miss ratio, eliminates unavailability-induced miss ratio spikes, and reduces write load imbalance by 99%.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Content Delivery Networks (CDNs) deliver much of the world\u2019s web and video content to users from thousands of clusters deployed at the \u201cedges\u201d of the Internet. Maintain- ing consistent performance in this large distributed system is challenging. Through analysis of month-long logs from over 2000 clusters of a large CDN, we study the patterns of server unavailability. For a CDN with no redundancy, each server unavailability causes a sudden loss in performance as the objects previously cached on that server are not accessible, which leads to a miss ratio spike. The state-of-the-art miti- gation technique used by large CDNs is to replicate objects across multiple servers within a cluster. We find that although replication reduces miss ratio spikes, spikes remain a perfor- mance challenge. We present C2DN, the first CDN design that achieves a lower miss ratio, higher availability, higher resource efficiency, and close-to-perfect write load balancing. The core of our design is to introduce erasure coding into the CDN architecture and use the parity chunks to re-balance the write load across servers. We implement C2DN on top of open-source production software and demonstrate that com- pared to replication-based CDNs, C2DN obtains 11% lower byte miss ratio, eliminates unavailability-induced miss ratio spikes, and reduces write load imbalance by 99%.<\/div>
Close<\/a><\/p><\/div>
13.<\/td>

Yang, Lin; Zeynali, Ali; Hajiesmaili, Mohammad H.; Sitaraman, Ramesh K.; Towsley, Don<\/p>

Competitive Algorithms for Online Multidimensional Knapsack Problems<\/a> Journal Article<\/span> <\/p>

In: <\/span>Proceedings of the ACM on the Measurement Analysis of Computing Systems (POMACS), <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

14.<\/td>

Kumar, Dhruv; Ahmad, Sohaib; Chandra, Abhishek; Sitaraman, Ramesh K.<\/p>

AggNet: Cost-Aware Aggregation Networks for Geo-distributed Streaming Analytics<\/a> Conference<\/span> <\/p>

ACM\/IEEE Symposium on Edge Computing (SEC '21), <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

15.<\/td>

Sabnis, Anirudh; Sitaraman, Ramesh<\/p>

TRAGEN: A Synthetic Trace Generator for Realistic Cache Simulations<\/a> Conference<\/span> <\/p>

ACM Internet Measurement Conference (IMC), <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

16.<\/td>

Wang, Lingdong; Hajiesmaili, Mohammad; Sitaraman, Ramesh K<\/p>

FOCAS: Practical Video Super Resolution using Foveated Rendering<\/a> Conference<\/span> <\/p>

ACM International Conference on Multimedia (MM \u201921), <\/span>2021<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@conference{WangHS21,
\r\ntitle = {FOCAS: Practical Video Super Resolution using Foveated Rendering},
\r\nauthor = {Lingdong Wang and Mohammad Hajiesmaili and Ramesh K Sitaraman},
\r\nurl = {https:\/\/groups.cs.umass.edu\/ramesh\/wp-content\/uploads\/sites\/3\/2021\/08\/FOCAS_Practical_Video_Super_Resolution_using_Foveated_Rendering__Final.pdf},
\r\nyear  = {2021},
\r\ndate = {2021-10-20},
\r\nbooktitle = {ACM International Conference on Multimedia (MM \u201921)},
\r\nabstract = {Super-resolution (SR) is a well-studied technique for reconstructing high-resolu- tion (HR) images from low-resolution (LR) ones. SR holds great promise for video streaming since an LR video segment can be transmitted from the video server to the client that then reconstructs the HR version using SR, resulting in a significant reduction in network bandwidth. However, SR is seldom used in practice for real-time video streaming, because the computational overhead of frame reconstruction results in large latency and low frame rate.
\r\n
\r\nTo reduce the computational overhead and make SR practi- cal, we propose a deep-learning-based SR method called Foveated Cascaded Video Super Resolution (FOCAS). FOCAS relies on the fact that human eyes only have high acuity in a tiny central foveal region of the retina. FOCAS uses more neural network blocks in the foveal region to provide higher video quality, while using fewer blocks in the periphery as lower quality is sufficient. To optimize the computational resources and reduce reconstruction latency, FOCAS formulates and solves a convex optimization problem to decide the number of neural network blocks to use in each region of the frame. Using extensive experiments, we show that FOCAS reduces the latency by 50% \u2212 70% while maintaining comparable visual quality as traditional (non-foveated) SR. Further, FOCAS provides a 12 \u2212 16\u00d7 reduction in the client-to-server network bandwidth in comparison with sending the full HR video segments.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Super-resolution (SR) is a well-studied technique for reconstructing high-resolu- tion (HR) images from low-resolution (LR) ones. SR holds great promise for video streaming since an LR video segment can be transmitted from the video server to the client that then reconstructs the HR version using SR, resulting in a significant reduction in network bandwidth. However, SR is seldom used in practice for real-time video streaming, because the computational overhead of frame reconstruction results in large latency and low frame rate.
\r\n
\r\nTo reduce the computational overhead and make SR practi- cal, we propose a deep-learning-based SR method called Foveated Cascaded Video Super Resolution (FOCAS). FOCAS relies on the fact that human eyes only have high acuity in a tiny central foveal region of the retina. FOCAS uses more neural network blocks in the foveal region to provide higher video quality, while using fewer blocks in the periphery as lower quality is sufficient. To optimize the computational resources and reduce reconstruction latency, FOCAS formulates and solves a convex optimization problem to decide the number of neural network blocks to use in each region of the frame. Using extensive experiments, we show that FOCAS reduces the latency by 50% \u2212 70% while maintaining comparable visual quality as traditional (non-foveated) SR. Further, FOCAS provides a 12 \u2212 16\u00d7 reduction in the client-to-server network bandwidth in comparison with sending the full HR video segments.<\/div>
Close<\/a><\/p><\/div>
17.<\/td>

