Pub Date : 2021-11-08DOI: 10.1109/CloudNet53349.2021.9657121
P. Casas, Matteo Romiti, Peter Holzer, Sami Ben Mariem, B. Donnet, Bernhard Haslhofer
Proposed in 2016 and launched in 2018, the Bitcoin (BTC) Lightning Network (LN) can scale-up the capacity of the BTC blockchain network to process a significantly higher amount of transactions, in a faster, cheaper, and more privacy preserving manner. The number of LN nodes has been significantly increasing since 2018, and today there are more than twelve thousand nodes actively participating of so-called LN payment channels. The upcoming Taproot upgrade to the Bitcoin protocol would further boost the development and adoption of the LN. Taproot is the most significant upgrade to the Bitcoin network since the block size increase of 2017, and it will make LN transactions cheaper, more flexible, and more private. We focus on the characterization of the LN network topology, using network active measurements. By crawling the underlying P2P network supporting the Bitcoin LN over a span of 10-months, we unveil the LN in terms of size and location of its nodes as well as connectivity protocols, comparing it to the P2P IP network supporting the BTC blockchain. Among our findings, we show that IP addresses exposed by LN nodes correspond mainly to customer networks, even if most BTC nodes are actually deployed at major cloud providers, and that LN nodes significantly rely on anonymized networks and protocols such as Onion, with more than 40% of LN nodes connect through Tor.
{"title":"Where is the Light(ning) in the Taproot Dawn? Unveiling the Bitcoin Lightning (IP) Network","authors":"P. Casas, Matteo Romiti, Peter Holzer, Sami Ben Mariem, B. Donnet, Bernhard Haslhofer","doi":"10.1109/CloudNet53349.2021.9657121","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657121","url":null,"abstract":"Proposed in 2016 and launched in 2018, the Bitcoin (BTC) Lightning Network (LN) can scale-up the capacity of the BTC blockchain network to process a significantly higher amount of transactions, in a faster, cheaper, and more privacy preserving manner. The number of LN nodes has been significantly increasing since 2018, and today there are more than twelve thousand nodes actively participating of so-called LN payment channels. The upcoming Taproot upgrade to the Bitcoin protocol would further boost the development and adoption of the LN. Taproot is the most significant upgrade to the Bitcoin network since the block size increase of 2017, and it will make LN transactions cheaper, more flexible, and more private. We focus on the characterization of the LN network topology, using network active measurements. By crawling the underlying P2P network supporting the Bitcoin LN over a span of 10-months, we unveil the LN in terms of size and location of its nodes as well as connectivity protocols, comparing it to the P2P IP network supporting the BTC blockchain. Among our findings, we show that IP addresses exposed by LN nodes correspond mainly to customer networks, even if most BTC nodes are actually deployed at major cloud providers, and that LN nodes significantly rely on anonymized networks and protocols such as Onion, with more than 40% of LN nodes connect through Tor.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"229 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121531623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.1109/CloudNet53349.2021.9657150
J. M. L. Filho, Maiara de Souza Coelho, C. Melo
According to a Cisco report, mobile network speeds will more than triple by 2023, from 13.2 Mbps in 2018 to 43.9 Mbps in 2023. The average 5G connection speed is forecasted to reach 575 Mbps by 2023. This increase in bandwidth on mobile networks, along with the growing demand for streaming video content, has imposed unprecedented challenges on the backhaul networks that interconnect mobile networks to the Internet core. A trend to mitigate this problem has been to bring the source of content closer to the users, bringing it from the cloud to multi-access edge computing (MEC), therefore shifting the traffic pattern from the Internet core to the edge. In this article, we propose a framework called live streaming with super-resolution (LiveSR) that uses deep neural network-based super-resolution. In the LiveSR, live video moves in low resolution down to MEC and upscales to high resolution before being delivered to viewers over high-bandwidth mobile networks. We evaluate the proposed framework in scenarios with real 5G network traces. When we compare the proposed framework and a cloud-based video delivery system in a network defined by congested backhaul links, results show that the LiveSR framework can increase the quality of experience (QoE) in adaptive live videos by 49%, 51%, and 58% for the LoL+, BOLA, and L2A-LL adaptive algorithms, respectively. A considerable reduction in traffic in the backhaul is also recorded, ranging from 97.36% to 98.18%.
