Difference between revisions of "Resource:Seminar"

From MobiNetS
Jump to: navigation, search
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract=This paper presents CellFusion, a system designed for high-quality, real-time video streaming from vehicles to the cloud. It leverages an innovative blend of multipath QUIC transport and network coding. Surpassing the limitations of individual cellular carriers, CellFusion uses a unique last-mile overlay that integrates multiple cellular networks into a single, unified cloud connection. This integration is made possible through the use of in-vehicle Customer Premises Equipment (CPEs) and edge-cloud proxy servers. In order to effectively handle unstable cellular connections prone to intense burst losses and unexpected latency spikes as a vehicle moves, CellFusion introduces XNC. This innovative network coding-based transport solution enables efficient and resilient multipath transport. XNC aims to accomplish low latency, minimal traffic redundancy, and reduced computational complexity all at once. CellFusion is secure and transparent by nature and does not require modifications for vehicular apps connecting to it. We tested CellFusion on 100 self-driving vehicles for over six months with our cloud-native back-end running on 50 CDN PoPs. Through extensive road tests, we show that XNC reduced video packet delay by 71.53% at the 99th percentile versus 5G. At 30Mbps, CellFusion achieved 66.11% ~ 80.62% reduction in video stall ratio versus state-of-the-art multipath transport solutions with less than 10% traffic redundancy.
|abstract=Recent years have witnessed the wide adoption of RDMA in the cloud to accelerate first-party workloads and achieve cost savings by freeing up CPU cycles. Now cloud providers are working towards supporting RDMA in general-purpose guest VMs to benefit third-party workloads. To this end, cloud providers must provide strong performance isolation so that the RDMA workloads of one tenant do not adversely impact the RDMA performance of another tenant. Despite many efforts on network performance isolation in the public cloud, we find that RDMA brings unique challenges due to its complex NIC microarchitecture resources (e.g., the NIC cache).In this paper, we aim to systematically understand the impact of RNIC microarchitecture resources on performance isolation. We present a model that represents how RDMA operations use RNIC resources. Using this model, we develop a test suite to evaluate RDMA performance isolation solutions. Our test suite can break all existing solutions in various scenarios. Our results are acknowledged and reproduced by one of the largest RDMA NIC vendors. Finally, based on the test results, we summarize new insights on designing future RDMA performance isolation solutions.
|confname=SIGCOMM '23
|confname=NSDI '23
|link=https://dl.acm.org/doi/10.1145/3603269.3604832
|link=https://www.usenix.org/conference/nsdi23/presentation/kong
|title=CellFusion: Multipath Vehicle-to-Cloud Video Streaming with Network Coding in the Wild
|title=Understanding RDMA Microarchitecture Resources for Performance Isolation
|speaker=Rong Cong
|speaker=Xinyu Zhang
|date=2023-10-08}}
|date=2023-10-19}}
{{Latest_seminar
{{Latest_seminar
|abstract=Resource disaggregation offers a cost effective solution to resource scaling, utilization, and failure-handling in data centers by physically separating hardware devices in a server. Servers are architected as pools of processor, memory, and storage devices, organized as independent failure-isolated components interconnected by a high-bandwidth network. A critical challenge, however, is the high performance penalty of accessing data from a remote memory module over the network. Addressing this challenge is difficult as disaggregated systems have high runtime variability in network latencies/bandwidth, and page migration can significantly delay critical path cache line accesses in other pages. This paper conducts a