Advanced Computer Networks. Scheduling

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Advanced Computer Networks. Scheduling"

Transcription

1 Oriana Riva, Department of Computer Science ETH Zürich Advanced Computer Networks Scheduling Patrick Stuedi, Qin Yin and Timothy Roscoe Spring Semester 2015

2 Outline Last time Load balancing Layer-4 switching Layer-7 switching TCP Splicing Ananta Today Scheduling Data transfer orchestration 2 research projects 2

3 Cluster computing frameworks Cluster computing frameworks like MapReduce, Spark, etc., are an important class of applications in data centers Web search, machine learning, etc Performance and efficiency of these frameworks is of major interest Challenges for networking discussed in lecture 6 (TCP) Incast Buffer buildup at switches due to mice & elephant traffic patterns Missed deadlines (and SLAs) Poor utilization of multiple paths 3

4 Remember: Partition / Aggregate Pattern Partition Work Aggregate Results 4

5 Two key traffic patterns in MapReduce Map Stage Broadcast One-to-many Partition work Job Shuffle Many-to-many Reduce Stage Aggregate resultswith end-system provision 5

6 MapReduce logs from Facebook cluster Weeklong trace of 188'000 mapreduce jobs from a 3000-node cluster 33% of time in shuffle on average 6

7 In lecture 6 (TCP) we have......discussed several techniques to improve networking in partition/aggregate type of applications: Fine-grain TCP timers: reduces long tail effects DCTCP: reduces queue buildup D3 and D2DCTCP: meet deadlines and SLAs MPTCP: leverage multiple network paths These approaches are all working on a per-flow basis None of them looks at the collective behavior of flows by taking job semantics into account No coordination between individual network transfers within a single job 7

8 In lecture 6 (TCP) we have......discussed several techniques to improve networking in partition/aggregate type of applications: Fine-grain TCP timers: reduces long tail effects DCTCP: reduces queue buildup D3 and D2DCTCP: meet deadlines and SLAs MPTCP: leverage multiple network paths These approaches are all working on a per-flow basis None of them looks at the collective behavior of flows by taking job semantics into account No coordination between individual network transfers within a single job 8

9 Lack of coordination can hurt the performance 9

10 Scalability of Netflix recommendation system Bottlenecked by communication as cluster size increases 10

11 Two research projects Orchestra Coordinate all transfers within a mapreduce job FastPass Coordinate packet transfers and path selection among all flows 11

12 Orchestra: Managing Data Transfers in Computer Clusters

13 Orchestra: key idea Optimize at the level of transfers instead of individual flows Transfer: set of all flows transporting data between two stages of a job Coordination done through three control components Cornet: cooperative broadcast Weighted shuffle scheduling: shuffle coordination Inter-transfer controller (ITC): global coordination 13

14 Cornet: cooperative broadcast Key idea: Broadcasting in MapReduce is very similar to data distribution mechanisms in the Internet like BitTorrent BitTorrent-like protocol optimized for data centers Split data up into blocks and distribute them across nodes in the data center On receiving: request/gather blocks from various nodes Receivers of blocks become part of sender set (BitTorrent) Cornet differs from classical BitTorrent: Blocks are much larger (4MB) Data center is assumed to have high-bandwidth No need for incentives No selfish peers in the data center Topology aware Topology of data center is known Receiver chooses sender in the same rack 14

15 Cornet performance Experiment: 100GB data to 100 receivers on Amazon EC2 cluster Traditional broadcast implementations use distributed file system to store and retrieve broadcast data Cornet is about 4-5 times more efficient 15

16 Shuffle: Status Quo (1) Reducers Mappers Map output To receivers (top) need to fetch separate pieces of data from each sender If every sender has equal amount of data, all links are equally loaded and utilized What if data sizes are unbalanced? 16

17 Shuffle: Status Quo (1) Reducers Mappers Map output To receivers (top) need to fetch separate pieces of data from each sender If every sender has equal amount of data, all links are equally loaded and utilized What if data sizes are unbalanced? 17

18 Shuffle: Sender Bottleneck Reducers Mappers Map output Senders s1, s2, s4 and s5 have one data unit for each receiver Sender s3 has two data units for both receivers The link of the sender s3 becomes the bottleneck if flows share bandwidth in fair way 18

19 Orchestra: Weighted Shuffle Scheduling (WSS) Key idea: Assign weights to each flow in a shuffle Make the weight proportional to the data that needs to be transported 19

20 Example: shuffle with fair bandwidth sharing Reducers Mappers Map output Each receiver fetches data at 1/3 units/seconds from the three senders (three flows sharing bandwidth at receiver) After 3 seconds, all data from s1, s2, s4 and s5 is fetched But one unit of data left for both receivers at s3 s3 transmits the two remaining units at 1/2 units per seconds to each receiver (two flows sharing the bandwidth at sender) After two more seconds all units are transferred Total time = 5 seconds 20

21 Example: shuffle with fair bandwidth sharing Reducers Mappers Map output Each receiver fetches data at 1/3 units/seconds from the three senders (three flows sharing bandwidth at receiver) After 3 seconds, all data from s1, s2, s4 and s5 is fetched But one unit of data left for both receivers at s3 s3 transmits the two remaining units at 1/2 units per seconds to each receiver (two flows sharing the bandwidth at sender) After two more seconds all units are transferred Total time = 5 seconds 21

22 Example: shuffle with fair bandwidth sharing Reducers Mappers Map output Each receiver fetches data at 1/3 units/seconds from the three senders (three flows sharing bandwidth at receiver) After 3 seconds, all data from s1, s2, s4 and s5 is fetched But one unit of data left for both receivers at s3 s3 transmits the two remaining units at 1/2 units per seconds to each receiver (two flows sharing the bandwidth at sender) After two more seconds all units are transferred Total time = 5 seconds 22

