Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Sheng, Shuran; Chen, Peng; Chen, Zhimin; Wu, Lenan; Yao, Yuxuan

doi:10.3390/s21051666

Cited by 97 publications

(51 citation statements)

References 37 publications

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“…The authors in [6] analyze a task scheduling problem in edge computing formulated as a Markov Decision Process. They propose a deep reinforcement learning approach to solve this problem, seeking to optimize the mapping of tasks to virtual machines, considering heterogeneity in terms of the VMs computing capacity.…”

Section: Related Workmentioning

confidence: 99%

“…These systems are also increasingly incorporating different types of storage and multi-NUMA servers. Recently, few efforts have tackled the problems of data placement separately on heterogeneous storage infrastructure [4] and virtual machine allocation on servers with multiple NUMA domains [5], and intelligent cloud resource allocation [6,7]. However, these approaches exclude some cloud storage types and shared memory patterns of applications, which are meaningful considerations for efficient resource allocation.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Marquez¹,

Mondragon²,

González³

2021

Applied Sciences

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Cloud computing systems are rapidly evolving toward multicloud architectures supported on heterogeneous hardware. Cloud service providers are widely offering different types of storage infrastructures and multi-NUMA architecture servers. Existing cloud resource allocation solutions do not comprehensively consider this heterogeneous infrastructure. In this study, we present a novel approach comprised of a hierarchical framework based on genetic programming to solve problems related to data placement and virtual machine allocation for analytics applications running on heterogeneous hardware with a variety of storage types and nonuniform memory access. Our approach optimizes data placement using the Hadoop File System on heterogeneous storage devices on multicloud systems. It guarantees the efficient allocation of virtual machines on physical machines with multiple NUMA (nonuniform memory access) domains by minimizing contention between workloads. We prove that our solutions for data placement and virtual machine allocation outperform other state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

Marquez¹,

Mondragon²,

González³

2021

Applied Sciences

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“…This learning is an off-policy method in which the agent acts on the basis of a previous policy and decides on the current state of the system. In RL the efficiency of resource allocation and cloud computing and load calculation are stabilized [52]. The v * policy estimates a status of the next state of action (s, a) based on the previous state of an action.…”

Section: Reinforcement Learning Process In Energy Consumption Management By Consideringmentioning

confidence: 99%

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

Mohammadian

Zarrabi

Jabbehdari

et al. 2021

Preprint

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In this paper, using edge processing in IoT network ,a method to decide on task offloading to edge devices and improve management of consuming energy in network of edge devices is introduced. First, by defining the problem of maximizing utility and then by decomposing it based on the status of task offloading from end devices to smart gateway with the lowest battery consumption and the possible lowest use of the communication bandwidth, independent optimal models are obtained and then combining. Then the general problem of maximizing the usefulness and increasing the lifetime of the end devices with restrictions of processing time and energy resources will be obtained. Due to the unknown environment of the problem and how the end devices and the edge of the network, an iterative reinforcement learning algorithm is used to generate the optimal answer in order to maximize the utility gain.The results show the existence of processing overhead and network load with increasing number of devices. The proposed method, while improving energy consumption in existence of a small number of devices in end of edge, reduces latency and increases processing speed and maximizes system performance compared to the central cloud system. The operating efficiency of the whole system is improved by 36% and the energy consumption at the edge of the network is optimized by 12.5%. It should be noted that, with the addition of a large number of end devices, our method outperforms similar works.

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“…By conducting an extensive literature review of current advances in the field of task scheduling on heterogeneous distributed systems [6,7], we identified a pressing need for more clarity and formalism in the performance assessment of existing scheduling algorithms such as Min-Min and Max-Min, introduced in [8].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

Stan

Băjenaru

Negru

et al. 2021

Sensors

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This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.

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Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing

Cited by 97 publications

References 37 publications

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

An Intelligent Approach to Resource Allocation on Heterogeneous Cloud Infrastructures

The Reinforcement Learning Method in Deciding on Task Offloading and Energy Consumption Management at the Edge of IoT Network

Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

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