Comparison of Execution Time of Mobile Application Using Equal Division and Profile-Based Algorithm in Mobile Cloud Computing

2019 ◽  
pp. 59-72
Author(s):  
Kailas K. Devadkar ◽  
Dhananjay R. Kalbande
Keyword(s):  
Cloud Computing ◽  
Execution Time ◽  
Mobile Application ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Equal Division

Mobile Learning Services on Cloud

2015 ◽  
pp. 230-255
Author(s):  
Dušan Barać ◽  
Miloš Radenković ◽  
Branislav Jovanić
Keyword(s):  
Cloud Computing ◽  
Mobile Learning ◽  
Mobile Application ◽  
Management System ◽  
Mobile Cloud Computing ◽  
Learning Management System ◽  
Learning System ◽  
Mobile Cloud ◽  
E Learning ◽  
Learning Management

This chapter discusses providing mobile learning services on cloud. Mobile cloud computing brings numerous benefits and enables overcoming technical constraints of mobile learning. The main techniques and approaches in mobile cloud computing are analyzed. A model for mobile learning services delivering through cloud computing is proposed. Several examples of mobile learning services implementations on cloud are presented: Android native application that provides Moodle learning management system features and a SMS service and mobile application for managing the infrastructure of e-learning system.

scholarly journals Job Scheduling Based on Dependent and Independent Task using Particle Swarm Optimization

2020 ◽  
Vol 9 (4) ◽  
pp. 1600-1606
Keyword(s):  
Cloud Computing ◽  
Particle Swarm Optimization ◽  
Execution Time ◽  
Mobile Cloud Computing ◽  
Job Scheduling ◽  
Dynamic Scheduling ◽  
Particle Swarm ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Dependent Tasks

Mobile Cloud Computing is an accumulation of both Cloud Computing and Mobile Computing. In cloud computing resources are deployed to a client on-demand basis. Mobile cloud computing is similar to cloud computing except that some devices involved in mobile cloud computing should be mobile. The demand for MCC has been increasing due to its scalability, reliability, high QOS (Quality Of Services), longer battery life, large storage capacity. Mobile cloud computing aims to take benefit of limited resources provided by a cloud provider. Task scheduling is a major concept involved in executing a task. In cloud computing job scheduling is required to execute each job without any deadlock. But the scheduling of dependent tasks is a problem in cloud systems. This problem is an NP-complete problem and can be solved using various heuristic and metaheuristic approaches. These approaches give high-quality solutions with reasonable execution time. Particle Swarm Optimization (PSO) is one of these meta-heuristic approaches that solve the problem of grid scheduling. In this paper, we address the problem encounter in dynamic scheduling. In dynamic scheduling, each task has its own deadline completion time. The task that arrived earlier in the system occupied the resources first and later arrived tasks are rejected because their execution time exceeds the deadline. In this paper, we proposed PSO with a variable job identifier that identifies independent and dependent tasks from the population. The particles are arranged with a grid dynamically and influence swarm to minimize execution time and waiting time simultaneously. The experimental studies show that the proposed approach is more efficient than other PSO based approaches as described in the literature

scholarly journals Application of mobile cloud computing in emergency health care

2019 ◽  
Vol 8 (3) ◽  
pp. 1088-1095
Author(s):  
Shihab A. Hameed ◽  
Ali Nirabi ◽  
Mohamed Hadi Habaebi ◽  
Alaa Haddad
Keyword(s):  
Health Care ◽  
Cloud Computing ◽  
Data Storage ◽  
Mobile Application ◽  
Mobile Cloud Computing ◽  
Medical Center ◽  
Professional Service ◽  
Mobile Cloud ◽  
Emergency Health Care ◽  
Cloud Computing System

Mobile applications in emergency health care help maintain patient confidentiality and manage patient records, data storage. Compiles and analyzes care of better quality care. new implementations come with new goals and technologies like using mobile application with cloud computing system and reducing the responding time to safe the patient life and give the patient best health care professional service transition to using of mobile application in emergency healthcare, this paper will present (MCCEH) mobile cloud computing in emergency health care model, mainly reducing the wasting time in emergency health care, The process starting once the accident occurred and the patient run the application, mobile application will detect the patient location and allow him to book nearest medical center or specialist in some emergency cases once the patient did the booking will send help request to medical center this process will include an online pre-register patient in the medical center to save time of patient registration, MCCEH model allows the patients to review the previous feedback and experiences of each specialist or medical center and allows doctors to be able to stay in contact with their patients more often and by communication through mobiles applications and share messages and photos of the accident or emergency case itself.

