Sketch-based measurement has emerged as a promising alternative to the traditional sampling-based network measurement approaches due to its high accuracy and resource efficiency. While there have been various designs around sketches, they focus on measuring one particular flow key, and it is infeasible to support many keys based on these sketches. In this work, we take a significant step towards supporting arbitrary partial key queries, where we only need to specify a full range of possible flow keys that are of interest before measurement starts, and in query time, we can extract the information of any key in that range. We design CocoSketch, which casts arbitrary partial key queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. To realize desirable resource-accuracy tradeoffs in software and hardware platforms, we propose two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. We implement CocoSketch on four popular platforms (CPU, Open vSwitch, P4, and FPGA) and show that compared to baselines that use traditional single-key sketches, CocoSketch improves average packet processing throughput by 27.2x and accuracy by 10.4x when measuring six flow keys.
{"title":"CocoSketch","authors":"Yinda Zhang, Zaoxing Liu, Ruixin Wang, Tong Yang, Jizhou Li, Ruijie Miao, Peng Liu, Ruwen Zhang, Junchen Jiang","doi":"10.1145/3452296.3472892","DOIUrl":"https://doi.org/10.1145/3452296.3472892","url":null,"abstract":"Sketch-based measurement has emerged as a promising alternative to the traditional sampling-based network measurement approaches due to its high accuracy and resource efficiency. While there have been various designs around sketches, they focus on measuring one particular flow key, and it is infeasible to support many keys based on these sketches. In this work, we take a significant step towards supporting arbitrary partial key queries, where we only need to specify a full range of possible flow keys that are of interest before measurement starts, and in query time, we can extract the information of any key in that range. We design CocoSketch, which casts arbitrary partial key queries to the subset sum estimation problem and makes the theoretical tools for subset sum estimation practical. To realize desirable resource-accuracy tradeoffs in software and hardware platforms, we propose two techniques: (1) stochastic variance minimization to significantly reduce per-packet update delay, and (2) removing circular dependencies in the per-packet update logic to make the implementation hardware-friendly. We implement CocoSketch on four popular platforms (CPU, Open vSwitch, P4, and FPGA) and show that compared to baselines that use traditional single-key sketches, CocoSketch improves average packet processing throughput by 27.2x and accuracy by 10.4x when measuring six flow keys.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81500936","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}
This paper proposes 1Pipe, a novel communication abstraction that enables different receivers to process messages from senders in a consistent total order. More precisely, 1Pipe provides both unicast and scattering (i.e., a group of messages to different destinations) in a causally and totally ordered manner. 1Pipe provides a best effort service that delivers each message at most once, as well as a reliable service that guarantees delivery and provides restricted atomic delivery for each scattering. 1Pipe can simplify and accelerate many distributed applications, e.g., transactional key-value stores, log replication, and distributed data structures. We propose a scalable and efficient method to implement 1Pipe inside data centers. To achieve total order delivery in a scalable manner, 1Pipe separates the bookkeeping of order information from message forwarding, and distributes the work to each switch and host. 1Pipe aggregates order information using in-network computation at switches. This forms the “control plane” of the system. On the “data plane”, 1Pipe forwards messages in the network as usual and reorders them at the receiver based on the order information. Evaluation on a 32-server testbed shows that 1Pipe achieves scalable throughput (80M messages per second per host) and low latency (10𝜇s) with little CPU and network overhead. 1Pipe achieves linearly scalable throughput and low latency in transactional key-value store, TPC-C, remote data structures, and replication that outperforms traditional designs by 2∼20x.
