Estimating power consumption in modern Cloud environments is essential for
carbon quantification toward green computing. Specifically, it is important to
properly account for the power consumed by each of the running applications,
which are packaged as containers. This paper examines multiple challenges
associated with this goal. The first challenge is that multiple customers are
sharing the same hardware platform (multi-tenancy), where information on the
physical servers is mostly obscured. The second challenge is the overhead in
power consumption that the Cloud platform control plane induces. This paper
addresses these challenges and introduces a novel pipeline framework for power
model training. This allows versatile power consumption approximation of
individual containers on the basis of available performance counters and other
metrics. The proposed model utilizes machine learning techniques to predict the
power consumed by the control plane and associated processes, and uses it for
isolating the power consumed by the user containers, from the server power
consumption. To determine how well the prediction results in an isolation, we
introduce a metric termed isolation goodness. Applying the proposed power model
does not require online power measurements, nor does it need information on the
physical servers, configuration, or information on other tenants sharing the
same machine. The results of cross-workload, cross-platform experiments
demonstrated the higher accuracy of the proposed model when predicting power
consumption of unseen containers on unknown platforms, including on virtual
machines.

本文研究了现代云环境中估计功耗的重要性，提出了一个新的管道框架用于训练功耗模型，通过机器学习技术预测云平台控制平面和相关进程所消耗的功耗，并利用该模型来隔离用户容器消耗的功耗。实验结果表明该模型在预测未知平台和虚拟机上的未知容器功耗时具有更高的准确性。

云原生运行时能耗指标导出的强大功率模型训练框架

A Robust Power Model Training Framework for Cloud Native Runtime Energy  Metric Exporter

Vector databases have emerged as key enablers for bridging intelligent
applications with unstructured data, providing generic search and management
support for embedding vectors extracted from the raw unstructured data. As
multiple data users can share the same database infrastructure, multi-tenancy
support for vector databases is increasingly desirable. This hinges on an
efficient filtered search operation, i.e., only querying the vectors accessible
to a particular tenant. Multi-tenancy in vector databases is currently achieved
by building either a single, shared index among all tenants, or a per-tenant
index. The former optimizes for memory efficiency at the expense of search
performance, while the latter does the opposite. Instead, this paper presents
Curator, an in-memory vector index design tailored for multi-tenant queries
that simultaneously achieves the two conflicting goals, low memory overhead and
high performance for queries, vector insertion, and deletion. Curator indexes
each tenant's vectors with a tenant-specific clustering tree and encodes these
trees compactly as sub-trees of a shared clustering tree. Each tenant's
clustering tree adapts dynamically to its unique vector distribution, while
maintaining a low per-tenant memory footprint. Our evaluation, based on two
widely used data sets, confirms that Curator delivers search performance on par
with per-tenant indexing, while maintaining memory consumption at the same
level as metadata filtering on a single, shared index.

Curator 是一种专为多租户查询设计的内存向量索引，实现了低内存开销和高查询、向量插入和删除性能，并在两个常用数据集上得到了验证。

Curator: 多租户矢量数据库的高效索引

Curator: Efficient Indexing for Multi-Tenant Vector Databases

Multi-tenancy in public clouds may lead to co-location interference on shared
resources, which possibly results in performance degradation of cloud
applications. Cloud providers want to know when such events happen and how
serious the degradation is, to perform interference-aware migrations and
alleviate the problem. However, virtual machines (VM) in
Infrastructure-as-a-Service public clouds are black-boxes to providers, where
application-level performance information cannot be acquired. This makes
performance monitoring intensely challenging as cloud providers can only rely
on low-level metrics such as CPU usage and hardware counters.
We propose a novel machine learning framework, Alioth, to monitor the
performance degradation of cloud applications. To feed the data-hungry models,
we first elaborate interference generators and conduct comprehensive
co-location experiments on a testbed to build Alioth-dataset which reflects the
complexity and dynamicity in real-world scenarios. Then we construct Alioth by
(1) augmenting features via recovering low-level metrics under no interference
using denoising auto-encoders, (2) devising a transfer learning model based on
domain adaptation neural network to make models generalize on test cases unseen
in offline training, and (3) developing a SHAP explainer to automate feature
selection and enhance model interpretability. Experiments show that Alioth
achieves an average mean absolute error of 5.29% offline and 10.8% when testing
on applications unseen in the training stage, outperforming the baseline
methods. Alioth is also robust in signaling quality-of-service violation under
dynamicity. Finally, we demonstrate a possible application of Alioth's
interpretability, providing insights to benefit the decision-making of cloud
operators. The dataset and code of Alioth have been released on GitHub.

