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

Abstract

The Cloud Computing(CC) paradigm has transformed the information technology horizon in past years and has emerged as an important computing utility. Government bodies, industries and academia have given significant attention to the CC. Cloud has become the backbone of the current economy by posing subscription-based facilities anywhere, anytime resulting in a pay-as-you-go model. CC supports properties such as scalable resources handling and elasticity through resource management. The management of the resources is being handled through monitoring and prediction . The present challenge in CC environment is to identify the possible violations in the SLA proactively. Also reacting to this state by taking appropriate actions to avoid penalties and manage the resources effectively. This research work addresses resource monitoring and prediction mechanism to handle users’ demands in an efficient way in the CC environment through the Service Level Agreement Management using Monitoring and Prediction (SLAMMP) framework. The framework integrates the concepts of Deep Learning (DL), Hidden Markov Model (HMM), and Smart Contracts (SC); and is mapped to four-fold layers. First, the workload generation has been implemented through Reinforcement Learning (RL); secondly, the anti-patterns of the workloads were checked by using HMM, thirdly the SLAs has been maintained using a smart contract, and fourth, is the utilization of resources has been predicted using Long Short Term Memory (LSTM) approach. The SLAMMP framework discussed here ensures timely monitoring and prediction of the cloud infrastructure, which results in the analysis of the realistic (real-time) behavior of the IaaS cloud and take precautionary actions for the management of cloud resources during peak time/high demand. This mechanism is better for capacity planning, Admission control, and SLA process management. The experiment shows that the proposed SLAMMP framework effectively manages the cloud resources using monitoring and prediction methodologies. The SLAMMP framework is evaluated against other techniques such as Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest (RF), Decision Tree (DT), Support Vector Regression (SVR), Gated Recurrent Unit(GRU), and Autoregressive integrated moving average (ARIMA) . Various parameters such as CPU utilization, disk write throughput, disk read through put, memory usages, network received throughput, and network transmitted throughput are used to validate the framework. The evaluation based on the performance metrics and statistical t testing shows that the mentioned framework makes a substantial improvement in resource management. The results are validated for both patterns and anti-patterns based resource utilities. This also meets the SLA and also restricts the violations. Overall, we conclude that based on the experimental results, the designed and implemented SLAMMP framework works efficiently as compared to the other online machine learning and deep learning techniques. The framework manages resources optimally while dealing with the patterns and anti-patterns. This research work contributes towards an overall performance and Quality of Service (QoS) enhancement for resource management in the CC ecosystem.