{"title":"1Pipe","authors":"Bojie Li, Gefei Zuo, Wei Bai, Lintao Zhang","doi":"10.1145/3452296.3472909","DOIUrl":"https://doi.org/10.1145/3452296.3472909","url":null,"abstract":"This paper proposes 1Pipe, a novel communication abstraction that enables different receivers to process messages from senders in a consistent total order. More precisely, 1Pipe provides both unicast and scattering (i.e., a group of messages to different destinations) in a causally and totally ordered manner. 1Pipe provides a best effort service that delivers each message at most once, as well as a reliable service that guarantees delivery and provides restricted atomic delivery for each scattering. 1Pipe can simplify and accelerate many distributed applications, e.g., transactional key-value stores, log replication, and distributed data structures. We propose a scalable and efficient method to implement 1Pipe inside data centers. To achieve total order delivery in a scalable manner, 1Pipe separates the bookkeeping of order information from message forwarding, and distributes the work to each switch and host. 1Pipe aggregates order information using in-network computation at switches. This forms the “control plane” of the system. On the “data plane”, 1Pipe forwards messages in the network as usual and reorders them at the receiver based on the order information. Evaluation on a 32-server testbed shows that 1Pipe achieves scalable throughput (80M messages per second per host) and low latency (10𝜇s) with little CPU and network overhead. 1Pipe achieves linearly scalable throughput and low latency in transactional key-value store, TPC-C, remote data structures, and replication that outperforms traditional designs by 2∼20x.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73621185","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}
For the widely deployed ECN-based congestion control schemes, the marking threshold is the key to deliver high bandwidth and low latency. However, due to traffic dynamics in the high-speed production networks, it is difficult to maintain persistent performance by using the static ECN setting. To meet the operational challenge, in this paper we report the design and implementation of an automatic run-time optimization scheme, ACC, which leverages the multi-agent reinforcement learning technique to dynamically adjust the marking threshold at each switch. The proposed approach works in a distributed fashion and combines offline and online training to adapt to dynamic traffic patterns. It can be easily deployed based on the common features supported by major commodity switching chips. Both testbed experiments and large-scale simulations have shown that ACC achieves low flow completion time (FCT) for both mice flows and elephant flows at line-rate. Under heterogeneous production environments with 300 machines, compared with the well-tuned static ECN settings, ACC achieves up to 20% improvement on IOPS and 30% lower FCT for storage service. ACC has been applied in high-speed datacenter networks and significantly simplifies the network operations.
{"title":"ACC: automatic ECN tuning for high-speed datacenter networks","authors":"Siyu Yan, Xiaoliang Wang, Xiaolong Zheng, Yinben Xia, Derui Liu, Weishan Deng","doi":"10.1145/3452296.3472927","DOIUrl":"https://doi.org/10.1145/3452296.3472927","url":null,"abstract":"For the widely deployed ECN-based congestion control schemes, the marking threshold is the key to deliver high bandwidth and low latency. However, due to traffic dynamics in the high-speed production networks, it is difficult to maintain persistent performance by using the static ECN setting. To meet the operational challenge, in this paper we report the design and implementation of an automatic run-time optimization scheme, ACC, which leverages the multi-agent reinforcement learning technique to dynamically adjust the marking threshold at each switch. The proposed approach works in a distributed fashion and combines offline and online training to adapt to dynamic traffic patterns. It can be easily deployed based on the common features supported by major commodity switching chips. Both testbed experiments and large-scale simulations have shown that ACC achieves low flow completion time (FCT) for both mice flows and elephant flows at line-rate. Under heterogeneous production environments with 300 machines, compared with the well-tuned static ECN settings, ACC achieves up to 20% improvement on IOPS and 30% lower FCT for storage service. ACC has been applied in high-speed datacenter networks and significantly simplifies the network operations.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82069135","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}
A. Mahimkar, Carlos Eduardo de Andrade, R. Sinha, Giritharan Rana
Change management has been a long-standing challenge for network operations. The large scale and diversity of networks, their complex dependencies, and continuous evolution through technology and software updates combined with the risk of service impact create tremendous challenges to effectively manage changes. In this paper, we use data from a large service provider and experiences of their operations teams to highlight the need for quick and easy adaptation of change management capabilities and keep up with the continuous network changes. We propose a new framework CORNET (COmposition fRamework for chaNge managEmenT) with key ideas of modularization of changes into building blocks, flexible composition into change workflows, change plan optimization, change impact verification, and automated translation of high-level change management intent into low-level implementations and mathematical models. We demonstrate the effectiveness of CORNET using real-world data collected from 4G and 5G cellular networks and virtualized services such as VPN and SDWAN running in the cloud as well as experiments conducted on a testbed of virtualized network functions. We also share our operational experiences and lessons learned from successfully using CORNET within a large service provider network over the last three years.
