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.

该数据集通过合成方式模拟了多种发动机在运行过程中的传感器监测数据,旨在构建一个用于机械系统故障检测的基准资源,特别适用于汽车领域的诊断分析。数据按固定时间间隔采集,涵盖了发动机性能指标、异常状态以及工作模式等多维度信息。 时间戳:数据类型为日期时间,记录了每个数据点的采集时刻。序列起始于2024年12月24日10:00,并以5分钟为间隔持续生成,体现了对发动机运行状态的连续监测。 温度(摄氏度):以浮点数形式记录发动机的温度读数。其数值范围通常处于60至120摄氏度之间,反映了发动机在常规工况下的典型温度区间。 转速(转/分钟):以浮点数表示发动机曲轴的旋转速度。该参数在1000至4000转/分钟的范围内随机生成,符合多数发动机在正常运转时的转速特征。 燃油效率(公里/升):浮点型变量,用于衡量发动机的燃料利用效能,即每升燃料所能支持的行驶里程。其取值范围设定在15至30公里/升之间。 振动_X、振动_Y、振动_Z:这三个浮点数列分别记录了发动机在三维空间坐标系中各轴向的振动强度。测量值标准化至0到1的标度,较高的数值通常暗示存在异常振动,可能与潜在的机械故障相关。 扭矩(牛·米):以浮点数表征发动机输出的旋转力矩,数值区间为50至200牛·米,体现了发动机的负载能力。 功率输出(千瓦):浮点型变量,描述发动机单位时间内做功的速率,取值范围为20至100千瓦。 故障状态:整型分类变量,用于标识发动机的异常程度,共分为四个等级:0代表正常状态,1表示轻微故障,2对应中等故障,3指示严重故障。该列作为分类任务的目标变量,支持基于传感器数据预测故障等级。 运行模式:字符串类型变量,描述发动机当前的工作状态,主要包括:怠速(发动机运转但无负载)、巡航(发动机在常规负载下平稳运行)、重载(发动机承受高负荷或高压工况)。 数据集整体包含1000条记录,每条记录对应特定时刻的发动机性能快照。其中故障状态涵盖从正常到严重故障的四级分类,有助于训练模型实现故障预测与诊断。所有数据均为合成生成,旨在模拟真实的发动机性能变化与典型故障场景,所包含的温度、转速、燃油效率、振动、扭矩及功率输出等关键传感指标,均为影响发动机故障判定的重要因素。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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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