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.

内容概要:本文详细介绍了一个基于Java和Vue的联邦学习隐私保护推荐系统的设计与实现。系统采用联邦学习架构,使用户数据在本地完成模型训练,仅上传加密后的模型参数或梯度,通过中心服务器进行联邦平均聚合,从而实现数据隐私保护与协同建模的双重目标。项目涵盖完整的系统架构设计,包括本地模型训练、中心参数聚合、安全通信、前后端解耦、推荐算法插件化等模块,并结合差分隐私与同态加密等技术强化安全性。同时,系统通过Vue前端实现用户行为采集与个性化推荐展示,Java后端支撑高并发服务与日志处理,形成“本地训练—参数上传—全局聚合—模型下发—个性化微调”的完整闭环。文中还提供了关键模块的代码示例,如特征提取、模型聚合、加密上传等,增强了项目的可实施性与工程参考价值。 适合人群:具备一定Java和Vue开发基础,熟悉Spring Boot、RESTful API、分布式系统或机器学习相关技术,从事推荐系统、隐私计算或全栈开发方向的研发人员。 使用场景及目标:①学习联邦学习在推荐系统中的工程落地方法;②掌握隐私保护机制(如加密传输、差分隐私)与模型聚合技术的集成;③构建高安全、可扩展的分布式推荐系统原型;④实现前后端协同的个性化推荐闭环系统。 阅读建议:建议结合代码示例深入理解联邦学习流程,重点关注本地训练与全局聚合的协同逻辑,同时可基于项目架构进行算法替换与功能扩展,适用于科研验证与工业级系统原型开发。
源码来自:https://pan.quark.cn/s/a4b39357ea24 遗传算法 - 简书 遗传算法的理论是根据达尔文进化论而设计出来的算法: 人类是朝着好的方向(最优解)进化,进化过程中,会自动选择优良基因,淘汰劣等基因。 遗传算法(英语:genetic algorithm (GA) )是计算数学中用于解决最佳化的搜索算法,是进化算法的一种。 进化算法最初是借鉴了进化生物学中的一些现象而发展起来的,这些现象包括遗传、突变、自然选择、杂交等。 搜索算法的共同特征为: 首先组成一组候选解 依据某些适应性条件测算这些候选解的适应度 根据适应度保留某些候选解,放弃其他候选解 对保留的候选解进行某些操作,生成新的候选解 遗传算法流程 遗传算法的一般步骤 my_fitness函数 评估每条染色体所对应个体的适应度 升序排列适应度评估值,选出 前 parent_number 个 个体作为 待选 parent 种群(适应度函数的值越小越好) 从 待选 parent 种群 中随机选择 2 个个体作为父方和母方。 抽取父母双方的染色体,进行交叉,产生 2 个子代。 (交叉概率) 对子代(parent + 生成的 child)的染色体进行变异。 (变异概率) 重复3,4,5步骤,直到新种群(parentnumber + childnumber)的产生。 循环以上步骤直至找到满意的解。 名词解释 交叉概率:两个个体进行交配的概率。 例如,交配概率为0.8,则80%的“夫妻”会生育后代。 变异概率:所有的基因中发生变异的占总体的比例。 GA函数 适应度函数 适应度函数由解决的问题决定。 举一个平方和的例子。 简单的平方和问题 求函数的最小值,其中每个变量的取值区间都是 [-1, ...
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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