Quantifying energy consumption is a fundamental element of green computing. Power models trained by resource utilization allow quantifying the energy number and enable energy-efficient resource management systems without raising the concerns of complexity, cost, and security. However, energy consuming behavior on different machines varies by several factors. In this paper, we address the challenges of power modeling for cloud instances where information about these factors is obscured or unseen in the training set, and propose a best-effort method to train and serve a power model as precise as possible by leveraging a large, industry-standard power database. The proposed method prioritizes the modeling precision, and offers similarity and uncertainty indicators to elucidate the confidence level when serving an unseen instance. The results have demonstrated feasibility and precision of the proposed method against comparable approaches.

Determining the root cause of performance regression for microservices is challenging. The topological cascading performance implications among microservices hide the source of the problem. Additionally, the lack of knowledge about application phases can potentially lead to false-positive critical service detection. Service resource utilization is an imperfect proxy for application performance, potentially leading to false positives. Therefore, in this work, we propose a new performance testing framework that leverages hidden Berkeley Packet Filter (BPF) kernel metrics to locate root causes of performance regression. The framework applies a systematic multi-level approach to analyze microservice performance without intrusive code instrumentation. First, the framework constructs an attributed graph with microservice requests, scores the services to identify the critical paths, and ranks the low-level metrics to highlight the root cause of performance regression. Through judiciously designed experiments, we evaluated the metric collection overhead, showing less than 18% more latency when the application is running across hosts and 9% within the same host. In addition, depending on the application, no overhead is experienced, while the state-of-the-art approach presented up to 1060% more latency. The microservice benchmark evaluation shows that MicroLens can successfully identify the set of root causes and that the causes vary when the application is running in different infrastructures.

Containerization on the cloud offers several crucial benefits. However, these benefits are negated by the effects of virtual network stack and address encapsulation, especially for workloads that require intense communication. Socket replacement is a promising approach to breach this wall without changing the underlay infrastructure by replacing a nested network stack with a simple host network stack. Current state-of-the-art approaches perform this replacement by preloading the overridden socket library in a containerized process. However, the preloading approach requires user effort to modify the deploying manifests and a compromised security policy configuration of privileged containers to access the host namespace. This paper introduces a new replacement framework where a secured control plane agent performs the replacement by utilizing low-overhead BPF kernel tracing technology. As a result, containers can obtain host-native network performance and neither modification nor escalated privileges are required for user containers. Experiments on multiple benchmarks including iPerf, MPI, memslap, and GROMACS have been conducted to confirm efficacy.

Containerization and application variety bring many challenges in automating evaluations for performance tuning and comparison among infrastructure choices. Due to the tightly-coupled design of benchmarks and evaluation tools, the present automated tools on Kubernetes are limited to trivial microbenchmarks and cannot be extended to complex cloudnative architectures such as microservices and serverless, which are usually managed by customized operators for setting up workload dependencies. In this paper, we propose AutoDECK, a performance evaluation framework with a fully declarative manner. The proposed framework automates configuring, deploying, evaluating, summarizing, and visualizing the benchmarking workload. It seamlessly integrates mature Kubernetes-native systems and extends multiple functionalities such as tracking the image-build pipeline, and auto-tuning. We present five use cases of evaluations and analysis through various kinds of bench-marks including microbenchmarks and HPC/AI benchmarks. The evaluation results can also differentiate characteristics such as resource usage behavior and parallelism effectiveness between different clusters. Furthermore, the results demonstrate the benefit of integrating an auto-tuning feature in the proposed framework, as shown by the 10% transferred memory bytes in the Sysbench benchmark.

We propose a method to detect both software and hardware bottlenecks in a web service consisting of microservices. A bottleneck is a resource that limits the maximum performance of the entire web service. Bottlenecks often include both software resources such as threads, locks, and channels, and hardware resources such as processors, memories, and disks. Bottlenecks form a layered structure since a single request can utilize multiple software resources and a hardware resource simultaneously. The microservice architecture makes the detection of layered bottlenecks challenging due to the lack of a uniform analysis perspective across languages, libraries, frameworks, and middle-ware.We detect layered bottlenecks in microservices by profiling numbers and status of working threads in each microservice and dependency among microservices via network connections. Our approach can be applied to various programming languages since it relies only on standard debugging tools. Nevertheless, our approach not only detects which microservice is a bottleneck but also enables us to understand why it becomes a bottleneck. This is enabled by a novel visualization method to show layered bottlenecks in microservices at a glance. We demonstrate that our approach successfully detects and visualizes layered bottlenecks in the state-of-the-art microservice benchmarks, DeathStarBench and Acme Air microservices. This enables us to optimize the microservices themselves to achieve a higher throughput per re-source utilization rate compared with simply scaling the number of replicas of microservices.

