Online Resource Scheduling under Concave Pricing for Cloud Computing - 2016


With the booming cloud computing business, computational resources are readily and elastically available to the purchasers. In order to attract customers with numerous demands, most Infrastructure-as-a-service (IaaS) cloud service suppliers offer several pricing ways such as pay as you go, pay less per unit when you employ a lot of (so referred to as volume discount), and pay even less when you reserve. The numerous pricing schemes among completely different IaaS service providers or maybe in the same provider kind a complex economic landscape that nurtures the market of cloud brokers. By strategically scheduling multiple customers' resource requests, a cloud broker will totally exploit the discounts offered by cloud service suppliers. In this paper, we target how a broker can facilitate a group of consumers to totally utilize the quantity discount pricing strategy offered by cloud service suppliers through cost-economical online resource scheduling. We have a tendency to gift a randomized online stack-centric scheduling algorithm (ROSA) and theoretically prove the lower certain of its competitive ratio. Three special cases of the offline concave cost scheduling downside and the corresponding optimal algorithms are introduced. Our simulation shows that ROSA achieves a competitive ratio shut to the theoretical lower bound underneath the special cases. Trace-driven simulation using Google cluster data demonstrates that ROSA is superior to the traditional online scheduling algorithms in terms of value saving.

Did you like this research project?

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

PROJECT TITLE : Classification of Online Toxic Comments Using Machine Learning Algorithms ABSTRACT: Toxic comments are online remarks that are insulting, abusive, or inappropriate, and frequently cause other users to quit a
PROJECT TITLE : Reviewer Credibility and Sentiment Analysis Based User Profile Modelling for Online Product Recommendation ABSTRACT: Even for humans, deciphering user buying preferences, likes and dislikes is a difficult undertaking,
PROJECT TITLE : Active Learning From Imbalanced Data A Solution of Online Weighted Extreme Learning Machine ABSTRACT: Active learning is well known for its ability to improve the quality of a classification model while also reducing
PROJECT TITLE : Online ADMM-based Extreme Learning Machine for Sparse Supervised Learning ABSTRACT: In the field of machine learning, sparse learning is a useful strategy for selecting features and avoiding overfitting. An online
PROJECT TITLE : Online Subspace Learning from Gradient Orientations for Robust Image Alignment ABSTRACT: Robust and effective picture alignment remains a difficult task due to the size and complexity of images as well as fluctuations

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

Project Enquiry