A Study on the Evaluation of HPC Microservices in Containerized Environment
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Microbenchmarks in Big Data
M Microbenchmark Overview Microbenchmarks constitute the first line of per- Nicolas Poggi formance testing. Through them, we can ensure Databricks Inc., Amsterdam, NL, BarcelonaTech the proper and timely functioning of the different (UPC), Barcelona, Spain individual components that make up our system. The term micro, of course, depends on the prob- lem size. In BigData we broaden the concept Synonyms to cover the testing of large-scale distributed systems and computing frameworks. This chap- Component benchmark; Functional benchmark; ter presents the historical background, evolution, Test central ideas, and current key applications of the field concerning BigData. Definition Historical Background A microbenchmark is either a program or routine to measure and test the performance of a single Origins and CPU-Oriented Benchmarks component or task. Microbenchmarks are used to Microbenchmarks are closer to both hardware measure simple and well-defined quantities such and software testing than to competitive bench- as elapsed time, rate of operations, bandwidth, marking, opposed to application-level – macro or latency. Typically, microbenchmarks were as- – benchmarking. For this reason, we can trace sociated with the testing of individual software microbenchmarking influence to the hardware subroutines or lower-level hardware components testing discipline as can be found in Sumne such as the CPU and for a short period of time. (1974). Furthermore, we can also find influence However, in the BigData scope, the term mi- in the origins of software testing methodology crobenchmarking is broadened to include the during the 1970s, including works such cluster – group of networked computers – acting as Chow (1978). One of the first examples of as a single system, as well as the testing of a microbenchmark clearly distinguishable from frameworks, algorithms, logical and distributed software testing is the Whetstone benchmark components, for a longer period and larger data developed during the late 1960s and published sizes. -
Overview of the SPEC Benchmarks
9 Overview of the SPEC Benchmarks Kaivalya M. Dixit IBM Corporation “The reputation of current benchmarketing claims regarding system performance is on par with the promises made by politicians during elections.” Standard Performance Evaluation Corporation (SPEC) was founded in October, 1988, by Apollo, Hewlett-Packard,MIPS Computer Systems and SUN Microsystems in cooperation with E. E. Times. SPEC is a nonprofit consortium of 22 major computer vendors whose common goals are “to provide the industry with a realistic yardstick to measure the performance of advanced computer systems” and to educate consumers about the performance of vendors’ products. SPEC creates, maintains, distributes, and endorses a standardized set of application-oriented programs to be used as benchmarks. 489 490 CHAPTER 9 Overview of the SPEC Benchmarks 9.1 Historical Perspective Traditional benchmarks have failed to characterize the system performance of modern computer systems. Some of those benchmarks measure component-level performance, and some of the measurements are routinely published as system performance. Historically, vendors have characterized the performances of their systems in a variety of confusing metrics. In part, the confusion is due to a lack of credible performance information, agreement, and leadership among competing vendors. Many vendors characterize system performance in millions of instructions per second (MIPS) and millions of floating-point operations per second (MFLOPS). All instructions, however, are not equal. Since CISC machine instructions usually accomplish a lot more than those of RISC machines, comparing the instructions of a CISC machine and a RISC machine is similar to comparing Latin and Greek. 9.1.1 Simple CPU Benchmarks Truth in benchmarking is an oxymoron because vendors use benchmarks for marketing purposes. -
Hypervisors Vs. Lightweight Virtualization: a Performance Comparison
2015 IEEE International Conference on Cloud Engineering Hypervisors vs. Lightweight Virtualization: a Performance Comparison Roberto Morabito, Jimmy Kjällman, and Miika Komu Ericsson Research, NomadicLab Jorvas, Finland [email protected], [email protected], [email protected] Abstract — Virtualization of operating systems provides a container and alternative solutions. The idea is to quantify the common way to run different services in the cloud. Recently, the level of overhead introduced by these platforms and the lightweight virtualization technologies claim to offer superior existing gap compared to a non-virtualized environment. performance. In this paper, we present a detailed performance The remainder of this paper is structured as follows: in comparison of traditional hypervisor based virtualization and Section II, literature review and a brief description of all the new lightweight solutions. In our measurements, we use several technologies and platforms evaluated is provided. The benchmarks tools in order to understand the strengths, methodology used to realize our performance comparison is weaknesses, and anomalies introduced by these different platforms in terms of processing, storage, memory and network. introduced in Section III. The benchmark results are presented Our results show that containers achieve generally better in Section IV. Finally, some concluding remarks and future performance when compared with traditional virtual machines work are provided in Section V. and other recent solutions. Albeit containers offer clearly more dense deployment of virtual machines, the performance II. BACKGROUND AND RELATED WORK difference with other technologies is in many cases relatively small. In this section, we provide an overview of the different technologies included in the performance comparison. -
