• The You'reFat treadmill company has decided to enhance its treadmills to allow users to run workout programs from an audio program (either online streaming audio or from a CD). The program will have ...
• Treadmill System Based on Kinect
• m still to receive any response from the development team on a timeline for treadmill device support. <p>Currently it seems that the new input binding system only allows for 2 controllers (left and ...
• This is a problem for my driver, as my treadmill (vr::TrackedDeviceClass_Controller with treadmill role) always registers before the vive controllers and therefore the treadmill is assigned to one of ...
• <div><p><strong>FEATURE REQUEST - FILTER INPUT FOR TREADMILL In the SteamVR Plugin for Unity, you can select input sources when reading inputs. This way you can, for example, make an input action only...
• <p>We had a brief period where Treadmill devices were becoming quite universal in terms of compatibility, but these recent updates have really damaged the support Treadmill device developers can offer...
• <p>So, perhaps my request is that a treadmill run with no GAP still works. I think this could be accomplished by assuming an entirely flat course. I'm new to this code, but maybe that could be ...
• <div><p>Does anyone use RunnerUp to track treadmill activities? I have a compatible heart rate monitor and tend to do my hill repeats on the treadmill because it is convenient (no downhill before ...
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• <p>If you start the treadmill function on the Mi Band 3, it starts and shows info like time, HR, steps/min, steps and distance. All of them are displayed correctly except distance. It never changes ...
• Treadmill:通过精确的负载测试和统计推理识别尾延迟来源 摘要 管理请求的尾部延迟已经成为大规模Internet服务的主要挑战之一。 数据中心正在快速发展，服务运营商经常希望对部署的软件和生产硬件配置进行更改。 ...

题目：
摘要
管理请求的尾部延迟已经成为大规模Internet服务的主要挑战之一。 数据中心正在快速发展，服务运营商经常希望对部署的软件和生产硬件配置进行更改。 这种变化需要对对服务的影响，特别是其对尾部延迟的影响（例如，服务的响应延迟的95％或99％）的理解。
评估对尾部的影响是具有挑战性的，因为其固有的变异性。用于测量这些影响的现有工具和方法存在许多缺陷，包括差的负载测试仪设计，统计上不准确的聚合和不正确的效应归因。 如文中所示，这些陷阱常常会导致误导性的结论。
前言
减少尾部延迟（即延迟分布的高分位数）提高了大规模互联网服务的服务质量[1]。 高尾延迟已被确定为现代数据中心设计面临的主要挑战之一，因为它导致用户体验不佳，特别是对于诸如网络搜索和社交网络之类的交互式服务。这些服务由机群集群供电，其中单个请求以“扇出”模式分布在大量服务器之间。
在这种设计中，这种系统的整体性能取决于响应速度最慢的机器[2]。 最近的工作试图控制和理解这些尾部请求，无论是在个别服务器还是整个集群级别[3]。
对于数据中心运营商来说，精确测量尾部延迟而不会中断生产系统的能力很重要，原因有很多。首先，服务器通常大量（例如每次1000s）获取，因此选择最佳设计并仔细提供资源很重要。
评估硬件配置需要对提高的工作负载进行大量和准确的测量。第二，有必要在不破坏生产系统的情况下，忠实地测量效能。 软件和硬件变化的高频率使得评估这些生产变化非常困难或不可能，因为它可以很容易地导致用户可见的事件。 相反，最好在安全但准确的负载测试环境中了解性能关键决定的影响。
