Is Fabric Computing the Future of Cloud?

织物计算作为一种新兴概念,正迅速获得关注。它通过将存储、网络和处理等功能紧密集成来提升数据中心效率。本文探讨了从分离到再整合的数据处理方式转变,并分析了这种趋势如何与软件定义的基础设施相结合,进而推动云计算的发展。

 

原文地址:http://cloudcomputing.sys-con.com/node/1755128  感谢作者:GREGOR PETRI

 

云计算还没有站稳脚跟,新的概念又来了!

 

The term fabric computing is gaining rapid popularity, but currently mostly within the hardware community. In fact, according to a recent report, over 50% of attendees at the recent Datacenter Summit had implemented, or are in the process of implementing, fabric computing. Time to take a look at what fabric computing means for software and for (cloud) computing as a whole.

 

Depending on which dictionary you choose, you can find anywhere between two and seven meanings for "fabric." Etymology-wise, it comes from the French fabrique and the Latin fabricare, and the Dutch Fabriek actually means factory. But in an IT context, fabric has little to do with our often used manufacturing or supply chain analogies; instead it actually relates much closer to fabric in its meaning of cloth, a material produced (fabricated) by weaving fibers.

If we check our handy Wikipedia for fabric computing, we get:

Fabric computing or unified computing involves the creation of a computing fabric consisting of interconnected nodes that look like a 'weave' or a 'fabric' when viewed collectively from a distance.[1]

 

Usually this refers to a consolidated high-performance computing system consisting of loosely coupled storage, networking and parallel processing functions linked by high bandwidth interconnects ...

 

 

 

In the context of data centers it means a move from having distinct boxes for handling storage, network and processing towards a fabric where these functions are much more intertwined or even integrated. Most people started to note the move to fabric or unified computing when Cisco started to include servers inside their switches, which they did partly in response to HP including more and more switches in their server deals. Cisco's UCS (Unified Computing System), and its bigger sibling, VCE, are the first hardware examples of this trend (although inside the box you can still distinguish the original components).

One reason to move to such a fabric design is that by moving data, network and compute closer together (integrating them) you can improve performance. Juniper's recent QFabric architecture announcement is another similar example. But, the idea of closer integration of data, processing, and communication is actually much older. In some respects, we may even conclude IT is coming full circle with this trend.

Let me explain.

Many years ago I spoke to Professor Scheer, founder of IDS Scheer and a pioneer in the field of Business Process Management (BPM). (Disclosure: years later IDS Scheer became part of my former employer: Software AG.) He spoke about how - in the old days of IT - data and logic were seen as one. Literally! If - while walking with your stack of punch cards to the computer room (back then it was a computer the size of a room, not a room with a computer in it) - you dropped your stack of punch cards, both data and logic would be in one pile on the floor. You would spend the rest of your afternoon sorting them again. There was just one stack: first the processing/algorithm logic, and then the data. Scheer's point was that just like we figured out after a while that data did not belong there and we moved it to its own place (typically a relational database), we should now separate the process flow instructions from the algorithms and move these to a workflow process engine (preferably of course his BPM engine). All valid and true - at that time.

 

But not long after, Object Oriented programming became the norm, and we started to move data back with the logic that understood how to handle that data, and treat them as objects. This of course created a new issue of having these objects perform in an even more remotely acceptable way, as we used relational databases to store or persist the data inside these objects. You could compare this to disassembling your car every night into its original pieces in order to put it in your garage. Over the years the industry figured out how to do this better,in part by creating new databases which design-wise looked remarkably similar to the (hierarchical) databases we used back in the day of punch cards.

 

And now , under the new shiny name of fabric computing we are moving all these processes back in the same physical box.

 

But this is not the whole story -- there is another revolution happening. As an industry we are moving from using dedicated hardware for specialized tasks to generic hardware with specialized software instead. For example, you might use a software virtualization layer to simply emulate a certain piece of specific hardware.

 

Or, look at a firewall: traditionally it was a piece of dedicated hardware built to do one thing (keeping non-allowed traffic out). Today, most firewalls are software-based. We use a generic processor to take care of that task. And we're seeing this trend unfold with more equipment in the data center. Even switches, load balancers and network-attached storage are becoming software-based (virtual appliance seems to be the preferred marketing buzzword for this trend).

 

Using software is more efficient than having loads of dedicated hardware, and we can't ignore the fact that software, because of its completely different economic and management characteristics, has numerous inherent advantages over hardware. For example, you can copy, change, delete and distribute software, all remotely, without having to leave your seat, and even do automatically. You'd need some pretty advanced robots to do that with hardware (if it could be done today).

 

So how do these two trends relate to cloud computing?

By combining the idea of moving stuff that needs to work together closer together (the idea of fabric) and the idea of doing that by using software instead of hardware (which gives us the economics and manageability of software) we can create higher performance, lower cost and easier to manage clouds.

 

Virtualization has been on a similar path. First we virtualized servers, then storage and networking, but all remained in their separate silos. Now we are virtualizing all of it in the same "fabric." This means that managing the entire stack gets simpler, with one tool to define it, make it work and monitor it. And that's something that should make any IT pro smile.

 

In my next post, I'll share my thoughts on why I think this approach has the power to change IT as we know it, based on some of my own epiphanies.

