Analysis of Reinforcement Learning for determining task replication in workflows

• URL: http://arxiv.org/abs/2209.13531v1
• Date: Wed, 14 Sep 2022 12:53:21 GMT
• Title: Analysis of Reinforcement Learning for determining task replication in workflows
• Authors: Andrew Stephen McGough, Matthew Forshaw
• Abstract summary: Executing on volunteer computing resources leads to unpredictability and often significantly increases execution time. This comes at the expense of a potentially significant increase in system and energy consumption. We propose the use of Reinforcement Learning (RL) such that a system may learn' the best' number of replicas to run to increase the number of load which complete promptly whilst minimising the additional workload on the system when replicas are not beneficial.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Executing workflows on volunteer computing resources where individual tasks may be forced to relinquish their resource for the resource's primary use leads to unpredictability and often significantly increases execution time. Task replication is one approach that can ameliorate this challenge. This comes at the expense of a potentially significant increase in system load and energy consumption. We propose the use of Reinforcement Learning (RL) such that a system may `learn' the `best' number of replicas to run to increase the number of workflows which complete promptly whilst minimising the additional workload on the system when replicas are not beneficial. We show, through simulation, that we can save 34% of the energy consumption using RL compared to a fixed number of replicas with only a 4% decrease in workflows achieving a pre-defined overhead bound.

Related papers

• Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning [11.657154571216234]
  This study addresses the challenge of resource scheduling optimization in edge-cloud collaborative computing using deep reinforcement learning (DRL) The proposed DRL-based approach improves task processing efficiency, reduces overall processing time, enhances resource utilization, and effectively controls task migrations.
  arXiv  Detail & Related papers  (2025-02-26T03:05:11Z)
• Exploring RL-based LLM Training for Formal Language Tasks with Programmed Rewards [49.7719149179179]
  This paper investigates the feasibility of using PPO for reinforcement learning (RL) from explicitly programmed reward signals. We focus on tasks expressed through formal languages, such as programming, where explicit reward functions can be programmed to automatically assess quality of generated outputs. Our results show that pure RL-based training for the two formal language tasks is challenging, with success being limited even for the simple arithmetic task.
  arXiv  Detail & Related papers  (2024-10-22T15:59:58Z)
• VinePPO: Unlocking RL Potential For LLM Reasoning Through Refined Credit Assignment [66.80143024475635]
  We propose VinePPO, a straightforward approach to compute unbiased Monte Carlo-based estimates. We show that VinePPO consistently outperforms PPO and other RL-free baselines across MATH and GSM8K datasets.
  arXiv  Detail & Related papers  (2024-10-02T15:49:30Z)
• Self-Supervised Curriculum Generation for Autonomous Reinforcement Learning without Task-Specific Knowledge [25.168236693829783]
  A significant bottleneck in applying current reinforcement learning algorithms to real-world scenarios is the need to reset the environment between every episode. We propose a novel ARL algorithm that can generate a curriculum adaptive to the agent's learning progress without task-specific knowledge.
  arXiv  Detail & Related papers  (2023-11-15T18:40:10Z)
• REBOOT: Reuse Data for Bootstrapping Efficient Real-World Dexterous Manipulation [61.7171775202833]
  We introduce an efficient system for learning dexterous manipulation skills withReinforcement learning. The main idea of our approach is the integration of recent advances in sample-efficient RL and replay buffer bootstrapping. Our system completes the real-world training cycle by incorporating learned resets via an imitation-based pickup policy.
  arXiv  Detail & Related papers  (2023-09-06T19:05:31Z)
• LaGR-SEQ: Language-Guided Reinforcement Learning with Sample-Efficient Querying [71.86163159193327]
  Large language models (LLMs) have recently demonstrated their impressive ability to provide context-aware responses via text. This ability could potentially be used to predict plausible solutions in sequential decision making tasks pertaining to pattern completion. We introduce LaGR, which uses this predictive ability of LLMs to propose solutions to tasks that have been partially completed by a primary reinforcement learning (RL) agent.
  arXiv  Detail & Related papers  (2023-08-21T02:07:35Z)
• Actively Learning Costly Reward Functions for Reinforcement Learning [56.34005280792013]
  We show that it is possible to train agents in complex real-world environments orders of magnitudes faster. By enabling the application of reinforcement learning methods to new domains, we show that we can find interesting and non-trivial solutions.
  arXiv  Detail & Related papers  (2022-11-23T19:17:20Z)
• Teacher Forcing Recovers Reward Functions for Text Generation [21.186397113834506]
  We propose a task-agnostic approach that derives a step-wise reward function directly from a model trained with teacher forcing. We additionally propose a simple modification to stabilize the RL training on non-parallel datasets with our induced reward function.
  arXiv  Detail & Related papers  (2022-10-17T02:48:58Z)
• PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications [16.812611987082082]
  We propose PROMPT, a novel resource allocation framework using proactive prediction to guide a reinforcement learning controller. We show that PROMPT incurs 4.2x fewer violations, reduces severity of policy violations by 12.7x, improves best-effort workload performance, and improves overall power efficiency over prior work.
  arXiv  Detail & Related papers  (2022-01-19T23:34:34Z)
• Persistent Reinforcement Learning via Subgoal Curricula [114.83989499740193]
  Value-accelerated Persistent Reinforcement Learning (VaPRL) generates a curriculum of initial states. VaPRL reduces the interventions required by three orders of magnitude compared to episodic reinforcement learning.
  arXiv  Detail & Related papers  (2021-07-27T16:39:45Z)
• On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocation [20.20889051697198]
  We show that task allocation with task importance for Multi-task Transfer Learning (MTL) is a variant of the NP-complete Knapsack problem. We propose a Data-driven Cooperative Task Allocation (DCTA) approach to solve TATIM with high computational efficiency. Our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM.
  arXiv  Detail & Related papers  (2021-07-06T08:24:25Z)
• Generalized Hindsight for Reinforcement Learning [154.0545226284078]
  We argue that low-reward data collected while trying to solve one task provides little to no signal for solving that particular task. We present Generalized Hindsight: an approximate inverse reinforcement learning technique for relabeling behaviors with the right tasks.
  arXiv  Detail & Related papers  (2020-02-26T18:57:05Z)

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.