构建这样的负载测试环境以实现精确的尾部延迟测量是特别具有挑战性的。这主要是因为与传统的单服务器工作负载（例如SPEC CPU2006，PARSEC）相比，大规模Internet服务工作负载（例如，分布式服务器端软件，网络连接等）所涉及的系统和资源显着增加。
尽管已经有几个以前的工作[4,5,6,7,8,9,10]试图弥合这个差距，但是在他们的负载测试设计中有几个陷阱，我们将在后面的文章中给出。 不幸的是，这些工具通常用于研究出版物进行评估，陷阱可能导致误导性的结论。类似于学术界，工业界还缺乏准确的尾部延迟测量测试台，导致不必要的资源过度供应[11]和不明原因的性能回归[12]。
此外，为了能够控制这些互联网服务的尾部延迟，需要彻底和正确地理解尾部延迟的来源。这些互联网服务与广泛的系统和资源（包括操作系统，网络堆栈和服务器硬件）进行交互，从而定量地将尾部延迟来源归因于个人资源的能力是至关重要的。 虽然以前的一些作品[13,14,15,16,3,17,18,19,20,21,22,23,24,25]研究了个别资源对尾部潜伏期的影响，但许多资源复杂的相互作用行为，无法在孤立的研究中被捕获。
例如，Turbo Boost和DVFS调速器可以通过竞争热余量来间接地相互作用。 请注意，识别尾部延迟源的能力取决于上述第一个挑战。
换句话说，没有对尾部延迟的精确测量，我们将无法将其正确归因于各种来源。在本文中，我们首先调查研究界使用的现有绩效评估方法。 我们已经确定了这些工具的一组常见错误：
•查询到达后生成 -
负载测试软件通常用于简化软件。 我们发现常用的编程范例创建一个隐式排队模型，其近似于闭环系统，并且系统地低估了尾部延迟。相反，我们展示了精确定时的开环负载测试仪是正确运行系统排队行为所必需的。
•统计汇总 -
由于请求率高，必须采用采样来控制测量开销。必须仔细执行这些延迟样本的在线聚合。我们发现，单数统计（例如，第95或99个周期延迟的点估计）不能捕获详细信息;其他负载测试仪中使用的静态直方图也表现出偏差。
•客户端排队偏差 - 由于许多商业系统中的高吞吐率（每秒100k-1M的请求），我们证明，必须使用多台客户机来测试单个服务器。没有使用轻松的客户端，延迟测量迅速开始受到客户端排队的影响，产生有偏见的结果。    •性能“滞后” -
我们观察到在收集足够数量的样本之后估计的延迟收敛的现象，但是在再次运行负载测试时，测试收敛到不同的值。这是由于底层系统状态的变化，如逻辑内存，线程和连接到物理资源的映射。我们将其称为滞后，因为没有合理数量的附加样本可以使两个运行收敛到同一点。相反，我们发现实验必须重新启动多次，并且收敛的值应该被聚合。
[27]。使用我们的尾部延迟归因程序，我们能够识别许多反直觉的性能行为，包括不同硬件资源之间的复杂交互，这些交互不能通过孤立的各个硬件功能的先前研究来捕获。最后，我们展示了我们成功捕获了mcrouter系统中90％的性能变化，并为Memcached捕获了95％以上的性能变化。通过仔细调整归因建议的硬件功能，我们将第99百分位数延迟减少了43％，方差99位的百分比减少了93％。
总之，本文有三个主要贡献：
•对现有负荷测试方法中常见的陷阱进行调查 -
对相关工作现有方法进行调查，并对其缺点进行实证分析。我们将这些镜子分类为未来从业人员的四大原则。

•精确的基于群集的绩效评估方法 -
我们介绍了一个强大的实验方法的设计，以及作为开源软件发布的软件负载测试工具跑步机。这两个系统都符合我们原则的要求，并且易于扩展以供采纳。

•归因于尾部延迟的来源 -
通过我们的方法实现的高精度测量使得可以使用分位数回归来理解尾部延迟方差的来源。我们成功地将大部分方差归因于几个高级硬件特征及其间的相互作用。通过仔细调整归因结果推荐的硬件配置，我们显着降低了尾部延迟及其差异。
现代艺术方法论中的讽刺
为了了解评估试验台的要求，我们首先调查了以前工作中现有的方法和工具。许多工具可用于研究服务器端软件的性能，包括YCSB [4]，Faban[28]，Mutilate
[29]和CloudSuite[6]。 这些工具已经广泛应用于标准的基准套件，包括SPEC2010 jEnterprise [30]，SPEC2011 SIP基础设施[31]，CloudSuite
[6]和BigDataBench [8]，从而引起了许多最近发表的研究项目。
通过研究这些现有工具，我们发现了几个常见的陷阱。我们将它们分为以下四个主题。
A.查询到达后生成
性能评估测试台需要一个负载测试器，一个以受控的方式向服务器发出请求的软件。客户机将运行负载测试器，周期性地构建，发送和接收请求以实现期望的吞吐量。通常采用两种类型的控制回路来创建这些定时：开环控制和闭环控制[32]。闭环控制器具有反馈回路，其中仅在已经接收到对先前请求的响应之后才尝试发送另一个请求。相比之下，开环控制器在定义的时间内发送请求，而不管响应的状态如何。几乎所有的现代数据中心服务器端软件都是为了处理开环设置而建立的，所以每个服务器线程在忙碌处理之前都不会拒绝来自客户端的请求。
然而，由于软件的简单性，许多负载测试仪被实现为闭环控制器，包括Faban，YCSB和Mutilate，如表I所示。通常，使用发出网络请求时阻止的工作线程生成负载。然后可以通过增加或减少负载发生器中的螺纹量来控制负载量。不幸的是，这种模式完全类似于闭环控制器;每个线程正好代表一个潜在未完成的请求
B.统计汇总