通过构建一个新颖的机器学习框架 Alioth 来监控云应用性能降低，并提供对决策的洞察力。

Alioth: 公有云上基于机器学习的多租户应用干扰感知性能监测器

Alioth: A Machine Learning Based Interference-Aware Performance Monitor  for Multi-Tenancy Applications in Public Cloud

Smart IoT-based systems often desire continuous execution of multiple
latency-sensitive Deep Learning (DL) applications. The edge servers serve as
the cornerstone of such IoT-based systems, however, their resource limitations
hamper the continuous execution of multiple (multi-tenant) DL applications. The
challenge is that, DL applications function based on bulky "neural network (NN)
models" that cannot be simultaneously maintained in the limited memory space of
the edge. Accordingly, the main contribution of this research is to overcome
the memory contention challenge, thereby, meeting the latency constraints of
the DL applications without compromising their inference accuracy. We propose
an efficient NN model management framework, called Edge-MultiAI, that ushers
the NN models of the DL applications into the edge memory such that the degree
of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI
leverages NN model compression techniques, such as model quantization, and
dynamically loads NN models for DL applications to stimulate multi-tenancy on
the edge server. We also devise a model management heuristic for Edge-MultiAI,
called iWS-BFE, that functions based on the Bayesian theory to predict the
inference requests for multi-tenant applications, and uses it to choose the
appropriate NN models for loading, hence, increasing the number of warm-start
inferences. We evaluate the efficacy and robustness of Edge-MultiAI under
various configurations. The results reveal that Edge-MultiAI can stimulate the
degree of multi-tenancy on the edge by at least 2X and increase the number of
warm-starts by around 60% without any major loss on the inference accuracy of
the applications.

该研究主要针对 IoT 系统中的深度学习多租户应用的内存争用问题，提出了一种名为 Edge-MultiAI 的有效神经网络模型管理框架，结合量化技术和贝叶斯理论等多种策略，使得边缘服务器上的 NN 模型能够同时维护多个应用请求，从而更快的响应更多租户请求，且不会明显损失推理精度。

Edge-MultiAI: 边缘多租户环境下低延迟深度学习应用的多租户实现

Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge

Edge computing is the practice of placing computing resources at the edges of
the Internet in close proximity to devices and information sources. This, much
like a cache on a CPU, increases bandwidth and reduces latency for applications
but at a potential cost of dependability and capacity. This is because these
edge devices are often not as well maintained, dependable, powerful, or robust
as centralized server-class cloud resources. This article explores
dependability and deployment challenges in the field of edge computing, what
aspects are solvable with today's technology, and what aspects call for new
solutions.
The first issue addressed is failures, both hard (crash, hang, etc.) and soft
(performance-related), and real-time constraint violation. In this domain, edge
computing bolsters real-time system capacity through reduced end-to-end
latency. However, much like cache misses, overloaded or malfunctioning edge
computers can drive latency beyond tolerable limits. Second, decentralized
management and device tampering can lead to chain of trust and security or
privacy violations. Authentication, access control, and distributed intrusion
detection techniques have to be extended from current cloud deployments and
need to be customized for the edge ecosystem. The third issue deals with
handling multi-tenancy in the typically resource-constrained edge devices and
the need for standardization to allow for interoperability across vendor
products.
We explore the key challenges in each of these three broad issues as they
relate to dependability of edge computing and then hypothesize about promising
avenues of work in this area.

论文探讨了边缘计算的可信性和部署挑战，分别是故障处理、安全和多租户处理，并提出了解决这些挑战的解决方案。

边缘计算中的可靠性

Dependability in Edge Computing

Public infrastructure-as-a-service clouds, such as Amazon EC2, Google Compute
Engine (GCE) and Microsoft Azure allow clients to run virtual machines (VMs) on
shared physical infrastructure. This practice of multi-tenancy brings economies
of scale, but also introduces the risk of sharing a physical server with an
arbitrary and potentially malicious VM. Past works have demonstrated how to
place a VM alongside a target victim (co-location) in early-generation clouds
and how to extract secret information via side- channels. Although there have
been numerous works on side-channel attacks, there have been no studies on
placement vulnerabilities in public clouds since the adoption of stronger
isolation technologies such as Virtual Private Clouds (VPCs).
We investigate this problem of placement vulnerabilities and quantitatively
evaluate three popular public clouds for their susceptibility to co-location
attacks. We find that adoption of new technologies (e.g., VPC) makes many prior
attacks, such as cloud cartography, ineffective. We find new ways to reliably
test for co-location across Amazon EC2, Google GCE, and Microsoft Azure. We
also found ways to detect co-location with victim web servers in a multi-tiered
cloud application located behind a load balancer.
We use our new co-residence tests and multiple customer accounts to launch VM
instances under different strategies that seek to maximize the likelihood of
co-residency. We find that it is much easier (10x higher success rate) and
cheaper (up to $114 less) to achieve co-location in these three clouds when
compared to a secure reference placement policy.

研究了公共云基础设施（如 Amazon EC2，Google Compute Engine（GCE）和 Microsoft Azure）的容易受到 co-location 攻击性漏洞，并在三个受欢迎的公共云上定量评估了其易感程度。新技术的采用（例如 VPC）使得许多以前的攻击方式失效，但是作者发现对于最近的云漏洞，攻击的成功率更高，成本更低。