S. K. Khadar Babu

Bio: S. K. Khadar Babu is an academic researcher from VIT University. The author has contributed to research in topics: Cloud computing & Time series. The author has an hindex of 4, co-authored 8 publications receiving 43 citations.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Abstract: The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service QoS oriented computing platform. To meet these requirements, virtualization and Virtual Machine VM consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm A-GA based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time MMT, Maximum Correlation MC and Random Selection RS, along with different CPU utilization estimation approaches like Inter Quartile Range IQR, Local Regression LR, Local Robust Regression LRR, static THReshold THR and Median Absolute Deviation MAD has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization ACO and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement SLA violation and QoS assurance is inevitable.

An Adaptive Genetic Algorithm based Robust QoS Oriented Green Computing Scheme for VM Consolidation in Large Scale Cloud Infrastructures

Abstract: Backgrounds: The high pace increase in cloud applications requires an optimal computing platform like, Virtual Machine (VM) Consolidationor virtualization to ensure optimal computational efficiency, energy consumption and minimal SLA violation. Methods: In this paper, an evolutionary computing approach called Adaptive Genetic Algorithm (A-GA) has been proposed for VM placement policy, to be used in VM consolidation. In the proposed model, the modified Robust Local Regression (LRR) and Inter-Quartile Range (IQR) schemes estimate the dynamic CPU utilization for overload detection, which is followed by Maximum Correlation (MC) and Minimum Migration Time (MMT) based VM selectionand A-GA based VM placement. Findings: The comparative performance analysis for the proposed system with Planet Lab cloud benchmark dataset has exhibited that the proposed model exhibits better results as compared to other heuristic approaches such as Best Fit Decreasing (BFD) algorithm and Ant Colony Optimization (ACO). The implementation of the proposed A-GA based consolidation with modified IQR and LRR, and MMT selection policyhas performed better in terms of energy efficiency and SLA violation as compared to the other heuristic approaches for placement such as Best Fit Decreasing (BFD) algorithm with conventional IQR, Local Regression (LR), robust local regression, Static Threshold (THR) and Median Absolute Deviation (MAD) based CPU utilization threshold estimation schemes. Furthermore, the proposed A-GA based scheme has outperformed Ant Colony Optimization (ACO) based consolidation scheme. The performance analysis with two distinct VM selection policies, MC and MMT has revealed that A-GA performs better with MMT selection policy and provides higher host shutdown, minimal VM migration and SLA violation, and minimal energy consumption. Applications: The proposed A-GAbased VM consolidation scheme can be significant for energy aware and QoS oriented virtualization application in large scale cloud infrastructures.

An Evolutionary Computing based Energy Efficient VM Consolidation Scheme for Optimal Resource Utilization and QoS Assurance

Abstract: Background: The increase in Cloud applications have demanded efficient cloud computing systems like Virtual Machine (VM) consolidation that intends to facilitate optimal resource utilization, energy conservation and quality of service. Methods: In this paper, an evolutionary computing technique called Adaptive Genetic Algorithm (A-GA) has been proposed for VM consolidation that encompasses under load and overload utilization detection, VM selection and placement, where the modified robust local regression and interquartile range schemes estimate the dynamic CPU utilization threshold for overload detection, minimum migration time works as VM selection policy, while A-GA optimizes VM placement across network to reduce energy consumption and SLA violation. Findings: PlanetLab Cloud benchmark data based simulation results confirms that the proposed VM consolidation scheme exhibits better than other existing approaches such as Ant Colony Optimization (ACO), Static Threshold (THR), Local Regression (LR), Conventional Inter Quartile Range (IQR) and Median Absolute Deviation (MAD) based virtualization schemes. The proposed system has exhibited minimal host shutdown, VM migration, energy consumption and SLA violation as compared to other existing approaches. Applications: Thus, the efficiency of the proposed VM consolidation scheme signifies that it can be a potential VM consolidation solution for large scale Cloud data centers.

Stochastic time series analysis of hydrology data for water resources

Abstract: The prediction to current publication of stochastic time series analysis in hydrology and seasonal stage. The different statistical tests for predicting the hydrology time series on Thomas-Fiering model. The hydrology time series of flood flow have accept a great deal of consideration worldwide. The concentration of stochastic process areas of time series analysis method are expanding with develop concerns about seasonal periods and global warming. The recent trend by the researchers for testing seasonal periods in the hydrologic flowseries using stochastic process on Thomas-Fiering model. The present article proposed to predict the seasonal periods in hydrology using Thomas-Fiering model.

