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Towards efficient processing of latency-sensitive serverless DAGs at the edge

Authors:

Ludmila Cherkasova,

Gabriel Parmer,

Timothy WoodAuthors Info & Claims

EdgeSys '22: Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking

Pages 49 - 54

https://doi.org/10.1145/3517206.3526274

Published: 05 April 2022 Publication History

Abstract

Many emerging novel applications expect "near real-time" processing and responses, which can not be guaranteed by today's Cloud and would require processing at the Edge. Serverless computing is a particularly promising architecture for edge environments since it offers to improve efficiency by precisely scaling resources to meet application needs. As the edge applications grow more complex and get composed from a subset of simpler functions or microservices, there is a need to support more complicated function topologies which can be represented as directed acyclic graphs (DAGs). However, running DAG functions on a serverless platform poses new challenges related to interconnecting, instantiating, and scheduling function sandboxes. In this paper¹, we explore how Sledge, a Wasm-based serverless runtime, can be extended to support DAG functions. Sledge's unique design allows for extremely lightweight sandbox instantiation - a new sandbox can be started for each function invocation in under 30μsec - which mitigates the cold start problems that can be especially detrimental to DAGs. Rather than relying on expensive coordination via shared storage, the enhanced Sledge framework provides a fast memory communication channel to propagate data through the DAG. We consider the DAGs with service level objectives, defined by their execution deadlines. To ensure the DAGs meet their performance requirements, we consider, analyze, and compare two deadline-aware pluggable schedulers (that we implemented in Sledge) on a variety of realistic workloads.

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Cited By

Giagkos DTzenetopoulos AMasouros DXydis SCatthoor FSoudris D(2024)AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the EdgeInformation10.3390/info1508048015:8(480)Online publication date: 13-Aug-2024
https://doi.org/10.3390/info15080480
Russo Russo GCardellini VLo Presti F(2024)A framework for offloading and migration of serverless functions in the Edge–Cloud ContinuumPervasive and Mobile Computing10.1016/j.pmcj.2024.101915100:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101915
Russo Russo GFerrarelli DPasquali DCardellini VLo Presti F(2024)QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuumFuture Generation Computer Systems10.1016/j.future.2024.02.019156:C(1-15)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.future.2024.02.019
Show More Cited By

Index Terms

Towards efficient processing of latency-sensitive serverless DAGs at the edge
1. Computer systems organization
  1. Real-time systems
    1. Real-time system architecture
2. Computing methodologies
  1. Distributed computing methodologies

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cover image ACM Conferences

EdgeSys '22: Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking

April 2022

67 pages

ISBN:9781450392532

DOI:10.1145/3517206

General Chairs:
Aaron Ding
TU Delft, Netherlands
,
Volker Hilt
Nokia Bell Labs, Germany

Copyright © 2022 ACM.

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Published: 05 April 2022

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Overall Acceptance Rate 10 of 23 submissions, 43%

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Cited By

Giagkos DTzenetopoulos AMasouros DXydis SCatthoor FSoudris D(2024)AI-Driven QoS-Aware Scheduling for Serverless Video Analytics at the EdgeInformation10.3390/info1508048015:8(480)Online publication date: 13-Aug-2024
https://doi.org/10.3390/info15080480
Russo Russo GCardellini VLo Presti F(2024)A framework for offloading and migration of serverless functions in the Edge–Cloud ContinuumPervasive and Mobile Computing10.1016/j.pmcj.2024.101915100:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.pmcj.2024.101915
Russo Russo GFerrarelli DPasquali DCardellini VLo Presti F(2024)QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuumFuture Generation Computer Systems10.1016/j.future.2024.02.019156:C(1-15)Online publication date: 18-Jul-2024
https://dl.acm.org/doi/10.1016/j.future.2024.02.019
Russo GMannucci TCardellini VPresti F(2023)Serverledge: Decentralized Function-as-a-Service for the Edge-Cloud Continuum2023 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM56429.2023.10099372(131-140)Online publication date: 13-Mar-2023
https://doi.org/10.1109/PERCOM56429.2023.10099372
Luo KOuyang TZhou ZChen X(2023)Behavior Tree-based Workflow Modeling and Scheduling for Serverless Edge Computing2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS57875.2023.00100(955-956)Online publication date: Jul-2023
https://doi.org/10.1109/ICDCS57875.2023.00100
Lyu X(2022)Balancing Three Important Goals for Runtimes - Isolation, High Performance, and Resource Efficiency2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOSC56246.2022.00031(60-62)Online publication date: Sep-2022
https://doi.org/10.1109/ACSOSC56246.2022.00031

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