SLAMMP Framework for Efficient Resource Monitoring and Prediction at an IaaS Cloud

Abstract

The Cloud Computing(CC) paradigm has transformed the information technology newlinehorizon in past years and has emerged as an important computing utility. Government newlinebodies, industries and academia have given significant attention to the CC. Cloud has newlinebecome the backbone of the current economy by posing subscription-based facilities anywhere, newlineanytime resulting in a pay-as-you-go model. CC supports properties such as scalable newlineresources handling and elasticity through resource management. The management of newlinethe resources is being handled through monitoring and prediction . The present challenge newlinein CC environment is to identify the possible violations in the SLA proactively. Also newlinereacting to this state by taking appropriate actions to avoid penalties and manage the newlineresources effectively. This research work addresses resource monitoring and prediction newlinemechanism to handle users demands in an efficient way in the CC environment through newlinethe Service Level Agreement Management using Monitoring and Prediction (SLAMMP) newlineframework. newlineThe framework integrates the concepts of Deep Learning (DL), Hidden Markov Model newline(HMM), and Smart Contracts (SC); and is mapped to four-fold layers. First, the workload newlinegeneration has been implemented through Reinforcement Learning (RL); secondly, newlinethe anti-patterns of the workloads were checked by using HMM, thirdly the SLAs has newlinebeen maintained using a smart contract, and fourth, is the utilization of resources has newlinebeen predicted using Long Short Term Memory (LSTM) approach. The SLAMMP framework newlinediscussed here ensures timely monitoring and prediction of the cloud infrastructure, newlinewhich results in the analysis of the realistic (real-time) behavior of the IaaS cloud and newlinetake precautionary actions for the management of cloud resources during peak time/high newlinedemand. This mechanism is better for capacity planning, Admission control, and SLA newlineprocess management. The experiment shows that the proposed SLAMMP framework newlineeffectively manages the cloud resources using monitoring and prediction methodolog