由于请求率高，负载测试软件需要至少执行一些延迟样本的统计汇总，以避免保持大量样本的开销。我们发现在这个过程中必须小心，可能会发生两种类型的错误。首先，负载测试人员必须保持随时间变化的延迟内部直方图。那些维护直方图的负载测试仪常常会导致静态设置直方图桶的错误。当服务器被高度利用时，非自适应直方图分组将会中断，因为延迟在达到稳定状态之前会保持增加，从而超过直方图的上限。    此外，如果请求具有不同的特征（例如，不同的请求类型，由不同的机器发送等），我们观察到由于不正确的统计聚合而可能发生偏差。例如，在图2中，我们演示了一种场景，其中四个客户端用于将请求发送到同一个Memcached服务器，“客户端1”位于与其他客户端和服务器不同的机架上。在x轴上的每个延迟点，每个阴影区域表示来自四个客户端之一的样本的比例。随着分数量的增加，人们可以清楚地看到，大多数样本来自“客户端1”。这种偏差是有问题的，因为系统的性能估计成为一个单一客户端的功能。相反，应该单独提取每个客户端的感兴趣度量（例如，第99百分位数延迟），并将其合理聚合。    C.客户端排队偏差    在操作负载测试器时，纯粹测量服务器端延迟的影响很重要。对于服务时间长（例如，复杂的MySQL查询）的工作负载，客户端不必发出许多使服务器饱和的请求。然而，对于像Memcached这样的工作负载，客户端和网络本身的请求率相当高。这可能导致客户端和网络本身的排队效应，从而偏差延迟测量。
YCSB和CloudSuite由于其单个客户端配置而受到这样的偏见，如表I所示。    图3显示了客户端和网络利用率如何对延迟测量进行偏差的示例。在“单客户端安装”中，客户端和网络具有与服务器相同的利用率。可以看出，客户端延迟和网络延迟随着服务器利用率的增加而增长，并且在端对端延迟中占有重要的比例。我们发现设计一个可以使服务器充分饱和的单客户端负载测试器是非常具有挑战性的，即使不是不可能，也不会对以微秒级延迟运行的现代数据中心工作负载造成重大的客户端排队。相反，有必要建立一个多客户机，并且拥有足够数量的机器，使得客户端和网络延迟保持较低。在“MultiClient Setup”中，我们增加了客户端计算机的数量以最小化这些偏差。经过调整后，大部分测量的延迟来自服务器。
D.性能“迟滞”
图4.差异存在，而不管单个运行的样本大小。单次运行对于小样本量（即，运行早期）表现出很大的变化。具有足够大的样本量，第99百分位数延迟的估计收敛。
然而，从经验上我们发现每个运行都可以收敛到一个不同的值。 虽然每次运行的测试程序都会产生紧密的间隔，每次运行的结果明显变化（与平均值相差15-67％）。
通过实验，我们发现一个通常的行为，如何估计收敛，我们称之为性能“迟滞”。图4表明，随着更多样本的收集，第99百分位延迟的估计开始收敛到一个奇异值。但是，如果重新启动服务器并执行另一个运行，则估计可以收敛到不同的值。    在这种情况下，估计的样本量很大，我们预计每个估计值的“确定”都很高，但显然在运行中仍然存在差异。事实上，这些估计与平均值的差异高达67％。这种现象意味着只能通过“运行更长时间”来实现更高的统计准确度，并且与STABILIZER[33]中观察到的效果相似。相反，有必要多次重新启动整个过程并聚合结果。然而，现有的负载测试仪都没有足够的足够强大来处理这种情况，如表I所示。

注：内容太多，只有1、2章节，我的操作系统课程作业。


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• <div><p>It would be awesome to get ...m trying to figure out how to make those treadmill workouts show up in my fitness trend. Thanks!</p><p>该提问来源于开源项目：thomaschampagne/elevate</p></div>
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• 0001317f 32 FILE_TREADMILL_RECORD(timestamp=1442857142, distance=7902.68212891, calories=645, steps=8021, step_length=0) 00013190 25 FILE_HEART_RATE_RECORD(heart_rate=134, _...
• When walking room scale, it has the effect of walking against a very slow treadmill. Not a big thing, but it makes it Arenascale a bit less fun. </p><p>该提问来源于开源项目：DrBeef/Lambda1VR...
• Should You Listen to Music While You Work? "Whistle while you work" is classic advice, straight from Snow ... Science backs it to a certain extent, too — listening to music on the treadmill, for i...
Should You Listen to Music While You Work?
"Whistle while you work" is classic advice, straight from Snow White. Science backs it to a certain extent, too — listening to music on the treadmill, for instance, helps people persevere through their runs. But does music create a productive backdrop for more mental work? What about for any kind of work?Music + Work = It's Complicated