Mobile Learning Services on Cloud

2016 ◽  
pp. 1027-1052
Author(s):  
Dušan Barać ◽  
Miloš Radenković ◽  
Branislav Jovanić
Keyword(s):  
Cloud Computing ◽  
Mobile Learning ◽  
Mobile Application ◽  
Management System ◽  
Mobile Cloud Computing ◽  
Learning Management System ◽  
Learning System ◽  
Mobile Cloud ◽  
E Learning ◽  
Learning Management

This chapter discusses providing mobile learning services on cloud. Mobile cloud computing brings numerous benefits and enables overcoming technical constraints of mobile learning. The main techniques and approaches in mobile cloud computing are analyzed. A model for mobile learning services delivering through cloud computing is proposed. Several examples of mobile learning services implementations on cloud are presented: Android native application that provides Moodle learning management system features and a SMS service and mobile application for managing the infrastructure of e-learning system.

Mobile Learning Services on Cloud

2014 ◽  
pp. 147-172 ◽  
Author(s):  
Dušan Barać ◽  
Miloš Radenković ◽  
Branislav Jovanić
Keyword(s):  
Cloud Computing ◽  
Mobile Learning ◽  
Mobile Application ◽  
Management System ◽  
Mobile Cloud Computing ◽  
Learning Management System ◽  
Learning System ◽  
Mobile Cloud ◽  
E Learning ◽  
Learning Management

This chapter discusses providing mobile learning services on cloud. Mobile cloud computing brings numerous benefits and enables overcoming technical constraints of mobile learning. The main techniques and approaches in mobile cloud computing are analyzed. A model for mobile learning services delivering through cloud computing is proposed. Several examples of mobile learning services implementations on cloud are presented: Android native application that provides Moodle learning management system features and a SMS service and mobile application for managing the infrastructure of e-learning system.

Mobile Learning Services on Cloud

2016 ◽  
pp. 81-106
Author(s):  
Dušan Barać ◽  
Miloš Radenković ◽  
Branislav Jovanić
Keyword(s):  
Cloud Computing ◽  
Mobile Learning ◽  
Mobile Application ◽  
Management System ◽  
Mobile Cloud Computing ◽  
Learning Management System ◽  
Learning System ◽  
Mobile Cloud ◽  
E Learning ◽  
Learning Management

This chapter discusses providing mobile learning services on cloud. Mobile cloud computing brings numerous benefits and enables overcoming technical constraints of mobile learning. The main techniques and approaches in mobile cloud computing are analyzed. A model for mobile learning services delivering through cloud computing is proposed. Several examples of mobile learning services implementations on cloud are presented: Android native application that provides Moodle learning management system features and a SMS service and mobile application for managing the infrastructure of e-learning system.

scholarly journals A Multi-Objective Genetic Algorithm-Based Resource Scheduling in Mobile Cloud Computing

2021 ◽  
Vol 15 (3) ◽  
pp. 58-73
Author(s):  
Somula Ramasubbareddy ◽  
Evakattu Swetha ◽  
Ashish Kumar Luhach ◽  
T. Aditya Sai Srinivas
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Load Balancing ◽  
Response Time ◽  
Execution Time ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Heterogeneous Environments ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Mobile cloud computing is an emerging technology in recent years. This technology reduces battery consumption and execution time by executing mobile applications in remote cloud server. The virtual machine (VM) load balancing among cloudlets in MCC improves the performance of application in terms of response time. Genetic algorithm (GA) is popular for providing optimal solution for load balancing problems. GA can perform well in both homogeneous and heterogeneous environments. In this paper, the authors consider multi-objective genetic algorithm for load balancing in MCC (MOGALMCC) environment. In MOGALMCC, they consider distance, bandwidth, memory, and cloudlet server load to find optimal cloudlet before scheduling VM in another cloudlet. The framework MOGALMCC aims to improve response time as well as minimizes VM failure rate. The experiment result shows that proposed model performed well by reducing execution time and task waiting time at server.

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