A Benchmark Suite for Microservices
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ENOS: a Holistic Framework for Conducting Scientific
ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau, Adrien Lebre, Dimitri Pertin, Anthony Simonet, Matthieu Simonin To cite this version: Ronan-Alexandre Cherrueau, Adrien Lebre, Dimitri Pertin, Anthony Simonet, Matthieu Simonin. ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack. [Technical Report] RT-0485, Inria Rennes Bretagne Atlantique; Nantes. 2016. hal-01415522v2 HAL Id: hal-01415522 https://hal.inria.fr/hal-01415522v2 Submitted on 13 Dec 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau , Adrien Lebre , Dimitri Pertin , Anthony Simonet , Matthieu Simonin TECHNICAL REPORT N° 485 November 2016 Project-Teams Ascola and ISSN 0249-0803 ISRN INRIA/RT--485--FR+ENG Myriads ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau , Adrien Lebre , Dimitri Pertin , Anthony Simonet , Matthieu Simonin Project-Teams Ascola and Myriads Technical Report n° 485 — version 2 — initial version November 2016 — revised version Décembre 2016 — 13 pages Abstract: By massively adopting OpenStack for operating small to large private and public clouds, the industry has made it one of the largest running software project. -
Towards Measuring and Understanding Performance in Infrastructure- and Function-As-A-Service Clouds
Thesis for the Degree of Licentiate of Engineering Towards Measuring and Understanding Performance in Infrastructure- and Function-as-a-Service Clouds Joel Scheuner Division of Software Engineering Department of Computer Science & Engineering Chalmers University of Technology and Gothenburg University Gothenburg, Sweden, 2020 Towards Measuring and Understanding Performance in Infrastructure- and Function-as-a-Service Clouds Joel Scheuner Copyright ©2020 Joel Scheuner except where otherwise stated. All rights reserved. Technical Report ISSN 1652-876X Department of Computer Science & Engineering Division of Software Engineering Chalmers University of Technology and Gothenburg University Gothenburg, Sweden This thesis has been prepared using LATEX. Printed by Chalmers Reproservice, Gothenburg, Sweden 2020. ii ∼100% of benchmarks are wrong. - Brendan Gregg, Senior Performance Architect iv Abstract Context Cloud computing has become the de facto standard for deploying modern software systems, which makes its performance crucial to the efficient functioning of many applications. However, the unabated growth of established cloud services, such as Infrastructure-as-a-Service (IaaS), and the emergence of new services, such as Function-as-a-Service (FaaS), has led to an unprecedented diversity of cloud services with different performance characteristics. Objective The goal of this licentiate thesis is to measure and understand performance in IaaS and FaaS clouds. My PhD thesis will extend and leverage this understanding to propose solutions for building performance-optimized FaaS cloud applications. Method To achieve this goal, quantitative and qualitative research methods are used, including experimental research, artifact analysis, and literature review. Findings The thesis proposes a cloud benchmarking methodology to estimate application performance in IaaS clouds, characterizes typical FaaS applications, identifies gaps in literature on FaaS performance evaluations, and examines the reproducibility of reported FaaS performance experiments. -
Accordion: Better Memory Organization for LSM Key-Value Stores
Accordion: Better Memory Organization for LSM Key-Value Stores Edward Bortnikov Anastasia Braginsky Eshcar Hillel Yahoo Research Yahoo Research Yahoo Research [email protected] [email protected] [email protected] Idit Keidar Gali Sheffi Technion and Yahoo Research Yahoo Research [email protected] gsheffi@oath.com ABSTRACT of applications for which they are used continuously in- Log-structured merge (LSM) stores have emerged as the tech- creases. A small sample of recently published use cases in- nology of choice for building scalable write-intensive key- cludes massive-scale online analytics (Airbnb/ Airstream [2], value storage systems. An LSM store replaces random I/O Yahoo/Flurry [7]), product search and