characterization analysis on different data movement strategies in fully disaggregated systems, evaluates their performance overheads in a variety of workloads, and introduces DaeMon, the first software-transparent mechanism to significantly alleviate data movement overheads in fully disaggregated systems. First, to enable scalability to multiple hardware components in the system, we enhance each compute and memory unit with specialized engines that transparently handle data migrations. Second, to achieve high performance and provide robustness across various network, architecture and application characteristics, we implement a synergistic approach of bandwidth partitioning, link compression, decoupled data movement of multiple granularities, and adaptive granularity selection in data movements. We evaluate DaeMon in a wide variety of workloads at different network and architecture configurations using a state-of-the-art simulator. DaeMon improves system performance and data access costs by 2.39× and 3.06×, respectively, over the widely-adopted approach of moving data at page granularity.
|abstract=LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This paper presents FLog, a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a 3D model of the orchards. Once we have the location of a sensor and a gateway, we know the mediums that the wireless signal traverse. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents (PLE) of all mediums can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely-used propagation model.
|confname=SigMetrics '23
|confname=IPSN '23
|link=https://dl.acm.org/doi/abs/10.1145/3579445
|link=https://dl.acm.org/doi/10.1145/3583120.3586969
|title=DaeMon: Architectural Support for Efficient Data Movement in Fully Disaggregated Systems
|title=Link Quality Modeling for LoRa Networks in Orchards
|speaker=Jiyi
|speaker=Jiacheng Li
|date=2023-10-08}}
|date=2023-10-19}}
{{Latest_seminar
{{Latest_seminar
|abstract=LoRa has emerged as a key wireless communication technology for a gateway to provide geographically-distributed IoT devices with low-rate, long-range connections. In this paper, we present MaLoRaGW, the first-of-its-kind Multi-antenna LoRa GateWay that enables multi-user MIMO (MU-MIMO) LoRa communications in both uplink and downlink. MaLoRaGW was inspired by the success of MU-MIMO in cellular and Wi-Fi networks. The key component of MaLoRaGW is a joint baseband PHY design for uplink packet detection and downlink beamforming. Its innovation lies in three modules: spatial signal projection, accurate channel estimation, and implicit beamforming, all of which reside only in a LoRa gateway and require no modification on LoRa client devices. We have built a prototype of two-antenna MaLoRaGW on a USRP device and extensively evaluated its performance with commercial LoRa dongles in three scenarios: lab, office building, and university campus. Our experimental results show that, compared to the state-of-the-art, the two-antenna MaLoRaGW increases uplink throughput by 10% and downlink throughput by 95%.
|abstract=Cooperative perception significantly enhances the perception performance of connected autonomous vehicles. Instead of purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data to perceive the environment collaboratively. Through our study, we realize that the performance of cooperative perception systems is limited in real-world deployment due to (1) out-of-sync sensor data during data fusion and (2) inaccurate localization of occluded areas. To address these challenges, we develop RAO, an innovative, effective, and lightweight cooperative perception system that merges asynchronous sensor data from different vehicles through our novel designs of motion-compensated occupancy flow prediction and on-demand data sharing, improving both the accuracy and coverage of the perception system. Our extensive evaluation, including real-world and emulation-based experiments, demonstrates that RAO outperforms state-of-the-art solutions by more than 34% in perception coverage and by up to 14% in perception accuracy, especially when asynchronous sensor data is present. RAO consistently performs well across a wide variety of map topologies and driving scenarios. RAO incurs negligible additional latency (8.5 ms) and low data transmission overhead (10.9 KB per frame), making cooperative perception feasible.
