Today's cloud data centers are often distributed geographically to provide
robust data services. But these geo-distributed data centers (GDDCs) have a
significant associated environmental impact due to their increasing carbon
emissions and water usage, which needs to be curtailed. Moreover, the energy
costs of operating these data centers continue to rise. This paper proposes a
novel framework to co-optimize carbon emissions, water footprint, and energy
costs of GDDCs, using a hybrid workload management framework called SHIELD that
integrates machine learning guided local search with a decomposition-based
evolutionary algorithm. Our framework considers geographical factors and
time-based differences in power generation/use, costs, and environmental
impacts to intelligently manage workload distribution across GDDCs and data
center operation. Experimental results show that SHIELD can realize 34.4x
speedup and 2.1x improvement in Pareto Hypervolume while reducing the carbon
footprint by up to 3.7x, water footprint by up to 1.8x, energy costs by up to
1.3x, and a cumulative improvement across all objectives (carbon, water, cost)
of up to 4.8x compared to the state-of-the-art.

本研究提出了一个名为 SHIELD 的混合工作负载管理框架，利用机器学习引导的局部搜索和基于分解的进化算法来共同优化地理分布数据中心的碳排放、水足迹和能源成本，实现了 34.4 倍的加速比、2.1 倍的帕累托超体积改进，并将碳足迹最多减少 3.7 倍、水足迹最多减少 1.8 倍、能源成本最多减少 1.3 倍，与现有技术相比，所有目标（碳、水、成本）的累积改进最多可达到 4.8 倍。

SHIELD：碳，废水和能源感知数据中心管理的可持续混合进化学习框架

SHIELD: Sustainable Hybrid Evolutionary Learning Framework for Carbon,  Wastewater, and Energy-Aware Data Center Management

The core of the computer business now offers subscription-based on-demand
services with the help of cloud computing. We may now share resources among
multiple users by using virtualization, which creates a virtual instance of a
computer system running in an abstracted hardware layer. It provides infinite
computing capabilities through its massive cloud datacenters, in contrast to
early distributed computing models, and has been incredibly popular in recent
years because to its continually growing infrastructure, user base, and hosted
data volume. This article suggests a conceptual framework for a workload
management paradigm in cloud settings that is both safe and
performance-efficient. A resource management unit is used in this paradigm for
energy and performing virtual machine allocation with efficiency, assuring the
safe execution of users' applications, and protecting against data breaches
brought on by unauthorised virtual machine access real-time. A secure virtual
machine management unit controls the resource management unit and is created to
produce data on unlawful access or intercommunication. Additionally, a workload
analyzer unit works simultaneously to estimate resource consumption data to
help the resource management unit be more effective during virtual machine
allocation. The suggested model functions differently to effectively serve the
same objective, including data encryption and decryption prior to transfer,
usage of trust access mechanism to prevent unauthorised access to virtual
machines, which creates extra computational cost overhead.

本文提出了一种云计算工作负载管理范式的概念框架，通过虚拟化创建计算机系统的虚拟实例来共享资源并保证数据安全性，包括资源管理单元、安全的虚拟机管理单元和工作量分析器单元等部分。

云计算环境下具备信任访问控制的智能资源管理机制

A smart resource management mechanism with trust access control for cloud computing environment

As the emerging trend of graph-based deep learning, Graph Neural Networks
(GNNs) excel for their capability to generate high-quality node feature vectors
(embeddings). However, the existing one-size-fits-all GNN implementations are
insufficient to catch up with the evolving GNN architectures, the
ever-increasing graph sizes, and the diverse node embedding dimensionalities.
To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime
system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor
explores and identifies several performance-relevant features from both the GNN
model and the input graph, and uses them as a new driving force for GNN
acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D
workload management, tailored for GNN computation to improve GPU utilization
and performance under different application settings. Third, GNNAdvisor
capitalizes on the GPU memory hierarchy for acceleration by gracefully
coordinating the execution of GNNs according to the characteristics of the GPU
memory structure and GNN workloads. Furthermore, to enable automatic runtime
optimization, GNNAdvisor incorporates a lightweight analytical model for an
effective design parameter search. Extensive experiments show that GNNAdvisor
outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph
Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$
faster), on mainstream GNN architectures across various datasets.

GNNAdvisor 是一种自适应、高效的运行时系统，旨在加速 GPU 平台上各种 GNN 工作负载，使用了新的驱动力、二维工作负载管理和 GPU 内存层次优化等技术，实现了自动运行时优化。