recommendation (Al- with sequential I/O by accumulating large batches of writes ibaba [13]), graph storage (Facebook/Dragon [5], Pinter- in a memory store prior to flushing them to log-structured est/Zen [19]), and many more. disk storage; the latter is continuously re-organized in the The leading approach for implementing write-intensive background through a compaction process for efficiency of key-value storage is log-structured merge (LSM) stores [31]. reads. Though inherent to the LSM design, frequent com- This technology is ubiquitously used by popular key-value pactions are a major pain point because they slow down storage platforms [9, 14, 16, 22,4,1, 10, 11]. The premise data store operations, primarily writes, and also increase for using LSM stores is the major disk access bottleneck, disk wear. Another performance bottleneck in today's state- exhibited even with today's SSD hardware [14, 33, 34]. -
Artificial Intelligence for Understanding Large and Complex
Artificial Intelligence for Understanding Large and Complex Datacenters by Pengfei Zheng Department of Computer Science Duke University Date: Approved: Benjamin C. Lee, Advisor Bruce M. Maggs Jeffrey S. Chase Jun Yang Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science in the Graduate School of Duke University 2020 Abstract Artificial Intelligence for Understanding Large and Complex Datacenters by Pengfei Zheng Department of Computer Science Duke University Date: Approved: Benjamin C. Lee, Advisor Bruce M. Maggs Jeffrey S. Chase Jun Yang An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Computer Science in the Graduate School of Duke University 2020 Copyright © 2020 by Pengfei Zheng All rights reserved except the rights granted by the Creative Commons Attribution-Noncommercial Licence Abstract As the democratization of global-scale web applications and cloud computing, under- standing the performance of a live production datacenter becomes a prerequisite for making strategic decisions related to datacenter design and optimization. Advances in monitoring, tracing, and profiling large, complex systems provide rich datasets and establish a rigorous foundation for performance understanding and reasoning. But the sheer volume and complexity of collected data challenges existing techniques, which rely heavily on human intervention, expert knowledge, and simple statistics. In this dissertation, we address this challenge using artificial intelligence and make the case for two important problems, datacenter performance diagnosis and datacenter workload characterization. The first thrust of this dissertation is the use of statistical causal inference and Bayesian probabilistic model for datacenter straggler diagnosis. -
Learning Key-Value Store Design
Learning Key-Value Store Design Stratos Idreos, Niv Dayan, Wilson Qin, Mali Akmanalp, Sophie Hilgard, Andrew Ross, James Lennon, Varun Jain, Harshita Gupta, David Li, Zichen Zhu Harvard University ABSTRACT We introduce the concept of design continuums for the data Key-Value Stores layout of key-value stores. A design continuum unifies major Machine Databases K V K V … K V distinct data structure designs under the same model. The Table critical insight and potential long-term impact is that such unifying models 1) render what we consider up to now as Learning Data Structures fundamentally different data structures to be seen as \views" B-Tree Table of the very same overall design space, and 2) allow \seeing" Graph LSM new data structure designs with performance properties that Store Hash are not feasible by existing designs. The core intuition be- hind the construction of design continuums is that all data Performance structures arise from the very same set of fundamental de- Update sign principles, i.e., a small set of data layout design con- Data Trade-offs cepts out of which we can synthesize any design that exists Access Patterns in the literature as well as new ones. We show how to con- Hardware struct, evaluate, and expand, design continuums and we also Cloud costs present the first continuum that unifies major data structure Read Memory designs, i.e., B+tree, Btree, LSM-tree, and LSH-table. Figure 1: From performance trade-offs to data structures, The practical benefit of a design continuum is that it cre- key-value stores and rich applications. -