23 Example: shuffle with fair bandwidth sharing Reducers Mappers Map output Each receiver fetches data at 1/3 units/seconds from the three senders (three flows sharing bandwidth at receiver) After 3 seconds, all data from s1, s2, s4 and s5 is fetched But one unit of data left for both receivers at s3 s3 transmits the two remaining units at 1/2 units per seconds to each receiver (two flows sharing the bandwidth at sender) After two more seconds all units are transferred Total time = 5 seconds 23

24 Example: shuffle with fair bandwidth sharing Reducers Mappers Map output Each receiver fetches data at 1/3 units/seconds from the three senders (three flows sharing bandwidth at receiver) After 3 seconds, all data from s1, s2, s4 and s5 is fetched But one unit of data left for both receivers at s3 s3 transmits the two remaining units at 1/2 units per seconds to each receiver (two flows sharing the bandwidth at sender) After two more seconds all units are transferred Total time = 5 seconds 24

25 Example: shuffle with weighted scheduling Reducers Mappers Map output Receivers fetch data at 1/4 units/seconds from s1, s2, s4 and s5...and: fetch data at 1/2 units/seconds s3 Fetching data from s1, s2, s4 and s5: 4 seconds Fetching data from s3: 4 seconds Total time = 4 seconds (25% faster than fair sharing) 25

26 Example: shuffle with weighted scheduling Reducers Mappers Map output Receivers fetch data at 1/4 units/seconds from s1, s2, s4 and s5...and: fetch data at 1/2 units/seconds s3 Fetching data from s1, s2, s4 and s5: 4 seconds Fetching data from s3: 4 seconds Total time = 4 seconds (25% faster than fair sharing) 26

27 Example: shuffle with weighted scheduling Reducers Mappers Map output Receivers fetch data at 1/4 units/seconds from s1, s2, s4 and s5...and: fetch data at 1/2 units/seconds s3 Fetching data from s1, s2, s4 and s5: 4 seconds Fetching data from s3: 4 seconds Total time = 4 seconds (25% faster than fair sharing) 27

28 Orchestra: End-to-end evaluation 1.9x faster on 90 nodes 28

29 Fastpass: A Centralized Zero-Queue Datacenter Network

30 Fastpass: key idea Instead of flows sending packets uncoordinated..use a central and datacenter wide arbiter to schedule and assign paths to all packets! No need for queues at switches Very high utilization Fastpass Arbiter 30

31 Example: Packet from A to B 31

32 Fastpass challenges Fine-grained timing: can an arbiter make scheduling decisions at the latencies required? Time to transfer a 1500-byte MTU-sized packet at 10Gbit/s is 1230 nanoseconds Assign batches of timeslots in one go Scalability: arbiter needs to serve requests from many nodes Efficient parallelization of requests on a multicore system 32

33 Fastpass system design Client application issues send() call on a socket Fastpass library intercepts call and sends demand request message (source, destination, data size) to arbiter The arbiter processes each request, performing two functions: Timeslot allocation: assignment of a set of timeslots to transmit the data Path selection: assignment of a path through the network for each packet Arbiter communicates timeslot and path information to the client 33

34 Timeslot allocation Input: set of demands (source/destination pairs, number of time slots required per pair) Arbiter sorts demands by last timeslot allocated to each pair (fairness) Arbiter processes demands in sorted order while making sure no slot is double booked In example: 3 rd demand cannot be allocated since destination slot is already taken 34

35 Path selection using edge coloring ToR 2-tier network topology core Example in a network with two tiers (ToR and core): Each ToR switch connected to a subset of core switches Path selection entails assigning a core switch to each packet Path selection through edge coloring in four steps: (1) Input: matching of src/dst pairs (2) Bipartite graph of ToR switches where src/dst pairs of every packet are connected (3) Color edges so that no two edges incident on the same ToR have the same color (4) Colors identify which core switch a packet is using 35

36 Path selection using edge coloring ToR 2-tier network topology core Example in a network with two tiers (ToR and core): Each ToR switch connected to a subset of core switches Path selection entails assigning a core switch to each packet Path selection through edge coloring in four steps: (1) Input: matching of src/dst pairs (2) Bipartite graph of ToR switches where src/dst pairs of every packet are connected (3) Color edges so that no two edges incident on the same ToR have the same color (4) Colors identify which core switch a packet is using (1) (2) (3) (4) 36

37 Path selection using edge coloring ToR 2-tier network topology core Example in a network with two tiers (ToR and core): Each ToR switch connected to a subset of core switches Path selection entails assigning a core switch to each packet Path selection through edge coloring in four steps: (1) Input: matching of src/dst pairs (2) Bipartite graph of ToR switches where src/dst pairs of every packet are connected (3) Color edges so that no two edges incident on the same ToR have the same color (4) Colors identify which core switch a packet is using (1) (2) (3) (4) 37

38 Path selection using edge coloring ToR 2-tier network topology core Example in a network with two tiers (ToR and core): Each ToR switch connected to a subset of core switches Path selection entails assigning a core switch to each packet Path selection through edge coloring in four steps: (1) Input: matching of src/dst pairs (2) Bipartite graph of ToR switches where src/dst pairs of every packet are connected (3) Color edges so that no two edges incident on the same ToR have the same color (4) Colors identify which core switch a packet is using (1) (2) (3) (4) 38

39 Path selection using edge coloring ToR 2-tier network topology core Example in a network with two tiers (ToR and core): Each ToR switch connected to a subset of core switches Path selection entails assigning a core switch to each packet Path selection through edge coloring in four steps: (1) Input: matching of src/dst pairs (2) Bipartite graph of ToR switches where src/dst pairs of every packet are connected (3) Color edges so that no two edges incident on the same ToR have the same color (4) Colors identify which core switch a packet is using (1) (2) (3) (4) 39

40 Ping roundtrip times Setup: Single rack with 32 servers 4 servers generate traffic to a single receiver Fastpass reduces the end-to-end latency by 15x 40

41 Queue length Setup: Single rack with 32 servers 4 servers generate traffic to a single receiver Fastpass reduces the queue length by 242x 41

