Agent-Based System for Mobile Service Adaptation Using Online Machine Learning and Mobile Cloud Computing Paradigm

In recent years, cloud workflow task scheduling has always been an important research topic in the business world. Cloud workflow task scheduling means that the workflow tasks submitted by users are allocated to appropriate computing resources for execution, and the corresponding fees are paid in real time according to the usage of resources. For most ordinary users, they are mainly concerned with the two service quality indicators of workflow task completion time and execution cost. Therefore, how cloud service providers design a scheduling algorithm to optimize task completion time and cost is a very important issue. This paper proposes research on workflow scheduling based on mobile cloud computing machine learning, and this paper conducts research by using literature research methods, experimental analysis methods, and other methods. This article has deeply studied mobile cloud computing, machine learning, task scheduling, and other related theories, and a workflow task scheduling system model was established based on mobile cloud computing machine learning from different algorithms used in processing task completion time, task service costs, task scheduling, and resource usage The situation and the influence of different tasks on the experimental results are analyzed in many aspects. The algorithm in this paper speeds up the scheduling time by about 7% under a different number of tasks and reduces the scheduling cost by about 2% compared with other algorithms. The algorithm in this paper has been obviously optimized in time scheduling and task scheduling.

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Mobile Cloud Computing

Applications of Security, Mobile, Analytic, and Cloud (SMAC) Technologies for Effective Information Processing and Management - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-4044-1.ch006 ◽

2018 ◽

pp. 105-123

Author(s):

Parkavi R ◽

Priyanka C ◽

Sujitha S. ◽

Sheik Abdullah A

Keyword(s):

Cloud Computing ◽

Mobile Computing ◽

Mobile Devices ◽

Mobile Cloud Computing ◽

Cloud Services ◽

Mobile Cloud ◽

Mobile Service ◽

Service Users ◽

Security Issues ◽

Mobile Computing Environment

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry ring words and a major conversation thread in the IT world with an explosive development of the mobile applications and emerging of cloud computing idea, the MCC has become a possible technology for the mobile service users. The concepts of Cloud computing are naturally meshed with mobile devices to allow on-the-go functionalities and benefits. The mobile cloud computing is emerging as one of the most important branches of cloud computing and it is expected to expand the mobile ecosystems. As more mobile devices enter the market and evolve, certainly security issues will grow as well. Also, enormous growth in the variety of devices connected to the Internet will further drive security needs. MCC provides a platform where mobile users make use of cloud services on mobile devices. The use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing environment.

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A Machine Learning Algorithm TsF K-NN Based on Automated Data Classification for Securing Mobile Cloud Computing Model

2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS) ◽

10.1109/ccoms.2019.8821756 ◽

2019 ◽

Author(s):

Anunaya Inani ◽

Chakradhar Verma ◽

Suvrat Jain

Keyword(s):

Machine Learning ◽

Cloud Computing ◽

Mobile Cloud Computing ◽

Learning Algorithm ◽

Data Classification ◽

Mobile Cloud ◽

Machine Learning Algorithm ◽

Computing Model

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Agent-Based Resource Management for Mobile Cloud

Mobile Computing and Wireless Networks ◽

10.4018/978-1-4666-8751-6.ch010 ◽

2016 ◽

pp. 200-216

Author(s):

Zhili Sun ◽

Yichao Yang ◽

Yanbo Zhou ◽

Haitham Cruickshank

Keyword(s):

Cloud Computing ◽

Resource Management ◽

Mobile Cloud Computing ◽

Mobile Network ◽

Mobile Cloud ◽

Mobile Environment ◽

Agent Architectures ◽

Agent Based ◽

Multiple Data ◽

User Demand

Mobile cloud computing is a new computing paradigm to integrate cloud computing technology into the mobile environment. It takes full advantages of cloud computing with great potential to transform a large part of the IT industry. The objectives of mobile cloud computing are to meet user demand, efficiently utilize a pool of resources, including mobile network, storage, and computation resources, and optimize energy on mobile devices. Here, the authors review the current mobile cloud computing technologies, highlight the main issues and challenges for the future development, and focus on resource management. Then, combining the current agent architectures and resource optimization strategies, they present an agent-based resource management to deal with multiple data and computation intensive applications of user demand. The chapter offers a promising solution of selecting the best service provider and efficiently utilizing mobile network resources given the user's request constraint.

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Multilayer Architecture Model for Mobile Cloud Computing Paradigm

Complexity ◽

10.1155/2019/3951495 ◽

2019 ◽

Vol 2019 ◽

pp. 1-13 ◽

Cited By ~ 5

Author(s):

Higinio Mora ◽

Francisco J. Mora Gimeno ◽

María Teresa Signes-Pont ◽

Bruno Volckaert

Keyword(s):

Cloud Computing ◽

Mobile Cloud Computing ◽

Mobile Cloud ◽

Comprehensive Model ◽

Architecture Model ◽

Network Layers ◽

Computing Paradigm ◽

Multilayer Architecture ◽

Computing Platforms ◽

Cloud Servers

Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.

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