Motivated by an imperative to reduce the carbon emissions of cloud data
centers, this paper studies the online carbon-aware resource scaling problem
with unknown job lengths (OCSU) and applies it to carbon-aware resource scaling
for executing computing workloads. The task is to dynamically scale resources
(e.g., the number of servers) assigned to a job of unknown length such that it
is completed before a deadline, with the objective of reducing the carbon
emissions of executing the workload. The total carbon emissions of executing a
job originate from the emissions of running the job and excess carbon emitted
while switching between different scales (e.g., due to checkpoint and resume).
Prior work on carbon-aware resource scaling has assumed accurate job length
information, while other approaches have ignored switching losses and require
carbon intensity forecasts. These assumptions prohibit the practical deployment
of prior work for online carbon-aware execution of scalable computing workload.
We propose LACS, a theoretically robust learning-augmented algorithm that
solves OCSU. To achieve improved practical average-case performance, LACS
integrates machine-learned predictions of job length. To achieve solid
theoretical performance, LACS extends the recent theoretical advances on online
conversion with switching costs to handle a scenario where the job length is
unknown. Our experimental evaluations demonstrate that, on average, the carbon
footprint of LACS lies within 1.2% of the online baseline that assumes perfect
job length information and within 16% of the offline baseline that, in addition
to the job length, also requires accurate carbon intensity forecasts.
Furthermore, LACS achieves a 32% reduction in carbon footprint compared to the
deadline-aware carbon-agnostic execution of the job.

本文研究在线碳感知资源伸缩问题，并将其应用于执行计算工作负载的碳感知资源伸缩，以减少云数据中心的碳排放。通过提出的 LACS 算法，本研究实现了对未知作业长度的在线碳感知执行，并与基线算法进行了比较，展示了 LACS 相对于理想的作业长度信息和准确的碳强度预测算法的平均碳足迹减少。

LACS: 基于学习的算法对碳意识进行资源调整和需求不确定性管理

LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling  with Uncertain Demand

In the rapidly evolving field of serverless computing, efficient function
scheduling and resource scaling are critical for optimizing performance and
cost. This paper presents a comprehensive review of the application of Deep
Reinforcement Learning (DRL) techniques in these areas. We begin by providing
an overview of serverless computing, highlighting its benefits and challenges,
with a particular focus on function scheduling and resource scaling. We then
delve into the principles of deep reinforcement learning (DRL) and its
potential for addressing these challenges. A systematic review of recent
studies applying DRL to serverless computing is presented, covering various
algorithms, models, and performances. Our analysis reveals that DRL, with its
ability to learn and adapt from an environment, shows promising results in
improving the efficiency of function scheduling and resource scaling in
serverless computing. However, several challenges remain, including the need
for more realistic simulation environments, handling of cold starts, and the
trade-off between learning time and scheduling performance. We conclude by
discussing potential future directions for this research area, emphasizing the
need for more robust DRL models, better benchmarking methods, and the
exploration of multi-agent reinforcement learning for more complex serverless
architectures. This review serves as a valuable resource for researchers and
practitioners aiming to understand and advance the application of DRL in
serverless computing.

该论文综述了深度强化学习技术在无服务器计算的函数调度和资源扩展领域的应用，分析了各种算法、模型和性能，发现深度强化学习在提高函数调度和资源扩展效率方面取得了有希望的结果，同时也指出了需要解决的挑战和未来研究方向。