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README.md

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+# Energy Aware Operating System Based On Diaggregated System
+
+## Introduction
+
+<p align="center"><img src="EAOS_logo.png" width="128"></p>
+
+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.