|confname=SenSys '22
|confname=MobiCom '23
|link=https://dl.acm.org/doi/pdf/10.1145/3560905.3568533
|link=https://dl.acm.org/doi/abs/10.1145/3570361.3613271
|title=MaLoRaGW: Multi-User MIMO Transmission for LoRa
|title=Robust Real-time Multi-vehicle Collaboration on Asynchronous Sensors
|speaker=Kai Chen
|speaker=Yang Wang
|date=2023-10-08}}
|date=2023-10-19}}
{{Latest_seminar
{{Latest_seminar
|abstract=On-boarding new devices into an existing SDN network is a pain for network operations (NetOps) teams, because much expert effort is required to bridge the gap between the configuration models of the new devices and the unified data model in the SDN controller. In this work, we present an assistant framework NAssim, to help NetOps accelerate the process of assimilating a new device into a SDN network. Our solution features a unified parser framework to parse diverse device user manuals into preliminary configuration models, a rigorous validator that confirm the correctness of the models via formal syntax analysis, model hierarchy validation and empirical data validation, and a deep-learning-based mapping algorithm that uses state-of-the-art neural language processing techniques to produce human-comprehensible recommended mapping between the validated configuration model and the one in the SDN controller. In all, NAssim liberates the NetOps from most tedious tasks by learning directly from devices' manuals to produce data models which are comprehensible by both the SDN controller and human experts. Our evaluation shows, NAssim can accelerate the assimilation process by 9.1x. In this process, we also identify and correct 243 errors in four mainstream vendors' device manuals, and release a validated and expert-curated dataset of parsed manual corpus for future research.
|abstract=Serverless computing provides fine-grained resource elasticity for data analytics---a job can flexibly scale its resources for each stage, instead of sticking to a fixed pool of resources throughout its lifetime. Due to different data dependencies and different shuffling overheads caused by intra- and inter-server communication, the best degree of parallelism (DoP) for each stage varies based on runtime conditions. We present Ditto, a job scheduler for serverless analytics that leverages fine-grained resource elasticity to optimize for job completion time (JCT) and cost. The key idea of Ditto is to use a new scheduling granularity---stage group---to decouple parallelism configuration from function placement. Ditto bundles stages into stage groups based on their data dependencies and IO characteristics. It exploits the parallelized time characteristics of the stages to determine the parallelism configuration, and prioritizes the placement of stage groups with large shuffling traffic, so that the stages in these groups can leverage zero-copy intra-server communication for efficient shuffling. We build a system prototype of Ditto and evaluate it with a variety of benchmarking workloads. Experimental results show that Ditto outperforms existing solutions by up to 2.5× on JCT and up to 1.8× on cost.
|confname=SIGCOMM '22
|confname=SIGCOMM '23
|link=https://dl.acm.org/doi/10.1145/3544216.3544244
|link=https://dl.acm.org/doi/abs/10.1145/3603269.3604816
|title=Software-defined network assimilation: bridging the last mile towards centralized network configuration management with NAssim
|title=Ditto: Efficient Serverless Analytics with Elastic Parallelism
|speaker=Yaliang
|speaker=Mengqi Ma
|date=2023-10-08}}
|date=2023-10-19}}
=== History ===
=== History ===


