Towards A Robust Meta-Reinforcement Learning-Based Scheduling Framework for Time Critical Tasks in Cloud Environments

Liu, Hongyun; Chen, Peng; Zhao, Zhiming

doi:10.1109/cloud53861.2021.00082

Cited by 8 publications

(5 citation statements)

References 44 publications

(43 reference statements)

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“…Such DRL-based algorithms require a long retraining of the model. To address this challenge, meta learning has attracted the attention of many researches and is widely used in task offloading and caching problem [33][34][35][36][37][38]. A general framework was proposed by He et al [33] to combine meta learning with hierarchical reinforcement learning, enabling rapid adaptive resource allocation for dynamic vehicular networks.…”

Section: Related Workmentioning

confidence: 99%

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A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

Guo,

Du,

Liu

2023

Applied Sciences

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In order to achieve reliability, security, and scalability, the request flow in the Internet of Things (IoT) needs to pass through the service function chain (SFC), which is composed of series-ordered virtual network functions (VNFs), then reach the destination application in multiaccess edge computing (MEC) for processing. Since there are usually multiple identical VNF instances in the network and the network environment of IoT changes dynamically, placing the SFC for the IoT request flow is a significant challenge. This paper decomposes the dynamic SFC placement problem of the IoT-MEC network into two subproblems: VNF placement and path determination of routing. We first formulate these two subproblems as Markov decision processes. We then propose a meta reinforcement learning and fuzzy logic-based dynamic SFC placement approach (MRLF-SFCP). The MRLF-SFCP contains an inner model that focuses on making SFC placement decisions and an outer model that focuses on learning the initial parameters considering the dynamic IoT-MEC environment. Specifically, the approach uses fuzzy logic to pre-evaluate the link status information of the network by jointly considering available bandwidth, delay, and packet loss rate, which is helpful for model training and convergence. In comparison to existing algorithms, simulation results demonstrate that the MRLF-SFCP algorithm exhibits superior performance in terms of traffic acceptance rate, throughput, and the average reward.

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Section: Related Workmentioning

confidence: 99%

“…A general framework was proposed by He et al [33] to combine meta learning with hierarchical reinforcement learning, enabling rapid adaptive resource allocation for dynamic vehicular networks. To improve scheduling robustness, Liu et al [34] design a meta-gradient reinforcement learning algorithm for time-critical task scheduling. Refs.…”

Section: Related Workmentioning

confidence: 99%

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

Guo,

Du,

Liu

2023

Applied Sciences

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“…QoS Latency Critical Rate (QLCR) [5]: total percentage of executed tasks that meet latency required by QoS.…”

Section: A Evaluation Measurementsmentioning

confidence: 99%

“…Robustness measurements [5] includes: Offloading Performance Deviation(OPD) and Adaptation Steps and Data Usage for Performance Recovery(ASDUPR). They are formulated as follows:…”

Section: A Evaluation Measurementsmentioning

confidence: 99%

“…To cope with the offloading problem, lots of attention has been paid to Machine Learning-based approaches [3] [4]. Among them, Reinforcement-Learning(RL)-based approaches [5] [6] [4] also has been investigated a lot because it does not ask for data labeling. Many works have been done to investigate the offloading problem also in the context of heterogeneous environment [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum

Liu

Xin

Chen

et al. 2022

2022 IEEE 15th International Conference on Cloud Computing (CLOUD)

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Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical singleobjective RL-based offloading methods.

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Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method

Hao,

Chen,

Chen

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Towards A Robust Meta-Reinforcement Learning-Based Scheduling Framework for Time Critical Tasks in Cloud Environments

Cited by 8 publications

References 44 publications

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

A Meta Reinforcement Learning Approach for SFC Placement in Dynamic IoT-MEC Networks

Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum

Efficiently Detecting Anomalies in IoT: A Novel Multi-Task Federated Learning Method

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