Myrocks in Mariadb
MyRocks in MariaDB Sergei Petrunia <[email protected]> MariaDB Shenzhen Meetup November 2017 2 What is MyRocks ● #include <Yoshinori’s talk> ● This talk is about MyRocks in MariaDB 3 MyRocks lives in Facebook’s MySQL branch ● github.com/facebook/mysql-5.6 – Will call this “FB/MySQL” ● MyRocks lives there in storage/rocksdb ● FB/MySQL is easy to use if you are Facebook ● Not so easy if you are not :-) 4 FB/mysql-5.6 – user perspective ● No binaries, no packages – Compile yourself from source ● Dependencies, etc. ● No releases – (Is the latest git revision ok?) ● Has extra features – e.g. extra counters “confuse” monitoring tools. 5 FB/mysql-5.6 – dev perspective ● Targets a CentOS-type OS – Compiler, cmake version, etc. – Others may or may not [periodically] work ● MariaDB/Percona file pull requests to fix ● Special command to compile – https://github.com/facebook/mysql-5.6/wiki/Build-Steps ● Special command to run tests – Test suite assumes a big machine ● Some tests even a release build 6 Putting MyRocks in MariaDB ● Goals – Wider adoption – Ease of use – Ease of development – Have MyRocks in MariaDB ● Use it with MariaDB features ● Means – Port MyRocks into MariaDB – Provide binaries and packages 7 Status of MyRocks in MariaDB 8 Status of MyRocks in MariaDB ● MariaDB 10.2 is GA (as of May, 2017) ● It includes an ALPHA version of MyRocks plugin – Working to improve maturity ● It’s a loadable plugin (ha_rocksdb.so) ● Packages – Bintar, deb, rpm, win64 zip + MSI – deb/rpm have MyRocks .so and tools in a separate package. 9 Packaging for MyRocks in MariaDB 10 MyRocks and RocksDB library ● MyRocks is tied RocksDB@revno MariaDB – RocksDB is a github submodule – No compatibility with other versions MyRocks ● RocksDB is always compiled with RocksDB MyRocks S Z n ● l i And linked-in statically a b p ● p Distros have a RocksDB package y – Not using it. -
Myrocks Deployment at Facebook and Roadmaps
MyRocks deployment at Facebook and Roadmaps Yoshinori Matsunobu Production Engineer / MySQL Tech Lead, Facebook Feb/2018, #FOSDEM #mysqldevroom Agenda ▪ MySQL at Facebook ▪ MyRocks overview ▪ Production Deployment ▪ Future Plans MySQL “User Database (UDB)” at Facebook▪ Storing Social Graph ▪ Massively Sharded ▪ Low latency ▪ Automated Operations ▪ Pure Flash Storage (Constrained by space, not by CPU/IOPS) What is MyRocks ▪ MySQL on top of RocksDB (RocksDB storage engine) ▪ Open Source, distributed from MariaDB and Percona as well MySQL Clients SQL/Connector Parser Optimizer Replication etc InnoDB RocksDB MySQL http://myrocks.io/ MyRocks Initial Goal at Facebook InnoDB in main database MyRocks in main database CPU IO Space CPU IO Space Machine limit Machine limit 45% 90% 21% 15% 45% 20% 15% 21% 15% MyRocks features ▪ Clustered Index (same as InnoDB) ▪ Bloom Filter and Column Family ▪ Transactions, including consistency between binlog and RocksDB ▪ Faster data loading, deletes and replication ▪ Dynamic Options ▪ TTL ▪ Online logical and binary backup MyRocks vs InnoDB ▪ MyRocks pros ▪ Much smaller space (half compared to compressed InnoDB) ▪ Gives better cache hit rate ▪ Writes are faster = Faster Replication ▪ Much smaller bytes written (can use more afordable fash storage) ▪ MyRocks cons (improvements in progress) ▪ Lack of several features ▪ No SBR, Gap Lock, Foreign Key, Fulltext Index, Spatial Index support. Need to use case sensitive collation for perf ▪ Reads are slower, especially if your data fts in memory ▪ More dependent on flesystem -
Etriks Analytical Environment: a Practical Platform for Medical Big Data Analysis