{"title":"Super-resolution on Edge Computing for Improved Adaptive HTTP Live Streaming Delivery","authors":"J. M. L. Filho, Maiara de Souza Coelho, C. Melo","doi":"10.1109/CloudNet53349.2021.9657150","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657150","url":null,"abstract":"According to a Cisco report, mobile network speeds will more than triple by 2023, from 13.2 Mbps in 2018 to 43.9 Mbps in 2023. The average 5G connection speed is forecasted to reach 575 Mbps by 2023. This increase in bandwidth on mobile networks, along with the growing demand for streaming video content, has imposed unprecedented challenges on the backhaul networks that interconnect mobile networks to the Internet core. A trend to mitigate this problem has been to bring the source of content closer to the users, bringing it from the cloud to multi-access edge computing (MEC), therefore shifting the traffic pattern from the Internet core to the edge. In this article, we propose a framework called live streaming with super-resolution (LiveSR) that uses deep neural network-based super-resolution. In the LiveSR, live video moves in low resolution down to MEC and upscales to high resolution before being delivered to viewers over high-bandwidth mobile networks. We evaluate the proposed framework in scenarios with real 5G network traces. When we compare the proposed framework and a cloud-based video delivery system in a network defined by congested backhaul links, results show that the LiveSR framework can increase the quality of experience (QoE) in adaptive live videos by 49%, 51%, and 58% for the LoL+, BOLA, and L2A-LL adaptive algorithms, respectively. A considerable reduction in traffic in the backhaul is also recorded, ranging from 97.36% to 98.18%.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133958661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.1109/CloudNet53349.2021.9657137
Kohei Ogawa, Kouto Miyazawa, Saneyasu Yamaguchi, A. Kobayashi
Abstract—TCP BBR (Bottleneck Bandwidth and Round-trip time) is one of the most promising transport layer algorithms in the near future. This algorithm provides higher throughput than existing algorithms. However, it was reported that the throughput fairness between TCP BBR connections that share the bottleneck link is not high in some cases. In this situation, the throughputs of some TCP BBR connections are not high enough. We think that this should be solved for TCP BBR to become popular. In this paper, we focus on the TCP BBR implementation of the Linux kernel and discuss the throughput fairness between TCP BBR connections. First, we evaluate the throughput fairness and show that the fairness is not high in some cases. Second, we reveal that the cause of this unfairness. Third, we propose a method for improving fairness by fixing a parameter in this implementation. Fourth, we evaluate the proposed method and show that the method can improve fairness significantly. In the cases of optimized parameters, the fairness index improved more than four times.
{"title":"Throughput Distribution and Stabilization on TCP BBR Connections","authors":"Kohei Ogawa, Kouto Miyazawa, Saneyasu Yamaguchi, A. Kobayashi","doi":"10.1109/CloudNet53349.2021.9657137","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657137","url":null,"abstract":"Abstract—TCP BBR (Bottleneck Bandwidth and Round-trip time) is one of the most promising transport layer algorithms in the near future. This algorithm provides higher throughput than existing algorithms. However, it was reported that the throughput fairness between TCP BBR connections that share the bottleneck link is not high in some cases. In this situation, the throughputs of some TCP BBR connections are not high enough. We think that this should be solved for TCP BBR to become popular. In this paper, we focus on the TCP BBR implementation of the Linux kernel and discuss the throughput fairness between TCP BBR connections. First, we evaluate the throughput fairness and show that the fairness is not high in some cases. Second, we reveal that the cause of this unfairness. Third, we propose a method for improving fairness by fixing a parameter in this implementation. Fourth, we evaluate the proposed method and show that the method can improve fairness significantly. In the cases of optimized parameters, the fairness index improved more than four times.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125124829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-08DOI: 10.1109/CloudNet53349.2021.9657156
K. Kontodimas, P. Soumplis, A. Kretsis, P. Kokkinos, Emmanouel A. Varvarigos
Distributed storage systems place data in multiple cloud datacenters, leading to increased availability and flexibility. However, limitations are present when strict bandwidth and latency requirements are posed by the applications, as the data are stored into different, probably distant locations. The incorporation of edge resources in distributed storage services enables the placement of the data closer to their source, serving better the applications’ demands. Erasure coding offers a way to increase the availability and longevity of data during hosting. In our work, we develop mechanisms that perform resource allocation and store data at edge and cloud resources taking advantage of their different characteristics, while also exploiting the erasure coding technique. Initially, we provide a mixed integer linear programming formulation of the considered problem. As the search space can be vast and the execution time prohibitively large for real size problems, we also propose a heuristic approach which makes use of the rollout policy to efficiently trade-off performance with execution time. A set of simulation experiments is performed to showcase the validity of the proposed methods.
{"title":"Secure Distributed Storage on Cloud-Edge Infrastructures","authors":"K. Kontodimas, P. Soumplis, A. Kretsis, P. Kokkinos, Emmanouel A. Varvarigos","doi":"10.1109/CloudNet53349.2021.9657156","DOIUrl":"https://doi.org/10.1109/CloudNet53349.2021.9657156","url":null,"abstract":"Distributed storage systems place data in multiple cloud datacenters, leading to increased availability and flexibility. However, limitations are present when strict bandwidth and latency requirements are posed by the applications, as the data are stored into different, probably distant locations. The incorporation of edge resources in distributed storage services enables the placement of the data closer to their source, serving better the applications’ demands. Erasure coding offers a way to increase the availability and longevity of data during hosting. In our work, we develop mechanisms that perform resource allocation and store data at edge and cloud resources taking advantage of their different characteristics, while also exploiting the erasure coding technique. Initially, we provide a mixed integer linear programming formulation of the considered problem. As the search space can be vast and the execution time prohibitively large for real size problems, we also propose a heuristic approach which makes use of the rollout policy to efficiently trade-off performance with execution time. A set of simulation experiments is performed to showcase the validity of the proposed methods.","PeriodicalId":369247,"journal":{"name":"2021 IEEE 10th International Conference on Cloud Networking (CloudNet)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123962428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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