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<h4 id="ctcdflow代码框架"><a href="#ctcdflow代码框架" class="headerlink" title="ctcdflow代码框架"></a>ctcdflow代码框架</h4><h3 id="流程框架"><a href="#流程框架" class="headerlink" title="流程框架"></a>流程框架</h3><figure class="highlight plain"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><span class="line">st1=>start: ctcdflow framework</span><br><span class="line">e=>end: category implement</span><br><span class="line">op1=>operation: opreation dict</span><br><span class="line">op2=>operation: category factory</span><br><span class="line">st1->op1</span><br><span class="line">op1->op2</span><br></pre></td></tr></table></figure>
<h3 id="framework概述"><a href="#framework概述" class="headerlink" title="framework概述"></a>framework概述</h3><p>framework基于python的argparse的子命令方式，将各个step的任务处理进行了解耦</p>
<p>以下是framework下的目录架构除tools目录外其他每个目录代表ctcd流程中的某一步step，与该操作相关的所有代码文件都聚合到该目录下，每一个step作为一个插件接入到framework里。</p>
<blockquote>
<p>├── engine_deploy<br>├── model_deploy<br>├── model_evaluate<br>├── model_predict<br>├── model_prepare<br>├── model_recycle<br>├── model_train<br>├── serving_check<br>└── tools<br>如果需要新增子命令时，只需要遵循相关的插件定义方式即可</p>
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<figure class="highlight aspectj"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br></pre></td><td class="code"><pre><span class="line">OP_DICT = {</span><br><span class="line"> <span class="string">'model_train'</span>: ModelTrainOp(),</span><br><span class="line"> 'model_prepare': ModelPrepareOp(),</span><br><span class="line"> 'model_predict': ModelPredictOp(),</span><br><span class="line"> 'model_evaluate': ModelEvaluateOp(),</span><br><span class="line"> 'model_deploy': ModelDeployOp(),</span><br><span class="line"> 'model_recycle': ModelRecycleOp(),</span><br><span class="line"> 'serving_check': ServingCheckOp(),</span><br><span class="line"> 'engine_deploy':EngineDeployOp()</span><br><span class="line">}</span><br></pre></td></tr></table></figure>
<p>然后具体到每个Step内部，代码结构如下：</p>
<blockquote>
<p>├── factory.py<br>├── op.py<br>├── base<br>├── fm<br>├── lr</p>
</blockquote>
<p>op.py:<br>其中op.py中定义了step接入framework的方式，其中包括</p>
<ul>
<li>定义framwork中与该step相关的输入参数</li>
<li>定义framwork调用该step时的入口操作函数<figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br></pre></td><td class="code"><pre><span class="line"><span class="class"><span class="keyword">class</span> <span class="title">ModelPrepareOp</span><span class="params">()</span>:</span></span><br><span class="line"><span class="meta"> @staticmethod</span></span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">set_argument</span><span class="params">(subparser)</span>:</span></span><br><span class="line"> subparser.add_argument(<span class="string">'--model_name'</span>,help = <span class="string">"model name"</span>)</span><br><span