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Energy-Efficiency and Sustainability in New Generation Cloud Computing: A Vision and Directions for Integrated Management of Data Centre Resources and Workloads
Rajkumar Buyya, Shashikant Ilager, Patricia Arroba
TL;DR
Growing data-centre energy use and carbon emissions motivate approaches that reduce energy while maintaining service quality. The paper proposes learning-centric integrated management of cloud resources and workloads. Early results report lower total and cooling consumption while maintaining QoS against baselines.
Problem
Growing data-centre energy use motivates management approaches that reduce consumption and carbon footprint while preserving service quality.
Source: §1 · pp. 1–2Method
The paper envisions learning-centric algorithms for integrated management of data-centre resources and workloads while maintaining users’ QoS.
Source: §1 · p. 3Results
14% lower average total Cloud data-centre consumption and 58% lower cooling power consumption; PUE fell from 1.37 to 1.16 while QoS was maintained against baselines.
Source: §6.2 · p. 12Takeaways & Limitations
The early experiments support the potential of integrating DVFS, room-temperature behavior, and computing and cooling management for energy-efficient Clouds.
Source: §7 · p. 12RL algorithms require substantial learning time and face safety, reward-function, and real-time inference challenges.
Source: §5.6 · p. 9
Abstract
from arXiv · showhide
Cloud computing has become a critical infrastructure for modern society, like electric power grids and roads. As the backbone of the modern economy, it offers subscription-based computing services anytime, anywhere, on a pay-as-you-go basis. Its use is growing exponentially with the continued development of new classes of applications driven by a huge number of emerging networked devices. However, the success of Cloud computing has created a new global energy challenge, as it comes at the cost of vast energy usage. Currently, data centres hosting Cloud services world-wide consume more energy than most countries. Globally, by 2025, they are projected to consume 20% of global electricity and emit up to 5.5% of the world's carbon emissions. In addition, a significant part of the energy consumed is transformed into heat which leads to operational problems, including a reduction in system reliability and the life expectancy of devices, and escalation in cooling requirements. Therefore, for future generations of Cloud computing to address the environmental and operational consequences of such significant energy usage, they must become energy-efficient and environmentally sustainable while continuing to deliver high-quality services. In this paper, we propose a vision for learning-centric approach for the integrated management of new generation Cloud computing environments to reduce their energy consumption and carbon footprint while delivering service quality guarantees. In this paper, we identify the dimensions and key issues of integrated resource management and our envisioned approaches to address them. We present a conceptual architecture for energy-efficient new generation Clouds and early results on the integrated management of resources and workloads that evidence its potential benefits towards energy efficiency and sustainability.
1. Introduction
Cloud data centres’ growing energy use and carbon footprint motivate integrated management that reduces energy while preserving service quality. The paper envisions learning-centric approaches across distributed data-centre resources and workloads.
1. Introduction
- Cloud services support applications including enterprise, banking, transport, and healthcare, with demand also driven by IoT, Big Data, and AI.Source: §1 · p. 1
- Data centres are projected to contribute up to 5.5% of global carbon emissions by 2025 and consume about 8,000 TWh by 2030.The paper also reports that there are over 8 million active data centres worldwide, projected to grow over 12% per year.Source: §1 · pp. 1–2
- The vision aims to reduce worldwide data-centre energy use by up to 80%, from 8,000 TWh in 2030 (worst case) to about 1,200 TWh.Source: §1 · p. 2
- The proposed approach combines monitoring agents and brokering agents to offload tasks across data centres, supporting edge affinity, green-energy use, lower costs, and QoS.The paper describes distributed deployments with different capabilities and energy sources.Source: §1 · p. 2
- The paper proposes reducing energy through sustainable resource use while maintaining QoS, studying all resource types, and shifting from control-centric to dynamic learning-centric management.Source: §1 · p. 3
2. Significance and Innovation of Proposed Vision
The vision calls for integrated, learning-centric management across data-centre resources and service models. It aims to balance energy efficiency with QoS amid diverse workloads and heterogeneous infrastructure.
