Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks

• URL: http://arxiv.org/abs/2009.02186v1
• Date: Tue, 1 Sep 2020 13:36:34 GMT
• Title: Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks
• Authors: Shreshth Tuli, Shashikant Ilager, Kotagiri Ramamohanarao and Rajkumar Buyya
• Abstract summary: A-Advantage-Actor-Critic (A3C) learning is known to quickly adapt to dynamic scenarios with less data and Residual Recurrent Neural Network (R2N2) to quickly update model parameters. We use the R2N2 architecture to capture a large number of host and task parameters together with temporal patterns to provide efficient scheduling decisions. Experiments conducted on real-world data set show a significant improvement in terms of energy consumption, response time, ServiceLevelAgreement and running cost by 14.4%, 7.74%, 31.9%, and 4.64%, respectively.
• Score: 30.61220416710614
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The ubiquitous adoption of Internet-of-Things (IoT) based applications has resulted in the emergence of the Fog computing paradigm, which allows seamlessly harnessing both mobile-edge and cloud resources. Efficient scheduling of application tasks in such environments is challenging due to constrained resource capabilities, mobility factors in IoT, resource heterogeneity, network hierarchy, and stochastic behaviors. xisting heuristics and Reinforcement Learning based approaches lack generalizability and quick adaptability, thus failing to tackle this problem optimally. They are also unable to utilize the temporal workload patterns and are suitable only for centralized setups. However, Asynchronous-Advantage-Actor-Critic (A3C) learning is known to quickly adapt to dynamic scenarios with less data and Residual Recurrent Neural Network (R2N2) to quickly update model parameters. Thus, we propose an A3C based real-time scheduler for stochastic Edge-Cloud environments allowing decentralized learning, concurrently across multiple agents. We use the R2N2 architecture to capture a large number of host and task parameters together with temporal patterns to provide efficient scheduling decisions. The proposed model is adaptive and able to tune different hyper-parameters based on the application requirements. We explicate our choice of hyper-parameters through sensitivity analysis. The experiments conducted on real-world data set show a significant improvement in terms of energy consumption, response time, Service-Level-Agreement and running cost by 14.4%, 7.74%, 31.9%, and 4.64%, respectively when compared to the state-of-the-art algorithms.

Related papers

• Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach [34.00679567444125]
  We develop a dynamic scheduling and resource allocation algorithm to address the inherent randomness in data arrivals and resource availability under long-term energy constraints. Our proposed algorithm makes adaptive decisions on device scheduling, computational capacity adjustment, and allocation of bandwidth and transmit power in every round. The effectiveness of our scheme is verified through simulation results, demonstrating improved learning performance and energy efficiency as compared to baseline schemes.
  arXiv  Detail & Related papers  (2024-05-20T14:13:22Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Latency-aware Unified Dynamic Networks for Efficient Image Recognition [72.8951331472913]
  LAUDNet is a framework to bridge the theoretical and practical efficiency gap in dynamic networks. It integrates three primary dynamic paradigms-spatially adaptive computation, dynamic layer skipping, and dynamic channel skipping. It can notably reduce the latency of models like ResNet by over 50% on platforms such as V100,3090, and TX2 GPUs.
  arXiv  Detail & Related papers  (2023-08-30T10:57:41Z)
• 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)
• Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning [11.007816552466952]
  This paper focuses on the problem of scheduling inference queries on Deep Neural Networks in edge networks at short timescales. By means of simulations, we analyze several policies in the realistic network settings and workloads of a large ISP. We design ASET, a Reinforcement Learning based scheduling algorithm able to adapt its decisions according to the system conditions.
  arXiv  Detail & Related papers  (2023-01-31T13:23:34Z)
• Multi-Agent Reinforcement Learning for Long-Term Network Resource Allocation through Auction: a V2X Application [7.326507804995567]
  We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction mechanism that incentivizes such agents to align private and system goals by balancing between competition and cooperation. We propose a novel multi-agent online learning algorithm that learns with partial, delayed and noisy state information.
  arXiv  Detail & Related papers  (2022-07-29T10:29:06Z)
• An optimised deep spiking neural network architecture without gradients [7.183775638408429]
  We present an end-to-end trainable modular event-driven neural architecture that uses local synaptic and threshold adaptation rules. The architecture represents a highly abstracted model of existing Spiking Neural Network (SNN) architectures.
  arXiv  Detail & Related papers  (2021-09-27T05:59:12Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Learning to Continuously Optimize Wireless Resource in a Dynamic Environment: A Bilevel Optimization Perspective [52.497514255040514]
  This work develops a new approach that enables data-driven methods to continuously learn and optimize resource allocation strategies in a dynamic environment. We propose to build the notion of continual learning into wireless system design, so that the learning model can incrementally adapt to the new episodes. Our design is based on a novel bilevel optimization formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2021-05-03T07:23:39Z)
• Learning to Continuously Optimize Wireless Resource In Episodically Dynamic Environment [55.91291559442884]
  This work develops a methodology that enables data-driven methods to continuously learn and optimize in a dynamic environment. We propose to build the notion of continual learning into the modeling process of learning wireless systems. Our design is based on a novel min-max formulation which ensures certain fairness" across different data samples.
  arXiv  Detail & Related papers  (2020-11-16T08:24:34Z)
• Reinforcement Learning on Computational Resource Allocation of Cloud-based Wireless Networks [22.06811314358283]
  Wireless networks used for Internet of Things (IoT) are expected to largely involve cloud-based computing and processing. In a cloud environment, dynamic computational resource allocation is essential to save energy while maintaining the performance of the processes. This paper models this dynamic computational resource allocation problem into a Markov Decision Process (MDP) and designs a model-based reinforcement-learning agent to optimise the dynamic resource allocation of the CPU usage. The results show that our agent rapidly converges to the optimal policy, stably performs in different settings, outperforms or at least equally performs compared to a baseline algorithm in energy savings for different scenarios.
  arXiv  Detail & Related papers  (2020-10-10T15:16:26Z)

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.