| CONFIDENTIAL Leveraging Istio for Creating API Tests Low Effort API Testing for Microservices | CONFIDENTIAL • What has changed? – Migration to microservices triggering need for extensive testing earlier Create and maintain a balanced test pyramid Create different types of tests with low effort 7 What we need… End-to-end Component Service | CONFIDENTIAL REQUEST RESPONSE API MOCKS

ideas, the compression techniques. Compression techniques aim to reduce the model footprint (size, latency, memory etc.). We can reduce the model footprint by reducing the number of trainable parameters. prediction latency, RAM consumption and the quality metrics, such as accuracy, F1, precision and recall as shown in table 2-1. Footprint Metrics Quality Metrics ● Model Size ● Inference Latency on Target the model with respect to one or more of the footprint metrics such as the model size, inference latency, or training time required for convergence with a little quality compromise. Hence, it is important

number of parameters, the amount of training resources required to train the network, prediction latency, etc. Natural language models such as GPT-3 now cost millions of dollars to train just one iteration training process in terms of computation cost, memory cost, amount of training data, and the training latency. It addresses questions like: ● How long does the model take to train? ● How many devices are needed parameters does the model have, what is the disk size, RAM consumption during inference, inference latency, etc. Using the sensitive tweet classifier example, during the deployment phase the user will be

middle boxes) ● High performance networking ○ Much higher multi-Gbps peak data speeds ○ Ultra low latency ○ And of course, reduce overheads introduced! ● High availability ● CapEx, OpEx #IstioCon Overheads introduced ● No high performance data path support ○ Multi-Gbps bandwidth ○ Ultra low latency #IstioCon Performance Limitations: Solutions ● Software techniques ○ (eBPF-based) TCP/IP stack co-designs #IstioCon Latency Analysis ● ~3ms P90 latency added ○ Istio v1.6 ○ More for VM usage ● Hotspots ○ 1  2 ○ 3  4: 30%~50% ● Others ○ Latency between Pods ○ Latency introduced by C/S #IstioCon

connections and nodes to prune, and how to prune a given deep learning model to achieve storage and latency gains with a minimal performance tradeoff. Next, the chapter goes over weight sharing using clustering better understanding of the curvature of the loss function. The authors prune weights which have low magnitude of momentum, follow it up with fine-tuning the network, and then regrow some weights (allow saliency scores get axed. This is great, but there is one tiny wrinkle here. To get improvement in latency (inference or training), we also need to make sure that the hardware can exploit the sparsity in

环境 境变 变量 量 描述 描述 LATENCY_TEST_DE LAY 指定测试开始运行的时间（以秒为单位）。您可以使用变量来允许 CPU 管理器协 调循环来更新默认的 CPU 池。默认值为 0。 LATENCY_TEST_CP US 指定运行延迟测试的 pod 使用的 CPU 数量。如果没有设置变量，则默认配置包含 所有隔离的 CPU。 LATENCY_TEST_RU NTIME 指 HWLATDETECT_MA XIMUM_LATENCY 指定工作负载和操作系统的最大可接受硬件延迟（微秒）。如果您没有设置 HWLATDETECT_MAXIMUM_LATENCY 或 MAXIMUM_LATENCY 的值， 该工具会比较默认预期阈值(20μs)和工具本身中实际的最大延迟。然后，测试会失 败或成功。 CYCLICTEST_MAXI MUM_LATENCY 指定 cyclictest 您没有设置 CYCLICTEST_MAXIMUM_LATENCY 或 MAXIMUM_LATENCY 的值，该 工具会跳过预期和实际最大延迟的比较。 OSLAT_MAXIMUM_L ATENCY 指定 oslat 测试结果的最大可接受延迟（微秒）。如果您没有设置 OSLAT_MAXIMUM_LATENCY 或 MAXIMUM_LATENCY 的值，该工具会跳 过预期和实际最大延迟的比较。 MAXIMUM_LATENC

image, audio, video, etc. to a low-dimensional representation such as a fixed length vector of floating point numbers, thus performing dimensionality reduction1. b) The low-dimensional representation should behind them. 1 Dimensionality reduction is the process of transforming high-dimensional data into low-dimension, while retaining the properties from the high-dimensional representation. It is useful because features by hand (at least in the pre deep learning era). Techniques like Principal Components Analysis, Low-Rank Matrix Factorization, etc. are popular tools for dimensionality reduction. We will explain these

lMemory access latency issues: ØDirect reclaim • Background reclaim (kswapd) • Foreground reclaim (direct reclaim) ØPage fault • Major page fault will produce latency Memory Access Latency(Page Fault) Fault) alloc mem free mem? No Latency Page Reclaiming back-reclaim No Latency Direct Reclaim Y N Enough NOT Enough Latency lDirect reclaim Øvm.min_free_kbytes: 3g Øvm.extra_free_kbytes: 8g Used Used watermark high watermark min watermark low reclaim kswapd wakeup allocate extra free_kbytes 1.4万亿 低延迟分布式存储系统 – PageCache的毛刺内核源码分析 Entity Inode i_mapping i_data address_space radix_tree_root

lMemory access latency issues: ØDirect reclaim • Background reclaim (kswapd) • Foreground reclaim (direct reclaim) ØPage fault • Major page fault will produce latency Memory Access Latency(Page Fault) Fault) alloc mem free mem? No Latency Page Reclaiming back-reclaim No Latency Direct Reclaim Y N Enough NOT Enough Latency lDirect reclaim Øvm.min_free_kbytes: 3g Øvm.extra_free_kbytes: 8g Used Used watermark high watermark min watermark low reclaim kswapd wakeup allocate extra free_kbytes 1.4万亿 低延迟分布式存储系统 – PageCache的毛刺内核源码分析 Entity Inode i_mapping i_data address_space radix_tree_root

historical data • batched updates during downtimes, e.g. every night Streaming Data Warehouse • low-latency materialized view updates • pre-aggregated, pre-processed streams and historical data Data append-only Update rates relatively low high, bursty Processing Model query-driven / pull-based data-driven / push-based Queries ad-hoc continuous Latency relatively high low 5 Vasiliki Kalavri | Boston Boston University 2020 Traditional DW vs. SDW Traditional DW SDW Update Frequency low high Update propagation synchronized asynchronous Data historical recent and historical ETL process complex fast and