Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Keshavarznejad, Maryam; Rezvani, Mohammad Hossein; Adabi, Sepideh

doi:10.1007/s10586-020-03230-y

Cited by 76 publications

(26 citation statements)

References 52 publications

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“…The authors also presented a dynamic energy model, designed for physical machines and cloud communication components. In some researches such as [8,22,23] the GA has been used. In previous researches, besides the above-mentioned methods, other methods such as the artificial bee algorithm [7], the Cuckoo algorithm [9], etc.…”

Section: -Related Workmentioning

confidence: 99%

“…The placement operation must be performed in such a way that leads to a trade-off between response time and energy cost in the system. Finally, the optimization problem is formulated as follows: 19)- (23), and (31) The above multi-objective problem can be converted to a single-objective form as follows [36]…”

Section: -4-joint Cost and Scheduling Optimization Problemmentioning

confidence: 99%

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Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Abbasi-khazaei

Rezvani

2022

Preprint

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One of the most important concerns of cloud service providers is balancing renewable and fossil energy consumption. On the other hand, the policy of organizations and governments is to reduce energy consumption and greenhouse gas emissions in cloud data centers. Recently, a lot of research has been conducted to optimize the Virtual Machine (VM) placement on physical machines to minimize energy consumption. Many previous studies have not considered the deadline and scheduling of IoT tasks. Therefore, the previous modelings are mainly not well-suited to the IoT environments where requests are time-constraint. Unfortunately, both the sub-problems of energy consumption minimization and scheduling fall into the category of NP-hard issues. In this study, we propose a multi-objective VM placement to joint minimizing energy costs and scheduling. After presenting a modified memetic algorithm, we compare its performance with baseline methods as well as state-of-the-art ones. The simulation results on the CloudSim platform show that the proposed method can reduce energy costs, carbon footprints, SLA violations, and the total response time of IoT requests.

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Section: -Related Workmentioning

confidence: 99%

Section: -4-joint Cost and Scheduling Optimization Problemmentioning

confidence: 99%

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Abbasi-khazaei

Rezvani

2022

Preprint

Self Cite

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“…In that case, the resource-constrained fog node cannot process it, resulting in the task being unable to be completed. For the task offloading problem in fog computing, Keshavarznejad et al [23] proposed a multi-objective optimization problem of energy consumption and delay, which was solved using a hybrid heuristic algorithm. The results showed that the best trade-off was obtained between the probability of offloading and the energy consumption required for data transmission.…”

Section: Fog Computing Task Schedulingmentioning

confidence: 99%

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

Yin

et al. 2022

Sensors

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With the widespread use of industrial Internet technology in intelligent production lines, the number of task requests generated by smart terminals is growing exponentially. Achieving rapid response to these massive tasks becomes crucial. In this paper we focus on the multi-objective task scheduling problem of intelligent production lines and propose a task scheduling strategy based on task priority. First, we set up a cloud-fog computing architecture for intelligent production lines and built the multi-objective function for task scheduling, which minimizes the service delay and energy consumption of the tasks. In addition, the improved hybrid monarch butterfly optimization and improved ant colony optimization algorithm (HMA) are used to search for the optimal task scheduling scheme. Finally, HMA is evaluated by rigorous simulation experiments, showing that HMA outperformed other algorithms in terms of task completion rate. When the number of nodes exceeds 10, the completion rate of all tasks is greater than 90%, which well meets the real-time requirements of the corresponding tasks in the intelligent production lines. In addition, the algorithm outperforms other algorithms in terms of maximum completion rate and power consumption.

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“…On the basis of these methods, a security mechanism was introduced in IoT networks for the healthcare applications in different works. These studies implemented closely related existing schemes in the simulation, for instance, delay optimal long short-term memory (LSTM) [ 21 ], workflow metaheuristic system (WFMS), [ 22 ] and workflow metaheuristic cloud (WMC) [ 23 ]. These studies are closely related to our work to execute workflow applications on heterogeneous nodes in cloud computing.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

Lakhan

Sodhro

Majumdar

et al. 2022

Sensors

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Mobile-cloud-based healthcare applications are increasingly growing in practice. For instance, healthcare, transport, and shopping applications are designed on the basis of the mobile cloud. For executing mobile-cloud applications, offloading and scheduling are fundamental mechanisms. However, mobile healthcare workflow applications with these methods are widely ignored, demanding applications in various aspects for healthcare monitoring, live healthcare service, and biomedical firms. However, these offloading and scheduling schemes do not consider the workflow applications’ execution in their models. This paper develops a lightweight secure efficient offloading scheduling (LSEOS) metaheuristic model. LSEOS consists of light weight, and secure offloading and scheduling methods whose execution offloading delay is less than that of existing methods. The objective of LSEOS is to run workflow applications on other nodes and minimize the delay and security risk in the system. The metaheuristic LSEOS consists of the following components: adaptive deadlines, sorting, and scheduling with neighborhood search schemes. Compared to current strategies for delay and security validation in a model, computational results revealed that the LSEOS outperformed all available offloading and scheduling methods for process applications by 10% security ratio and by 29% regarding delays.

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Delay-aware optimization of energy consumption for task offloading in fog environments using metaheuristic algorithms

Cited by 76 publications

References 52 publications

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

Energy-aware and Carbon-efficient VM Placement Optimization in Cloud Datacenters Using Evolutionary Computing Methods

A Multi-Objective Task Scheduling Strategy for Intelligent Production Line Based on Cloud-Fog Computing

A Lightweight Secure Adaptive Approach for Internet-of-Medical-Things Healthcare Applications in Edge-Cloud-Based Networks

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