Summary of Safetail: Efficient Tail Latency Optimization in Edge Service Scheduling Via Computational Redundancy Management, by Jyoti Shokhanda et al.
SafeTail: Efficient Tail Latency Optimization in Edge Service Scheduling via Computational Redundancy Management
by Jyoti Shokhanda, Utkarsh Pal, Aman Kumar, Soumi Chattopadhyay, Arani Bhattacharya
First submitted to arxiv on: 30 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning framework is proposed to optimize tail latency in edge computing while efficiently managing computational resources. Emerging applications like augmented reality require low-latency services with high reliability on user devices with limited capabilities. Existing approaches focus on median latency but struggle with tail latency under uncertain network and computation conditions. The SafeTail framework addresses this challenge by selectively replicating services across multiple edge servers to meet target latencies, using a reward-based deep learning framework to learn optimal placement strategies. Through trace-driven simulations, SafeTail demonstrated near-optimal performance and outperformed most baseline strategies across three diverse services. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge computing needs better latency control! Some devices can’t handle heavy tasks so they rely on nearby servers for help. The problem is that network and computation speeds can be unpredictable. Most solutions focus on average speed, but not the slowest part (called tail latency). This new framework called SafeTail tries to solve this by copying services across multiple servers to meet targets while using minimal extra resources. It uses a smart learning system to figure out the best way to do this. The results are promising! |
Keywords
» Artificial intelligence » Deep learning » Machine learning