Zhang, Xiao; Sen, Tanmoy; Zhang, Zheyuan; April, Tim; Chandrasekaran, Balakrishnan; Choffnes, David; Maggs, Bruce M.; Shen, Haiying; Sitaraman, Ramesh K.; Yang, Xiaowei<\/p>

AnyOpt: Predicting and Optimizing IP Anycast Performance<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>Proceedings of the ACM SIGCOMM Conference, <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

18.<\/td>

Lee, Russell; Maghakian, Jessica; Hajiesmaili, Mohammad; Li, Jian; Sitaraman, Ramesh; Liu, Zhenhua<\/p>

Online Peak-Aware Energy Scheduling with Untrusted Advice<\/a> Conference<\/span> <\/p>

ACM International Conference on Future Energy Systems (ACM e-Energy), <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

19.<\/td>

Sabnis, Anirudh; Salem, Tareq Si; Neglia, Giovanni; Garetto, Michele; Leonardi, Emilio; Sitaraman, Ramesh K<\/p>

GRADES: Gradient Descent for Similarity Caching<\/a> Proceedings Article<\/span> <\/p>

In: <\/span>IEEE Conference on Computer Communications (INFOCOM), <\/span>2021<\/span>.<\/p>

Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

20.<\/td>

Park, Jounsup; Wu, Mingyuan; Lee, Eric; Chen, Bo; Nahrstedt, Klara; Zink, Michael; Sitaraman, Ramesh<\/p>

SEAWARE: Semantic Aware View Prediction System for 360-degree Video Streaming<\/a> Conference<\/span> <\/p>

IEEE International Symposium on Multimedia (ISM), <\/span>2020<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@conference{ParkWLCNZS2020,
\r\ntitle = {SEAWARE: Semantic Aware View Prediction System for 360-degree Video Streaming},
\r\nauthor = {Jounsup Park and Mingyuan Wu and Eric Lee and Bo Chen and Klara Nahrstedt and Michael Zink and Ramesh Sitaraman},
\r\nurl = {https:\/\/groups.cs.umass.edu\/ramesh\/wp-content\/uploads\/sites\/3\/2020\/10\/ISM2020__SEAWARE__CameraReady_.pdf},
\r\nyear  = {2020},
\r\ndate = {2020-12-02},
\r\nbooktitle = {IEEE International Symposium on Multimedia (ISM)},
\r\nabstract = {Future view prediction for a 360-degree video streaming system is important to save the network bandwidth and improve the Quality of Experience (QoE). Historical view data of a single viewer and multiple viewers have been used for future view prediction. Video semantic information is also useful to predict the viewer\u2019s future behavior. However, extracting video semantic information requires powerful computing hardware and large memory space to perform deep learning-based video analysis. It is not a desirable condition for most of client devices, such as small mobile devices or Head Mounted Display (HMD). Therefore, we develop an approach where video semantic analysis is executed on the media server, and the analysis results are shared with clients via the Semantic Flow Descriptor (SFD) and View-Object State Machine (VOSM). SFD and VOSM become new descriptive additions of the Media Presentation Description (MPD) and Spatial Relation Description (SRD) to support 360- degree video streaming via the DASH framework. Using the semantic-based approach, we design the Semantic-Aware View Prediction System (SEAWARE) to improve the overall view prediction performance. The evaluation results of 360-degree videos and real HMD view traces show that the SEAWARE system improves the view prediction performance and streams high-quality video with limited network bandwidth.
\r\n},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {conference}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Future view prediction for a 360-degree video streaming system is important to save the network bandwidth and improve the Quality of Experience (QoE). Historical view data of a single viewer and multiple viewers have been used for future view prediction. Video semantic information is also useful to predict the viewer\u2019s future behavior. However, extracting video semantic information requires powerful computing hardware and large memory space to perform deep learning-based video analysis. It is not a desirable condition for most of client devices, such as small mobile devices or Head Mounted Display (HMD). Therefore, we develop an approach where video semantic analysis is executed on the media server, and the analysis results are shared with clients via the Semantic Flow Descriptor (SFD) and View-Object State Machine (VOSM). SFD and VOSM become new descriptive additions of the Media Presentation Description (MPD) and Spatial Relation Description (SRD) to support 360- degree video streaming via the DASH framework. Using the semantic-based approach, we design the Semantic-Aware View Prediction System (SEAWARE) to improve the overall view prediction performance. The evaluation results of 360-degree videos and real HMD view traces show that the SEAWARE system improves the view prediction performance and streams high-quality video with limited network bandwidth.
\r\n<\/div>
Close<\/a><\/p><\/div>