Power Measurement Tutorial for the Green500 List
Power Measurement Tutorial for the Green500 List R. Ge, X. Feng, H. Pyla, K. Cameron, W. Feng June 27, 2007 Contents 1 The Metric for Energy-Efficiency Evaluation 1 2 How to Obtain P¯(Rmax)? 2 2.1 The Definition of P¯(Rmax)...................................... 2 2.2 Deriving P¯(Rmax) from Unit Power . 2 2.3 Measuring Unit Power . 3 3 The Measurement Procedure 3 3.1 Equipment Check List . 4 3.2 Software Installation . 4 3.3 Hardware Connection . 4 3.4 Power Measurement Procedure . 5 4 Appendix 6 4.1 Frequently Asked Questions . 6 4.2 Resources . 6 1 The Metric for Energy-Efficiency Evaluation This tutorial serves as a practical guide for measuring the computer system power that is required as part of a Green500 submission. It describes the basic procedures to be followed in order to measure the power consumption of a supercomputer. A supercomputer that appears on The TOP500 List can easily consume megawatts of electric power. This power consumption may lead to operating costs that exceed acquisition costs as well as intolerable system failure rates. In recent years, we have witnessed an increasingly stronger movement towards energy-efficient computing systems in academia, government, and industry. Thus, the purpose of the Green500 List is to provide a ranking of the most energy-efficient supercomputers in the world and serve as a complementary view to the TOP500 List. However, as pointed out in [1, 2], identifying a single objective metric for energy efficiency in supercom- puters is a difficult task. Based on [1, 2] and given the already existing use of the “performance per watt” metric, the Green500 List uses “performance per watt” (PPW) as its metric to rank the energy efficiency of supercomputers. -
Remote Attestation on Light VM
POLITECNICO DI TORINO Master's Degree in Computer Engineering Master Thesis Remote Attestation on light VM Supervisor prof. Antonio Lioy Candidate Fabio Vallone Accademic Year 2016-2017 To my parents, who helped me during this journey Summary In the last decade Cloud Computing has massively entered the IT world, changing the way services are offered to the final users. One important aspect of Cloud Computing is the needs to provide greater scalability and flexibility and this can be done by using a technique called Virtualization. This enables us to execute different virtualized ambient on a single piece of hardware. Each virtualized ambient can be seen as a node that offers some services to the final users by exchanging information with other nodes. For this reason the ability to trust each other is growing exponentially. This process is called Remote Attestation and it is from this consideration that the thesis work start. When we say that a node is trustworthy, we are saying that we are sure that all the file that are loaded into memory are the ones that are supposed to be loaded. In other word we need to be sure that the code, and in general the files loaded on that node, have not been tampered by anyone in order to produce a different behaviour of the node. It is important to note that we are not checking in anyway that the behaviour of the machine is logically correct, but we are simply checking that no one has modified it, so bug and logical error can still occurs in the code. -
The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster by Wong Chun Shiang 16138 Diss
The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster By Wong Chun Shiang 16138 Dissertation submitted in partial fulfilment of the requirements for the Bachelor of Technology (Hons) (Information and Communications Technology) MAY 2015 Universiti Teknologi PETRONAS Bandar Seri Iskandar 31750 Tronoh Perak Darul Ridzuan CERTIFICATION OF APPROVAL The High Performance Linpack (HPL) Benchmark Evaluation on UTP High Performance Computing Cluster by Wong Chun Shiang 16138 A project dissertation submitted to the Information & Communication Technology Programme Universiti Teknologi PETRONAS In partial fulfillment of the requirement for the BACHELOR OF TECHNOLOGY (Hons) (INFORMATION & COMMUNICATION TECHNOLOGY) Approved by, ____________________________ (DR. IZZATDIN ABDUL AZIZ) UNIVERSITI TEKNOLOGI PETRONAS TRONOH, PERAK MAY 2015 ii CERTIFICATION OF ORIGINALITY This is to certify that I am responsible for the work submitted in this project, that the original work is my own except as specified in the references and acknowledgements, and that the original work contained herein have not been undertaken or done by unspecified sources or persons. ________________ WONG CHUN SHIANG iii ABSTRACT UTP High Performance Computing Cluster (HPCC) is a collection of computing nodes using commercially available hardware interconnected within a network to communicate among the nodes. This campus wide cluster is used by researchers from internal UTP and external parties to compute intensive applications. However, the HPCC has never been benchmarked before. It is imperative to carry out a performance study to measure the true computing ability of this cluster. This project aims to test the performance of a campus wide computing cluster using a selected benchmarking tool, the High Performance Linkpack (HPL). -
