Multi-Context Based Optimal Resource Provisioning in Mobile Cloud Environments

2018 ◽  
Vol 15 (5) ◽  
pp. 1762-1768
Author(s):  
S Durga ◽  
S Mohan ◽  
J. Dinesh Peter
Keyword(s):  
Resource Provisioning ◽  
Mobile Cloud ◽  
Optimal Resource ◽  
Cloud Environments

Optimal Resource Provisioning in Federated-Cloud Environments

2015 ◽  
pp. 84-101
Author(s):  
Veena Goswami ◽  
Choudhury N. Sahoo
Keyword(s):  
Service Providers ◽  
Queueing Network ◽  
Resource Provisioning ◽  
New Paradigm ◽  
Management Framework ◽  
Software Applications ◽  
Optimal Resource ◽  
Cloud Environments ◽  
Optimal Resource Management ◽  
Multiple Clouds

Cloud computing has emerged as a new paradigm for accessing distributed computing resources such as infrastructure, hardware platform, and software applications on-demand over the internet as services. Multiple Clouds can collaborate in order to integrate different service-models or service providers for end-to-end-requirements. Intercloud Federation and Service delegation models are part of Multi-Cloud environment where the broader target is to achieve infinite pool of resources. This chapter presents an optimal resource management framework for Federated-cloud environments. Each service model caters to specific type of requirements and there are already number of players with own customized products/services offered. They propose an analytical queueing network model to improve the efficiency of the system. Numerical results indicate that the proposed provisioning technique detects changes in arrival pattern, resource demands that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.

A Mobility-Aware Optimal Resource Allocation Architecture for Big Data Task Execution on Mobile Cloud in Smart Cities

2018 ◽  
Vol 56 (2) ◽  
pp. 110-117 ◽  
Author(s):  
Asma Enayet ◽  
Md. Abdur Razzaque ◽  
Mohammad Mehedi Hassan ◽  
Atif Alamri ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Big Data ◽  
Smart Cities ◽  
Mobile Cloud ◽  
Task Execution ◽  
Optimal Resource Allocation ◽  
Optimal Resource

scholarly journals Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Monika Kumari ◽  
G. Sahoo
Keyword(s):  
Genetic Algorithm ◽  
Differential Evolution ◽  
Resource Provisioning ◽  
Vertical Scaling ◽  
Predator Prey ◽  
Average Completion Time ◽  
Predator Prey Model ◽  
De Algorithm ◽  
Optimal Resource ◽  
And Performance

Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.

scholarly journals Cooperative Runtime Offloading Decision Algorithm for Mobile Cloud Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Xiaomin Jin ◽  
Zhongmin Wang ◽  
Wenqiang Hua
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Data Transmission ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Wireless Data ◽  
Decision Algorithm ◽  
Wireless Data Transmission ◽  
Cloud Environments ◽  
New Challenges

Mobile cloud computing (MCC) provides a platform for resource-constrained mobile devices to offload their tasks. MCC has the characteristics of cloud computing and its own features such as mobility and wireless data transmission, which bring new challenges to offloading decision for MCC. However, most existing works on offloading decision assume that mobile cloud environments are stable and only focus on optimizing the consumption of offloaded applications but ignore the consumption caused by offloading decision algorithms themselves. This paper focuses on runtime offloading decision in dynamic mobile cloud environments with the consideration of reducing the offloading decision algorithm’s consumption. A cooperative runtime offloading decision algorithm, which takes advantage of the cooperation of online machine learning and genetic algorithm to make offloading decisions, is proposed to address this problem. Simulations show that the proposed algorithm helps offloaded applications save more energy and time while consuming fewer computing resources.

Resource Provisioning and Scheduling Techniques of IoT Based Applications in Fog Computing

2019 ◽  
Vol 2 (2) ◽  
pp. 57-70 ◽  
Author(s):  
Rajni Gupta
Keyword(s):  
Oil And Gas ◽  
Health Care Systems ◽  
Fog Computing ◽  
Fast Response ◽  
Resource Provisioning ◽  
Time Data ◽  
Computing Paradigm ◽  
Care Systems ◽  
Cloud Environments ◽  
Suitable Technique

Internet of Things (IoT) has emerged as a computing paradigm to develop smart applications such e-health care systems, smart city, smart waste management systems, etc. It contains a large number of different devices and heterogeneous networks, which make it difficult to provide secure and fast response to the end user. To provide the faster response services, there is a need to use the concept of Fog computing Recently, the use of fog computing is a rapidly increasing in many industries for the development of applications such as manufacturing, e-health, oil and gas, As more and more users have started to store/process their real-time data in Fog-based Cloud environments, resource provisioning and scheduling of IoT based applications becomes a key element of consideration for efficient execution of these applications. This article will help to select the most suitable technique for processing smart IoT based applications in Fog computing environments.

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