Description
Abstract
With the exponential growth of cloud services, data centers are experiencing an unprecedented increase in energy consumption, leading to both environmental and economic concerns. The "CloudEnerOpt" project aims to explore and devise novel algorithms and methodologies for optimizing energy consumption in cloud data centers without compromising service quality. By marrying traditional cloud resource management strategies with real-time energy consumption data, we seek to facilitate dynamic resource allocation that's both performance-aware and energy-efficient.
Methodology
Data Collection:
Obtain real-time energy consumption metrics from state-of-the-art cloud data centers.
Gather typical workload data, including CPU, memory, storage, and network utilization statistics for standard cloud services.
Baseline Establishment:
Using traditional resource allocation algorithms, determine the energy consumption patterns for given workloads without any optimization.
Development of Energy-Aware Algorithms:
Incorporate energy consumption metrics into existing cloud resource allocation strategies.
Develop new algorithms prioritizing energy efficiency, factoring in parameters like dynamic voltage and frequency scaling (DVFS) and server idle states.
Simulation and Testing:
Deploy a cloud simulation tool (e.g., CloudSim) to simulate the real-world environment of a cloud data center.
Test the energy-aware algorithms against the baseline to determine potential energy savings and any compromises on performance.
Real-world Testing:
Partner with an operational cloud data center for a pilot test.
Implement the proposed energy-aware algorithms in a controlled environment and monitor performance and energy metrics in real-time.
Analysis and Refinement:
Compare simulated results with real-world outcomes.
Refine algorithms based on discrepancies, unexpected results, or performance bottlenecks.
Documentation and Dissemination:
Document findings, best practices, and recommendations in a comprehensive report.