A Comparative Analysis Between Traditional Virtualization and Container Based Virtualization for Advancement of Open Stack Cloud
ISSN (Online) 2394-2320 International Journal of Engineering Research in Computer Science and Engineering (IJERCSE) Vol 5, Issue 4, April 2018 A Comparative Analysis between Traditional Virtualization and Container Based Virtualization for Advancement of Open Stack Cloud [1] Krishna Parihar, [2] Arjun Choudhary, [3] Vishrant Ojha [1][2] Department of Computer Science and Engineering, Sardar Patel University of police, Security and Criminal Justice, Jodhpur Abstract: - LXD based machine containerized new concept in the virtualization world. Its give performance to the bare metal virtualizations. A Centralized system is accessed through IP and can be scalable on demand as per requirement, Technology that eases the user problem like hardware, as well as software most popular technologies, is known as Cloud Computing. As per enterprise and industries demand cloud computing holds a big part of IT world. An OpenStack is a way to manage public and private cloud computing platform. A Linux Container Hypervisor brings Cloud Computing to the next generation Hypervisor with its features Fast, Simple, Secure. The main goal of this paper is factual calibrate is determine various virtualization technique and performance analysis. Keywords— OpenStack, LXD, Cloud Computing, Virtualization, Container, Hypervisor power their public cloud infrastructure.[2] The core I. INTRODUCTION component of virtualization is Hypervisors. Analysis of structured and consistent data has seen A B. Hypervisor cloud computing technologies have been rapidly devel- It is a software which provides isolation for virtual ma- oping. resources are dynamically enlarged, virtualized as chines running on top of physical hosts. The thin layer of well as feed as a service over the Internet, it permits software that typically provides capabilities to virtual providers to provide users connected to a virtually parti-tioning that runs directly on hardware, It provides a unlimited number of resources i.e. -
Application Container Security Guide
NIST Special Publication 800-190 Application Container Security Guide Murugiah Souppaya John Morello Karen Scarfone This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.800-190 C O M P U T E R S E C U R I T Y NIST Special Publication 800-190 Application Container Security Guide Murugiah Souppaya Computer Security Division Information Technology Laboratory John Morello Twistlock Baton Rouge, Louisiana Karen Scarfone Scarfone Cybersecurity Clifton, Virginia This publication is available free of charge from: https://doi.org/10.6028/NIST.SP.800-190 September 2017 U.S. Department of Commerce Wilbur L. Ross, Jr., Secretary National Institute of Standards and Technology Kent Rochford, Acting Under Secretary of Commerce for Standards and Technology and Acting Director NIST SP 800-190 APPLICATION CONTAINER SECURITY GUIDE Authority This publication has been developed by NIST in accordance with its statutory responsibilities under the Federal Information Security Modernization Act (FISMA) of 2014, 44 U.S.C. § 3551 et seq., Public Law (P.L.) 113-283. NIST is responsible for developing information security standards and guidelines, including minimum requirements for federal information systems, but such standards and guidelines shall not apply to national security systems without the express approval of appropriate federal officials exercising policy authority over such systems. This guideline is consistent with the requirements of the Office of Management and Budget (OMB) Circular A-130. Nothing in this publication should be taken to contradict the standards and guidelines made mandatory and binding on federal agencies by the Secretary of Commerce under statutory authority. -
Separating Protection and Management in Cloud Infrastructures
SEPARATING PROTECTION AND MANAGEMENT IN CLOUD INFRASTRUCTURES A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Zhiming Shen December 2017 c 2017 Zhiming Shen ALL RIGHTS RESERVED SEPARATING PROTECTION AND MANAGEMENT IN CLOUD INFRASTRUCTURES Zhiming Shen, Ph.D. Cornell University 2017 Cloud computing infrastructures serving mutually untrusted users provide se- curity isolation to protect user computation and resources. Additionally, clouds should also support flexibility and efficiency, so that users can customize re- source management policies and optimize performance and resource utiliza- tion. However, flexibility and efficiency are typically limited due to security requirements. This dissertation investigates the question of how to offer flexi- bility and efficiency as well as strong security in cloud infrastructures. Specifically, this dissertation addresses two important platforms in cloud in- frastructures: the containers and the Infrastructure as a Service (IaaS) platforms. The containers platform supports efficient container provisioning and execut- ing, but does not provide sufficient security and flexibility. Different containers share an operating system kernel which has a large attack surface, and kernel customization is generally not allowed. The IaaS platform supports secure shar- ing of cloud resources among mutually untrusted users, but does not provide sufficient flexibility and efficiency. Many powerful management primitives en- abled by the underlying virtualization platform are hidden from users, such as live virtual machine migration and consolidation. The main contribution of this dissertation is the proposal of an approach in- spired by the exokernel architecture that can be generalized to any multi-tenant system to improve security, flexibility, and efficiency. -
