Energy Aware Operating System Based On Diaggregated System

Introduction

This project aims to develop a novel energy-aware operating system based on a disaggregated system. By implementing energy-aware scheduling algorithms, optimizing kernel synchronization, and leveraging lightweight kernel techniques, we aim to significantly reduce energy consumption in data centers. Additionally, we explore energy-efficient storage management techniques, including data placement optimization and remote storage access.

Objectives

• Develop an energy-aware scheduling algorithm for micro-partitions.
• Optimize kernel synchronization for energy efficiency.
• Implement a lightweight kernel for reduced overhead.
• Control the energy consumption of storage devices.
• Minimize data movement within storage systems.
• Develop an energy-efficient remote storage access mechanism.
• Utilize DPUs for low-power cryptographic offloading.

Core Technologies

• Energy-Aware Micro-Partition Scheduling: Develop a scheduling algorithm that efficiently allocates resources to micro-partitions based on their workload and energy consumption characteristics.
• Energy-Efficient Kernel Synchronization: Optimize kernel synchronization primitives to reduce energy consumption and improve performance.
• Lightweight Kernel: Implement a lightweight kernel that provides essential operating system services with minimal overhead.
• Storage Energy Management: Develop techniques to control the energy consumption of storage devices, including power management and data placement optimization.
• Data Movement Minimization: Minimize data movement within storage systems through techniques such as data compression and deduplication.
• Energy-Efficient Remote Storage Access: Develop a remote storage access mechanism that minimizes network traffic and energy consumption.
• DPU-Based Cryptographic Offloading: Utilize DPUs to offload cryptographic operations, reducing the energy consumption of the main CPU.

Expected Outcomes

• Reduced Energy Consumption: Significantly reduce the energy consumption of data centers by optimizing resource allocation and minimizing idle time.
• Improved Performance: Enhance system performance through optimized scheduling and reduced overhead.
• Enhanced Reliability: Improve system reliability through the use of isolated architectures and fault-tolerant mechanisms.
• Increased Flexibility: Provide a flexible platform for developing energy-efficient applications.