42 Summary Two typical traffic patterns in data processing applications running in datacenters are Broadcast Shuffle Uncoordinated network transfers lead to inefficiences Two research efforts: Orchestra: coordinate network transfers within a MapReduce job Fastpass: coordinate packet transfers and paths used across a data center 42

43 References Managing Data Transfers in Computer Clusters with Orchestra, Sigcomm 2011 Fastpass: A centralized Zero-Queue Datacenter Network, Sigcomm

Advanced Computer Networks. Datacenter Network Fabric

Advanced Computer Networks. Datacenter Network Fabric Advanced Computer Networks 263 3501 00 Datacenter Network Fabric Patrick Stuedi Spring Semester 2014 Oriana Riva, Department of Computer Science ETH Zürich 1 Outline Last week Today Supercomputer networking

More information

Advanced Computer Networks. Layer-7-Switching and Loadbalancing

Advanced Computer Networks. Layer-7-Switching and Loadbalancing Oriana Riva, Department of Computer Science ETH Zürich Advanced Computer Networks 263-3501-00 Layer-7-Switching and Loadbalancing Patrick Stuedi, Qin Yin and Timothy Roscoe Spring Semester 2015 Outline

More information

Load Balancing in Data Center Networks

Load Balancing in Data Center Networks Load Balancing in Data Center Networks Henry Xu Computer Science City University of Hong Kong HKUST, March 2, 2015 Background Aggregator Aggregator Aggregator Worker Worker Worker Worker Low latency for

More information

Fastpass: A Centralized Zero-Queue Datacenter Network

Fastpass: A Centralized Zero-Queue Datacenter Network Fastpass: A Centralized Zero-Queue Datacenter Network Jonathan Perry, Amy Ousterhout, Hari Balakrishnan, Devavrat Shah, Hans Fugal M.I.T. Computer Science & Artificial Intelligence Lab Facebook http://fastpass.mit.edu/

More information

Managing Data Transfers in Computer Clusters with Orchestra

Managing Data Transfers in Computer Clusters with Orchestra Managing Data Transfers in Computer Clusters with Orchestra Mosharaf Chowdhury, Matei Zaharia, Justin Ma, Michael I. Jordan, Ion Stoica University of California, Berkeley {mosharaf, matei, jtma, jordan,

More information

Advanced Computer Networks. Introduction

Advanced Computer Networks. Introduction Oriana Riva, Department of Computer Science ETH Zürich Advanced Computer Networks 263-3501-00 Introduction Patrick Stuedi, Qin Yin and Timothy Roscoe Spring Semester 2015 Information about the course http://www.systems.ethz.ch/courses/spring2014/acn

More information

Decentralized Task-Aware Scheduling for Data Center Networks

Decentralized Task-Aware Scheduling for Data Center Networks Decentralized Task-Aware Scheduling for Data Center Networks Fahad R. Dogar, Thomas Karagiannis, Hitesh Ballani, Ant Rowstron Presented by Eric Dong (yd2dong) October 30, 2015 Tasks in data centers Applications

More information

From GWS to MapReduce: Google s Cloud Technology in the Early Days

From GWS to MapReduce: Google s Cloud Technology in the Early Days Large-Scale Distributed Systems From GWS to MapReduce: Google s Cloud Technology in the Early Days Part II: MapReduce in a Datacenter COMP6511A Spring 2014 HKUST Lin Gu lingu@ieee.org MapReduce/Hadoop

More information

BM 465E Distributed Systems

BM 465E Distributed Systems BM 465E Distributed Systems Lecture 4 Networking (cont.) Mehmet Demirci Today Overlay networks Data centers Content delivery networks Overlay Network A virtual network built on top of another network Overlay

More information

MMPTCP: A Novel Transport Protocol for Data Centre Networks

MMPTCP: A Novel Transport Protocol for Data Centre Networks MMPTCP: A Novel Transport Protocol for Data Centre Networks Morteza Kheirkhah FoSS, Department of Informatics, University of Sussex Modern Data Centre Networks FatTree It provides full bisection bandwidth

More information

Networking in the Hadoop Cluster

Networking in the Hadoop Cluster Hadoop and other distributed systems are increasingly the solution of choice for next generation data volumes. A high capacity, any to any, easily manageable networking layer is critical for peak Hadoop

More information

Load Balancing Mechanisms in Data Center Networks

Load Balancing Mechanisms in Data Center Networks Load Balancing Mechanisms in Data Center Networks Santosh Mahapatra Xin Yuan Department of Computer Science, Florida State University, Tallahassee, FL 33 {mahapatr,xyuan}@cs.fsu.edu Abstract We consider

More information

Software Defined Network Support for Real Distributed Systems

Software Defined Network Support for Real Distributed Systems Software Defined Network Support for Real Distributed Systems Chen Liang Project for Reading and Research Spring 2012, Fall 2012 Abstract Software defined network is an emerging technique that allow users

More information

Coflow! A Networking Abstraction For Cluster Applications! Mosharaf Chowdhury Ion Stoica! UC#Berkeley#

Coflow! A Networking Abstraction For Cluster Applications! Mosharaf Chowdhury Ion Stoica! UC#Berkeley# Coflow A Networking Abstraction For Cluster Applications Mosharaf Chowdhury Ion Stoica UC#Berkeley# Cluster Applications Multi-Stage Data Flows» Computation interleaved with communication Computation»

More information

Improving Flow Completion Time for Short Flows in Datacenter Networks

Improving Flow Completion Time for Short Flows in Datacenter Networks Improving Flow Completion Time for Short Flows in Datacenter Networks Sijo Joy, Amiya Nayak School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada {sjoy028, nayak}@uottawa.ca

More information

D1.2 Network Load Balancing

D1.2 Network Load Balancing D1. Network Load Balancing Ronald van der Pol, Freek Dijkstra, Igor Idziejczak, and Mark Meijerink SARA Computing and Networking Services, Science Park 11, 9 XG Amsterdam, The Netherlands June ronald.vanderpol@sara.nl,freek.dijkstra@sara.nl,

