Abstract
The network representation used for conveying an application's objective in cloud environments, which we refer to as the Application Network Interface (ANI), has steadily evolved - from packet to flow and flowlet, and more complex abstractions such as coflow. In this paper, we argue that state-of-the-art ANIs still fail to capture important application needs. Using distributed deep learning as a representative application, we show that application performance achievable using current ANIs are up to 25% lower than optimal. We analyze these ANIs to understand the missing pieces and put forward CadentFlow, an ANI with per-flow metrics and an optimization objective, to capture application requirements effectively. We discuss the opportunity for real-world implementation of a more expressive ANI and its implications on the design of network controllers and scheduling algorithms.
Original language | English (US) |
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State | Published - 2020 |
Event | 12th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2020, co-located with USENIX ATC 2020 - Virtual, Online Duration: Jul 13 2020 → Jul 14 2020 |
Conference
Conference | 12th USENIX Workshop on Hot Topics in Cloud Computing, HotCloud 2020, co-located with USENIX ATC 2020 |
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City | Virtual, Online |
Period | 7/13/20 → 7/14/20 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Software