Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Qu, Zhiguo; Wang, Yilin; Sun, Le; Peng, Dandan; Zheng, Li

doi:10.1155/2020/8964165

Cited by 15 publications

(11 citation statements)

References 87 publications

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“…Increasing in number of the active end devices, caused lack of the edge resources and deadlocked the task processing on edge of the network. In this simulation, the average energy consumption of the entire edge system is middling improved by 0.36 with the iterative reinforcement learning method compared to the processing transfer to the cloud system [56,58], and the lifetime of the final devices is increased. As the remote processing inputs and the volume of communication bandwidth between the end devices and the edge devices are constantly changing, as well as the rapid changes of the data of the end devices, so to avoid the standby energy consumption, which is comparable with the energy consumption of the working mode of the ballast device, in the simulation model, the on/off technique is used.…”

Section: Simulation Resultsmentioning

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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Section: Simulation Resultsmentioning

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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show abstract

“…For this, in each simulation run, we took five sets with different ranges of cloudlets each consisting of 100, 200, 300, 400, and 500 cloudlets. In addition, each simulation run was carried over different settings of VMs and cloudlets having random lengths acquired by the queue scheduling schemes, i.e., FCFS [7], Priority-Based [8], and QoS-aware HQS in this article, whereas the cost incurred per cloud resource (CPU, RAM and BW) in each VM can be defined by the following three Equations ( 11)-( 13…”

Section: Objective Function: the Cost Minimizationmentioning

confidence: 99%

“…In order to cope with these challenges, coupling the Internet of Things (IoT)-based networks [5,6] with cloud computing [7,8] will play an important role in the development of an improved Smart Grid architecture. With the induction of these two modern networking technologies, the residential users (here, SMs) will keep no concerns about the IT resource allocation (hardware, software) and application services (here, database application) because cloud computing primarily focuses on providing these services in a flexible and pay-as per-use manner over the Internet.…”

Section: Introductionmentioning

confidence: 99%

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

Khan

Umar

Shirazi

et al. 2022

Sensors

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Cloud computing coupled with Internet of Things technology provides a wide range of cloud services such as memory, storage, computational processing, network bandwidth, and database application to the end users on demand over the Internet. More specifically, cloud computing provides efficient services such as “pay as per usage”. However, Utility providers in Smart Grid are facing challenges in the design and implementation of such architecture in order to minimize the cost of underlying hardware, software, and network services. In Smart Grid, smart meters generate a large volume of different traffics, due to which efficient utilization of available resources such as buffer, storage, limited processing, and bandwidth is required in a cost-effective manner in the underlying network infrastructure. In such context, this article introduces a QoS-aware Hybrid Queue Scheduling (HQS) model that can be seen over the IoT-based network integrated with cloud environment for different advanced metering infrastructure (AMI) application traffic, which have different QoS levels in the Smart Grid network. The proposed optimization model supports, classifies, and prioritizes the AMI application traffic. The main objective is to reduce the cost of buffer, processing power, and network bandwidth utilized by AMI applications in the cloud environment. For this, we developed a simulation model in the CloudSim simulator that uses a simple mathematical model in order to achieve the objective function. During the simulations, the effects of various numbers of cloudlets on the cost of virtual machine resources such as RAM, CPU processing, and available bandwidth have been investigated in cloud computing. The obtained simulation results exhibited that our proposed model successfully competes with the previous schemes in terms of minimizing the processing, memory, and bandwidth cost by a significant margin. Moreover, the simulation results confirmed that the proposed optimization model behaves as expected and is realistic for AMI application traffic in the Smart Grid network using cloud computing.

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“…Efficient fog resource management can offer various benefits namely, energy efficiency, network load reduction, profit maximization, load balancing, and minimization of Service Level Agreement (SLA) violations. The scheduling approaches reduce SLA violation risks and optimize revenues in resource allocation [10]. It can lead to Green Fog Computing by providing workload consolidation and reduction in carbon emissions [11,12].…”

Section: Introductionmentioning

confidence: 99%

Efficient Resource Allocation in Fog Computing Using QTCS Model

Iyapparaja¹,

Alshammari²,

Kumar³

et al. 2022

Computers, Materials &Amp; Continua

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Infrastructure of fog is a complex system due to the large number of heterogeneous resources that need to be shared. The embedded devices deployed with the Internet of Things (IoT) technology have increased since the past few years, and these devices generate huge amount of data. The devices in IoT can be remotely connected and might be placed in different locations which add to the network delay. Real time applications require high bandwidth with reduced latency to ensure Quality of Service (QoS). To achieve this, fog computing plays a vital role in processing the request locally with the nearest available resources by reduced latency. One of the major issues to focus on in a fog service is managing and allocating resources. Queuing theory is one of the most popular mechanisms for task allocation. In this work, an efficient model is designed to improve QoS with the efficacy of resource allocation based on a Queuing Theory based Cuckoo Search (QTCS) model which will optimize the overall resource management process.

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Study QoS Optimization and Energy Saving Techniques in Cloud, Fog, Edge, and IoT

Cited by 15 publications

References 87 publications

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

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

QoS-Aware Cost Minimization Strategy for AMI Applications in Smart Grid Using Cloud Computing

Efficient Resource Allocation in Fog Computing Using QTCS Model

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