More information

Large-Scale Distributed Systems. Datacenter Networks. COMP6511A Spring 2014 HKUST. Lin Gu lingu@ieee.org

Large-Scale Distributed Systems. Datacenter Networks. COMP6511A Spring 2014 HKUST. Lin Gu lingu@ieee.org Large-Scale Distributed Systems Datacenter Networks COMP6511A Spring 2014 HKUST Lin Gu lingu@ieee.org Datacenter Networking Major Components of a Datacenter Computing hardware (equipment racks) Power supply

More information

Load Balancing in Data Center Networks

Load Balancing in Data Center Networks Load Balancing in Data Center Networks Henry Xu Department of Computer Science City University of Hong Kong ShanghaiTech Symposium on Data Scinece June 23, 2015 Today s plan Overview of research in DCN

More information

Question: 3 When using Application Intelligence, Server Time may be defined as.

Question: 3 When using Application Intelligence, Server Time may be defined as. 1 Network General - 1T6-521 Application Performance Analysis and Troubleshooting Question: 1 One component in an application turn is. A. Server response time B. Network process time C. Application response

More information

Sustainable Network Resource Management System for Virtual Private Clouds

Sustainable Network Resource Management System for Virtual Private Clouds Sustainable Network Resource Management System for Virtual Private Clouds Takahiro Miyamoto Michiaki Hayashi Kosuke Nishimura KDDI R&D Laboratories Inc. Cloud computing environment Infrastructure as a

More information

Paolo Costa costa@imperial.ac.uk

Paolo Costa costa@imperial.ac.uk joint work with Ant Rowstron, Austin Donnelly, and Greg O Shea (MSR Cambridge) Hussam Abu-Libdeh, Simon Schubert (Interns) Paolo Costa costa@imperial.ac.uk Paolo Costa CamCube - Rethinking the Data Center

More information

Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani

Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani Improving the Scalability of Data Center Networks with Traffic-aware Virtual Machine Placement Xiaoqiao Meng, Vasileios Pappas, Li Zhang IBM T.J. Watson Research Center Presented by: Payman Khani Overview:

More information

Tech Report TR-WP3-6-2.9.2013 Analyzing Virtualized Datacenter Hadoop Deployments Version 1.0

Tech Report TR-WP3-6-2.9.2013 Analyzing Virtualized Datacenter Hadoop Deployments Version 1.0 Longitudinal Analytics of Web Archive data European Commission Seventh Framework Programme Call: FP7-ICT-2009-5, Activity: ICT-2009.1.6 Contract No: 258105 Tech Report TR-WP3-6-2.9.2013 Analyzing Virtualized

More information

3. MONITORING AND TESTING THE ETHERNET NETWORK

3. MONITORING AND TESTING THE ETHERNET NETWORK 3. MONITORING AND TESTING THE ETHERNET NETWORK 3.1 Introduction The following parameters are covered by the Ethernet performance metrics: Latency (delay) the amount of time required for a frame to travel

More information

CHAPTER 2. QoS ROUTING AND ITS ROLE IN QOS PARADIGM

CHAPTER 2. QoS ROUTING AND ITS ROLE IN QOS PARADIGM CHAPTER 2 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 22 QoS ROUTING AND ITS ROLE IN QOS PARADIGM 2.1 INTRODUCTION As the main emphasis of the present research work is on achieving QoS in routing, hence this

More information

CSE-E5430 Scalable Cloud Computing Lecture 2

CSE-E5430 Scalable Cloud Computing Lecture 2 CSE-E5430 Scalable Cloud Computing Lecture 2 Keijo Heljanko Department of Computer Science School of Science Aalto University keijo.heljanko@aalto.fi 14.9-2015 1/36 Google MapReduce A scalable batch processing

More information

Providing Reliable Service in Data-center Networks

Providing Reliable Service in Data-center Networks Providing Reliable Service in Data-center Networks A.Suresh 1, S. Jaya Kumar 2 1 M.Tech (CSE) Student, Department of CSE, SRM University, Ramapuram, Chennai, India suresh_hce2004@yahoo.co.in 2 Assistant

More information

Broadcom Smart-Buffer Technology in Data Center Switches for Cost-Effective Performance Scaling of Cloud Applications

Broadcom Smart-Buffer Technology in Data Center Switches for Cost-Effective Performance Scaling of Cloud Applications Broadcom Smart-Buffer Technology in Data Center Switches for Cost-Effective Performance Scaling of Cloud Applications Sujal Das Product Marketing Director Network Switching Rochan Sankar Associate Product

More information

Outline. VL2: A Scalable and Flexible Data Center Network. Problem. Introduction 11/26/2012

Outline. VL2: A Scalable and Flexible Data Center Network. Problem. Introduction 11/26/2012 VL2: A Scalable and Flexible Data Center Network 15744: Computer Networks, Fall 2012 Presented by Naveen Chekuri Outline Introduction Solution Approach Design Decisions Addressing and Routing Evaluation

More information

Switching Architectures for Cloud Network Designs

Switching Architectures for Cloud Network Designs Overview Networks today require predictable performance and are much more aware of application flows than traditional networks with static addressing of devices. Enterprise networks in the past were designed

More information

ICTCP: Incast Congestion Control for TCP in Data Center Networks

ICTCP: Incast Congestion Control for TCP in Data Center Networks ICTCP: Incast Congestion Control for TCP in Data Center Networks Haitao Wu, Zhenqian Feng, Chuanxiong Guo, Yongguang Zhang {hwu, v-zhfe, chguo, ygz}@microsoft.com, Microsoft Research Asia, China School

More information

Apache Hadoop. Alexandru Costan

Apache Hadoop. Alexandru Costan 1 Apache Hadoop Alexandru Costan Big Data Landscape No one-size-fits-all solution: SQL, NoSQL, MapReduce, No standard, except Hadoop 2 Outline What is Hadoop? Who uses it? Architecture HDFS MapReduce Open