{"title":"A composition framework for change management","authors":"A. Mahimkar, Carlos Eduardo de Andrade, R. Sinha, Giritharan Rana","doi":"10.1145/3452296.3472901","DOIUrl":"https://doi.org/10.1145/3452296.3472901","url":null,"abstract":"Change management has been a long-standing challenge for network operations. The large scale and diversity of networks, their complex dependencies, and continuous evolution through technology and software updates combined with the risk of service impact create tremendous challenges to effectively manage changes. In this paper, we use data from a large service provider and experiences of their operations teams to highlight the need for quick and easy adaptation of change management capabilities and keep up with the continuous network changes. We propose a new framework CORNET (COmposition fRamework for chaNge managEmenT) with key ideas of modularization of changes into building blocks, flexible composition into change workflows, change plan optimization, change impact verification, and automated translation of high-level change management intent into low-level implementations and mathematical models. We demonstrate the effectiveness of CORNET using real-world data collected from 4G and 5G cellular networks and virtualized services such as VPN and SDWAN running in the cloud as well as experiments conducted on a testbed of virtualized network functions. We also share our operational experiences and lessons learned from successfully using CORNET within a large service provider network over the last three years.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82292515","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}
Jaehong Min, Ming G. Liu, Tapan Chugh, Chenxingyu Zhao, Andrew Wei, I. Doh, A. Krishnamurthy
Emerging SmartNIC-based disaggregated NVMe storage has become a promising storage infrastructure due to its competitive IO performance and low cost. These SmartNIC JBOFs are shared among multiple co-resident applications, and there is a need for the platform to ensure fairness, QoS, and high utilization. Unfortunately, given the limited computing capability of the SmartNICs and the non-deterministic nature of NVMe drives, it is challenging to provide such support on today's SmartNIC JBOFs. This paper presents Gimbal, a software storage switch that orchestrates IO traffic between Ethernet ports and NVMe drives for co-located tenants. It enables efficient multi-tenancy on SmartNIC JBOFs using the following techniques: a delay-based SSD congestion control algorithm, dynamic estimation of SSD write costs, a fair scheduler that operates at the granularity of a virtual slot, and an end-to-end credit-based flow control channel. Our prototyped system not only achieves up to x6.6 better utilization and 62.6% less tail latency but also improves the fairness for complex workloads. It also improves a commercial key-value store performance in a multi-tenant environment with x1.7 better throughput and 35.0% less tail latency on average.
{"title":"Gimbal","authors":"Jaehong Min, Ming G. Liu, Tapan Chugh, Chenxingyu Zhao, Andrew Wei, I. Doh, A. Krishnamurthy","doi":"10.1145/3452296.3472940","DOIUrl":"https://doi.org/10.1145/3452296.3472940","url":null,"abstract":"Emerging SmartNIC-based disaggregated NVMe storage has become a promising storage infrastructure due to its competitive IO performance and low cost. These SmartNIC JBOFs are shared among multiple co-resident applications, and there is a need for the platform to ensure fairness, QoS, and high utilization. Unfortunately, given the limited computing capability of the SmartNICs and the non-deterministic nature of NVMe drives, it is challenging to provide such support on today's SmartNIC JBOFs. This paper presents Gimbal, a software storage switch that orchestrates IO traffic between Ethernet ports and NVMe drives for co-located tenants. It enables efficient multi-tenancy on SmartNIC JBOFs using the following techniques: a delay-based SSD congestion control algorithm, dynamic estimation of SSD write costs, a fair scheduler that operates at the granularity of a virtual slot, and an end-to-end credit-based flow control channel. Our prototyped system not only achieves up to x6.6 better utilization and 62.6% less tail latency but also improves the fairness for complex workloads. It also improves a commercial key-value store performance in a multi-tenant environment with x1.7 better throughput and 35.0% less tail latency on average.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80761101","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}
Qizhe Cai, Shubham Chaudhary, Midhul Vuppalapati, Jaehyun Hwang, R. Agarwal
Traditional end-host network stacks are struggling to keep up with rapidly increasing datacenter access link bandwidths due to their unsustainable CPU overheads. Motivated by this, our community is exploring a multitude of solutions for future network stacks: from Linux kernel optimizations to partial hardware offload to clean-slate userspace stacks to specialized host network hardware. The design space explored by these solutions would benefit from a detailed understanding of CPU inefficiencies in existing network stacks. This paper presents measurement and insights for Linux kernel network stack performance for 100Gbps access link bandwidths. Our study reveals that such high bandwidth links, coupled with relatively stagnant technology trends for other host resources (e.g., CPU speeds and capacity, cache sizes, NIC buffer sizes, etc.), mark a fundamental shift in host network stack bottlenecks. For instance, we find that a single core is no longer able to process packets at line rate, with data copy from kernel to application buffers at the receiver becoming the core performance bottleneck. In addition, increase in bandwidth-delay products have outpaced the increase in cache sizes, resulting in inefficient DMA pipeline between the NIC and the CPU. Finally, we find that traditional loosely-coupled design of network stack and CPU schedulers in existing operating systems becomes a limiting factor in scaling network stack performance across cores. Based on insights from our study, we discuss implications to design of future operating systems, network protocols, and host hardware.
