1 Enabling Large Scale Network Simulation with 100 Million Nodes using Grid Infrastructure Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.

Slides:

Advertisements

Similar presentations

Misbah Mubarak, Christopher D. Carothers

Advertisements

Achieving Elasticity for Cloud MapReduce Jobs Khaled Salah IEEE CloudNet 2013 – San Francisco November 13, 2013.

WHITE – Achieving Fair Bandwidth Allocation with Priority Dropping Based on Round Trip Time Name : Choong-Soo Lee Advisors : Mark Claypool, Robert Kinicki.

Congestion Control Algorithms: Open Questions Benno Overeinder NLnet Labs.

Web Server Benchmarking Using the Internet Protocol Traffic and Network Emulator Carey Williamson, Rob Simmonds, Martin Arlitt et al. University of Calgary.

Simulating Large Networks using Fluid Flow Model Yong Liu Joint work with Francesco LoPresti, Vishal Misra Don Towsley, Yu Gu.

Scalable and Crash-Tolerant Load Balancing based on Switch Migration

Receiver-driven Layered Multicast S. McCanne, V. Jacobsen and M. Vetterli SIGCOMM 1996.

Katz, Stoica F04 EECS 122 Introduction to Computer Networks (Fall 2003) Network simulator 2 (ns-2) Department of Electrical Engineering and Computer Sciences.

Beneficial Caching in Mobile Ad Hoc Networks Bin Tang, Samir Das, Himanshu Gupta Computer Science Department Stony Brook University.

CS603 Summer II 2003 Homework #2 Presentation Weng Liong Low.

Multiple constraints QoS Routing Given: - a (real time) connection request with specified QoS requirements (e.g., Bdw, Delay, Jitter, packet loss, path.

A simulation-based comparative evaluation of transport protocols for SIP Authors: M.Lulling*, J.Vaughan Department of Computer science, University college.

Computer Networks NS. Features F Event driven simulator –developed at UC Berkeley F Network Topologies –nodes (connectivity), links (bandwidth, delay)

Questions  RED vs. DropTail  What’s the key difference?  Why RED drops packet randomly?  What’s the major effect if using RED.

Performance Evaluation on Buddy-TCP By Felix. Simulation Setup S C1C1 CNCN … … T_Sink1 T_SinkN … T1T1 TNTN U U_Sink 4N Mbps 50 ms L Types of traffic:

1 PERFORMANCE EVALUATION H Often in Computer Science you need to: – demonstrate that a new concept, technique, or algorithm is feasible –demonstrate that.

Probability Grid: A Location Estimation Scheme for Wireless Sensor Networks Presented by cychen Date ： 3/7 In Secon (Sensor and Ad Hoc Communications and.

1 Sonia Fahmy Ness Shroff Students: Roman Chertov Rupak Sanjel Center for Education and Research in Information Assurance and Security (CERIAS) Purdue.

Color Aware Switch algorithm implementation The Computer Communication Lab (236340) Spring 2008.

1 Simulation Methodology H Plan: –Introduce basics of simulation modeling –Define terminology and methods used –Introduce simulation paradigms u Time-driven.

Analysis of Active Queue Management Jae Chung and Mark Claypool Computer Science Department Worcester Polytechnic Institute Worcester, Massachusetts, USA.

Computer Science 1 Characterizing Link Properties Using “Loss-pairs” Jun Liu (joint work with Prof. Mark Crovella)

Performance Evaluation Techniques Professor Bob Kinicki Computer Science Department.

1 Performance Evaluation of Ring- based Peer-to-Peer Virtual Private Network (RING-P2P-VPN) Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.

EstiNet Network Simulator & Emulator 2014/06/ 尉遲仲涵.

Edge Based Cloud Computing as a Feasible Network Paradigm(1/27) Edge-Based Cloud Computing as a Feasible Network Paradigm Joe Elizondo and Sam Palmer.

