Energy Efficient Resource Scheduling Using Optimization Based Neural Network in Mobile Cloud Computing

Akki, Praveena; Vijayarajan, V.

doi:10.1007/s11277-020-07448-2

Cited by 13 publications

(10 citation statements)

References 24 publications

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“…Here, the comparison of proposed MFCSNN approach is compared with the BAT and NN approach presented by Akki and Vijayarajan [8]. III.…”

Section: Resultsmentioning

confidence: 99%

“…III. The comparison has been made with the existing work performed by Akki and Vijayarajan (2020) [8] using BAT with ANN approach to optimize the power by selecting the best value among the available values of the generated solution. The aim of the researchers is to route mobile users to the server without interruption in services as well as with minimum power consumption.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, the minimization of energy becomes a serious problem to the researchers. This requires mechanism to expand the energy efficiency of MCC data centers while maintaining the specified level of operations [8].…”

Section: Introductionmentioning

confidence: 99%

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Cuckoo-Neural Approach for Secure Execution and Energy Management in Mobile Cloud Computing

Vishal¹,

Kaur²,

Jangra³

2021

IJACSA

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Along with an explosive growth in mobile applications and the emergence of the concept of cloud computing, in mobile cloud computing (MCC) has been familiarized as a potential technology for mobile users. Employing MCC to enable mobile users to realize the benefits of cloud computing in an environment friendly way is an effective strategy to meet today's industrial demands. With the ever-increasing demand for MCC technology, energy efficiency has become extremely relevant in mobile cloud computing infrastructure. The concept of mobile cloud computing offers low cost and high availability to the mobile cloud users on pay-per-use basis. However, the challenges such as resource management and energy consumption are still faced by mobile cloud providers. If the allocation of the resources is not managed in a secure manner, the false allocation will lead to more energy consumption. This article demonstrates the importance of energy-saving mechanisms in cloud data centers and elaborates the importance of the "energy efficiency" relationship to promote the adoption of these mechanisms in practical scenarios. The utilization of resources are being maximized by minimizing the energy consumption. To achieve this, an integrated approach using Cuckoo Search (CS) with Artificial Neural network (ANN) is presented here. Initially, the Virtual Machines (VMs) are sorted as per their CPU utilization using Modified Best Fit Decreasing Approach (MBFD). This suffers from the increase in Service Level Agreement (SLA) violation along with many VM migrations. If the migration is not done at an appropriate host, which can hold the VM for long, Service Level Agreement Violation (SLAV) will be high.

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“…Here, the comparison of proposed MFCSNN approach is compared with the BAT and NN approach presented by Akki and Vijayarajan [8]. III.…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Cuckoo-Neural Approach for Secure Execution and Energy Management in Mobile Cloud Computing

Vishal¹,

Kaur²,

Jangra³

2021

IJACSA

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“…With respect to using the Mobile Cloud Computing concept, an important aspect is that the optimization of resources concerns not only the mobile device but the cloud as well [23] [24]. In [23], the authors demonstrate that it is possible to optimize resource usage in MCC by applying common patterns used in traditional cloud computing, whereas in [24], the authors propose optimization the power consumption in the data center based on neural network.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning

Nawrocki

Śnieżyński

2020

Wireless Pers Commun

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In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.

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“…As these services are paid according to their use model, mobility, cost, and interactivity are the main challenges to the existing MCC paradigm. Another challenge for the MCC paradigm for microservices-based applications is cost-efficient resource scheduling for Mobile processes/tasks [12]. Task Scheduling is one of the most concerning topics in mobile cloud computing due to the limited capabilities of mobile devices, storage restrictions, Task processing capabilities, and network bandwidth requirements.…”

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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Energy Efficient Resource Scheduling Using Optimization Based Neural Network in Mobile Cloud Computing

Cited by 13 publications

References 24 publications

Cuckoo-Neural Approach for Secure Execution and Energy Management in Mobile Cloud Computing

Cuckoo-Neural Approach for Secure Execution and Energy Management in Mobile Cloud Computing

Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning

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

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