class="line"> subparser.add_argument(<span class="string">'--model_version'</span>,help= <span class="string">"model version"</span>)</span><br><span class="line"> subparser.set_defaults(func = ModelPrepareOp.run)</span><br><span class="line"><span class="meta"> @staticmethod</span></span><br><span class="line"> <span class="function"><span class="keyword">def</span> <span class="title">run</span><span class="params">(args)</span>:</span></span><br><span class="line"> module_logger.info(<span class="string">"args:%s"</span> % args)</span><br><span class="line"> instance = ModelPrepareFactory.create(args.model_source)</span><br><span class="line"> instance.init(args)</span><br><span class="line"> instance.process()</span><br></pre></td></tr></table></figure>
</li>
</ul>
<p>factory.py:<br>因为每个step执行时，根据某些类型不一致，具体的执行流程也不一致，以上述例子为例，不同的算法对应不同的执行流程。所以在这里定义了定义了工厂类，根据入参来反射查找对应的执行类<br>base:<br>base目录下存储了通用的操作定义，所有具体执行类的基础类接口interface也在此定义；具体执行类必须继承自interface接口进行重载实现。在上述例子中fm与lr目录下即存放了具体执行子类的代码逻辑</p>
<h3 id="step-操作说明"><a href="#step-操作说明" class="headerlink" title="step 操作说明"></a>step 操作说明</h3><h4 id="model-prepare"><a href="#model-prepare" class="headerlink" title="model_prepare"></a>model_prepare</h4><p>模型准备，在weips模式下，内部流程包括模型保存、模型转化、模型库注册等</p>
<h4 id="model-evaluate"><a href="#model-evaluate" class="headerlink" title="model_evaluate"></a>model_evaluate</h4><p>模型评估</p>
<h4 id="model-deploy"><a href="#model-deploy" class="headerlink" title="model_deploy"></a>model_deploy</h4><p>模型部署</p>
<h4 id="engine-deploy"><a href="#engine-deploy" class="headerlink" title="engine_deploy"></a>engine_deploy</h4>
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<h2 id="1-分布式存储定义"><a href="#1-分布式存储定义" class="headerlink" title="1. 分布式存储定义"></a>1. 分布式存储定义</h2><ul>
<li>大量普通PC服务器通过internet互联，对外作为一个整体提供存储服务</li>
</ul>
<h2 id="2-特性"><a href="#2-特性" class="headerlink" title="2. 特性"></a>2. 特性</h2><ul>
<li>扩展性，低成本，高性能，易用性</li>
</ul>
<h2 id="3-挑战难点"><a href="#3-挑战难点" class="headerlink" title="3. 挑战难点"></a>3. 挑战难点</h2><ul>
<li>数据与状态信息的持久化</li>
<li>在自动迁移与自读容错，并发读写的过程中保证数据的一致性</li>
</ul>
<h2 id="4-数据类型分类"><a href="#4-数据类型分类" class="headerlink" title="4. 数据类型分类"></a>4. 数据类型分类</h2><ul>
<li>非结构化数据</li>
<li>结构化数据</li>
<li>半结构化数据</li>
</ul>
<h2 id="5-分布式存储分类："><a href="#5-分布式存储分类：" class="headerlink" title="5.分布式存储分类："></a>5.分布式存储分类：</h2><ul>
<li>分布式文件系统</li>
<li>作为分布式表格系统与分布式数据库的的底层存储 <ul>
<li>Facebook Haystack</li>
<li>TFS\GFS\Amazon EBS</li>
</ul>
</li>
<li>分布式键值系统<ul>
<li>memcache\Tair\Amazon Dynamo</li>
</ul>
</li>
<li>分布式表格系统 <ul>
<li>Google Bigtable</li>
<li>megastore</li>
<li>Microsoft azure table storage</li>
<li>Amazon DynamoDB</li>
</ul>
</li>
<li>分布式数据库<ul>
<li>MySQL sharding</li>
<li>Amazon RDS</li>
<li>Microsoft SQL azure</li>
<li>Google spanner</li>
<li>OceanBase</li>
</ul>
</li>
</ul>
<h2 id="6-OTHER"><a href="#6-OTHER" class="headerlink" title="6. OTHER"></a>6. OTHER</h2><ul>