Lesson 5 – Looking at the Output MATSim Tutorial, 2011, Shanghai 1.

The Georgia Tech Network Simulator (GTNetS) ECE6110 August 25, 2008 George F. Riley.

Location-aware MapReduce in Virtual Cloud 2011 IEEE computer society International Conference on Parallel Processing Yifeng Geng1,2, Shimin Chen3, YongWei.

Routing Protocol Evaluation David Holmer

Modeling and Performance Evaluation of Network and Computer Systems Introduction (Chapters 1 and 2) 10/4/2015H.Malekinezhad1.

Performance Evaluation of Computer Systems Introduction

1 Performance Evaluation of Computer Systems and Networks Introduction, Outlines, Class Policy Instructor: A. Ghasemi Many thanks to Dr. Behzad Akbari.

Example: Sorting on Distributed Computing Environment Apr 20,

1 Network Simulator Tutorial. 2 Network Simulation * Motivation: Learn fundamentals of evaluating network performance via simulation Overview: fundamentals.

1 Modeling and Performance Evaluation of DRED (Dynamic Random Early Detection) using Fluid-Flow Approximation Hideyuki Yamamoto, Hiroyuki Ohsaki Graduate.

ICOM 6115: Computer Systems Performance Measurement and Evaluation August 11, 2006.

1 Network Emulation Mihai Ivanovici Dr. Razvan Beuran Dr. Neil Davies.

1 Evaluation of Cooperative Web Caching with Web Polygraph Ping Du and Jaspal Subhlok Department of Computer Science University of Houston presented at.

1 On Scalable Edge-based Flow Control Mechanism for VPN Tunnels --- Part 2: Scalability and Implementation Issues Hiroyuki Ohsaki Graduate School of Information.

15744 Course Project1 Evaluation of Queue Management Algorithms Ningning Hu, Liu Ren, Jichuan Chang 30 April 2001.

1 On Dynamic Parallelism Adjustment Mechanism for Data Transfer Protocol GridFTP Takeshi Itou, Hiroyuki Ohsaki Graduate School of Information Sci. & Tech.

Analysis of QoS Arjuna Mithra Sreenivasan. Objectives Explain the different queuing techniques. Describe factors affecting network voice quality. Analyse.

Interconnect simulation. Different levels for Evaluating an architecture Numerical models – Mathematic formulations to obtain performance characteristics.

Efficient Cache Structures of IP Routers to Provide Policy-Based Services Graduate School of Engineering Osaka City University

Network Simulator 2. Introduction Open source network simulator NS uses two languages: C++ and OTcl  C++ is fast to run but slower to change Kernel 

1 Analysis of a window-based flow control mechanism based on TCP Vegas in heterogeneous network environment Hiroyuki Ohsaki Cybermedia Center, Osaka University,

An Efficient Gigabit Ethernet Switch Model for Large-Scale Simulation Dong (Kevin) Jin.

OPERATING SYSTEMS CS 3530 Summer 2014 Systems and Models Chapter 03.

CATNIP – Context Aware Transport/Network Internet Protocol Carey Williamson Qian Wu Department of Computer Science University of Calgary.

H. OhsakiITCom A control theoretical analysis of a window-based flow control mechanism for TCP connections with different propagation delays Hiroyuki.

Internet research Needs Better Models Sally Floyd, Eddie Kohler ISCI Center for Internet Research, Berkeley, California Presented by Max Podlesny.

On the Placement of Web Server Replicas Yu Cai. Paper On the Placement of Web Server Replicas Lili Qiu, Venkata N. Padmanabhan, Geoffrey M. Voelker Infocom.

1 Sheer volume and dynamic nature of video stresses network resources PIE: A lightweight latency control to address the buffer problem issue Rong Pan,

Cluster Computers. Introduction Cluster computing –Standard PCs or workstations connected by a fast network –Good price/performance ratio –Exploit existing.