More information

Trevi: Watering down storage hotspots with cool fountain codes. Toby Moncaster University of Cambridge

Trevi: Watering down storage hotspots with cool fountain codes. Toby Moncaster University of Cambridge Trevi: Watering down storage hotspots with cool fountain codes Toby Moncaster University of Cambridge Trevi summary Ø Trevi is a cool new approach to data centre storage Ø based on exis;ng ideas that are

More information

Open source software framework designed for storage and processing of large scale data on clusters of commodity hardware

Open source software framework designed for storage and processing of large scale data on clusters of commodity hardware Open source software framework designed for storage and processing of large scale data on clusters of commodity hardware Created by Doug Cutting and Mike Carafella in 2005. Cutting named the program after

More information

Strategies. Addressing and Routing

Strategies. Addressing and Routing Strategies Circuit switching: carry bit streams original telephone network Packet switching: store-and-forward messages Internet Spring 2007 CSE 30264 14 Addressing and Routing Address: byte-string that

More information

Distributed Systems. 23. Content Delivery Networks (CDN) Paul Krzyzanowski. Rutgers University. Fall 2015

Distributed Systems. 23. Content Delivery Networks (CDN) Paul Krzyzanowski. Rutgers University. Fall 2015 Distributed Systems 23. Content Delivery Networks (CDN) Paul Krzyzanowski Rutgers University Fall 2015 November 17, 2015 2014-2015 Paul Krzyzanowski 1 Motivation Serving web content from one location presents

More information

Big Data Processing with Google s MapReduce. Alexandru Costan

Big Data Processing with Google s MapReduce. Alexandru Costan 1 Big Data Processing with Google s MapReduce Alexandru Costan Outline Motivation MapReduce programming model Examples MapReduce system architecture Limitations Extensions 2 Motivation Big Data @Google:

More information

PACE Your Network: Fair and Controllable Multi- Tenant Data Center Networks

PACE Your Network: Fair and Controllable Multi- Tenant Data Center Networks PACE Your Network: Fair and Controllable Multi- Tenant Data Center Networks Tiago Carvalho Carnegie Mellon University and Universidade de Lisboa Hyong S. Kim Carnegie Mellon University Pittsburgh, PA,

More information

Advanced Computer Networks. High Performance Networking I

Advanced Computer Networks. High Performance Networking I Advanced Computer Networks 263 3501 00 High Performance Networking I Patrick Stuedi Spring Semester 2014 1 Oriana Riva, Department of Computer Science ETH Zürich Outline Last week: Wireless TCP Today:

More information

Giving life to today s media distribution services

Giving life to today s media distribution services Giving life to today s media distribution services FIA - Future Internet Assembly Athens, 17 March 2014 Presenter: Nikolaos Efthymiopoulos Network architecture & Management Group Copyright University of

More information

Advanced Computer Networks Project 2: File Transfer Application

Advanced Computer Networks Project 2: File Transfer Application 1 Overview Advanced Computer Networks Project 2: File Transfer Application Assigned: April 25, 2014 Due: May 30, 2014 In this assignment, you will implement a file transfer application. The application

More information

Big Data With Hadoop

Big Data With Hadoop With Saurabh Singh singh.903@osu.edu The Ohio State University February 11, 2016 Overview 1 2 3 Requirements Ecosystem Resilient Distributed Datasets (RDDs) Example Code vs Mapreduce 4 5 Source: [Tutorials

More information

Application Performance Analysis and Troubleshooting

Application Performance Analysis and Troubleshooting Exam : 1T6-520 Title : Application Performance Analysis and Troubleshooting Version : DEMO 1 / 6 1. When optimizing application efficiency, an improvement in efficiency from the current 90% to an efficiency

More information

Computer Networks COSC 6377

Computer Networks COSC 6377 Computer Networks COSC 6377 Lecture 25 Fall 2011 November 30, 2011 1 Announcements Grades will be sent to each student for verificagon P2 deadline extended 2 Large- scale computagon Search Engine Tasks

More information

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh

Hadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets

More information

MapReduce Jeffrey Dean and Sanjay Ghemawat. Background context

MapReduce Jeffrey Dean and Sanjay Ghemawat. Background context MapReduce Jeffrey Dean and Sanjay Ghemawat Background context BIG DATA!! o Large-scale services generate huge volumes of data: logs, crawls, user databases, web site content, etc. o Very useful to be able

More information

Introduction to Big Data! with Apache Spark" UC#BERKELEY#

Introduction to Big Data! with Apache Spark UC#BERKELEY# Introduction to Big Data! with Apache Spark" UC#BERKELEY# This Lecture" The Big Data Problem" Hardware for Big Data" Distributing Work" Handling Failures and Slow Machines" Map Reduce and Complex Jobs"

More information

RAMCloud and the Low- Latency Datacenter. John Ousterhout Stanford University

RAMCloud and the Low- Latency Datacenter. John Ousterhout Stanford University RAMCloud and the Low- Latency Datacenter John Ousterhout Stanford University Most important driver for innovation in computer systems: Rise of the datacenter Phase 1: large scale Phase 2: low latency Introduction

More information

The Software Defined Hybrid Packet Optical Datacenter Network SDN AT LIGHT SPEED TM. 2012-13 CALIENT Technologies www.calient.

