Priority-based task scheduling and resource allocation in edge computing for health monitoring system

Sharif, Zubair; Jung, Low Tang; Ayaz, Muhammad; Yahya, Mazlaini

doi:10.1016/j.jksuci.2023.01.001

Cited by 25 publications

(6 citation statements)

References 40 publications

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“…Despite the increasing attention given to FC resources and task scheduling in recent times, the administration of FC resources and task scheduling are currently at the early stages of this paradigm [13]. For example, a placement strategy was introduced by Arshed et al [14] to perform resource optimization in a FC environment.…”

Section: Resource Allocation In Cloud/fog Computingmentioning

confidence: 99%

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Huda Qasim ALGawwam

2024

jes

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Recent advancements in service offerings and the emergence of new application areas have reveal the limitations of cloud computing as a solely solution. One of these new areas is the industrial internet of things, where industrial processes are controlled in a network-based and automated way which brings many new challenges. To solve the challenges, fog computing has been introduced as a complementary paradigm to the cloud. Resource management is one of those challenges need further investigation. The industrial internet of things by its own characteristics has led traditional methods to be inefficient within this evolving ecosystem. Therefore, fully utilizing potential of the new system necessitates effective resource management. This manuscript aims to investigate the problem of resource management in the emerging cloud-fog ecosystem of the industrial internet of things. Considering the dynamic nature of users, this study proposes the algorithms for dynamic resource allocation and provisioning based on deadlines. We formulate the problem as a multiple criteria stochastic problem and introduced an online solution. Our proposed solution takes into account the unique characteristics of Fog computing and the dynamic behavior of users. To effectively handle the scheduling of requests, the research suggests utilizing the Lyapunov optimization method and Lyapunov drift theory. The Lyapunov method helps to merge and measure all the related objectives in terms drift. The objective function is optimized, in each time slot, using a drift-plus-penalty weight parameter. By adopting this approach, the system can operate efficiently while meeting the constraints and performance requirements of IoT applications.

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Section: Resource Allocation In Cloud/fog Computingmentioning

confidence: 99%

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Huda Qasim ALGawwam

2024

jes

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“…They proposed a computation task mechanism consisting of edge servers, cloud centers, and edge devices based on task coscheduling. Sharif et al [39] assigned different priorities to different tasks, performed priority-based task scheduling and resource allocation according to the urgency of the task, and decided the priority of each task. Zhou et al [40] investigated the joint impact of task prioritization and mobile computing services on MEC networks by measuring system performance through the computational utility of multiple users, where the effect of a wide range of task prioritization was considered.…”

Section: Related Workmentioning

confidence: 99%

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Pang,

Zheng,

Wang

et al. 2023

J Cloud Comp

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Recently, the development of Low Earth Orbit (LEO) satellites and the advancement of the Mobile Edge Computing (MEC) paradigm have driven the emergence of the Satellite Mobile Edge Computing (Sat-MEC). Sat-MEC has been developed to support communication and task computation for Internet of Things (IoT) Mobile Devices (IMDs) in the absence of terrestrial networks. However, due to the heterogeneity of tasks and Sat-MEC servers, it is still a great challenge to efficiently schedule tasks in Sat-MEC servers. Here, we propose a scheduling algorithm based on the Deep Reinforcement Learning (DRL) method in the Sat-MEC architecture to minimize the average task processing time. We consider multiple factors, including the cooperation between LEO satellites, the concurrency and heterogeneity of tasks, the dynamics of LEO satellites, the heterogeneity of the computational capacity of Sat-MEC servers, and the heterogeneity of the initial queue for task computation. Further, we use the self-attention mechanism to act as a Q-network to extract high-dimensional dynamic information of tasks and Sat-MEC servers. In this work, we model the Sat-MEC environment simulation at the application level and propose a DRL-based task scheduling algorithm. The simulation results confirm the effectiveness of our proposed scheduling algorithm, which reduces the average task processing time by 22.1$$\%$$ % , 30.6$$\%$$ % , and 41.3$$\%$$ % , compared to the genetic algorithm(GA), the greedy algorithm, and the random algorithm, respectively.

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“…The capacity to allocate resources rapidly and smoothly has brought about a new level of operational effectiveness, fundamentally reshaping the economics of IT. Consequently, the distribution of resources in cloud environments has become a significant problem, requiring the creation of sophisticated resource allocation techniques to adapt to the constantly changing and dynamic cloud environment [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning

Torana Kamble

2024

jes

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Cloud computing revolutionizes fast-changing technology. Companies' computational resource use is changing. Businesses can quickly adapt to changing market conditions and operational needs with cloud-based solutions' adaptability, scalability, and cost-efficiency. IT operations and service delivery have changed due to widespread computational resource access. Cloud computing efficiently allocates resources in cloud environments, making it crucial to this transformation. Resource allocation impacts efficiency, cost, performance, and SLAs. Users and providers can allocate cloud resources based on workloads using elasticity, scalability, and on-demand provisioning. IT economics and operational effectiveness have changed due to rapid and flexible resource allocation. Proactive versus reactive resource allocation is key to understanding cloud resource management challenges and opportunities. Reactive strategies allocate resources only when shortages or surpluses occur at demand. This responsive strategy often leads to inefficiencies like over- or under-allocation, which raises costs and lowers performance. Predictive analysis and workload forecasting predict resource needs in proactive resource allocation. Optimize resource use to avoid shortages and over-provisioning. Attention has been drawn to proactive predictive resource allocation. These methods predict resource needs using historical data, machine learning, and predictive analytics. Predictive strategies optimize resource allocation by considering future decisions. Reduced bottlenecks boost user satisfaction and lower operational costs. Matching resource distribution to workloads optimizes cloud resource management. Resource allocation prediction improves with deep learning. CNN, LSTM, and Transformer cloud resource forecasting algorithms are promising. New tools for accurate and flexible workload predictions have come from their ability to spot intricate patterns in historical data. This paper compares CNN, LSTM, and Transformer deep learning algorithms for cloud computing resource allocation forecasting. This study determines the best predictive accuracy and workload ada[1]ptability algorithm using Google Cluster Data (GCD). The study evaluates upgrading cloud computing resource allocation with the Transformer model. This study advances predictive resource allocation strategies, which can help cloud service providers and organizations improve resource utilization, cost-effectiveness, and performance in the face of rapid technological change.

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Priority-based task scheduling and resource allocation in edge computing for health monitoring system

Cited by 25 publications

References 40 publications

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things

Minimize average tasks processing time in satellite mobile edge computing systems via a deep reinforcement learning method

Predictive Resource Allocation Strategies for Cloud Computing Environments Using Machine Learning

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