Querying the Internet with PIER CS294-4 Paul Burstein 11/10/2003.

1 Evaluation of Cooperative Web Caching with Web Polygraph Ping Du and Jaspal Subhlok Department of Computer Science University of Houston presented at.

MicroGrid Update & A Synthetic Grid Resource Generator Xin Liu, Yang-suk Kee, Andrew Chien Department of Computer Science and Engineering Center for Networked.

PATH DIVERSITY WITH FORWARD ERROR CORRECTION SYSTEM FOR PACKET SWITCHED NETWORKS Thinh Nguyen and Avideh Zakhor IEEE INFOCOM 2003.

1 Scalability and Accuracy in a Large-Scale Network Emulator Nov. 12, 2003 Byung-Gon Chun.

OPERATING SYSTEMS CS 3502 Fall 2017

ECE 544: Traffic engineering (supplement)

Karthik Sadasivam Banuprasad Samudrala

Computer Systems Performance Evaluation

ModelNet: A Large-Scale Network Emulator for Wireless Networks Priya Mahadevan, Ken Yocum, and Amin Vahdat Duke University, Goal:

by Xiang Mao and Qin Chen

Performance Evaluation of Computer Networks

Computer Systems Performance Evaluation

Performance Evaluation of Computer Networks

Presentation transcript:

1 Enabling Large Scale Network Simulation with 100 Million Nodes using Grid Infrastructure Hiroyuki Ohsaki Graduate School of Information Sci. & Tech. Osaka University, Japan

2 1. State Goals and Define the System Goals Show effectiveness of our network model partitioning, TI-PART Compare performance of TI-PART with random partitioning Find optimal parameter configuration of TI-PART on ``alpha’’ System Definition Parallel distributed simulation environment including... Computing resources running PDNS version 2.27-v1a Networking resources Network model partitioning software implementing TI-PART

3 2. List Services and Outcomes Services Provided Discrete-event simulation for a given network model and traffic matrix Outcomes Simulation event log for each packet processing

4 3. Select Metrics Speed (case of successful service case) Individual None Global Time required for completing all simulation events Reliability (case of error) None Availability (case of unavailability) None

5 4. List Parameters System parameters Networking resource related Topology Link bandwidth, latency, loss ratio Queue size and disciplie (e.g., DropTail or RED) Computing resource related # of resources CPU type, speed, # of CPUs Memory size, speed TI-PART related Balancing factor ``alpha’’ Workload parameters Network model Topology, # of nodes, degree Link bandwidth, latency, loss ratio Queue size and disciplie (e.g., DropTail or RED) Traffic matrix

6 5. Select Factors to Study System parameters Networking resource related Topology Link bandwidth, latency, loss ratio Queue size and disciplie (e.g., DropTail or RED) Computing resource related # of resources (1, 2, 4, 6, 8) CPU type, speed (use cpufreq?), # of CPUs Memory size, speed TI-PART related Balancing factor ``alpha’’ (0.1, 0.2, 0.3, 0.4, 0.5) Workload parameters Network model Topology, # of nodes, degree Link bandwidth, latency, loss ratio Queue size and disciplie (e.g., DropTail or RED) Traffic matrix

7 6. Select Evaluation Technique Use analytical modeling? No Use simulation? No Use measurement of real system? Yes

8 7. Select Workload Generate random network model with the following parameters # of nodes 10, 100, 1000, 10000, degree 2, 2.5, 3, 3.5, 4 Link bandwidth Uniformly distributed between 1 and 10, 100, 1000 Mbps Link latency Uniformly distributed between 0.1 and 1, 10, 100 ms Traffic matrix # of TCP flows: # of nodes × 0.1, 1, 10

9 8. Design Experiments ？

10 9. Analyze and Interpret Data ？

Present Results Running time vs. # of computing resources for different... # of nodes, degree Link bandwidth and latency Traffic matrix