Reduced power consumption by resource scheduling in mobile cloud using optimized neural network

Kaliappan, Vishnu Kumar; Gnanamurthy, S.; Kumar, C. Santhosh; Thangaraj, Rajasekaran; Sundaram, K. Mohana

doi:10.1016/j.matpr.2021.03.496

Cited by 3 publications

(4 citation statements)

References 17 publications

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“…Other researchers [19] who wanted to optimise memory used genetic algorithms. A variety of runtime and compilation approaches were jointly disclosed in [20] by the authors to hide the heterogeneity of cellphones from developers. In the MCC environment, a task of the mobile cloud is a relatively autonomous computation component that is raised inside a work flow and is executed on a cloud server or a mobile device.…”

Section: Review Of Literaturementioning

confidence: 99%

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

Khatri

Sharma

2022

ijfrcsce

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D2D communication technology enables the User Equipment (UE) in 5G networks to instantly connect with other UEs, with or without partial infrastructure involvement. In a Cloud Assisted energy harvesting system, it has improved user numbers and data transmission between mobile nodes. This research propose energy harvesting for mobile cloud computing in enhancing the QoS and latency of the network. The main aim of this research is to enhance energy optimization using discrete energy efficient offloading algorithm. The routing has been optimized using fuzzy logic cognitive Bellman-Ford routing algorithm. To identify the failing node and find an alternative node to deliver the seamless services, an unique weight-based approach has been presented. The method relies on two working node parameters: execution time and failure rate. Threshold values are specified for the parameters of the chosen master node. By contrasting the values with the threshold values, the alternative node is chosen. The experimental results shows comparative analysis in terms of throughput of 96%, QoS of 96%, latency of 28%, energy consumption of 51%, end-end delay of 41%, average power consumption of 41% and PDR of 85%

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Section: Review Of Literaturementioning

confidence: 99%

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

Khatri

Sharma

2022

ijfrcsce

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“…are now smart enough to collect, handle, and transmit the data without interruption for the concerned, intelligent devices and their effective environment [4]. This paper aims to outspread the Task Scheduling capabilities of mobile devices using Mobile Cloud Computing (MCC) [5]. However, intelligent mobile devices still face bandwidth utilization, power and battery capacity, poor CPU speed, and lower power and intensive operational capacities.…”

Section: Introductionmentioning

confidence: 99%

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

Ali

Iqbal²

2022

IEEE Access

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The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloudbased frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.INDEX TERMS Cloud computing, mobile cloud computing, task offloading, task sequencing, task scheduling, microservices.

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“…Cloud computing has been adopted to meet resource demands. The cost-effective cloud solutions offer numerous advantages in information technology and ensure that users receive essential computing, storage, and communication services based on their needs [1]. The tremendous applications based on IoT networks enhance the quality of life (Fig.…”

Section: Introductionmentioning

confidence: 99%

“…where 1  and 2  represents the weighted average functions, the learning rate is represented as  , the squares of gradients before bias correction is represented as…”

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confidence: 99%

Resource scheduling in edge computing IoT networks using hybrid deep learning algorithm

Vijayasekaran

Duraipandian

2022

SRIT

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The proliferation of the Internet of Things (IoT) and wireless sensor networks enhances data communication. The demand for data communication rapidly increases, which calls the emerging edge computing paradigm. Edge computing plays a major role in IoT networks and provides computing resources close to the users. Moving the services from the cloud to users increases the communication, storage, and network features of the users. However, massive IoT networks require a large spectrum of resources for their computations. In order to attain this, resource scheduling algorithms are employed in edge computing. Statistical and machine learning-based resource scheduling algorithms have evolved in the past decade, but the performance can be improved if resource requirements are analyzed further. A deep learning-based resource scheduling in edge computing IoT networks is presented in this research work using deep bidirectional recurrent neural network (BRNN) and convolutional neural network algorithms. Before scheduling, the IoT users are categorized into clusters using a spectral clustering algorithm. The proposed model simulation analysis verifies the performance in terms of delay, response time, execution time, and resource utilization. Existing resource scheduling algorithms like a genetic algorithm (GA), Improved Particle Swarm Optimization (IPSO), and LSTM-based models are compared with the proposed model to validate the superior performances.

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Reduced power consumption by resource scheduling in mobile cloud using optimized neural network

Cited by 3 publications

References 17 publications

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

Energy Harvesting based Mobile Cloud Network in Latency and QoS Improvement using 5G Systems by Energy Routing Optimization

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

Resource scheduling in edge computing IoT networks using hybrid deep learning algorithm

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