The Software Defined Hybrid Packet Optical Datacenter Network SDN AT LIGHT SPEED TM. 2012-13 CALIENT Technologies www.calient. The Software Defined Hybrid Packet Optical Datacenter Network SDN AT LIGHT SPEED TM 2012-13 CALIENT Technologies www.calient.net 1 INTRODUCTION In datacenter networks, video, mobile data, and big data

More information

Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks

Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks Friends, not Foes Synthesizing Existing Transport Strategies for Data Center Networks Ali Munir Michigan State University Ghufran Baig, Syed M. Irteza, Ihsan A. Qazi, Alex X. Liu, Fahad R. Dogar Data Center

More information

Beyond the Stars: Revisiting Virtual Cluster Embeddings

Beyond the Stars: Revisiting Virtual Cluster Embeddings Beyond the Stars: Revisiting Virtual Cluster Embeddings Matthias Rost Technische Universität Berlin September 7th, 2015, Télécom-ParisTech Joint work with Carlo Fuerst, Stefan Schmid Published in ACM SIGCOMM

More information

Airlift: Video Conferencing as a Cloud Service using Inter- Datacenter Networks

Airlift: Video Conferencing as a Cloud Service using Inter- Datacenter Networks Airlift: Video Conferencing as a Cloud Service using Inter- Datacenter Networks Yuan Feng Baochun Li Bo Li University of Toronto HKUST 1 Multi-party video conferencing 2 Multi-party video conferencing

More information

Data Center Network Topologies: FatTree

Data Center Network Topologies: FatTree Data Center Network Topologies: FatTree Hakim Weatherspoon Assistant Professor, Dept of Computer Science CS 5413: High Performance Systems and Networking September 22, 2014 Slides used and adapted judiciously

More information

Importance of Data locality

Importance of Data locality Importance of Data Locality - Gerald Abstract Scheduling Policies Test Applications Evaluation metrics Tests in Hadoop Test environment Tests Observations Job run time vs. Mmax Job run time vs. number

More information

Network Virtualization and Data Center Networks 263-3825-00 Data Center Virtualization - Basics. Qin Yin Fall Semester 2013

Network Virtualization and Data Center Networks 263-3825-00 Data Center Virtualization - Basics. Qin Yin Fall Semester 2013 Network Virtualization and Data Center Networks 263-3825-00 Data Center Virtualization - Basics Qin Yin Fall Semester 2013 1 Walmart s Data Center 2 Amadeus Data Center 3 Google s Data Center 4 Data Center

More information

Datacenter Operating Systems

Datacenter Operating Systems Datacenter Operating Systems CSE451 Simon Peter With thanks to Timothy Roscoe (ETH Zurich) Autumn 2015 This Lecture What s a datacenter Why datacenters Types of datacenters Hyperscale datacenters Major

More information

Applications. Network Application Performance Analysis. Laboratory. Objective. Overview

Applications. Network Application Performance Analysis. Laboratory. Objective. Overview Laboratory 12 Applications Network Application Performance Analysis Objective The objective of this lab is to analyze the performance of an Internet application protocol and its relation to the underlying

More information

Hadoop. History and Introduction. Explained By Vaibhav Agarwal

Hadoop. History and Introduction. Explained By Vaibhav Agarwal Hadoop History and Introduction Explained By Vaibhav Agarwal Agenda Architecture HDFS Data Flow Map Reduce Data Flow Hadoop Versions History Hadoop version 2 Hadoop Architecture HADOOP (HDFS) Data Flow

More information

基 於 SDN 與 可 程 式 化 硬 體 架 構 之 雲 端 網 路 系 統 交 換 器

基 於 SDN 與 可 程 式 化 硬 體 架 構 之 雲 端 網 路 系 統 交 換 器 基 於 SDN 與 可 程 式 化 硬 體 架 構 之 雲 端 網 路 系 統 交 換 器 楊 竹 星 教 授 國 立 成 功 大 學 電 機 工 程 學 系 Outline Introduction OpenFlow NetFPGA OpenFlow Switch on NetFPGA Development Cases Conclusion 2 Introduction With the proposal

More information

Three Key Design Considerations of IP Video Surveillance Systems

Three Key Design Considerations of IP Video Surveillance Systems Three Key Design Considerations of IP Video Surveillance Systems 2012 Moxa Inc. All rights reserved. Three Key Design Considerations of IP Video Surveillance Systems Copyright Notice 2012 Moxa Inc. All

More information

Definition. A Historical Example

Definition. A Historical Example Overlay Networks This lecture contains slides created by Ion Stoica (UC Berkeley). Slides used with permission from author. All rights remain with author. Definition Network defines addressing, routing,

More information

Latency Monitoring Tool on Cisco Nexus Switches: Troubleshoot Network Latency

Latency Monitoring Tool on Cisco Nexus Switches: Troubleshoot Network Latency White Paper Latency Monitoring Tool on Cisco Nexus Switches: Troubleshoot Network Latency Introduction Networks often encounter problems related to latency. Troubleshooting such problems can be complicated.

More information

Internet Management and Measurements Measurements

Internet Management and Measurements Measurements Internet Management and Measurements Measurements Ramin Sadre, Aiko Pras Design and Analysis of Communication Systems Group University of Twente, 2010 Measurements What is being measured? Why do you measure?

More information

LOAD BALANCING WITH SDN/NFV

LOAD BALANCING WITH SDN/NFV LOAD BALANCING WITH SDN/NFV Gert Grammel CTO OFFICE PROBLEM STATEMENT. Quality of Experience over a shared medium relies on flow identification and control. Applications have a limited capability to deal

More information

Lecture 15: Congestion Control. CSE 123: Computer Networks Stefan Savage

Lecture 15: Congestion Control. CSE 123: Computer Networks Stefan Savage Lecture 15: Congestion Control CSE 123: Computer Networks Stefan Savage Overview Yesterday: TCP & UDP overview Connection setup Flow control: resource exhaustion at end node Today: Congestion control Resource

More information

Centec s SDN Switch Built from the Ground Up to Deliver an Optimal Virtual Private Cloud

Centec s SDN Switch Built from the Ground Up to Deliver an Optimal Virtual Private Cloud Centec s SDN Switch Built from the Ground Up to Deliver an Optimal Virtual Private Cloud Table of Contents Virtualization Fueling New Possibilities Virtual Private Cloud Offerings... 2 Current Approaches

More information

DiFS: Distributed Flow Scheduling for Adaptive Routing in Hierarchical Data Center Networks

