Cloud providers can greatly benefit from accurate workload prediction.
However, the workload of cloud servers is highly variable, with occasional
heavy workload bursts. This makes workload prediction challenging.
There are mainly two categories of workload prediction methods: statistical
methods and neural-network-based ones. The former ones rely on strong
mathematical assumptions and have reported low accuracy when predicting highly
variable workload. The latter ones offer higher overall accuracy, yet they are
vulnerable to data imbalance between heavy workload and common one. This
impairs the prediction accuracy of neural network-based models on heavy
workload.
Either the overall inaccuracy of statistic methods or the heavy-workload
inaccuracy of neural-network-based models can cause service level agreement
violations.
Thus, we propose PePNet to improve overall especially heavy workload
prediction accuracy. It has two distinctive characteristics:
(i) A Periodicity-Perceived Mechanism to detect the existence of periodicity
and the length of one period automatically, without any priori knowledge.
Furthermore, it fuses periodic information adaptively, which is suitable for
periodic, lax periodic and aperiodic time series.
(ii) An Achilles' Heel Loss Function iteratively optimizing the most
under-fitting part in predicting sequence for each step, which significantly
improves the prediction accuracy of heavy load.
Extensive experiments conducted on Alibaba2018, SMD dataset and Dinda's
dataset demonstrate that PePNet improves MAPE for overall workload by 20.0% on
average, compared with state-of-the-art methods. Especially, PePNet improves
MAPE for heavy workload by 23.9% on average.

提出 PePNet 以改善工作负载的预测准确性，通过检测周期性、融合周期性信息以及迭代优化预测序列中最不适配部分，PePNet 在整体和重负载的预测准确性上相较于现有方法平均提高 20.0% 和 23.9%。

PePNet：一种支持稀有高负载出现的周期感知工作负载预测网络

PePNet: A Periodicity-Perceived Workload Prediction Network Supporting  Rare Occurrence of Heavy Workload

One of the main objectives of Cloud Providers (CP) is to guarantee the
Service-Level Agreement (SLA) of customers while reducing operating costs. To
achieve this goal, CPs have built large-scale datacenters. This leads, however,
to underutilized resources and an increase in costs. A way to improve the
utilization of resources is to reclaim the unused parts and resell them at a
lower price. Providing SLA guarantees to customers on reclaimed resources is a
challenge due to their high volatility. Some state-of-the-art solutions
consider keeping a proportion of resources free to absorb sudden variation in
workloads. Others consider stable resources on top of the volatile ones to fill
in for the lost resources. However, these strategies either reduce the amount
of reclaimable resources or operate on less volatile ones such as Amazon Spot
instance. In this paper, we proposed RISCLESS, a Reinforcement Learning
strategy to exploit unused Cloud resources. Our approach consists of using a
small proportion of stable on-demand resources alongside the ephemeral ones in
order to guarantee customers SLA and reduce the overall costs. The approach
decides when and how much stable resources to allocate in order to fulfill
customers' demands. RISCLESS improved the CPs' profits by an average of 15.9%
compared to state-of-the-art strategies. It also reduced the SLA violation time
by an average of 36.7% while increasing the amount of used ephemeral resources
by 19.5% on average

RISCLESS 提供一种使用强化学习策略来利用未使用云资源的方法，它通过使用小比例的稳定按需资源与临时资源相结合，以保证客户的 SLA 并降低成本，使云提供商的利润平均增加 15.9％，违反 SLA 的时间平均减少 36.7％，并平均增加 19.5％使用的临时资源。

RISCLESS：一种利用未使用云资源的强化学习策略

RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources

An increasing number of technology enterprises are adopting cloud-native
architectures to offer their web-based products, by moving away from
privately-owned data-centers and relying exclusively on cloud service
providers. As a result, cloud vendors have lately increased, along with the
estimated annual revenue they share. However, in the process of selecting a
provider's cloud service over the competition, we observe a lack of universal
common ground in terms of terminology, functionality of services and billing
models. This is an important gap especially under the new reality of the
industry where each cloud provider has moved towards his own service taxonomy,
while the number of specialized services has grown exponentially. This work
discusses cloud services offered by four dominant, in terms of their current
market share, cloud vendors. We provide a taxonomy of their services and
sub-services that designates major service families namely computing, storage,
databases, analytics, data pipelines, machine learning, and networking. The aim
of such clustering is to indicate similarities, common design approaches and
functional differences of the offered services. The outcomes are essential both
for individual researchers, and bigger enterprises in their attempt to identify
the set of cloud services that will utterly meet their needs without
compromises. While we acknowledge the fact that this is a dynamic industry,
where new services arise constantly, and old ones experience important updates,
this study paints a solid image of the current offerings and gives prominence
to the directions that cloud service providers are following.

本文对四个主导云供应商提供的云服务进行分类，并指出它们的相似之处和功能差异，提出了一种适用于个人和企业的选择云服务的方法。