FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems

• URL: http://arxiv.org/abs/2206.00065v1
• Date: Tue, 31 May 2022 19:19:40 GMT
• Title: FELARE: Fair Scheduling of Machine Learning Applications on Heterogeneous Edge Systems
• Authors: Ali Mokhtari, Pooyan Jamshidi, Mohsen Amini Salehi
• Abstract summary: Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. We study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.
• Score: 5.165692107696155
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Edge computing enables smart IoT-based systems via concurrent and continuous execution of latency-sensitive machine learning (ML) applications. These edge-based machine learning systems are often battery-powered (i.e., energy-limited). They use heterogeneous resources with diverse computing performance (e.g., CPU, GPU, and/or FPGAs) to fulfill the latency constraints of ML applications. The challenge is to allocate user requests for different ML applications on the Heterogeneous Edge Computing Systems (HEC) with respect to both the energy and latency constraints of these systems. To this end, we study and analyze resource allocation solutions that can increase the on-time task completion rate while considering the energy constraint. Importantly, we investigate edge-friendly (lightweight) multi-objective mapping heuristics that do not become biased toward a particular application type to achieve the objectives; instead, the heuristics consider "fairness" across the concurrent ML applications in their mapping decisions. Performance evaluations demonstrate that the proposed heuristic outperforms widely-used heuristics in heterogeneous systems in terms of the latency and energy objectives, particularly, at low to moderate request arrival rates. We observed 8.9% improvement in on-time task completion rate and 12.6% in energy-saving without imposing any significant overhead on the edge system.

Related papers

• AdaLog: Post-Training Quantization for Vision Transformers with Adaptive Logarithm Quantizer [54.713778961605115]
  Vision Transformer (ViT) has become one of the most prevailing fundamental backbone networks in the computer vision community. We propose a novel non-uniform quantizer, dubbed the Adaptive Logarithm AdaLog (AdaLog) quantizer.
  arXiv  Detail & Related papers  (2024-07-17T18:38:48Z)
• Characterization of Large Language Model Development in the Datacenter [55.9909258342639]
  Large Language Models (LLMs) have presented impressive performance across several transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster resources to develop LLMs. We present an in-depth characterization study of a six-month LLM development workload trace collected from our GPU datacenter Acme.
  arXiv  Detail & Related papers  (2024-03-12T13:31:14Z)
• ORIENT: A Priority-Aware Energy-Efficient Approach for Latency-Sensitive Applications in 6G [15.753216159980434]
  Growing concerns about increased energy consumption in computing and networking. The expected surge in connected devices and resource-demanding applications presents unprecedented challenges for energy resources. We investigate the joint problem of service instance placement and assignment, path selection, and request prioritization, dubbed PIRA.
  arXiv  Detail & Related papers  (2024-02-10T12:05:52Z)
• Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach [58.911515417156174]
  We propose a new definition of Age of Information (AoI) and, based on the redefined AoI, we formulate an online AoI problem for MEC systems. We introduce Post-Decision States (PDSs) to exploit the partial knowledge of the system's dynamics. We also combine PDSs with deep RL to further improve the algorithm's applicability, scalability, and robustness.
  arXiv  Detail & Related papers  (2023-12-01T01:30:49Z)
• Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing [6.872434270841794]
  This paper investigates the task graph offloading in MEC, considering the time-varying capabilities of edge computing devices. To adapt to environmental changes, we model the task graph scheduling for computation offloading as a Markov Decision Process. Then, we design a deep reinforcement learning algorithm (SATA-DRL) to learn the task scheduling strategy from the interaction with the environment.
  arXiv  Detail & Related papers  (2023-09-19T12:26:56Z)
• An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge [42.462246527457594]
  We present an energy-aware approach for the design and deployment of self-adaptive AI-based applications. We address the problem of determining the set of configurations that can be used to self-adapt the system with a meta-heuristic search procedure. Results show that our self-adaptive application can outperform non-adaptive baseline configurations by saving up to 81% of energy while loosing only between 2% and 6% in accuracy.
  arXiv  Detail & Related papers  (2023-08-31T09:33:44Z)
• Multi-objective Deep Reinforcement Learning for Mobile Edge Computing [11.966938107719903]
  Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. In this study, we formulate a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay. We introduce a well-designed state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption.
  arXiv  Detail & Related papers  (2023-07-05T16:36:42Z)
• Dynamic Scheduling for Federated Edge Learning with Streaming Data [56.91063444859008]
  We consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration.
  arXiv  Detail & Related papers  (2023-05-02T07:41:16Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• MCDS: AI Augmented Workflow Scheduling in Mobile Edge Cloud Computing Systems [12.215537834860699]
  Recently proposed scheduling methods leverage the low response times of edge computing platforms to optimize application Quality of Service (QoS) We propose MCDS: Monte Carlo Learning using Deep Surrogate Models to efficiently schedule workflow applications in mobile edge-cloud computing systems.
  arXiv  Detail & Related papers  (2021-12-14T10:00:01Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.