An approach toward design and development of an energy-aware VM selection policy with improved SLA violation in the domain of green cloud computing

Abstract: With the rapid demand for service-oriented computing in association with the growth of cloud computing technologies, large-scale virtualized data centers have been established throughout the globe. These huge data centers consume power at a large scale that results in a high operational cost. The massive carbon footprint from the energy generators is another great issue to deal global warming. It is essential to lower the rate of carbon emission and energy consumption as much as possible. The live-migration-enabled dynamic virtual machine consolidation results in high energy saving. But it also incurs the violation of service level agreement (SLA). Excessive migration may lead to performance degradation and SLA violation. The process of VM selection for migration plays a vital role in the domain of energy-aware cloud computing. Using VM selection policies, VMs are selected for migration. A new power-aware VM selection policy has been proposed in this research that helps in VM selection for migration. The proposed power-aware VM selection policy has been further evaluated using trace-based simulation environment.

Multi-Objective VM Consolidation Based on Thresholds and Ant Colony System in Cloud Computing

Abstract: With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing an effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption, and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection policies according to the host load status. The extensive experiment is conducted to compare our proposed approach with the state-of-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates thus achieving better comprehensive performance.

A Comprehensive Survey of Load Balancing Strategies Using Hadoop Queue Scheduling and Virtual Machine Migration

Abstract: The recent growth in the demand for scalable applications from the consumers of the services has motivated the application development community to build and deploy the applications on cloud in the form of services. The deployed applications have significant dependency on the infrastructure available with the application providers. Bounded by the limitations of available resource pools on-premises, many application development companies have migrated the applications to third party cloud environments called data centers. The data center owners or the cloud service providers are entitled to ensure high performance and high availability of the applications and at the same time the desired scalability for the applications. Also, the cloud service providers are also challenging in terms of cost reduction and energy consumption reductions for better manageability of the data center without degrading the performance of the deployed applications. It is to be noted that the performance of the application does not only depend on the responsiveness of the applications rather also must be measured in terms of service level agreements. The violation of the service level agreements or SLA can easily disprove the purpose of application deployments on cloud-based data centers. Thus, the data center owners apply multiple load balancing strategies for maintaining the desired outcomes from the application owners at the minimized cost of data center maintainability. Hence, the demand of the research is to thoroughly study and identify the scopes for improvements in the parallel research outcomes. As the number of applications ranging from small data-centric applications coming with the demand of frequent updates with higher computational capabilities to the big data-centric application as big data analytics applications coming with efficient algorithms for data and computation load managements, the data center owners are forced to think for efficient algorithms for load managements. The algorithms presented by various research attempts have engrossed on application specific demands for load balancing using virtual machine migrations and the solution as the proposed algorithms have become application problem specific. Henceforth, the further demand of the research is a guideline for selecting the appropriate load balancing algorithm via virtual machine migration for characteristics-based specific applications. Hence, this paper presents a comprehensive survey on existing virtual machine migration and selection processes to understand the specific application-oriented capabilities of these strategies with the advantages and bottlenecks. Also, with the understanding of the existing measures for load balancing, it is also important to furnish the further improvement strategies, which can be made possible with a detailed understanding of the parallel research outcomes. Henceforth, this paper also equips the study with guidelines for improvements and for further study. Nonetheless, the study cannot be completed without the mathematical analysis for better understanding and experimental analysis on different standards of datasets for better conclusive decisions. Hence, this paper also presents the discussion on mathematical models and experimental result analysis for the conclusive decision on the improvement factors and the usability of the migration methods for various purposes. Finally, this paper is a comprehensive survey on the background of the research, recent research outcomes using mathematical modeling and experimental studies on various available datasets, and finally identify the scopes of improvements considering various aspects such as execution time, mean time before a VM migration, mean time before a host shutdown, number of node shutdowns, SLA performance degradation, VM migrations, and energy consumption.

The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Abstract: Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

Evolutionary Computing Based on QoS Oriented Energy Efficient VM Consolidation Scheme for Large Scale Cloud Data Centers

Abstract: The high pace and increase in cloud computing technology and associated applications, especially large scale data centres, have demanded energy efficient and Quality of Service QoS oriented computing platform. To meet these requirements, virtualization and Virtual Machine VM consolidation has emerged as an effective solution. The optimization in VM consolidation by means of efficient dynamic resource-utilization prediction, VM selection and placement can achieve optimal solution for energy efficient and QoS oriented cloud computing system. In this paper, an evolutionary computing algorithm called Adaptive Genetic Algorithm A-GA based VM consolidation approach has been developed. A-GA based placement policy and its implementation with different VM selection policies like Minimum Migration Time MMT, Maximum Correlation MC and Random Selection RS, along with different CPU utilization estimation approaches like Inter Quartile Range IQR, Local Regression LR, Local Robust Regression LRR, static THReshold THR and Median Absolute Deviation MAD has revealed that A-GA based consolidation with MMT selection policy and combined IQR and LRR can enable optimal VM consolidation for large scale infrastructures. In addition, the proposed A-GA policy has exhibited better performance as compared to other meta-heuristics such as Ant Colony Optimization ACO and Best Fit Decreasing. The proposed consolidation system can be used for large scale cloud infrastructures where energy conservation, minimal Service Level Agreement SLA violation and QoS assurance is inevitable.