2. Significance and Innovation of Proposed Vision
- Existing approaches often optimize one cloud-stack level or resource independently, while the paper proposes integrated management of servers, cooling, and networks.The paper says existing approaches can achieve only up to 25% energy reduction and may create hotspots that raise cooling needs and risk failures.Source: §2 · p. 3
- The proposed innovations are intended to change how energy efficiency and sustainability are aligned and realized in new-generation Clouds.Source: §2 · p. 3
2.1 Delivering Service Quality Guarantees
- Time-critical, always-on services require QoS guarantees, and the proposed provisioning and scheduling approaches seek to balance these guarantees with energy-efficient execution.The section identifies diversity in workloads and infrastructure as a challenge to balancing energy efficiency with execution time and cost.Source: §2.1 · p. 4
2.2 Integrated Resource Management for Energy Efficiency
- VM consolidation can reduce active servers, but may increase hotspots; spreading load can mitigate hotspots while increasing network latency and impairing service quality.The paper calls for algorithms that manage these interacting trade-offs across resources and QoS.Source: §2.2 · p. 4
2.3 New Learning-based Algorithms for Cloud Resource Management Systems (RMS)
- The paper proposes ML- and RL-based methods to adapt resource management to complex, dynamic data-centre conditions using experience, system status, and workload characteristics.Source: §2.3 · p. 4
- The vision’s innovations aim to produce learning-centric algorithms and systems for integrated management that reduce energy consumption and carbon footprint.Source: §2.3 · p. 4
3. Dimensions and Research Challenges
The paper frames sustainable, energy-efficient Clouds as a multidimensional management challenge. Its research questions span QoS, placement and provisioning, thermal control, renewable energy, and geographic workload distribution.
3. Dimensions and Research Challenges
- Integrated management spans computing and cooling resources, application and workload characteristics, deployment models, energy sources, monitoring, and user QoS.The paper emphasizes that elements affect other domains and identifies learning-centric approaches as worth exploring.Source: §3 · p. 4
- The research questions ask how to trade off users’ QoS, select physical machines for VMs, and time resource provisioning or de-provisioning.Source: §3 · p. 4
- Further questions cover QoS-safe consolidation and frequency selection, hotspot avoidance, thermal prediction and placement, sensor-driven cooling, renewable-energy use, and load balancing across data centres.Source: §3 · p. 5
4. Architectural Framework
The architecture integrates energy-efficient management across cloud-stack layers, combining sensing, resource control, and request distribution. Its requirements include broad resource coverage, learning-centric management, sustainability, and QoS.
4. Architectural Framework
- The approach requires energy efficiency across data-centre resources, services, and application models, alongside learning-centric algorithms and sustainable resource use that meets QoS demands.The paper says these requirements can be met through integrated resource management, interplay between service layers, and machine-learning methods for proactive management.Source: §4 · p. 5
- The RMS manages requests, applications, and resources across edge and remote cloud data centres, integrating techniques from physical to application layers.The architecture includes heat sensors, power sensors, and programmable cooling systems.Source: §4 · p. 5
- The RMS provisions resources and schedules tasks, monitors server and cooling-system status, and uses a green broker to distribute requests across data centres.Admission control filters requests that cannot meet desired QoS; the RMS also controls cooling and server load distribution to avoid hotspots.Source: §4 · pp. 5–6
5. Research Issues and Envisioned Approaches as Future Directions
The paper outlines integrated, learning-centric approaches to manage Cloud workloads and resources for energy efficiency while maintaining QoS. Its research directions span workload-aware provisioning, scheduling, power states, consolidation, thermal management, monitoring, and multi-region energy use.
