1. Renewable Energy in IT Operations
- a. Solar and Wind Power: Companies are increasingly investing in solar panels and wind energy to power their data centres. By using on-site renewable energy or purchasing renewable energy credits, businesses can offset their carbon emissions and reduce reliance on fossil fuels.
- b. Hydroelectric and Biomass Energy: Data centres located near hydropower sources can take advantage of clean, consistent energy, while biomass energy, derived from organic materials, presents an alternative renewable source for powering IT operations.
2. Graphene and Advanced Semiconductors
- a. Graphene-Based Transistors: Graphene is emerging as a promising alternative to traditional silicon-based transistors due to its greater energy efficiency and faster processing speeds. This makes it a valuable material for reducing energy demands in IT systems.
- b. Advanced Semiconductor Materials: New materials like silicon carbide (SiC) and gallium nitride (GaN) are improving the power efficiency of semiconductors, which is especially beneficial for high-performance computing systems.
3. Immersive Liquid Cooling for Data Centres
- a. Immersive Cooling: Traditional air cooling systems in data centres consume significant energy. In contrast, immersive liquid cooling, where servers are submerged in non-conductive liquids, provides an efficient method for heat absorption, lowering overall energy consumption.
- b. Two-Phase Cooling: This advanced technique involves the transition from liquid to vapour, offering more efficient thermal management and reducing power consumption.
4. Energy-Efficient Chips
- a. ARM and RISC-V Architectures: These chip architectures are designed to maximize performance per watt, making them ideal for mobile devices, IoT applications, and cloud computing environments that require energy-efficient solutions.
- b. AI-Specific Chips: AI-focused processors, such as Google’s Tensor Processing Units (TPUs) and NVIDIA’s GPUs, are optimized for machine learning workloads, delivering higher efficiency and reduced energy demands in AI-driven applications.
5. Green Coding and Software Efficiency
- a. Energy-Efficient Algorithms: Developers are increasingly focused on creating algorithms that minimize computational power requirements, which helps reduce energy consumption.
- b. Demand Shifting and Demand Shaping: These techniques involve adjusting workloads based on energy availability and shifting non-critical workloads to off-peak hours, maximizing the use of renewable energy sources.
6. Green IT Architecture
- a. Edge Computing: By processing data closer to its source, edge computing reduces the need for long-distance data transfers, saving bandwidth and energy.
- b. Serverless Computing: Serverless architectures dynamically allocate computing resources based on demand, reducing idle time and minimizing energy waste.
- c. Micro-Services and Containerisation: These architectures allow applications to scale efficiently, using only the necessary computing power for each service, thus reducing energy consumption.
7. Sustainable Data Storage and Management
- a. Data Compression and Optimization: Techniques like data compression and deduplication reduce the amount of data stored and transmitted, thus minimizing storage and bandwidth requirements.
- b. Cold Data Storage: Archiving infrequently accessed data (cold data) on low-energy storage systems can reduce the energy needed for data storage.