Microservice architecture competes with the traditional monolithic design by offering benefits of agility, flexibility, reusability resilience, and ease of use. Nevertheless, due to the increase in internal communication complexity, care must be taken for resource-usage scaling in harmony with placement scheduling, and request balancing to prevent cascading performance degradation across microservices. We prototype Run Wild, a resource management system that controls all mechanisms in the microservice-deployment process covering scaling, scheduling, and balancing to optimize for desirable performance on the dynamic cloud driven by an automatic, united, and consistent deployment plan. In this paper, we also highlight the significance of co-location aware metrics on predicting the resource usage and computing the deployment plan. We conducted experiments with an actual cluster on the IBM Cloud platform. RunWild reduced the 90th percentile response time by 11% and increased average throughput by 10% with more than 30% lower resource usage for widely used autoscaling benchmarks on Kubernetes clusters.

A Java application sometimes raises an out-of-memory ex-ception. This is usually because it has exhausted the Java heap. However, a Java application can raise an out-of-memory exception when it exhausts the memory used by Java that is not in the Java heap. We call this area non-Java memory. For example, an out-of-memory exception in the non-Java memory can happen when the JVM attempts to load too many classes. Although it is relatively rare to ex-haust the non-Java memory compared to exhausting the Java heap, a Java application can consume a considerable amount of non-Java memory.This paper presents a quantitative analysis of non-Java memory. To the best of our knowledge, this is the first in-depth analysis of the non-Java memory. To do this we cre-ated a tool called Memory Analyzer for Redundant, Unused, and String Areas (MARUSA), which gathers memory statis-tics from both the OS and the Java virtual machine, break-ing down and visualizing the non-Java memory usage.We studied the use of non-Java memory for a wide range of Java applications, including the DaCapo benchmarks and Apache DayTrader. Our study is based on the IBM J9 Java Virtual Machine for Linux. Although some of our results may be specific to this combination, we believe that most of our observations are applicable to other platforms as well.

Programmers who develop Web applications often use dynamic scripting languages such as Perl, PHP, Python, and Ruby. For general purpose scripting language usage, interpreter-based implementations are efficient and popular but the server-side usage for Web application development implies an opportunity to significantly enhance Web server throughput. This paper summarizes a study of the optimization of PHP script processing. We developed a PHP processor, P9, by adapting an existing production-quality just-in-time (JIT) compiler for a Java virtual machine, for which optimization technologies have been well-established, especially for server-side application. This paper describes and contrasts microbenchmarks and SPECweb2005 benchmark results for a well-tuned configuration of a traditional PHP interpreter and our JIT compiler-based implementation, P9. Experimental results with the microbenchmarks show 2.5-9.5x advantage with P9, and the SPECweb2005 measurements show 20-30 % improvements. These results show that the acceleration of dynamic scripting language processing does matter in a realistic Web application server environment. CPU usage profiling shows our simple JIT compiler introduction reduces the PHP core runtime overhead from 45 % to 13 % for a SPECweb2005 scenario, implying that further improvements of dynamic compilers would provide little additional return unless other major overheads such as heavy memory copy between the language runtime and Web server frontend are reduced.

The performance of server-side applications is becoming increasingly important as more applications exploit the Web application model. Extensive work has been done to improve the performance of individual software components such as Web servers and programming language runtimes. This paper describes a novel approach to boost Web application performance by improving inter-process communication between a programming language runtime and Web server runtime. The approach reduces redundant processing for memory copying and the context switch overhead between user space and kernel space by exploiting the zero-copy data transfer methodology, such as the sendfile system call. In order to transparently utilize this optimization feature with existing Web applications, we propose enhancements of the PHP runtime, FastCGI protocol, and Web server. Our proposed approach achieves a 126% performance improvement with micro-benchmarks and a 44% performance improvement for a standard Web benchmark, SPECweb2005.

PHP is well known as a programming language in the Web 2.0 era enabling agile server-side software development. It has officially supported SOAP messaging since version 5 through a C-based built-in library. In this paper we perform a thorough study of the capability of PHP as a Web service engine in both qualitative and quantitative aspects while comparing it with other Web service engines implemented in Java and C. We used Axis2 for this purpose as it is an open source web service engine whose implementation is available both in Java and C. We report that PHP as a web service engine performs competitively with Axis2 Java for Web services involving small payloads, and greatly outperforms it for larger payloads by 5-17 times. As the authors expected, Axis2 C performs best, but the experimental results demonstrate that PHP performance is closer to Axis2 C with larger payloads. This performance difference comes from the fact that the SOAP engine within the PHP runtime is implemented in C with a monolithic architecture, whereas Axis2 uses a more modular architecture for the flexible insertation of handlers for an assorted set of WS-* standards, and also that Axis2 uses a different data binding mechanism known as ADB (Axis2 Data binding). This paper is the first attempt to compare Web services engines implemented in PHP, Java and C, and the authors believe that this boosts the development of SOAP-based Web services in PHP by letting people know its decent performance score and high productivity characteristics.