Deep-Hill: An Innovative Cloud Resource Optimization Algorithm by Predicting SaaS Instance Configuration Using Deep Learning

Abstract

The integration of Artificial Intelligence (AI) services within the framework of Software-as-a-Service (SaaS) cloud architecture has significantly permeated our everyday routines. These AI services diverge from traditional applications by offering a more personalized user experience. That is why a predefined instance configuration is not an optimal approach for these applications. The challenge is further compounded by the unpredictable nature of user demand, making optimal resource allocation to these instances a complex task. This paper introduces an innovative algorithm, termed Deep-Hill, designed to enhance cloud resource allocation through precise prediction of SaaS instance configurations. It is a combination of a 5-layer Deep Neural Network (DNN) and a Hill-Climbing algorithm. This unique approach classifies the instance configuration in one of the five classes with 96.33% accuracy, 90.83% precision, 90.96% recall, and 90.86% F1-score. On average, it reduces the number of active hosts by four, contributing to 13.33% less power consumption. The remarkable performance of the Deep-Hill algorithm underscores its potential to set a new benchmark in the optimization of SaaS cloud resources. It paves the way for more cost-effective SaaS applications, marking a significant step forward in the evolution of cloud computing.

Keywords

• Cloud computing
• deep learning
• optimization
• software-as-a-service
• instance configuration
• deep neural network