scholarly journals Load Predicting Model of Mobile Cloud Computing Based on Glowworm Swarm Optimization LSTM Network

2020 ◽  
Vol 14 (05) ◽  
pp. 150
Author(s):  
P. Sudhakaran ◽  
Subbiah Swaminathan ◽  
D. Yuvaraj ◽  
S.Shanmuga Priya
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Short Term Memory ◽  
Neural System ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Prediction Algorithms ◽  
Glowworm Swarm Optimization ◽  
Lstm Network ◽  
Host Load

Focusing on the issue of host load estimating in mobile cloud computing, the Long Short Term Memory networks (LSTM)is introduced, which is appropriate for the intricate and long-term arrangement information of the cloud condition and a heap determining calculation dependent on Glowworm Swarm Optimization LSTM neural system is proposed. Specifically, we build a mobile cloud load forecasting model using LSTM neural network, and the Glowworm Swarm Optimization Algorithm (GSO) is used to search for the optimal LSTM parameters based on the research and analysis of host load data in the mobile cloud computing data center. Finally, the simulation experiments are implemented and similar prediction algorithms are compared. The experimental results show that the prediction algorithms proposed in this paper are in prediction accuracy higher than equivalent prediction algorithms.

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

Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing

2020 ◽  
Vol 39 (6) ◽  
pp. 8285-8297
Author(s):  
V. Meena ◽  
Obulaporam Gireesha ◽  
Kannan Krithivasan ◽  
V.S. Shankar Sriram
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Cloud Computing ◽  
Cloud Services ◽  
Mobile Cloud ◽  
Swarm Optimization ◽  
Computational Offloading ◽  
Smart Mobile ◽  
Smart Mobile Devices

Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. However, their processing speeds, access delays, computation capability, residual memory and service charges are different which retards their usage, as it becomes time-consuming and ambiguous for making decisions. To address the aforementioned issues, this paper presents a Fuzzy Simplified Swarm Optimization based cloud Computational Offloading (FSSOCO) algorithm to achieve optimum multisite offloading. Fuzzy logic and simplified swarm optimization are employed for the identification of high powerful nodes and task decomposition respectively. The overall performance of FSSOCO is validated using the Specjvm benchmark suite and compared with the state-of-the-art offloading techniques in terms of the weighted total cost, energy consumption, and processing time.

mMedia: An Efficient Transmission Policy for Multimedia Applications using Mobile Cloud Computing

2019 ◽  
Vol 9 (1) ◽  
pp. 32-42 ◽  
Author(s):  
Rajesh Kumar Verma ◽  
Chhabi Rani Panigrahi ◽  
Bibudhendu Pati ◽  
Joy Lal Sarkar
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Optimization Technique ◽  
Multimedia Applications ◽  
Mobile Cloud ◽  
The People ◽  
Rich Media ◽  
Time Average ◽  
Audio Video ◽  
Efficient Transmission

Background & Objective: Multimedia aggregates various types of media such as audio, video, images, animations, etc., to form a rich media content which produces an everlasting effect in the minds of the people. Methods: In order to process multimedia applications using mobile devices, we encounter a big challenge as these devices have limited resources and power. To address these limitations, in this work, we have proposed an efficient approach named as mMedia, wherein multimedia applications will utilize the multi cloud environment using Mobile Cloud Computing (MCC), for faster processing. The proposed approach selects the best available network. The authors have also considered using the Lyapunov optimization technique for efficient transmission between the mobile device and the cloud. Results: The simulation results indicate that mMedia can be useful for various multimedia applications by considering the energy delay tradeoff decision. Conclusion: The results have been compared alongside the base algorithm SALSA on the basis of different parameters like time average queue backlog, delay and time average utility and indicate that the mMedia outperforms in all the aspects.

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