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。

多租户公共云的位置漏洞研究

A Placement Vulnerability Study in Multi-tenant Public Clouds

As computational work becomes more and more integral to many aspects of
scientific research, computational reproducibility has become an issue of
increasing importance to computer systems researchers and domain scientists
alike. Though computational reproducibility seems more straight forward than
replicating physical experiments, the complex and rapidly changing nature of
computer environments makes being able to reproduce and extend such work a
serious challenge. In this paper, I explore common reasons that code developed
for one research project cannot be successfully executed or extended by
subsequent researchers. I review current approaches to these issues, including
virtual machines and workflow systems, and their limitations. I then examine
how the popular emerging technology Docker combines several areas from systems
research - such as operating system virtualization, cross-platform portability,
modular re-usable elements, versioning, and a `DevOps' philosophy, to address
these challenges. I illustrate this with several examples of Docker use with a
focus on the R statistical environment.

本文探讨了计算再现性的重要性和挑战，介绍了当前处理此类问题的方法和局限性，并提出了基于 Docker 技术的解决方法，并给出了几个基于 R 统计环境的 Docker 使用案例。

Docker 简介与 R 环境下的可重复研究

An introduction to Docker for reproducible research, with examples from  the R environment

Cloud computing is an increasingly popular computing paradigm, now proving a
necessity for utility computing services. Each provider offers a unique service
portfolio with a range of resource configurations. Resource provisioning for
cloud services in a comprehensive way is crucial to any resource allocation
model. Any model should consider both computational resources and network
resources to accurately represent and serve practical needs. Another aspect
that should be considered while provisioning resources is energy consumption.
This aspect is getting more attention from industry and governments parties.
Calls of support for the green clouds are gaining momentum. With that in mind,
resource allocation algorithms aim to accomplish the task of scheduling virtual
machines on data center servers and then scheduling connection requests on the
network paths available while complying with the problem constraints. Several
external and internal factors that affect the performance of resource
allocation models are introduced in this paper. These factors are discussed in
detail and research gaps are pointed out. Design challenges are discussed with
the aim of providing a reference to be used when designing a comprehensive
energy aware resource allocation model for cloud computing data centers.

本篇论文讨论了云计算资源分配的核心问题，对计算资源、网络资源、能源消耗等多方面因素进行细致分析与探讨，为构建全面的、高效的、绿色可持续的云计算数据中心提供了参考。