This question is a bit simplistic, so it's no wonder that no one has satisfactorily answered it yet. The researchers who discovered the "Mozart effect," found that listening to Mozart sonatas before a mental activity sparked stronger spatial reasoning (it didn't actually make people smarter). Cool, but specific. What about Mozart sonatas during a task? What about death metal?
Hard to say. There have been various studies on music and work, but the results have been mixed. Perhaps, a recent study proposed, this is because music's effect on work performance is complicated. It's not just "good" or "bad" — it depends on the type of work, the type of music, and the worker's personality.
To test this hypothesis, the researchers recruited 142 undergraduate students and asked them to complete two tasks, one simple and one complex. The simple one was searching a list of words and crossing out the ones including the letter "a"; the complex one was studying pairs of words and then recalling them in a test setting.
Subjects did their tasks either in silence or with a soundtrack of instrumental music; the music was either simple or complex. (Both tracks had identical piano, strings, and synth elements, but the complex one had drums and bass layered in.)
Researchers assessed participants' personalities beforehand, too. Each study subject took an evaluation that gauged how much they enjoyed external stimulation. The 28-item questionnaire asked them to respond to statements like "It takes more stimulation to get me going than most people" and "I am seldom excited about my work."
The results were counterintuitive. Basically, researchers found that people with a preference for external stimulation — think people who check their phones while watching TV or actually play on the office swingset — were less able to handle working to music. They were the ones who most wanted to, ironically, but they performed best on the complex task when they worked in silence. Their peers who didn't prefer external stimuli performed best when music played.
This held true for the study's simple task, too. The external simulation seekers performed best to no music or simple music, whereas their peers who bored less easily performed best to complex music.
These results suggest that people who seek external stimulation have a huge, almost unwieldy amount of attention that they can give the world. Their attention splits easily when multiple activities — say, doing work and listening to music — compete for their attention. But isolated with a task, they can get very deeply absorbed. People on the other end of the spectrum, meanwhile, benefitted from the distraction, since it was just enough to keep their minds from wandering.
