SLAMMP Framework for Efficient Resource Monitoring and Prediction at an IaaS Cloud (Record no. 138713)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 04078ngm a22001577a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220702b |||||||| |||| 00| 0 eng d |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | TT000112 |
| Item number | PRA |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Prasad, Vivek Kumar |
| 245 ## - TITLE STATEMENT | |
| Title | SLAMMP Framework for Efficient Resource Monitoring and Prediction at an IaaS Cloud |
| Statement of responsibility, etc | by Vivek Kumar Prasad |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Ahmedabad |
| Name of publisher, distributor, etc | Nirma Institute of Technology |
| Date of publication, distribution, etc | March 2021 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 161p Ph. D. Thesis with Synopsis and CD |
| 500 ## - GENERAL NOTE | |
| General note | Guided by: Dr Madhuri Bhavsar With Synopsis and CD <br/>15EXTPHDE145<br/><br/>ABSTRACT:<br/>The Cloud Computing(CC) paradigm has transformed the information technology<br/>horizon in past years and has emerged as an important computing utility. Government<br/>bodies, industries and academia have given significant attention to the CC. Cloud has<br/>become the backbone of the current economy by posing subscription-based facilities anywhere,<br/>anytime resulting in a pay-as-you-go model. CC supports properties such as scalable<br/>resources handling and elasticity through resource management. The management of<br/>the resources is being handled through monitoring and prediction . The present challenge<br/>in CC environment is to identify the possible violations in the SLA proactively. Also<br/>reacting to this state by taking appropriate actions to avoid penalties and manage the<br/>resources effectively. This research work addresses resource monitoring and prediction<br/>mechanism to handle users’ demands in an efficient way in the CC environment through<br/>the Service Level Agreement Management using Monitoring and Prediction (SLAMMP)<br/>framework.<br/>The framework integrates the concepts of Deep Learning (DL), Hidden Markov Model<br/>(HMM), and Smart Contracts (SC); and is mapped to four-fold layers. First, the workload<br/>generation has been implemented through Reinforcement Learning (RL); secondly,<br/>the anti-patterns of the workloads were checked by using HMM, thirdly the SLAs has<br/>been maintained using a smart contract, and fourth, is the utilization of resources has<br/>been predicted using Long Short Term Memory (LSTM) approach. The SLAMMP framework<br/>discussed here ensures timely monitoring and prediction of the cloud infrastructure,<br/>which results in the analysis of the realistic (real-time) behavior of the IaaS cloud and<br/>take precautionary actions for the management of cloud resources during peak time/high<br/>demand. This mechanism is better for capacity planning, Admission control, and SLA<br/>process management. The experiment shows that the proposed SLAMMP framework<br/>effectively manages the cloud resources using monitoring and prediction methodologies.<br/>The SLAMMP framework is evaluated against other techniques such as Artificial Neural<br/>Network (ANN), Recurrent Neural Network (RNN), Random Forest (RF), Decision Tree<br/>(DT), Support Vector Regression (SVR), Gated Recurrent Unit(GRU), and Autoregressive<br/>integrated moving average (ARIMA) .<br/>Various parameters such as CPU utilization, disk write throughput, disk read through put, memory usages, network received throughput, and network transmitted throughput<br/>are used to validate the framework. The evaluation based on the performance metrics and<br/>statistical t testing shows that the mentioned framework makes a substantial improvement<br/>in resource management. The results are validated for both patterns and anti-patterns<br/>based resource utilities. This also meets the SLA and also restricts the violations. Overall,<br/>we conclude that based on the experimental results, the designed and implemented<br/>SLAMMP framework works efficiently as compared to the other online machine learning<br/>and deep learning techniques. The framework manages resources optimally while dealing<br/>with the patterns and anti-patterns. This research work contributes towards an overall<br/>performance and Quality of Service (QoS) enhancement for resource management in the<br/>CC ecosystem. |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Public note | Institute Repository (Campus Access) |
| Uniform Resource Identifier | https://repository.nirmauni.ac.in/jspui/handle/123456789/11137 |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Public note | Shodhganga |
| Uniform Resource Identifier | http://shodhganga.inflibnet.ac.in/handle/10603/384691 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Thesis |
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