Imperial College of Science, Technology and Medicine Department of Computing eTRIKS Analytical Environment: A Practical Platform for Medical Big Data Analysis Axel Oehmichen Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Computing of Imperial College London December 2018 Abstract Personalised medicine and translational research have become sciences driven by Big Data. Healthcare and medical research are generating more and more complex data, encompassing clinical investigations, 'omics, imaging, pharmacokinetics, Next Generation Sequencing and beyond. In addition to traditional collection methods, economical and numerous information sensing IoT devices such as mobile devices, smart sensors, cameras or connected medical de- vices have created a deluge of data that research institutes and hospitals have difficulties to deal with. While the collection of data is greatly accelerating, improving patient care by devel- oping personalised therapies and new drugs depends increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from that variety of large-scale heterogeneous data sources. As a result, the analysis of these datasets has become increasingly computationally expensive and has laid bare the limitations of current systems. From the patient perspective, the advent of electronic medical records coupled with so much personal data being collected have raised concerns about privacy. Many countries have intro- duced laws to protect people's privacy, however, many of these laws have proven to be less effective in practice. Therefore, along with the capacity to process the humongous amount of medical data, the addition of privacy preserving features to protect patients' privacy has become a necessity. -
Measuring Cloud Network Performance with Perfkit Benchmarker
Measuring Cloud Network Performance with PerfKit Benchmarker A Google Cloud Technical White Paper January 2020 For more information visit google.com/cloud Table of Contents 1. Abstract 3 2. Introduction 3 2.1 PerfKit Benchmarker 4 2.1.1 PerfKit Benchmarker Basic Example 5 2.1.2 PerfKit Benchmarker Example with Config file 5 3. Benchmark Configurations 6 3.1 Basic Benchmark Specific Configurations 7 3.1.1 Latency (ping and netperf TCP_RR) 7 3.1.2 Throughput (iperf and netperf) 7 3.1.3 Packets per Second 8 3.2 On-Premises to Cloud Benchmarks 9 3.3 Cross Cloud Benchmarks 10 3.4 VPN Benchmarks 11 3.5 Kubernetes Benchmarks 12 3.6 Intra-Zone Benchmarks 13 3.7 Inter-Zone Benchmarks 13 3.8 Inter-Region Benchmarks 14 3.9 Multi Tier 15 4. Inter-Region Latency Example and Results 16 5. Viewing and Analyzing Results 18 5.1 Visualizing Results with BigQuery and Data Studio 18 6. Summary 21 For more information visit google.com/cloud 1. Abstract Network performance is of vital importance to any business or user operating or running part of their infrastructure in a cloud environment. As such, it is also important to test the performance of a cloud provider’s network before deploying a potentially large number of virtual machines and other components of virtual infrastructure. Researchers at SMU’s AT&T Center for Virtualization (see smu.edu/provost/virtualization) have been working in conjunction with a team at Google to run network performance benchmarks across various cloud providers using automation built around PerfKit Benchmarker (see github.com/GoogleCloudPlatform/PerfKitBenchmarker) to track changes in network performance over time. -
ENOS: a Holistic Framework for Conducting Scientific Evaluations Of
ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau, Adrien Lebre, Dimitri Pertin, Anthony Simonet, Matthieu Simonin To cite this version: Ronan-Alexandre Cherrueau, Adrien Lebre, Dimitri Pertin, Anthony Simonet, Matthieu Si- monin. ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack . [Technical Report] RT-0485, Inria Rennes Bretagne Atlantique; Nantes. 2016. <hal- 01415522v2> HAL Id: hal-01415522 https://hal.inria.fr/hal-01415522v2 Submitted on 13 Dec 2016 HAL is a multi-disciplinary open access L'archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destin´eeau d´ep^otet `ala diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publi´esou non, lished or not. The documents may come from ´emanant des ´etablissements d'enseignement et de teaching and research institutions in France or recherche fran¸caisou ´etrangers,des laboratoires abroad, or from public or private research centers. publics ou priv´es. ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau , Adrien Lebre , Dimitri Pertin , Anthony Simonet , Matthieu Simonin TECHNICAL REPORT N° 485 November 2016 Project-Teams Ascola and ISSN 0249-0803 ISRN INRIA/RT--485--FR+ENG Myriads ENOS: a Holistic Framework for Conducting Scientific Evaluations of OpenStack Ronan-Alexandre Cherrueau , Adrien Lebre , Dimitri Pertin , Anthony Simonet , Matthieu Simonin Project-Teams Ascola and Myriads Technical Report n° 485 — version 2 — initial version November 2016 — revised version Décembre 2016 — 13 pages Abstract: By massively adopting OpenStack for operating small to large private and public clouds, the industry has made it one of the largest running software project. -