需求响应动态冰蓄冷系统与需求响应策略的优化研究(Matlab代码实现)内容概要:本文围绕“需求响应动态冰蓄冷系统与需求响应策略的优化研究”展开,基于Matlab代码实现,重点探讨了冰蓄冷系统在电力需求响应背景下的动态建模与优化调度策略。研究结合实际电力负荷与电价信号,构建系统能耗模型,利用优化算法对冰蓄冷系统的运行策略进行求解,旨在降低用电成本、平衡电网负荷,并提升能源利用效率。文中还提及该研究为博士论文复现,涉及系统建模、优化算法应用与仿真验证等关键技术环节,配套提供了完整的Matlab代码资源。; 适合人群:具备一定电力系统、能源管理或优化算法基础,从事科研或工程应用的研究生、高校教师及企业研发人员,尤其适合开展需求响应、综合能源系统优化等相关课题研究的人员。; 使用场景及目标:①复现博士论文中的冰蓄冷系统需求响应优化模型;②学习Matlab在能源系统建模与优化中的具体实现方法;③掌握需求响应策略的设计思路与仿真验证流程,服务于科研项目、论文写作或实际工程方案设计。; 阅读建议:建议结合提供的Matlab代码逐模块分析,重点关注系统建模逻辑与优化算法的实现细节,按文档目录顺序系统学习,并尝试调整参数进行仿真对比,以深入理解不同需求响应策略的效果差异。
综合能源系统零碳优化调度研究(Matlab代码实现)内容概要:本文围绕“综合能源系统零碳优化调度研究”,提供了基于Matlab代码实现的完整解决方案,重点探讨了在高比例可再生能源接入背景下,如何通过优化调度实现零碳排放目标。文中涉及多种先进优化算法(如改进遗传算法、粒子群优化、ADMM等)在综合能源系统中的应用,涵盖风光场景生成、储能配置、需求响应、微电网协同调度等多个关键技术环节,并结合具体案例(如压缩空气储能、光热电站、P2G技术等)进行建模与仿真分析,展示了从问题建模、算法设计到结果验证的全流程实现过程。; 适合人群:具备一定电力系统、能源系统或优化理论基础,熟悉Matlab/Simulink编程,从事新能源、智能电网、综合能源系统等相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①开展综合能源系统低碳/零碳调度的科研建模与算法开发;②复现高水平期刊(如SCI/EI)论文中的优化模型与仿真结果;③学习如何将智能优化算法(如遗传算法、灰狼优化、ADMM等)应用于实际能源系统调度问题;④掌握Matlab在能源系统仿真与优化中的典型应用方法。; 阅读建议:建议结合文中提供的Matlab代码与网盘资源,边学习理论模型边动手调试程序,重点关注不同优化算法在调度模型中的实现细节与参数设置,同时可扩展应用于自身研究课题中,提升科研效率与模型精度。
Journal of Cloud Computing: Advances, Systems and Applications 是一本专注于云计算领域的高质量学术期刊,涵盖了云计算的理论研究、系统实现和实际应用等多个方面。该期刊的官方网站为 [https://journalofcloudcomputing.springeropen.com/](https://journalofcloudcomputing.springeropen.com/),由 SpringerOpen 出版社负责出版。该期刊采用开放获取(Open Access)模式,所有文章均可免费阅读和下载。 该期刊的投稿范围包括但不限于以下主题: - 云计算架构与模型 - 虚拟化技术 - 云资源管理与调度 - 云安全与隐私保护 - 云存储与数据管理 - 分布式云与边缘计算 - 云服务质量(QoS)与性能优化 - 云应用案例与经验分享 - 云与大数据的融合 - 移动云计算与物联网(IoT)集成 [^1] 在该期刊上发表的论文通常包括原创研究论文(Research Articles)、综述论文(Review Articles)以及短篇通讯(Short Communications)。投稿者需通过其在线投稿系统(Editorial Manager)提交稿件,所有稿件均经过严格的同行评审流程。 例如,该期刊曾发表过以下具有代表性的论文: - **"A survey on virtual machine migration in cloud computing and its challenges"** 该综述论文系统性地总结了云计算中虚拟机迁移技术的研究进展,并分析了不同迁移策略在性能、能耗、安全等方面的优劣。 - **"Energy-efficient resource allocation in cloud computing: A survey"** 本文探讨了云计算环境下的节能资源分配策略,包括虚拟机放置、负载均衡、动态资源调度等关键技术。 - **"Security and privacy issues in cloud computing: A comprehensive survey"** 本研究综述了当前云计算中存在的主要安全与隐私问题,并对现有的防护机制、加密技术与访问控制方法进行了比较分析。 对于希望了解该期刊最新研究成果的读者,可以通过其官网访问最新发表的文章列表。此外,Google Scholar、Scopus、Web of Science 等学术数据库也收录了该期刊的全部论文内容,便于检索和引用。 ```python # 示例:使用 Python 请求 Journal of Cloud Computing 的 RSS Feed 获取最新文章信息 import feedparser rss_url = 'https://journalofcloudcomputing.springeropen.com/rss' feed = feedparser.parse(rss_url) for entry in feed.entries[:5]: # 获取最近5篇文章 print(f"Title: {entry.title}") print(f"Link: {entry.link}") print(f"Published: {entry.published}") print('-' * 40) ```
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