Here we present Singularity, software developed to bring containers and reproducibility to scientific computing. Using Singularity containers, developers can work in reproducible environments of their choosing and design, and these complete environments can easily be copied and executed on other platforms. Singularity is an open source initiative that harnesses the expertise of system and software engineers and researchers alike, and integrates seamlessly into common workflows for both of these groups. As its primary use case, Singularity brings mobility of computing to both users and HPC centers, providing a secure means to capture and distribute software and compute environments. This ability to create and deploy reproducible environments across these centers, a previously unmet need, makes Singularity a game changing development for computational science.

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Singularity: Scientific containers for mobility of compute.

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/

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A Survey of Research on Cloud Robotics and Automation

Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .

Resource Management in Clouds: Survey and Research Challenges

The cloud computing infrastructure relies on virtualized servers that provide isolation across guest OS's through sand boxing. This isolation was demonstrated to be imperfect in past work which exploited hardware level information leakages to gain access to sensitive information across co-located virtual machines (VMs). In response virtualization companies and cloud services providers have disabled features such as deduplication to prevent such attacks. In this work, we introduce a fine-grain cross-core cache attack that exploits access time variations on the last level cache. The attack exploits huge pages to work across VM boundaries without requiring deduplication. No configuration changes on the victim OS are needed, making the attack quite viable. Furthermore, only machine co-location is required, while the target and victim OS can still reside on different cores of the machine. Our new attack is a variation of the prime and probe cache attack whose applicability at the time is limited to L1 cache. In contrast, our attack works in the spirit of the flush and reload attack targeting the shared L3 cache instead. Indeed, by adjusting the huge page size our attack can be customized to work virtually at any cache level/size. We demonstrate the viability of the attack by targeting an Open SSL1.0.1f implementation of AES. The attack recovers AES keys in the cross-VM setting on Xen 4.1 with deduplication disabled, being only slightly less efficient than the flush and reload attack. Given that huge pages are a standard feature enabled in the memory management unit of OS's and that besides co-location no additional assumptions are needed, the attack we present poses a significant risk to existing cloud servers.

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S$A: A Shared Cache Attack That Works across Cores and Defies VM Sandboxing -- and Its Application to AES

Edge server placement in mobile edge computing

１ インフラを構築する（ＡＷＳにおけるインフラ；ＶＰＣを構成する；ＶＰＣとオンプレミス環境とを接続する） ２ ファイルオブジェクトを保存・共有・公開する（オブジェクトストレージＳ３の機能；ファイルストレージとして利用する；Ｗｅｂサーバーを構築する；信頼性とコストのバランスをとりたい） ３ アプリケーションサーバーを構築する（Ａｍａｚｏｎ ＥＣ２とＡＷＳ Ｌａｍｂｄａ；スケーラビリティーを高める；サーバーレスでプログラムを動かす；データベースサービスを活用する） ４ ＡＷＳシステムを管理する（リソース監視と異常検知・通報；耐障害性を高める仕組みとバックアップ＆リカバリー；構成管理）

和幸大嶋

Papers

Amazon Web Services完全ソリューションガイド

和幸 大嶋

Papers

Amazon Web Services完全ソリューションガイド

和幸大嶋