Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time=2021-10-22 8:40
|time='''2025-09-19 10:30'''
|addr=Main Building B1-612
|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]].
}}
}}


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{{Latest_seminar
{{Latest_seminar
|abstract=In this paper, an enhanced flooding-based routing protocol is designed based on random network coding (RNC) and clustering for swarm UAV networks, enabling the efficient routing process without any routing path discovery or network topology information. RNC can naturally accelerate the routing process, with which in some hops fewer generations need to be transmitted. To address the issue of numerous hops and further expedite routing process, a clustering method is leveraged, where UAV networks are partitioned into multiple clusters and generations are only flooded from representatives of each cluster rather than flooded from each UAV. By this way, the amount of hops can be significantly reduced. The technical details of the introduced routing protocol are designed. Moreover, to capture the dynamic network topology, the Poisson cluster process is employed to model UAV networks. Afterwards, stochastic geometry tools are utilized to derive the distance distribution between two random selected UAVs and analytically evaluate performance. Extensive simulation studies are conducted to prove the validation of performance analysis, demonstrate the effectiveness of our designed routing protocol, and reveal its design insight.
|abstract = With cloud-side computing and rendering, mobile cloud gaming (MCG) is expected to deliver high-quality gaming experiences to budget mobile devices. However, our measurement on representative MCG platforms reveals that even under good network conditions, all platforms exhibit high interactive latency of 112–403 ms, from a user-input action to its display response, that critically affects users’ quality of experience. Moreover, jitters in network latency often lead to significant fluctuations in interactive latency. In this work, we collaborate with a commercial MCG platform to conduct the first in-depth analysis on the interactive latency of cloud gaming. We identify VSync, the synchronization primitive of Android graphics pipeline, to be a key contributor to the excessive interactive latency; as many as five VSync events are intricately invoked, which serialize the complex graphics processing logic on both the client and cloud sides. To address this, we design an end-to-end VSync regulator, dubbed LoopTailor, which minimizes VSync events by decoupling game rendering from the lengthy cloud-side graphics pipeline and coordinating cloud game rendering directly with the client. We implement LoopTailor on the collaborated platform and commodity Android devices, reducing the interactive latency (by ∼34%) to stably below 100 ms.
|confname=INFOCOM 2021
|confname =NSDI'25
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9488721
|link = https://www.usenix.org/conference/nsdi25/presentation/li-yang
|title=Enhanced Flooding-Based Routing Protocol for Swarm UAV Networks: Random Network Coding Meets Clustering
|title= Dissecting and Streamlining the Interactive Loop of Mobile Cloud Gaming
|speaker=Luwei
|speaker= Li Chen
|date=2025-9-9
}}
}}
{{Latest_seminar
{{Latest_seminar
|abstract=In recent years, device-to-device (D2D) communication has attained significant attention in the research community. The advantages of D2D communication can be fully realized in multi-hop communication scenario. The integration of cellular and multi-hop networks not only provides guaranteed quality of service and reliability as a traditional cellular network, but also has the flexibility and adaptability as a multi-hop network. Routing in such multi-hop cellular D2D networks is a critical issue, since the multi-hop network can perform worse than a traditional cellular network if wrong routing decisions are made. This is because routing in these multi-hop networks needs to take care of the node mobility, dynamic network topology, and network fragmentation, which did not exist in traditional cellular networking. This paper provides a comprehensive survey of routing in multi-hop D2D networks. Some future research directions for the routing in D2D networks are also discussed at the end of this paper.
|abstract = The local deployment of large language models (LLMs) on mobile devices has garnered increasing attention due to its advantages in enhancing user privacy and enabling offline operation. However, given the limited computational resources of a single mobile device, only small language models (SLMs) with restricted capabilities can currently be supported. In this paper, we explore the potential of leveraging the collective computing power of multiple mobile devices to collaboratively support more efficient local LLM inference. We evaluate the feasibility and efficiency of existing parallelism techniques under the constraints of mobile devices and wireless network, identifying that chunked pipeline parallelism holds promise for realizing this vision. Building on this insight, we propose FlexSpark, a novel solution designed to achieve efficient and robust multi-device collaborative inference. FlexSpark incorporates priority scheduling, ordered communication, and elastic compression to maximize wireless bandwidth utilization, and thus accelerates distributed inference. Preliminary experimental results demonstrate that FlexSpark achieves up to a 2 × speedup compared to state-of-the-art frameworks, significantly enhancing the practicality and scalability of LLM deployment on mobile devices.
|confname=IEEE Communications Surveys & Tutorials 2018
|confname =APNet'25
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8386758
|link = https://dl.acm.org/doi/10.1145/3735358.3735368
|title=Routing in Multi-Hop Cellular Device-to-Device(D2D) Networks: A Survey
|title= FlexSpark: Robust and Efficient Multi-Device Collaborative Inference over Wireless Network
|speaker=Wenjie
|speaker=Ruizhen
|date=2025-9-19
}}
}}
{{Latest_seminar
|abstract=Internet path failure recovery relies on routing protocols, such as BGP. However, routing can take minutes to detect failures and reconverge; in some cases, like partial failures or severe performance degradation, it may never intervene. For large scale network outages, such as those caused by route leaks, bypassing the affected party completely may be the only effective solution. This paper presents Connection Path Reselection (CPR), a novel system that operates on edge networks such as Content Delivery Networks and edge peering facilities and augments TCP to deliver transparent, scalable, multipath-aware end-to-end path failure recovery. The key intuition behind it is that edge networks need not rely on BGP to learn of path impairments: they can infer the status of a path by monitoring transport-layer forward progress, and then reroute stalled flows onto healthy paths. Unlike routing protocols such as BGP, CPR operates at the timescale of round-trip times, providing connection recovery in seconds rather than minutes. By delegating routing responsibilities to the edge hosts themselves, CPR achieves per-connection re-routing protection for all destination prefixes without incurring additional costs reconstructing transport protocol state within the network. Unlike previous multipath-aware transport protocols, CPR is unilaterally deployable and has been running in production at a large edge network for over two years.
|confname=NSDI 2021
|link=https://www.usenix.org/system/files/nsdi21-landa.pdf
|title=Staying Alive: Connection Path Reselection at the Edge
|speaker=Zhuoliu
}}
=== History ===
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Latest revision as of 18:03, 18 September 2025