{"title":"Understanding host network stack overheads","authors":"Qizhe Cai, Shubham Chaudhary, Midhul Vuppalapati, Jaehyun Hwang, R. Agarwal","doi":"10.1145/3452296.3472888","DOIUrl":"https://doi.org/10.1145/3452296.3472888","url":null,"abstract":"Traditional end-host network stacks are struggling to keep up with rapidly increasing datacenter access link bandwidths due to their unsustainable CPU overheads. Motivated by this, our community is exploring a multitude of solutions for future network stacks: from Linux kernel optimizations to partial hardware offload to clean-slate userspace stacks to specialized host network hardware. The design space explored by these solutions would benefit from a detailed understanding of CPU inefficiencies in existing network stacks. This paper presents measurement and insights for Linux kernel network stack performance for 100Gbps access link bandwidths. Our study reveals that such high bandwidth links, coupled with relatively stagnant technology trends for other host resources (e.g., CPU speeds and capacity, cache sizes, NIC buffer sizes, etc.), mark a fundamental shift in host network stack bottlenecks. For instance, we find that a single core is no longer able to process packets at line rate, with data copy from kernel to application buffers at the receiver becoming the core performance bottleneck. In addition, increase in bandwidth-delay products have outpaced the increase in cache sizes, resulting in inefficient DMA pipeline between the NIC and the CPU. Finally, we find that traditional loosely-coupled design of network stack and CPU schedulers in existing operating systems becomes a limiting factor in scaling network stack performance across cores. Based on insights from our study, we discuss implications to design of future operating systems, network protocols, and host hardware.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86539323","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}
Michael Reininger, Arushi Arora, Stephen Herwig, Nicholas Francino, Jayson Hurst, Christina Garman, Dave Levin
Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor—it is largely relegated to proxies and hidden services—or to nimbly react to new forms of attack. Conversely, “non-anonymous” Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes. This paper seeks to close this gap by introducing programmable middleboxes into the Tor network. In this architecture, users can install and run sophisticated “functions” on willing Tor routers. We demonstrate a wide range of functions that improve anonymity, resilience to attack, performance of hidden services, and more. We present the design and implementation of an architecture, Bento, that protects middlebox nodes from the functions they run—and protects the functions from the middleboxes they run on. We evaluate Bento by running it on the live Tor network. We show that, with just a few lines of Python, we can significantly extend the capabilities of Tor to meet users' anonymity needs and nimbly react to new threats. We will be making our code and data publicly available.
{"title":"Bento","authors":"Michael Reininger, Arushi Arora, Stephen Herwig, Nicholas Francino, Jayson Hurst, Christina Garman, Dave Levin","doi":"10.1145/3452296.3472919","DOIUrl":"https://doi.org/10.1145/3452296.3472919","url":null,"abstract":"Tor is a powerful and important tool for providing anonymity and censorship resistance to users around the world. Yet it is surprisingly difficult to deploy new services in Tor—it is largely relegated to proxies and hidden services—or to nimbly react to new forms of attack. Conversely, “non-anonymous” Internet services are thriving like never before because of recent advances in programmable networks, such as Network Function Virtualization (NFV) which provides programmable in-network middleboxes. This paper seeks to close this gap by introducing programmable middleboxes into the Tor network. In this architecture, users can install and run sophisticated “functions” on willing Tor routers. We demonstrate a wide range of functions that improve anonymity, resilience to attack, performance of hidden services, and more. We present the design and implementation of an architecture, Bento, that protects middlebox nodes from the functions they run—and protects the functions from the middleboxes they run on. We evaluate Bento by running it on the live Tor network. We show that, with just a few lines of Python, we can significantly extend the capabilities of Tor to meet users' anonymity needs and nimbly react to new threats. We will be making our code and data publicly available.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73988744","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}
Xiao Zhang, Tanmoy Sen, Zheyuan Zhang, Tim April, B. Chandrasekaran, D. Choffnes, Bruce M. Maggs, Haiying Shen, R. Sitaraman, Xiaowei Yang
The key to optimizing the performance of an anycast-based system (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naïve approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naïve approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.