DiFS: Distributed Flow Scheduling for Adaptive Routing in Hierarchical Data Center Networks : Distributed Flow Scheduling for Adaptive Routing in Hierarchical Data Center Networks ABSTRACT Wenzhi Cui Department of Computer Science The University of Texas at Austin Austin, Texas, 78712 wc8348@cs.utexas.edu

More information

Energy Efficient MapReduce

Energy Efficient MapReduce Energy Efficient MapReduce Motivation: Energy consumption is an important aspect of datacenters efficiency, the total power consumption in the united states has doubled from 2000 to 2005, representing

More information

Weekly Report. Hadoop Introduction. submitted By Anurag Sharma. Department of Computer Science and Engineering. Indian Institute of Technology Bombay

Weekly Report. Hadoop Introduction. submitted By Anurag Sharma. Department of Computer Science and Engineering. Indian Institute of Technology Bombay Weekly Report Hadoop Introduction submitted By Anurag Sharma Department of Computer Science and Engineering Indian Institute of Technology Bombay Chapter 1 What is Hadoop? Apache Hadoop (High-availability

More information

MPLS-TP. Future Ready. Today. Introduction. Connection Oriented Transport

MPLS-TP. Future Ready. Today. Introduction. Connection Oriented Transport MPLS-TP Future Ready. Today Introduction As data traffic started dominating telecom networks, there was a need for transport data networks, as opposed to transport TDM networks. Traditional transport technologies

More information

Data Warehousing and Analytics Infrastructure at Facebook. Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com

Data Warehousing and Analytics Infrastructure at Facebook. Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com Data Warehousing and Analytics Infrastructure at Facebook Ashish Thusoo & Dhruba Borthakur athusoo,dhruba@facebook.com Overview Challenges in a Fast Growing & Dynamic Environment Data Flow Architecture,

More information

Large-Scale TCP Packet Flow Analysis for Common Protocols Using Apache Hadoop

Large-Scale TCP Packet Flow Analysis for Common Protocols Using Apache Hadoop Large-Scale TCP Packet Flow Analysis for Common Protocols Using Apache Hadoop R. David Idol Department of Computer Science University of North Carolina at Chapel Hill david.idol@unc.edu http://www.cs.unc.edu/~mxrider

More information

Investigation and Comparison of MPLS QoS Solution and Differentiated Services QoS Solutions

Investigation and Comparison of MPLS QoS Solution and Differentiated Services QoS Solutions Investigation and Comparison of MPLS QoS Solution and Differentiated Services QoS Solutions Steve Gennaoui, Jianhua Yin, Samuel Swinton, and * Vasil Hnatyshin Department of Computer Science Rowan University

More information

Big Data in the Enterprise: Network Design Considerations

Big Data in the Enterprise: Network Design Considerations White Paper Big Data in the Enterprise: Network Design Considerations What You Will Learn This document examines the role of big data in the enterprise as it relates to network design considerations. It

More information

Hadoop Cluster Applications

Hadoop Cluster Applications Hadoop Overview Data analytics has become a key element of the business decision process over the last decade. Classic reporting on a dataset stored in a database was sufficient until recently, but yesterday

More information

Implementing Replication for Predictability within Apache Thrift

Implementing Replication for Predictability within Apache Thrift Implementing Replication for Predictability within Apache Thrift Jianwei Tu The Ohio State University tu.118@osu.edu ABSTRACT Interactive applications, such as search, social networking and retail, hosted

More information

From Centralization to Distribution: A Comparison of File Sharing Protocols

From Centralization to Distribution: A Comparison of File Sharing Protocols From Centralization to Distribution: A Comparison of File Sharing Protocols Xu Wang, Teng Long and Alan Sussman Department of Computer Science, University of Maryland, College Park, MD, 20742 August, 2015

More information

Real-time apps and Quality of Service

Real-time apps and Quality of Service Real-time apps and Quality of Service Focus What transports do applications need? What network mechanisms provide which kinds of quality assurances? Topics Real-time versus Elastic applications Adapting

More information

This exam contains 13 pages (including this cover page) and 18 questions. Check to see if any pages are missing.

This exam contains 13 pages (including this cover page) and 18 questions. Check to see if any pages are missing. Big Data Processing 2013-2014 Q2 April 7, 2014 (Resit) Lecturer: Claudia Hauff Time Limit: 180 Minutes Name: Answer the questions in the spaces provided on this exam. If you run out of room for an answer,

More information

CH.1. Lecture # 2. Computer Networks and the Internet. Eng. Wafaa Audah. Islamic University of Gaza. Faculty of Engineering

CH.1. Lecture # 2. Computer Networks and the Internet. Eng. Wafaa Audah. Islamic University of Gaza. Faculty of Engineering Islamic University of Gaza Faculty of Engineering Computer Engineering Department Networks Discussion ECOM 4021 Lecture # 2 CH1 Computer Networks and the Internet By Feb 2013 (Theoretical material: page

More information

Globus Striped GridFTP Framework and Server. Raj Kettimuthu, ANL and U. Chicago

Globus Striped GridFTP Framework and Server. Raj Kettimuthu, ANL and U. Chicago Globus Striped GridFTP Framework and Server Raj Kettimuthu, ANL and U. Chicago Outline Introduction Features Motivation Architecture Globus XIO Experimental Results 3 August 2005 The Ohio State University

More information

A Simulation Study of Effect of MPLS on Latency over a Wide Area Network (WAN)

A Simulation Study of Effect of MPLS on Latency over a Wide Area Network (WAN) A Simulation Study of Effect of MPLS on Latency over a Wide Area Network (WAN) Adeyinka A. Adewale, Samuel N. John, and Charles Ndujiuba 1 Department of Electrical and Information Engineering, Covenant

More information

Introduction to Cloud Computing

Introduction to Cloud Computing Introduction to Cloud Computing Cloud Computing I (intro) 15 319, spring 2010 2 nd Lecture, Jan 14 th Majd F. Sakr Lecture Motivation General overview on cloud computing What is cloud computing Services

More information

NetAgg: Using Middleboxes for Application-specific On-path Aggregation in Data Centres