Optimal Bloom Filters and Adaptive Merging for LSM-Trees∗
Optimal Bloom Filters and Adaptive Merging for LSM-Trees∗ NIV DAYAN, Harvard University, USA MANOS ATHANASSOULIS, Harvard University, USA STRATOS IDREOS, Harvard University, USA In this paper, we show that key-value stores backed by a log-structured merge-tree (LSM-tree) exhibit an intrinsic trade-off between lookup cost, update cost, and main memory footprint, yet all existing designs expose a suboptimal and difficult to tune trade-off among these metrics. We pinpoint the problem tothefact that modern key-value stores suboptimally co-tune the merge policy, the buffer size, and the Bloom filters’ false positive rates across the LSM-tree’s different levels. We present Monkey, an LSM-tree based key-value store that strikes the optimal balance between the costs of updates and lookups with any given main memory budget. The core insight is that worst-case lookup cost is proportional to the sum of the false positive rates of the Bloom filters across all levels of the LSM-tree. Contrary to state-of-the-art key-value stores that assign a fixed number of bits-per-element to all Bloom filters, Monkey allocates memory to filters across different levels so as to minimize the sum of their false positive rates. We show analytically that Monkey reduces the asymptotic complexity of the worst-case lookup I/O cost, and we verify empirically using an implementation on top of RocksDB that Monkey reduces lookup latency by an increasing margin as the data volume grows (50% − 80% for the data sizes we experimented with). Furthermore, we map the design space onto a closed-form model that enables adapting the merging frequency and memory allocation to strike the best trade-off among lookup cost, update cost and main memory, depending onthe workload (proportion of lookups and updates), the dataset (number and size of entries), and the underlying hardware (main memory available, disk vs. -
Optimal Allocation of Virtual Machines in Multi-Cloud Environments with Reserved and On-Demand Pricing
Optimal allocation of virtual machines in multi-cloud environments with reserved and on-demand pricing Jos´eLuis D´ıaz∗, Joaqu´ınEntrialgo, Manuel Garc´ıa, Javier Garc´ıa, Daniel Fernando Garc´ıa Dept. of Informatics, University of Oviedo, Campus de Viesques, 33204 Gij´on(Spain) Abstract In the Cloud Computing market, a significant number of cloud providers offer Infrastructure as a Service (IaaS), including the capability of deploying virtual machines of many different types. The deployment of a service in a public provider generates a cost derived from the rental of the allocated virtual machines. In this paper we present LLOOVIA (Load Level based OpimizatiOn for VIrtual machine Allocation), an optimization technique designed for the optimal allocation of the virtual machines required by a service, in order to minimize its cost, while guaranteeing the required level of performance. LLOOVIA considers virtual machine types, different kinds of limits imposed by providers, and two price schemas for virtual machines: reserved and on-demand. LLOOVIA, which can be used with multi-cloud environments, provides two types of solutions: (1) the optimal solution and (2) the approximated solution based on a novel approach that uses binning applied on histograms of load levels. An extensive set of experiments has shown that when the size of the problem is huge, the approximated solution is calculated in a much shorter time and is very close to the optimal one. The technique presented has been applied to a set of case studies, based on the Wikipedia workload. These cases demonstrate that LLOOVIA can handle problems in which hundreds of virtual machines of many different types, multiple providers, and different kinds of limits are used.