<li>性能瓶颈: <ul>
<li>存储系统的性能瓶颈主要在于磁盘的随机读写</li>
</ul>
</li>
<li><p>优化方式：</p>
<ul>
<li><p>将随机写操作转化为顺序写</p>
<pre><code>>Log-structured Merge Tree 这套体系的核心思路就是，你的所有更新和删除操作，都会被转换为一个log并被追加到文件尾部，这样就可以将所有的原位更新都转换为顺序写入，代价则是更多的读取次数，不过因为NAND的读取不是瓶颈，所以代价是可以接受的.同时，后端有一组线程，从文件头开始顺序的读取所有的块，将块中未被标记为删除的数据找出来，凑够64个页后写到新的块中，然后调用块删除命令，清除那些已经完成誊写的数据块。
</code></pre></li>
<li><p>通过缓存减少磁盘随机读操作</p>
<pre><code>>缓存
</code></pre></li>
</ul>
</li>
</ul>
<ul>
<li>SSD随机读取友好，随机写入时存在写入放大问题</li>
</ul>
<h2 id="分布式系统衡量指标"><a href="#分布式系统衡量指标" class="headerlink" title="分布式系统衡量指标"></a>分布式系统衡量指标</h2><ul>
<li><strong>性能</strong><ul>
<li>吞吐能力与系统响应时间</li>
</ul>
</li>
<li>可用性<ul>
<li>指系统在面对各种异常时可以提供正常服务的能力，可用性可以用系统停服务的时间与正常服务的时间的比例来衡量</li>
</ul>
</li>
<li>一致性 <ul>
<li>越是强的一致性模型，用户使用起来越简单</li>
</ul>
</li>
<li>可扩展性</li>
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<a class="post-title-link" href="/2016/01/15/snapshot/" itemprop="url">使用带权计数算法对快照机制优化</a></h1>
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<h2 id="1-背景"><a href="#1-背景" class="headerlink" title="1. 背景"></a>1. 背景</h2><p>当前快照机制使用世代引用计数算法(generational referencecounting)，来判定对象是否需要删除。<br>但该算法无法实现legder对象空间复用，就导致了如果快照个数达到最大值，即使删除已创建快照，也无法创建新快照。<br>当前每次删除快照时，每删除一个对象都需要操作一个ledger记账对象，导致快照删除的性能很差<br>现在考虑使用带权计数算法进行优化，旨在解决ledger对象复用的问题，并尝试根据新的算法解决快照性能的不足。</p>
<h2 id="2-基于带权算法的快照机制"><a href="#2-基于带权算法的快照机制" class="headerlink" title="2. 基于带权算法的快照机制"></a>2. 基于带权算法的快照机制</h2><ol>
<li>每个数据对象创建后都有一个固定的总权重TW(total weight)，原始权重OW(original weight).最初时TW=OW=2^63，TW与OW值由ledger对象来管理</li>
<li>每个引用该对象的VDI都会维护一个对应的权重PW（partial weight），由于每个VDI由很多对象构成，会在inode中记录一个PW数组，以下以单个对象为例进行说明。</li>
<li>第一个VDI创建时，OW会传递1/2的权重到该VDI，即此时OW=2^62,PW1=2^62</li>
<li>每创建一个VDI，该VDI都会得到父VDI 1/2的权重，同时父VDI的权重变为原来的1/2<br><img src="/images/Image-1.png" alt="snapshot"></li>
<li><p>依此类推，在基于原始对象创建一个新的VDI（snapshot或者clone）时，当前VDI会将自身持有权重PW的1/2传递到下一个VDI中</p>
</li>
<li><p>VDI删除时，操作ledger对象 将这个VDI的权重加回到OW中</p>
</li>
<li>当TW==OW时，代表该对象已经没有VDI引用，触发删除操作</li>
<li>以上便是整个算法的骨架，该算法维护这样一个核心恒等式： TW =OW+(PW1+PW2+….+PWn), PWn表示第N个引用该对象的VDI的权重</li>
</ol>
<h2 id="3-边界处理与OW权重池"><a href="#3-边界处理与OW权重池" class="headerlink" title="3. 边界处理与OW权重池"></a>3. 边界处理与OW权重池</h2><h3 id="边界问题"><a href="#边界问题" class="headerlink" title="边界问题"></a>边界问题</h3><ul>
<li>如果一个VDI-x的权重已经为1,若还要基于其创建新VDI-y，需要判断ledger对象，进行如下流程：<ul>
<li>如果OW不为1，说明还有多余的权重可以分配，PW(x)=OW/2+PW(x), 从OW中分取一半的权重，继续进行后续的VDI创建流程. </li>
<li>如果OW为1, 说明该对象权重已经全部用完，无法继续分配。</li>
</ul>
</li>
</ul>
<h3 id="应对方案"><a href="#应对方案" class="headerlink" title="应对方案"></a>应对方案</h3><ul>
<li>针对此问题有两个方案可以解决:<ul>
<li>通过增加总权重TW的方式增加可用权重</li>
<li>创建新的权重对象OW2。</li>
</ul>
</li>
<li>此处参考相关资料使用间接块的想法，初步设计采用权重池的方式</li>
</ul>
<p>###权重池机制</p>
<ol>
<li>首先在ledger中维护32（个数待定）个OW权重数组OW[32]，每个OW初值均为TW</li>
<li>每个引用该对象的VDI，维护两个变量，generation与PW值，generation代表该权重所属的OW在OW数组中的位置。</li>