Not that people who seek external stimulation are unique. Across the board, people can enjoy only so much distraction. As the study authors put it: "While distractions may facilitate simple task performance to a degree, there is also a point at which distractions will overload task performers even during simple tasks." For external stimulation seekers, overload happens earlier, because they're actively seeking the sensation. Overload may be bad for performance, but it's definitely not boring.

转载于:https://www.cnblogs.com/huangbaobaoi/p/10708710.html
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• package com.example.treadmill.util;import java.security.cert.CertificateException;import java.security.cert.X509Certificate;import javax.net.ssl.X509TrustManager;/** * 类名: MyX509TrustManager <...

package com.example.treadmill.util;import java.security.cert.CertificateException;import java.security.cert.X509Certificate;import javax.net.ssl.X509TrustManager;/**  * 类名: MyX509TrustManager <br/>  * 描述:信任管理器 <br/>  * 开发人员： sy <br/>  * 创建时间：2016-9-27 <br/>  * 发布版本：V1.0 <br/>  */  public class MyX509TrustManager implements X509TrustManager {      // 检查客户端证书      public void checkClientTrusted(X509Certificate[] chain, String authType)              throws CertificateException {      }      // 检查服务器端证书      public void checkServerTrusted(X509Certificate[] chain, String authType)              throws CertificateException {      }      // 返回受信任的X509证书数组      public X509Certificate[] getAcceptedIssuers() {          return null;      }  }
package com.example.treadmill.util;/** 微信活动用的配置 * @author  lps * @time 2018年7月6日 下午4:47:21 * @version V1.0 */public class WxConfig {   /**  微信公众号通过code获取openid */   public static String wxUrl = "https://api.weixin.qq.com/sns/oauth2/access_token?appid=APPID&secret=SECRET&code=CODE&grant_type=authorization_code";   /** 获取用户基本信息（包括UnionID机制） */   public static String wxUserInfo = "https://api.weixin.qq.com/cgi-bin/user/info?access_token=ACCESS_TOKEN&openid=OPENID&lang=zh_CN";   /** 微信公众号配置 测试配置  */// public static final String APPID = "???";// public static final String SECRET= "???";   /** 微信公众号配置 正式配置  */   public static final String APPID = "???";   public static final String SECRET= "???";   /** 公众号和小程序均可以使用AppID和AppSecret调用本接口来获取access_token */   public static final String token = "https://api.weixin.qq.com/cgi-bin/token?grant_type=client_credential&appid=APPID&secret=APPSECRET";   /** jsapi_ticket是公众号用于调用微信JS接口的临时票据  */   public static final String ticket = "https://api.weixin.qq.com/cgi-bin/ticket/getticket?access_token=ACCESS_TOKEN&type=jsapi";   /** 用来保存access_token的xml文件 */    public static final String WXCONFIG_PATH = "wxconfig.xml";   /** 用来保存jsapi_ticket */   public static final String WX_JSAPI_TICKET = "jsapi_ticket.xml";}

转载于:https://www.cnblogs.com/sun27/p/11162854.html
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• 步态识别数据集整理 按照时间顺序，来自论文：... 数据集 提出时间 数据类型 个体数和序列总数 ...在Treadmill上走路 CASIA-A 2003 RGB 20/240 室外 3 正常走路 USF HumanID 2005 RG
• <div><p>Update GCE to v1.... Now that v1 has launched, the treadmill of releases with large changes should slow down to a more reasonable pace.</p><p>该提问来源于开源项目：apache/libcloud</p></div>
• 研究how the“patching treadmill”对于指导安全组织去理解patch的发展进程和patch的特征非常重要。研究现状安全patch的发展可以告知我们现有patch进行中的缺点，提供我们提高现有实践的思路。原文如下： it ...
• t have any streams (A treadmill run in this case) I get a 404 error and the program exits. It seems like a slightly weird situation because there doesn't seem to be a way to check directly from ...