An On-Line Virtual Machine Consolidation Strategy for Dual Improvement in Performance and Energy Conservation of Server Clusters in Cloud Data Centers


Improving the energy efficiency of Cloud Computing has become a primary focus of research in recent years as a direct response to the massive amounts of power that are consumed by data centers. However, it is difficult to decrease energy consumption while keeping system performance stable and without increasing the risk of violating a Service Level Agreement. The majority of the existing consolidation strategies for virtual machines (VMs) take into account system performance and Quality of Service (QoS) metrics as constraints. This typically results in a large scheduling overhead and makes it impossible to achieve effective improvement in energy efficiency without making some sacrifices in system performance as well as the quality of cloud services. First, we will define the metrics of peak power efficiency and optimal utilization for a variety of different physical machines in this article (PMs). Then, we put forward the idea of Peak Efficiency Aware Scheduling, or PEAS for short, which is an innovative method for the placement and reallocation of virtual machines that aims to simultaneously achieve an improvement in performance and a reduction in energy consumption from the point of view of server clusters. On-the-fly virtual machine allocation and reallocation is handled by PEAS, and every effort is made to keep physical machines operating at their highest possible level of power efficiency by consolidating VMs. Extensive testing on Cloudsim demonstrates that PEAS outperforms a number of energy-aware consolidation algorithms in terms of energy consumption, system performance, and a variety of quality of service metrics.

Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here

PROJECT TITLE : LibRoad: Rapid, Online, and Accurate Detection of TPLs on Android ABSTRACT: The detection of third-party libraries, also known as TPLs, is an extremely important part of Android malware analysis. The signature-based
PROJECT TITLE : Imitation Learning Enabled Task Scheduling for Online Vehicular Edge Computing ABSTRACT: The term "vehicular edge computing" (VEC) refers to a potentially useful paradigm that is based on the Internet of vehicles
PROJECT TITLE : Online Spatio-temporal Crowd Flow Distribution Prediction for Complex Metro System ABSTRACT: Crowd flow prediction (CFP), which is an essential part of contemporary traffic management, contributes to the success
PROJECT TITLE : ESA-Stream: Efficient Self-Adaptive Online Data Stream Clustering ABSTRACT: A wide variety of big data applications generate an enormous amount of streaming data that is high-dimensional, real-time, and constantly
PROJECT TITLE : Privacy-Preserving Diverse Keyword Search and Online Pre-Diagnosis in Cloud Computing ABSTRACT: With the development of the Mobile Healthcare Monitoring Network (MHMN), patients' data collected by body sensors

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry