We propose a root-causing procedure for accelerating system-level debug using
rule-based techniques. We describe the procedure and how it provides high
quality debug hints for reducing the debug effort. This includes the heuristics
for engineering features from logs of many tests, and the data analytics
techniques for generating powerful debug hints. As a case study, we used these
techniques for root-causing failures of the Power Management (PM) design
feature Package-C8 and showed their effectiveness. Furthermore, we propose an
approach for mining the root-causing experience and results for reuse, to
accelerate future debug activities and reduce dependency on validation experts.
We believe that these techniques are beneficial also for other validation
activities at different levels of abstraction, for complex hardware, software
and firmware systems, both pre-silicon and post-silicon.

通过基于规则的技术提出一种系统级调试加速的原因分析流程，使用多项测试的日志提取工程特征和数据分析技术生成高质量的调试提示，以 Power Management (PM) 设计功能 Package-C8 的故障排除为案例研究并展示了它们的有效性，还提出了一种挖掘原因分析经验和结果以加速未来调试活动并减少验证专家依赖的方法，我们相信这些技术对于复杂的硬件、软件和固件系统，以及不同抽象层次的验证活动，无论是硅前还是硅后，都是有益的。

利用规则学习和子群发现技术加速系统级调试

Accelerating System-Level Debug Using Rule Learning and Subgroup Discovery Techniques

As power management has become a primary concern in modern data centers,
computing resources are being scaled dynamically to minimize energy
consumption. We initiate the study of a variant of the classic online speed
scaling problem, in which machine learning predictions about the future can be
integrated naturally. Inspired by recent work on learning-augmented online
algorithms, we propose an algorithm which incorporates predictions in a
black-box manner and outperforms any online algorithm if the accuracy is high,
yet maintains provable guarantees if the prediction is very inaccurate. We
provide both theoretical and experimental evidence to support our claims.

本文介绍了一种基于机器学习的动态在线速度缩放算法，以最小化数据中心的能源消耗，并提供了理论和实验证据来支持其效果。

通过速度缩放学习增强能量最小化

Learning Augmented Energy Minimization via Speed Scaling

Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.

研究提出一种基于深度强化学习的层次化框架，用于云计算系统中的资源分配和电源管理问题，采用自动编码器和权重共享结构，以解决高维状态空间问题，采用 LSTM 建模工作负载预测，并采用分布式方式操作的无模型 RL 电源管理器处理本地部分问题。

基于深度强化学习的云资源分配和功耗管理的层级框架

A Hierarchical Framework of Cloud Resource Allocation and Power  Management Using Deep Reinforcement Learning

The problem of scheduling under resource constraints is widely applicable.
One prominent example is power management, in which we have a limited
continuous supply of power but must schedule a number of power-consuming tasks.
Such problems feature tightly coupled continuous resource constraints and
continuous temporal constraints.
We address such problems by introducing the Time Resource Network (TRN), an
encoding for resource-constrained scheduling problems. The definition allows
temporal specifications using a general family of representations derived from
the Simple Temporal network, including the Simple Temporal Network with
Uncertainty, and the probabilistic Simple Temporal Network (Fang et al.
(2014)).
We propose two algorithms for determining the consistency of a TRN: one based
on Mixed Integer Programing and the other one based on Constraint Programming,
which we evaluate on scheduling problems with Simple Temporal Constraints and
Probabilistic Temporal Constraints.

介绍了一种编码资源约束调度问题的方法 —— 时间资源网络，它可以使用简单时间网络（STN）系列的各种表示以及概率性 STN 进行时间规定， 两种算法可用于确定 TRN 的一致性：一种基于混合整数规划，另一种基于约束编程。