Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-10-08 16:20'''
|time='''Thursday 16:20-18:00'''
|addr=4th Research Building A518
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].

Revision as of 13:26, 15 October 2023

Time: Thursday 16:20-18:00
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

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

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