Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review

• URL: http://arxiv.org/abs/2203.12315v1
• Date: Wed, 23 Mar 2022 10:31:15 GMT
• Title: Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review
• Authors: Javad Hassannataj Joloudari, Roohallah Alizadehsani, Issa Nodehi, Sanaz Mojrian, Fatemeh Fazl, Sahar Khanjani Shirkharkolaie, H M Dipu Kabir, Ru-San Tan, U Rajendra Acharya
• Abstract summary: This paper presents a comprehensive literature review on the application of artificial intelligence (AI) methods for resource allocation optimization. To the best of our knowledge, there are no existing reviews on AI-based resource allocation approaches in different computational paradigms.
• Score: 7.738849852406729
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With the advent of smart devices, the demand for various computational paradigms such as the Internet of Things, fog, and cloud computing has increased. However, effective resource allocation remains challenging in these paradigms. This paper presents a comprehensive literature review on the application of artificial intelligence (AI) methods such as deep learning (DL) and machine learning (ML) for resource allocation optimization in computational paradigms. To the best of our knowledge, there are no existing reviews on AI-based resource allocation approaches in different computational paradigms. The reviewed ML-based approaches are categorized as supervised and reinforcement learning (RL). Moreover, DL-based approaches and their combination with RL are surveyed. The review ends with a discussion on open research directions and a conclusion.

Related papers

• DSMoE: Matrix-Partitioned Experts with Dynamic Routing for Computation-Efficient Dense LLMs [70.91804882618243]
  This paper proposes DSMoE, a novel approach that achieves sparsification by partitioning pre-trained FFN layers into computational blocks. We implement adaptive expert routing using sigmoid activation and straight-through estimators, enabling tokens to flexibly access different aspects of model knowledge. Experiments on LLaMA models demonstrate that under equivalent computational constraints, DSMoE achieves superior performance compared to existing pruning and MoE approaches.
  arXiv  Detail & Related papers  (2025-02-18T02:37:26Z)
• A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges [2.2448567386846916]
  Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI) This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms. We offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma.
  arXiv  Detail & Related papers  (2024-11-28T03:53:14Z)
• Accelerated AI Inference via Dynamic Execution Methods [0.562479170374811]
  We focus on Dynamic Execution techniques that optimize the computation flow based on input. The techniques discussed include early exit from deep networks, speculative sampling for language models, and adaptive steps for diffusion models. Experimental results demonstrate that these dynamic approaches can significantly improve latency and throughput without compromising quality.
  arXiv  Detail & Related papers  (2024-10-30T12:49:23Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• 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)
• A Review of Deep Reinforcement Learning in Serverless Computing: Function Scheduling and Resource Auto-Scaling [2.0722667822370386]
  This paper presents a comprehensive review of the application of Deep Reinforcement Learning (DRL) techniques in serverless computing. A systematic review of recent studies applying DRL to serverless computing is presented, covering various algorithms, models, and performances. Our analysis reveals that DRL, with its ability to learn and adapt from an environment, shows promising results in improving the efficiency of function scheduling and resource scaling.
  arXiv  Detail & Related papers  (2023-10-05T09:26:04Z)
• Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning [92.18524491615548]
  Contrastive self-supervised learning has been successfully integrated into the practice of (deep) reinforcement learning (RL) We study how RL can be empowered by contrastive learning in a class of Markov decision processes (MDPs) and Markov games (MGs) with low-rank transitions. Under the online setting, we propose novel upper confidence bound (UCB)-type algorithms that incorporate such a contrastive loss with online RL algorithms for MDPs or MGs.
  arXiv  Detail & Related papers  (2022-07-29T17:29:08Z)
• Resource-Efficient Deep Learning: A Survey on Model-, Arithmetic-, and Implementation-Level Techniques [10.715525749057495]
  Deep learning is pervasive in our daily life, including self-driving cars, virtual assistants, social network services, healthcare services, face recognition, etc. Deep neural networks demand substantial compute resources during training and inference. This article provides a survey on resource-efficient deep learning techniques in terms of model-, arithmetic-, and implementation-level techniques.
  arXiv  Detail & Related papers  (2021-12-30T17:00:06Z)
• Practical Machine Learning Safety: A Survey and Primer [81.73857913779534]
  Open-world deployment of Machine Learning algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities. New models and training techniques to reduce generalization error, achieve domain adaptation, and detect outlier examples and adversarial attacks. Our organization maps state-of-the-art ML techniques to safety strategies in order to enhance the dependability of the ML algorithm from different aspects.
  arXiv  Detail & Related papers  (2021-06-09T05:56:42Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)

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.