Fair Benchmarking for Cloud Computing Systems
Fair Benchmarking for Cloud Computing Systems Authors: Lee Gillam Bin Li John O’Loughlin Anuz Pranap Singh Tomar March 2012 Contents 1 Introduction .............................................................................................................................................................. 3 2 Background .............................................................................................................................................................. 4 3 Previous work ........................................................................................................................................................... 5 3.1 Literature ........................................................................................................................................................... 5 3.2 Related Resources ............................................................................................................................................. 6 4 Preparation ............................................................................................................................................................... 9 4.1 Cloud Providers................................................................................................................................................. 9 4.2 Cloud APIs ...................................................................................................................................................... 10 4.3 Benchmark selection ...................................................................................................................................... -
On the Performance of MPI-Openmp on a 12 Nodes Multi-Core Cluster
On the Performance of MPI-OpenMP on a 12 nodes Multi-core Cluster Abdelgadir Tageldin Abdelgadir1, Al-Sakib Khan Pathan1∗ , Mohiuddin Ahmed2 1 Department of Computer Science, International Islamic University Malaysia, Gombak 53100, Kuala Lumpur, Malaysia 2 Department of Computer Network, Jazan University, Saudi Arabia [email protected] , [email protected] , [email protected] Abstract. With the increasing number of Quad-Core-based clusters and the introduction of compute nodes designed with large memory capacity shared by multiple cores, new problems related to scalability arise. In this paper, we analyze the overall performance of a cluster built with nodes having a dual Quad-Core Processor on each node. Some benchmark results are presented and some observations are mentioned when handling such processors on a benchmark test. A Quad-Core-based cluster's complexity arises from the fact that both local communication and network communications between the running processes need to be addressed. The potentials of an MPI-OpenMP approach are pinpointed because of its reduced communication overhead. At the end, we come to a conclusion that an MPI-OpenMP solution should be considered in such clusters since optimizing network communications between nodes is as important as optimizing local communications between processors in a multi-core cluster. Keywords: MPI-OpenMP, hybrid, Multi-Core, Cluster. 1 Introduction The integration of two or more processors within a single chip is an advanced technology for tackling the disadvantages exposed by a single core when it comes to increasing the speed, as more heat is generated and more power is consumed by those single cores. -
Power Efficiency in High Performance Computing
Power Efficiency in High Performance Computing Shoaib Kamil John Shalf Erich Strohmaier LBNL/UC Berkeley LBNL/NERSC LBNL/CRD [email protected] [email protected] [email protected] ABSTRACT 100 teraflop successor consumes almost 1,500 KW without After 15 years of exponential improvement in microproces- even being in the top 10. The first petaflop-scale systems, sor clock rates, the physical principles allowing for Dennard expected to debut in 2008, will draw 2-7 megawatts of power. scaling, which enabled performance improvements without a Projections for exaflop-scale computing systems, expected commensurate increase in power consumption, have all but in 2016-2018, range from 60-130 megawatts [16]. Therefore, ended. Until now, most HPC systems have not focused on fewer sites in the US will be able to host the largest scale power efficiency. However, as the cost of power reaches par- computing systems due to limited availability of facilities ity with capital costs, it is increasingly important to com- with sufficient power and cooling capabilities. Following this pare systems with metrics based on the sustained perfor- trend, over time an ever increasing proportion of an HPC mance per watt. Therefore we need to establish practical center’s budget will be needed for supplying power to these methods to measure power consumption of such systems in- systems. situ in order to support such metrics. Our study provides The root cause of this impending crisis is that chip power power measurements for various computational loads on the efficiency is no longer improving at historical rates. Up until largest scale HPC systems ever involved in such an assess- now, Moore’s Law improvements in photolithography tech- ment.