{"title":"AnyOpt","authors":"Xiao Zhang, Tanmoy Sen, Zheyuan Zhang, Tim April, B. Chandrasekaran, D. Choffnes, Bruce M. Maggs, Haiying Shen, R. Sitaraman, Xiaowei Yang","doi":"10.1145/3452296.3472935","DOIUrl":"https://doi.org/10.1145/3452296.3472935","url":null,"abstract":"The key to optimizing the performance of an anycast-based system (e.g., the root DNS or a CDN) is choosing the right set of sites to announce the anycast prefix. One challenge here is predicting catchments. A naïve approach is to advertise the prefix from all subsets of available sites and choose the best-performing subset, but this does not scale well. We demonstrate that by conducting pairwise experiments between sites peering with tier-1 networks, we can predict the catchments that would result if we announce to any subset of the sites. We prove that our method is effective in a simplified model of BGP, consistent with common BGP routing policies, and evaluate it in a real-world testbed. We then present AnyOpt, a system that predicts anycast catchments. Using AnyOpt, a network operator can find a subset of anycast sites that minimizes client latency without using the naïve approach. In an experiment using 15 sites, each peering with one of six transit providers, AnyOpt predicted site catchments of 15,300 clients with 94.7% accuracy and client RTTs with a mean error of 4.6%. AnyOpt identified a subset of 12 sites, announcing to which lowers the mean RTT to clients by 33ms compared to a greedy approach that enables the same number of sites with the lowest average unicast latency.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"289 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74388996","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}
Rachee Singh, N. Bjørner, Sharon Shoham, Yawei Yin, John Arnold, J. Gaudette
In this work we propose Shoofly, a network design tool that minimizes hardware costs of provisioning long-haul capacity by optically bypassing network hops where conversion of signals from optical to electrical domain is unnecessary and uneconomical. Shoofly leverages optical signal quality and traffic demand telemetry from a large commercial cloud provider to identify optical bypasses in the cloud WAN that reduce the hardware cost of long-haul capacity by 40%. A key challenge is that optical bypasses cause signals to travel longer distances on fiber before re-generation, potentially reducing link capacities and resilience to optical link failures. Despite these challenges, Shoofly provisions bypass-enabled topologies that meet 8X the present-day demands using existing network hardware. Even under aggressive stochastic and deterministic link failure scenarios, these topologies save 32% of the cost of long-haul capacity.
{"title":"Cost-effective capacity provisioning in wide area networks with Shoofly","authors":"Rachee Singh, N. Bjørner, Sharon Shoham, Yawei Yin, John Arnold, J. Gaudette","doi":"10.1145/3452296.3472895","DOIUrl":"https://doi.org/10.1145/3452296.3472895","url":null,"abstract":"In this work we propose Shoofly, a network design tool that minimizes hardware costs of provisioning long-haul capacity by optically bypassing network hops where conversion of signals from optical to electrical domain is unnecessary and uneconomical. Shoofly leverages optical signal quality and traffic demand telemetry from a large commercial cloud provider to identify optical bypasses in the cloud WAN that reduce the hardware cost of long-haul capacity by 40%. A key challenge is that optical bypasses cause signals to travel longer distances on fiber before re-generation, potentially reducing link capacities and resilience to optical link failures. Despite these challenges, Shoofly provisions bypass-enabled topologies that meet 8X the present-day demands using existing network hardware. Even under aggressive stochastic and deterministic link failure scenarios, these topologies save 32% of the cost of long-haul capacity.","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75063978","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 : 2021-08-09DOI: 10.1007/978-3-662-48986-4_301467
J. Nolan, Kun Qian, Xinyu Zhang
{"title":"RoS","authors":"J. Nolan, Kun Qian, Xinyu Zhang","doi":"10.1007/978-3-662-48986-4_301467","DOIUrl":"https://doi.org/10.1007/978-3-662-48986-4_301467","url":null,"abstract":"","PeriodicalId":20487,"journal":{"name":"Proceedings of the 2021 ACM SIGCOMM 2021 Conference","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79038489","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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