Machine Learning Failure-Aware Scheme for Profit Maximization in the Cloud Market

Abstract

A successful cloud trading system requires suitable financial incentives for all parties involved. Cloud providers in the cloud market provide computing services to clients in order to perform their tasks and earn extra money. Unfortunately, the applications in the cloud are prone to failure for several reasons. Cloud service providers are responsible for managing the availability of scheduled computing tasks in order to provide high-level quality of service for their customers. However, the cloud market is extremely heterogeneous and distributed, making resource management a challenging problem. Protecting tasks against failure is a challenging and non-trivial mission due to the dynamic, heterogeneous, and largely distributed structure of the cloud environment. The existing works in the literature focus on task failure prediction and neglect the remedial (post) actions. To address these challenges, this paper suggests a fault-tolerant resource management scheme for the cloud computing market in which the optimal amount of computing resources is extracted at each system epoch to replace failed machines. When a cloud service provider detects a malfunctioning machine, they transfer the associated work to new machinery.

Improving Recommender Systems by a Further Factorization of the Factor Matrices

“…W ith the growth of the digital universe, a great diversity of information, products and services have become available on the Web [1], [2], [3]. An active user (AU) refers to a user who seeks recommendation via a recommender system (RS) to guide the AUs to obtain the best choice of the favourite items since end-users do not have sufficient time to investigate all available choices.…”