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1. Flocking-based live streaming of 360-degree video

   https://doi.org/10.1145/3339825.3391856  

   Sun, Liyang ; Mao, Yixiang ; Zong, Tongyu ; Liu, Yong ; Wang, Yao ( May 2020 , PubProceedings of the 11th ACM Multimedia Systems Conference)

   Streaming of live 360-degree video allows users to follow a live event from any view point and has already been deployed on some commercial platforms. However, the current systems can only stream the video at relatively low-quality because the entire 360-degree video is delivered to the users under limited bandwidth. In this paper, we propose to use the idea of "flocking" to improve the performance of both prediction of field of view (FoV) and caching on the edge servers for live 360-degree video streaming. By assigning variable playback latencies to all the users in a streaming session, a "streaming flock"more »is formed and led by low latency users in the front of the flock. We propose a collaborative FoV prediction scheme where the actual FoV information of users in the front of the flock are utilized to predict of users behind them. We further propose a network condition aware flocking strategy to reduce the video freeze and increase the chance for collaborative FoV prediction on all users. Flocking also facilitates caching as video tiles downloaded by the front users can be cached by an edge server to serve the users at the back of the flock, thereby reducing the traffic in the core network. We propose a latency-FoV based caching strategy and investigate the potential gain of applying transcoding on the edge server. We conduct experiments using real-world user FoV traces and WiGig network bandwidth traces to evaluate the gains of the proposed strategies over benchmarks. Our experimental results demonstrate that the proposed streaming system can roughly double the effective video rate, which is the video rate inside a user's actual FoV, compared to the prediction only based on the user's own past FoV trajectory, while reducing video freeze. Furthermore, edge caching can reduce the traffic in the core network by about 80%, which can be increased to 90% with transcoding on edge server.« less
2. MOSE: Practical Multi-User Oblivious Storage via Secure Enclaves

   https://doi.org/10.1145/3374664.3375749  

   Hoang, Thang ; Behnia, Rouzbeh ; Jang, Yeongjin ; Yavuz, Attila A. ( March 2020 , Proceedings of the Tenth {ACM} Conference on Data and Application Security and Privacy)

   Multi-user oblivious storage allows users to access their shared data on the cloud while retaining access pattern obliviousness and data confidentiality simultaneously. Most secure and efficient oblivious storage systems focus on the utilization of the maximum network bandwidth in serving concurrent accesses via a trusted proxy. How- ever, since the proxy executes a standard ORAM protocol over the network, the performance is capped by the network bandwidth and latency. Moreover, some important features such as access control and security against active adversaries have not been thoroughly explored in such proxy settings. In this paper, we propose MOSE, a multi-user obliviousmore »storage system that is efficient and enjoys from some desirable security properties. Our main idea is to harness a secure enclave, namely Intel SGX, residing on the untrusted storage server to execute proxy logic, thereby, minimizing the network bottleneck of proxy-based designs. In this regard, we address various technical design challenges such as memory constraints, side-channel attacks and scalability issues when enabling proxy logic in the secure enclave. We present a formal security model and analysis for secure enclave multi-user ORAM with access control. We optimize MOSE to boost its throughput in serving concurrent requests. We implemented MOSE and evaluated its performance on commodity hardware. Our evaluation confirmed the efficiency of MOSE, where it achieves approximately two orders of magnitudes higher throughput than the state-of-the-art proxy-based design, and also, its performance is scalable proportional to the available system resources.« less
3. Joint Caching and Routing in Congestible Networks of Arbitrary Topology

   Liu, B. ; Poularakis, K. ; Tassiulas, L. ; Jiang, T. ( August 2019 , IEEE internet of things journal)

   In-network caching constitutes a promising approach to reduce traffic loads and alleviate congestion in both wired and wireless networks. In this paper, we study the joint caching and routing problem in congestible networks of arbitrary topology (JoCRAT) as a generalization of previous efforts in this particular field. We show that JoCRAT extends many previous problems in the caching literature that are intractable even with specific topologies and/or assumed unlimited bandwidth of communications. To handle this significant but challenging problem, we develop a novel approximation algorithm with guaranteed performance bound based on a randomized rounding technique. Evaluation results demonstrate that ourmore »proposed algorithm achieves nearoptimal performance over a broad array of synthetic and real networks, while significantly outperforming the state-of-the-art methods.« less
4. Minimizing network bandwidth under latency constraints: The single node case

   Jiayi Song, Roch Guérin ( September 2021 , Proceedings 33rd International Teletraffic Congress (ITC 33))

   Much of today's traffic flows between datacenters over private networks. The operators of those networks have access to detailed traffic profiles with performance goals that need to be met as efficiently as possible, e.g., realizing latency guarantees with minimal network bandwidth. Of particular interest is the extent to which traffic (re)shaping can be of benefit. The paper focuses on the most basic network configuration, namely, a single link network, with extensions to more general, multi-node networks discussed in a companion paper. The main results are in the form of optimal solutions for different types of schedulers of varying complexity. Theymore »demonstrate how judicious traffic shaping can help lower complexity schedulers perform nearly as well as more complex ones.« less
5. A Federated Learning Approach to Routing in Challenged SDN-Enabled Edge Networks

   https://doi.org/10.1109/NetSoft48620.2020.9165506  

   Sacco, Alessio ; Esposito, Flavio ; Marchetto, Guido ( June 2020 , 2020 6th IEEE Conference on Network Softwarization (NetSoft))

   The edge computing paradigm allows computationally intensive tasks to be offloaded from small devices to nearby (more) powerful servers, via an edge network. The intersection between such edge computing paradigm and Machine Learning (ML), in general, and deep learning in particular, has brought to light several advantages for network operators: from automating management tasks, to gain additional insights on their networks. Most of the existing approaches that use ML to drive routing and traffic control decisions are valuable but rarely focus on challenged networks, that are characterized by continually varying network conditions and the high volume of traffic generated bymore »edge devices. In particular, recently proposed distributed ML-based architectures require either a long synchronization phase or a training phase that is unsustainable for challenged networks. In this paper, we fill this knowledge gap with Blaster, a federated architecture for routing packets within a distributed edge network, to improve the application's performance and allow scalability of data-intensive applications. We also propose a novel path selection model that uses Long Short Term Memory (LSTM) to predict the optimal route. Finally, we present some initial results obtained by testing our approach via simulations and with a prototype deployed over the GENI testbed. By leveraging a Federated Learning (FL) model, our approach shows that we can optimize the communication between SDN controllers, preserving bandwidth for the data traffic.« less