Time: 2025-09-19 10:30
Address: 4th Research Building A518
Useful links: 📚 Readling list; 📆 Schedules; 🧐 Previous seminars.

Latest

  1. [NSDI'25] Dissecting and Streamlining the Interactive Loop of Mobile Cloud Gaming, Li Chen
    Abstract: With cloud-side computing and rendering, mobile cloud gaming (MCG) is expected to deliver high-quality gaming experiences to budget mobile devices. However, our measurement on representative MCG platforms reveals that even under good network conditions, all platforms exhibit high interactive latency of 112–403 ms, from a user-input action to its display response, that critically affects users’ quality of experience. Moreover, jitters in network latency often lead to significant fluctuations in interactive latency. In this work, we collaborate with a commercial MCG platform to conduct the first in-depth analysis on the interactive latency of cloud gaming. We identify VSync, the synchronization primitive of Android graphics pipeline, to be a key contributor to the excessive interactive latency; as many as five VSync events are intricately invoked, which serialize the complex graphics processing logic on both the client and cloud sides. To address this, we design an end-to-end VSync regulator, dubbed LoopTailor, which minimizes VSync events by decoupling game rendering from the lengthy cloud-side graphics pipeline and coordinating cloud game rendering directly with the client. We implement LoopTailor on the collaborated platform and commodity Android devices, reducing the interactive latency (by ∼34%) to stably below 100 ms.
  2. [APNet'25] FlexSpark: Robust and Efficient Multi-Device Collaborative Inference over Wireless Network, Ruizhen
    Abstract: The local deployment of large language models (LLMs) on mobile devices has garnered increasing attention due to its advantages in enhancing user privacy and enabling offline operation. However, given the limited computational resources of a single mobile device, only small language models (SLMs) with restricted capabilities can currently be supported. In this paper, we explore the potential of leveraging the collective computing power of multiple mobile devices to collaboratively support more efficient local LLM inference. We evaluate the feasibility and efficiency of existing parallelism techniques under the constraints of mobile devices and wireless network, identifying that chunked pipeline parallelism holds promise for realizing this vision. Building on this insight, we propose FlexSpark, a novel solution designed to achieve efficient and robust multi-device collaborative inference. FlexSpark incorporates priority scheduling, ordered communication, and elastic compression to maximize wireless bandwidth utilization, and thus accelerates distributed inference. Preliminary experimental results demonstrate that FlexSpark achieves up to a 2 × speedup compared to state-of-the-art frameworks, significantly enhancing the practicality and scalability of LLM deployment on mobile devices.

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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