使用剪枝GRU神经网络高效在线预测主机工作负载
An Efficient Online Prediction of Host Workloads Using Pruned GRU Neural Nets
AI总结：
本文提出一种基于剪枝GRU的多变量多步主机负载在线预测模型，利用L1范数与随机剪枝提升预测速度，并通过在线学习适应新旧工作负载模式。
摘要：
主机负载预测对于云计算环境中的动态资源扩缩容和作业调度至关重要。在此背景下，工作负载预测因若干问题而具有挑战性。首先，预测必须准确，以实现精确的调度决策。其次，预测必须快速，以便在正确的时间进行调度。第三，模型必须能够考虑新的工作负载模式，从而在最新模式和旧模式上都表现良好。若无法做出准确且快速的预测，或无法预测新的使用模式，可能导致严重后果，例如服务水平协议（SLA）违约。我们的研究基于门控循环单元（GRU）训练了一个具备在线适应能力的快速模型，以缓解上述问题。我们采用多变量方法，使用内存使用率、CPU使用率、磁盘I/O使用率和磁盘空间等多个特征来准确执行预测。此外，我们进行多步超前预测，这对于提前做出调度决策至关重要。进一步地，我们使用L1范数和随机两种剪枝方法来生成稀疏模型，以实现更快的预测。最后，我们使用在线学习来创建一个能够随时间适应新工作负载模式的模型。
英文摘要：
Host load prediction is essential for dynamic resource scaling and job scheduling in a cloud computing environment. In this context, workload prediction is challenging because of several issues. First, it must be accurate to enable precise scheduling decisions. Second, it must be fast to schedule at the right time. Third, a model must be able to account for new patterns of workloads so it can perform well on the latest and old patterns. Not being able to make an accurate and fast prediction or the inability to predict new usage patterns can result in severe outcomes such as service level agreement (SLA) misses. Our research trains a fast model with the ability of online adaptation based on the gated recurrent unit (GRU) to mitigate the mentioned issues. We use a multivariate approach using several features, such as memory usage, CPU usage, disk I/O usage, and disk space, to perform the predictions accurately. Moreover, we predict multiple steps ahead, which is essential for making scheduling decisions in advance. Furthermore, we use two pruning methods: L1 norm and random, to produce a sparse model for faster forecasts. Finally, online learning is used to create a model that can adapt over time to new workload patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