5.1 Workload and Power-aware Dynamic Resource Provisioning and Placement
- Application-aware provisioning should balance QoS and energy use, while VM placement accounts for server use and application topology to preserve affinity.The authors note that one provisioning approach cannot optimize across the wide variation in application characteristics.Source: §5.1 · pp. 6–7
5.2 QoS-driven Energy-Efficient Scheduling of Applications
- DRL scheduling agents should use integrated server, network, and cooling-system states to learn resource policies and balance conflicting objectives while meeting QoS.The proposed scheduler can request more resources and configure processor frequencies when provisioned resources cannot meet QoS requirements.Source: §5.2 · p. 7
5.3 Application-Aware Management of Power States of Computing Elements
- Power-state management should dynamically configure frequencies across computing elements for different applications, using DVFS to reduce energy while meeting QoS.The authors note that power and performance responses to frequency changes vary across computing elements and applications.Source: §5.3 · p. 8
5.4 Energy-aware Dynamic Consolidation
- Consolidation should use historical workload behavior to reduce resource needs and migration while maintaining QoS, avoiding hotspots, and preserving VM affinity.Centralized control may suit settings where resource and workload information can be aggregated; decentralized approaches may better suit geographically distributed multi-cloud settings.Source: §5.4 · p. 8
5.5 Thermal-aware Management of Workloads and Cooling Systems
- Thermal-aware management should predict server temperatures from sensor data, move workloads from overheated nodes, and coordinate DVFS and cooling settings.Analytical and CFD temperature estimates may be too compute-intensive or sensitive to data-centre layout for online or real-time RMS decisions.Source: §5.5 · pp. 8–9
5.6 Monitoring, Analysis, and Actions for Rebalancing the System
- An integrated monitoring system should unify heterogeneous resource and cooling data with common semantics, then feed it to RMS modules for analysis and runtime actions.The authors also identify limited data, cold starts, RL training time, safety, reward design, and real-time inference as challenges; they propose transfer learning and asynchronous multi-agent solutions, including simulation training.Source: §5.6 · p. 9
5.7 Harnessing Multi-Region Edge and Cloud Data Centres with Varied Energy Sources
- Multi-region workload offloading should account for renewable-energy availability, user location, resource efficiency, energy mix, storage prices, and powered-on servers.Forecasting energy availability and workload levels can inform admission and batch-job delays; the authors also propose studying network-related offloading costs and Cloud participation in smart grids.Source: §5.7 · pp. 9–10
6. Case Study: Energy-Efficient Clouds for Business-critical Applications
The case study evaluates integrated energy- and thermal-aware resource management for business-critical Cloud workloads. Its approach combines ML-based prediction with digital-twin optimization, and reports lower total and cooling power consumption while maintaining QoS.
6. Case Study: Energy-Efficient Clouds for Business-critical Applications
- The case study targets business-critical applications whose low, highly variable resource use makes aggressive VM consolidation risky for QoS and thermal hotspots.Daily CPU peaks can exceed average utilization by up to 100 times.Source: §6 · p. 10
6.1 Integrated thermal-aware resource management
- The integrated system monitors cooling setpoint and server temperatures, then uses ML models and a digital twin to predict thermal and power behavior.The optimization uses the twin to select cooling setpoint, VM mapping, and server DVFS configuration.Source: §6.1 · pp. 10–11, Figure 6 · p. 11
6.2 Performance Evaluation
- The evaluation uses Bitbrains traces in an extended CloudSim setup, modeling cooling, server thermal behavior, and DVFS across 1,127 VMs and 1,200 hosts.The optimization predicts each VM placement's impact, then favors high server utilization with minimal frequency increase while accounting for temperature and QoS.Source: §6.2 · pp. 11–12
- 14% lower average total Cloud DC consumption and 58% lower cooling power were reported, with PUE reduced from 1.37 to 1.16 while maintaining QoS.The approach avoided server overload and selected dynamic operating frequencies to preserve performance for high-variability business-critical applications.Source: §6.2 · p. 12
7. Conclusions and Final Remarks
The paper envisions energy-efficient next-generation Clouds through integrated, learning-centric management that balances service quality with energy goals. Its future directions span data-centre architecture, resource and workload algorithms, thermal and carbon management, and operational software and demonstrators.
7. Conclusions and Final Remarks
- Early experiments indicate the potential of an energy-centric approach that integrates DVFS configuration and room thermal behaviour to manage computing and cooling energy.The paper frames this work as a vision for learning-centric algorithms and software systems managing resources and applications.Source: §7 · p. 12
- The paper calls for new algorithms to minimise the carbon footprint of Cloud services.This is one of the proposed future directions for integrated and sustainable Cloud management.Source: §7 · p. 13
- Future directions include an energy-efficient Cloud architecture and algorithms that integrate management of data-centre infrastructure, including servers and cooling systems.The algorithms are intended to minimise energy consumption across IT and non-IT resources.Source: §7 · p. 13
- The proposed algorithms and models would dynamically provision and schedule applications for energy efficiency while meeting QoS, using monitored system data to balance data-centre conditions.The directions also include thermal and hot-spot management for system resiliency.Source: §7 · p. 13
- The vision includes managing workloads across geographically distributed, green-energy-powered edge and Cloud data centres.It also calls for software systems and application demonstrators deployed in operational edge and Cloud data centres.Source: §7 · p. 13
- The proposed research shifts resource management from performance-centric, discrete, and control-centric approaches toward energy-centric, integrated, and learning-centric management.It aims to manage users’ QoS expectations alongside service providers’ energy-efficiency goals.Source: §7 · p. 13