{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 13:23, 15 October 2023

Time: 2023-10-08 16:20
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [NSDI '23] Understanding RDMA Microarchitecture Resources for Performance Isolation, Xinyu Zhang
    Abstract: Recent years have witnessed the wide adoption of RDMA in the cloud to accelerate first-party workloads and achieve cost savings by freeing up CPU cycles. Now cloud providers are working towards supporting RDMA in general-purpose guest VMs to benefit third-party workloads. To this end, cloud providers must provide strong performance isolation so that the RDMA workloads of one tenant do not adversely impact the RDMA performance of another tenant. Despite many efforts on network performance isolation in the public cloud, we find that RDMA brings unique challenges due to its complex NIC microarchitecture resources (e.g., the NIC cache).In this paper, we aim to systematically understand the impact of RNIC microarchitecture resources on performance isolation. We present a model that represents how RDMA operations use RNIC resources. Using this model, we develop a test suite to evaluate RDMA performance isolation solutions. Our test suite can break all existing solutions in various scenarios. Our results are acknowledged and reproduced by one of the largest RDMA NIC vendors. Finally, based on the test results, we summarize new insights on designing future RDMA performance isolation solutions.
  2. [IPSN '23] Link Quality Modeling for LoRa Networks in Orchards, Jiacheng Li
    Abstract: LoRa networks have been deployed in many orchards for environmental monitoring and crop management. An accurate propagation model is essential for efficiently deploying a LoRa network in orchards, e.g., determining gateway coverage and sensor placement. Although some propagation models have been studied for LoRa networks, they are not suitable for orchard environments, because they do not consider the shadowing effect on wireless propagation caused by the ground and tree canopies. This paper presents FLog, a propagation model for LoRa signals in orchard environments. FLog leverages a unique feature of orchards, i.e., all trees have similar shapes and are planted regularly in space. We develop a 3D model of the orchards. Once we have the location of a sensor and a gateway, we know the mediums that the wireless signal traverse. Based on this knowledge, we generate the First Fresnel Zone (FFZ) between the sender and the receiver. The intrinsic path loss exponents (PLE) of all mediums can be combined into a classic Log-Normal Shadowing model in the FFZ. Extensive experiments in almond orchards show that FLog reduces the link quality estimation error by 42.7% and improves gateway coverage estimation accuracy by 70.3%, compared with a widely-used propagation model.
  3. [MobiCom '23] Robust Real-time Multi-vehicle Collaboration on Asynchronous Sensors, Yang Wang
    Abstract: Cooperative perception significantly enhances the perception performance of connected autonomous vehicles. Instead of purely relying on local sensors with limited range, it enables multiple vehicles and roadside infrastructures to share sensor data to perceive the environment collaboratively. Through our study, we realize that the performance of cooperative perception systems is limited in real-world deployment due to (1) out-of-sync sensor data during data fusion and (2) inaccurate localization of occluded areas. To address these challenges, we develop RAO, an innovative, effective, and lightweight cooperative perception system that merges asynchronous sensor data from different vehicles through our novel designs of motion-compensated occupancy flow prediction and on-demand data sharing, improving both the accuracy and coverage of the perception system. Our extensive evaluation, including real-world and emulation-based experiments, demonstrates that RAO outperforms state-of-the-art solutions by more than 34% in perception coverage and by up to 14% in perception accuracy, especially when asynchronous sensor data is present. RAO consistently performs well across a wide variety of map topologies and driving scenarios. RAO incurs negligible additional latency (8.5 ms) and low data transmission overhead (10.9 KB per frame), making cooperative perception feasible.
  4. [SIGCOMM '23] Ditto: Efficient Serverless Analytics with Elastic Parallelism, Mengqi Ma
    Abstract: Serverless computing provides fine-grained resource elasticity for data analytics---a job can flexibly scale its resources for each stage, instead of sticking to a fixed pool of resources throughout its lifetime. Due to different data dependencies and different shuffling overheads caused by intra- and inter-server communication, the best degree of parallelism (DoP) for each stage varies based on runtime conditions. We present Ditto, a job scheduler for serverless analytics that leverages fine-grained resource elasticity to optimize for job completion time (JCT) and cost. The key idea of Ditto is to use a new scheduling granularity---stage group---to decouple parallelism configuration from function placement. Ditto bundles stages into stage groups based on their data dependencies and IO characteristics. It exploits the parallelized time characteristics of the stages to determine the parallelism configuration, and prioritizes the placement of stage groups with large shuffling traffic, so that the stages in these groups can leverage zero-copy intra-server communication for efficient shuffling. We build a system prototype of Ditto and evaluate it with a variety of benchmarking workloads. Experimental results show that Ditto outperforms existing solutions by up to 2.5× on JCT and up to 1.8× on cost.

History

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

Template loop detected: Resource:Previous Seminars

Instructions

请使用Latest_seminar和Hist_seminar模板更新本页信息.

    • 修改时间和地点信息
    • 将当前latest seminar部分的code复制到这个页面中
    • 将{{Latest_seminar... 修改为 {{Hist_seminar...，并增加对应的日期信息|date=
    • 填入latest seminar各字段信息
    • link请务必不要留空，如果没有link则填本页地址 https://mobinets.org/index.php?title=Resource:Seminar
  • 格式说明
    • Latest_seminar:

{{Latest_seminar
|confname=
|link=
|title=
|speaker=
}}

    • Hist_seminar

{{Hist_seminar
|confname=
|link=
|title=
|speaker=
|date=
}}

Retrieved from "http://mobinets.cn/site/index.php?title=Resource:Seminar&oldid=2739"