<li>开始创建第一个vdi时使用OW[0]作为原始权重，generation=0，按照第二节的规律后续的vdi依次创建</li>
<li>回到上面的问题，当某个VDI-x权重为1且需要基于该VDI进行创建新VDI-y，如果OW[0]为1，已经无法分配权重，此时我们进行如下操作：<ul>
<li>VDI-x将之前分配得到的权重还回OW[0]</li>
<li>选择OW[1]重进行分配,此时PW(x)= OW[1]/2；当VDI-x得到足够的权重，便可继续进行VDI创建</li>
<li>更新VDI-x的generation=1；</li>
</ul>
</li>
<li>当VDI删除时，根据自身的generation将PW还回到对应的OW中</li>
<li>当整个ledger数组的OW都等于TW时，该对象已经么有引用，触发删操作</li>
</ol>
<h2 id="4-结论讨论"><a href="#4-结论讨论" class="headerlink" title="4. 结论讨论"></a>4. 结论讨论</h2><ol>
<li>snapshot的原来的机制采用世代引用计数算法(GRC)，带权引用计数算法（WRC）与之对比如下：<br><img src="/images/Image-2.png" alt="snapshot"></li>
<li><p>针对该方案，进行初步理论值统计 </p>
<ul>
<li><p>MIN：在最差的情况下能够使用的快照个数。最差情况是指对同一个VDI只进行连续的快照操作，此时权重的消耗速度最快，判断为目前最差的情况,此种情况下总的快照次数计算公式为</p>
<blockquote>
<p>sum =n（n+1）/2, n为总权重的幂数 </p>
</blockquote>
</li>
<li><p>MAX: 整个VDI即有clone也有snapshot，这个VDI以二叉树的方式全部铺开（参考意义不大）<br><img src="/images/Image-3.jpg" alt="snapshot"></p>
</li>
</ul>
</li>
<li><p>当权重为1的VDI需要打快照时，就会导致快照创建时也会操作记账对象</p>
<blockquote>
<p>以2^31为TW总权重为例，在最差MIN情况下，每465快照中有30次快照，创建时会操作记账对象</p>
</blockquote>
</li>
<li><p>该优化方案，对代码的改动面较大，改动过程中需要考虑的比较全面，有出现新问题的风险，在做优化之前先把细节想清楚</p>
</li>
</ol>
<h2 id="5-基于WRC的快照性能优化（后续）"><a href="#5-基于WRC的快照性能优化（后续）" class="headerlink" title="5. 基于WRC的快照性能优化（后续）"></a>5. 基于WRC的快照性能优化（后续）</h2><ol>
<li>对于4T的VDI进行快照，若此时权重为1需要跟记账对象通信，按照每个对象4M计算，最差情况下大约有1M次的socket通信，可能会导致本次快照时间过长，快照的创建性能有会有波动。而且现在的机制中每个对象都对应一个ledger，在删除过程中最差情况会有1M次的socket通信，快照的删除性能比较差。</li>
<li>上述提到的问题，有两个优化方案<ul>
<li>由上一章节的理论数据可知，当ledger大小为1个字节时，数据对象支持快照个数可达4960个，如果这个个数已经达到要求的话，根据这一点我们可以用一个ledger对象管理一个VDI的所有对象，ledger中的每个字节与VDI中数据对象一一对应。对于4T的数据盘而言，ledger对象为4M大大减少了创建跟删除时的网络通信以及ledger对象的创建所耗费的时间。会对性能有很大的提升</li>
<li>第二个方案是一个ledger对象管理连续index的多个数据对象，通过hash的方式进行对象定位，通过这种方式，删除时理想情况下每删除256个对象，操作一次ledger对象</li>
</ul>
</li>
</ol>
<h2 id="6-参考文献"><a href="#6-参考文献" class="headerlink" title="6. 参考文献"></a>6. 参考文献</h2><p>Garbage Collection Algorithms For Automatic Dynamic Memory Management -Richard Jones<br>《垃圾收集》英文译- Richard Jones<br><a href="https://en.wikipedia.org/wiki/Reference_counting" target="_blank" rel="external">https://en.wikipedia.org/wiki/Reference_counting</a><br><a href="http://www.cse.scu.edu/~jholliday/COEN317S06/DC3Naming.ppt" target="_blank" rel="external">http://www.cse.scu.edu/~jholliday/COEN317S06/DC3Naming.ppt</a><br><a href="http://www.cs.nyu.edu/goldberg/pubs/gold89.pdf" target="_blank" rel="external">http://www.cs.nyu.edu/goldberg/pubs/gold89.pdf</a><br><a href="http://students.depaul.edu/~cbaier1/435/ds-log.html" target="_blank" rel="external">http://students.depaul.edu/~cbaier1/435/ds-log.html</a><br><a href="https://www.researchgate.net/publication/220758907_Cyclic_Weighted_Reference_Counting_without_Delay" target="_blank" rel="external">https://www.researchgate.net/publication/220758907_Cyclic_Weighted_Reference_Counting_without_Delay</a> </p>
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