NetAgg: Using Middleboxes for Application-specific On-path Aggregation in Data Centres : Using Middleboxes for Application-specific On-path regation in Data Centres Luo Mai Lukas Rupprecht Abdul Alim Paolo Costa Matteo Migliavacca Peter Pietzuch Alexander L. Wolf Imperial College London

More information

Empowering Software Defined Network Controller with Packet-Level Information

Empowering Software Defined Network Controller with Packet-Level Information Empowering Software Defined Network Controller with Packet-Level Information Sajad Shirali-Shahreza, Yashar Ganjali Department of Computer Science, University of Toronto, Toronto, Canada Abstract Packet

More information

An Investigation into Data Center Congestion Control with ECN

An Investigation into Data Center Congestion Control with ECN An Investigation into Data Center Congestion Control with ECN Randall R. Stewart Michael Tüxen George V. Neville-Neil February 24, 2011 Abstract Data centers pose a unique set of demands on any transport

More information

Cloud Computing Summary and Preparation for Examination

Cloud Computing Summary and Preparation for Examination Basics of Cloud Computing Lecture 8 Cloud Computing Summary and Preparation for Examination Satish Srirama Outline Quick recap of what we have learnt as part of this course How to prepare for the examination

More information

Stability of QOS. Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu

Stability of QOS. Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu Stability of QOS Avinash Varadarajan, Subhransu Maji {avinash,smaji}@cs.berkeley.edu Abstract Given a choice between two services, rest of the things being equal, it is natural to prefer the one with more

More information

Distributed Systems. 25. Content Delivery Networks (CDN) 2014 Paul Krzyzanowski. Rutgers University. Fall 2014

Distributed Systems. 25. Content Delivery Networks (CDN) 2014 Paul Krzyzanowski. Rutgers University. Fall 2014 Distributed Systems 25. Content Delivery Networks (CDN) Paul Krzyzanowski Rutgers University Fall 2014 November 16, 2014 2014 Paul Krzyzanowski 1 Motivation Serving web content from one location presents

More information

Final for ECE374 05/06/13 Solution!!

Final for ECE374 05/06/13 Solution!! 1 Final for ECE374 05/06/13 Solution!! Instructions: Put your name and student number on each sheet of paper! The exam is closed book. You have 90 minutes to complete the exam. Be a smart exam taker -

More information

Management & Analysis of Big Data in Zenith Team

Management & Analysis of Big Data in Zenith Team Management & Analysis of Big Data in Zenith Team Zenith Team, INRIA & LIRMM Outline Introduction to MapReduce Dealing with Data Skew in Big Data Processing Data Partitioning for MapReduce Frequent Sequence

More information

T. S. Eugene Ng Rice University

T. S. Eugene Ng Rice University T. S. Eugene Ng Rice University Guohui Wang, David Andersen, Michael Kaminsky, Konstantina Papagiannaki, Eugene Ng, Michael Kozuch, Michael Ryan, "c-through: Part-time Optics in Data Centers, SIGCOMM'10

More information

Data-Intensive Computing with Map-Reduce and Hadoop

Data-Intensive Computing with Map-Reduce and Hadoop Data-Intensive Computing with Map-Reduce and Hadoop Shamil Humbetov Department of Computer Engineering Qafqaz University Baku, Azerbaijan humbetov@gmail.com Abstract Every day, we create 2.5 quintillion

More information

Big Systems, Big Data

Big Systems, Big Data Big Systems, Big Data When considering Big Distributed Systems, it can be noted that a major concern is dealing with data, and in particular, Big Data Have general data issues (such as latency, availability,

More information

Multipath TCP design, and application to data centers. Damon Wischik, Mark Handley, Costin Raiciu, Christopher Pluntke

Multipath TCP design, and application to data centers. Damon Wischik, Mark Handley, Costin Raiciu, Christopher Pluntke Multipath TCP design, and application to data centers Damon Wischik, Mark Handley, Costin Raiciu, Christopher Pluntke Packet switching pools circuits. Multipath pools links : it is Packet Switching 2.0.

More information

PERFORMANCE MODELS FOR APACHE ACCUMULO:

PERFORMANCE MODELS FOR APACHE ACCUMULO: Securely explore your data PERFORMANCE MODELS FOR APACHE ACCUMULO: THE HEAVY TAIL OF A SHAREDNOTHING ARCHITECTURE Chris McCubbin Director of Data Science Sqrrl Data, Inc. I M NOT ADAM FUCHS But perhaps

More information

Portland: how to use the topology feature of the datacenter network to scale routing and forwarding

Portland: how to use the topology feature of the datacenter network to scale routing and forwarding LECTURE 15: DATACENTER NETWORK: TOPOLOGY AND ROUTING Xiaowei Yang 1 OVERVIEW Portland: how to use the topology feature of the datacenter network to scale routing and forwarding ElasticTree: topology control

More information

Lecture 18: Interconnection Networks. CMU 15-418: Parallel Computer Architecture and Programming (Spring 2012)

Lecture 18: Interconnection Networks. CMU 15-418: Parallel Computer Architecture and Programming (Spring 2012) Lecture 18: Interconnection Networks CMU 15-418: Parallel Computer Architecture and Programming (Spring 2012) Announcements Project deadlines: - Mon, April 2: project proposal: 1-2 page writeup - Fri,

More information

PART III. OPS-based wide area networks

PART III. OPS-based wide area networks PART III OPS-based wide area networks Chapter 7 Introduction to the OPS-based wide area network 7.1 State-of-the-art In this thesis, we consider the general switch architecture with full connectivity

More information

Small is Better: Avoiding Latency Traps in Virtualized DataCenters

Small is Better: Avoiding Latency Traps in Virtualized DataCenters Small is Better: Avoiding Latency Traps in Virtualized DataCenters SOCC 2013 Yunjing Xu, Michael Bailey, Brian Noble, Farnam Jahanian University of Michigan 1 Outline Introduction Related Work Source of

More information