Reducing AWS Spend Using Machine Learning Driven EC2 Instance Type Switching

In this blog, we will present a new feature of machine learning based “EC2 Instance Type Switching” through which all possible underutilized instances can be identified and appropriate “fitted” instance types are recommended to help reduce wasted AWS spend. Users … Read More

Real-Time Anomaly Detection – Protect yourself from malicious spend attacks on clouds

As stated in our previous blogs, FittedCloud applies many kinds of AI-driven algorithms to analyze contextual data and behaviors in your Cloud environment to identify opportunities to optimize system’s performance, increase resource utilization, and reduce your total cost. In this … Read More

Achieving significant cost savings using machine learning in elastic public clouds – A cost savings analysis

For many years, traditional information technology (IT) departments are required to build and maintain IT infrastructures (e.g., servers, networks, etc.) and services (e.g., data base management, email, technique issue solving, etc.) to support the company’s business. It is also known … Read More

Cloud Cost Management – Explainable Anomaly Detection with Cross Resources in Public Clouds

In our previous blog of contextual anomaly detection, we have discussed machine learning algorithms that are used to detect abnormal Cloud activities in a real-time manner. Several types of abnormal Cloud activities include but not limited to: Abnormal User Accesses … Read More

How to Measure Resource Cost Efficiency in Public Clouds

Public Clouds offer a broad set of products and services including compute, storage, databases, analytics, networking, mobile, and enterprise applications. These services offer a pay-as-you-go approach; customers only pay for the services they consume. Such services are often referred to … Read More

Feature Selection in Machine Learning for Dynamic Cloud Resource Optimization: Part II

In my previous post, I mentioned that there are three types of feature selection methods: wrappers, filters, and embedding methods. Since wrappers are commonly computationally heavy, we only discuss the latter two approaches in this post. Filter Approaches Filter approaches … Read More

Feature Selection in Machine Learning for Dynamic Cloud Resource Optimization: Part I

Introduction Feature selection, also known as attribution selection or feature subset selection, is a very important process in data mining and analytics, which aims to select a subset of relevant and irredundant features for use. Its significance in machine learning … Read More

Adaptive Machine Learning for Cloud Resource Provisioning

Overview of Machine Learning During the past few decades, we have witnessed tremendous growths in the study of machine learning. Machine learning has been widely and successfully used in many practical systems to bring intelligence or smartness to the systems, … Read More