PROJECT TITLE :

Dynamic Deployment and Cost-Sensitive Provisioning for Elastic MobileCloud Services - 2018

ABSTRACT:

As mobile customers gradually occupying the largest share of cloud service users, the effective and value-sensitive provisioning of mobile cloud services quickly becomes a main theme in Cloud Computing. The key issues involved are abundant more than simply enabling mobile users to access remote cloud resources through wireless networks. The resource limited and intermittent disconnection issues of mobile environments have intrinsic conflict with the continual connection assumption of the cloud service usage patterns. We have a tendency to advocate that seamless service provisioning in mobile cloud can only be achieved with full exploitation of all accessible resources around mobile users. An elastic framework is proposed to automatically and dynamically deploy cloud services on information center, base stations, consumer units, even peer devices. The simplest deployment location is dynamically determined based mostly on a context-aware and cost-sensitive analysis model. To facilitate straightforward adoption of the proposed framework, a service development model and associated semi-automatic tools are provided such that cloud service developers can simply convert a service for execution on totally different platforms without porting. Prototype implementation and evaluation on the Google Cloud and Android platforms demonstrate that our mechanism can successfully maintain seamless services with terribly low overhead.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Unsupervised Spectral Feature Selection with Dynamic Hyper-graph Learning ABSTRACT: In order to produce interpretable and discriminative results from unsupervised spectral feature selection (USFS) methods, an embedding
PROJECT TITLE : GloDyNE: Global Topology Preserving Dynamic Network Embedding ABSTRACT: Due to the time-evolving nature of many real-world networks, learning low-dimensional topological representations of networks in dynamic environments
PROJECT TITLE : Fully Dynamic kk-Center Clustering With Improved Memory Efficiency ABSTRACT: Any machine learning library worth its salt will include both static and dynamic clustering algorithms as core components. The sliding
PROJECT TITLE : Exploring Temporal Information for Dynamic Network Embedding ABSTRACT: The task of analyzing complex networks is a challenging one that is attracting an increasing amount of attention. One way to make the analysis
PROJECT TITLE : MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers ABSTRACT: Two of the most significant challenges for effective resource management in large-scale

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry