Advanced Computer Networks Network Topologies. Patrick Stuedi, Qin Yin, Timothy Roscoe Spring Semester 2015

Transcription

1 Advanced Computer Networks Network Topologies Patrick Stuedi, Qin Yin, Timothy Roscoe Spring Semester

2 What is Data Center? 2

3 Amadeus Data Center 3

4 DATA CENTER EVOLUTION 4

5 Data center evolution phases 5

6 Data Center Evolution 1.0 Computer rooms Large and special installations Supported mainframe systems since 1950 Accommodated IBM and other manufactures central processing units (CPU) and peripherals (storage devices, terminals, printers ) Features Based on a monolithic software architecture A tighter control from an IP perspective A high resource utilization IBM 7094 mainframe computer 6

7 Data Center Evolution 2.0 Background Client-server application model adopted in the 1980s PC became popular Mainframes -> low-end servers Features Low cost of computer hardware Scarce bandwidth of WAN links Closer to the clients and away from the centralized IT management Low-end servers accommodated into distributed and improvised DC Google s First Server 7

8 Data Center Evolution 3.0 Background Internet boom in the 1990s -> Internet data centers Internetworking and web-based applications -> Increased server utilization Client-server model -> Application tiers (dedicated servers) Problems Space and power saturation Low resource utilization 20% of the capacity on average Root cause: application silos 8

9 Data Center Evolution 3.0 Infrastructure consolidation Improve resource utilization Increase operational simplicity Objectives of virtualization technologies Isolation of environments in a consolidate structure Aggregation of discrete resources into a shared pool Simplification of operational procedures FB s data center in Sweden 9

10 HIGH PERFORMANCE COMPUTING 10

11 High-Performance Computing (HPC) Supercomputers Systems designed from scratch Densely packed Short rack-to-rack cables Expensive Built from custom high-end components Mostly run a single program at a time, e.g., message passing (MPI) applications 11

12 IBM Blue Gene P supercomputer 12

13 Blue Gene Supercomputer Overview Blue Gene is a family of supercomputers from IBM BlueGene/L (2004) BlueGene/P (2006) BlueGene/Q (2010) Blue Gene/L: 64K-node highly integrated supercomputer Many of the components (processor, network, router) on the same chip Blue Gene/L: #1 Supercomputer as ranked by Top500 list from November 2004 June

14 BlueGene: Dense Packaging 14

15 Design Motivation: Processor Clock Frequency Scaling Ends Three decades of exponential clock frequency (and electrical power!) and ILP scaling has been flatten Yet Moore s Law continues in transistor count What do we do with all those transistors to keep performance increasing to meet demand? Industry response: Multi-core (i.e. double the number of cores every 18 months instead of the clock frequency (and power!) But, added transistors can be used for other functions such as memory/storage controllers, embedded networks, etc. Source: The New Landscape of Parallel Computer Architecture, John Shalf,,

16 Blue Gene/P System-on-a-Chip Compute Node Network logic is a fraction of compute ASIC complexity/area. BG/P Node Card 16

17 WHAT IS CLOUD COMPUTING 17

18 Cloud Disclaimers Talk from Oracle CEO Larry Ellison We ve redefined Cloud Computing to include everything that we already do. I don t understand what we would do differently other than change the wording of some of our ads. Talk from Rich Stallman It's stupidity. It's worse than stupidity: it's a marketing hype campaign. Somebody is saying this is inevitable and whenever you hear somebody saying that, it's very likely to be a set of businesses campaigning to make it true. 18

19 Cloud Definitions NIST Definition from NIST(National Institute of Standards and Technology) Cloud computing is a model for enabling convenient, ondemand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model promotes availability and is composed of five essential characteristics, three service models, and four deployment models. 19

20 Cloud Definitions Vaquero et al. Clouds are a large pool of easily usable and accessible virtualized resources (such as hardware, development platforms and/or services). These resources can be dynamically reconfigured to adjust to a variable load (scale), allowing also for an optimum resource utilization. This pool of resources is typically exploited by a pay-per-use model guarantees are offered by the Infrastructure Provider by means of customized SLAs A break in the clouds: towards a cloud definition. Vaquero, L. M., Rodero- Merino, L., Caceres, J., and Lindner, M. SIGCOMM Comput. Commun. Rev. 39, 1 (Dec. 2008),

21 Cloud Definitions Berkeley Cloud computing refers to both the applications delivered as services over the Internet (SaaS) and the hardware and systems software in the data centers that provide those services (utility computing) Cloud: the datacenter hardware and software Public cloud: when a cloud is made available in a pay-as-you-go manner to the general public Private cloud: internal data centers of a business or other organization Utility computing: pay-as-you-go computing Illusion of infinite resources Fine-grained billing (e.g. hourly) Berkeley, UC Berkeley RAD Lab, Feb

22 Cloud Definitions Google Warehouse-scale computers, or WSCs Distinguishing feature: the massive scale of software infrastructure, data repositories, and hardware platform of the machines Differ significantly from traditional datacenters Belong to a single organization Use a relatively homogeneous hardware and system software platform Share a common systems management lay Much of the application, middleware, and system software is built inhouse compared to the predominance of third-party software running in conventional datacenters. WSCs run a smaller number of very large applications (or services) The common resource management infrastructure allows significant deployment flexibility The Datacenter as a Computer: an introduction to the Design of Warehouse-Scale Machines. U. Hoelzle et al st. Morgan and Claypool Publishers. 22

23 ESSENTIAL CHARACTERISTICS 23

24 Characteristics I On-demand self-service Unilaterally provision computing capabilities No human interaction with service provider Rapid elasticity Cloud services can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. 24

25 Characteristics II Resource pooling Computing resources are pooled together to serve multiple consumers using multiple-tenant model Different physical and virtual resources dynamically assigned and reassigned according to consumer demand. Resources include storage, processing, memory, network bandwidth, virtual machines, etc. Location independence: the customer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction. The pooling together of the resource builds economies of scale. 25

26 Characteristics III Broad network access Cloud capabilities are available over the network Accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms Measured service Cloud systems automatically control and optimize resource use by leveraging a metering capability Resource usage can be monitored, controlled and reported, providing transparency for the provider and consumer. Pay-per-use 26

27 Exmaple: EC2 Amazon Elastic Computer Cloud (EC2) Compute unit rental Standard On-Demand Instances Small (Default) Medium Large Extra Large $0.060 per Hour $0.120 per Hour $0.240 per Hour $0.480 per Hour No up-front cost, no contract Billing rounded to nearest hour 27

28 Why Now? Old idea: Software as a Service (SaaS) Software hosted in the infrastructure vs. installed on local servers or desktops Build-out of extremely large datacenters (1,000 s to 10,000 s of commodity computers) Economy of scale: 5-7x cheaper than provisioning a medium-sized (100 s machines) facility Build-out driven by demand growth (more users) Infrastructure software: GFS Operational expertise: failover, DDoS, firewalls... Other factors More pervasive broadband Internet x86 as universal ISA, fast virtualization Standard software stack, largely open source 28

29 CLOUD COMPUTING ECONOMICS 29

30 Cloud Computing Economics Static data center Provisioning for peak Wasteful, but necessary for SLA Data center in the cloud Pay-as-you-go Statistical multiplexing 30

31 Risks of underutilization Underutilization results if peak predictions are too optimistic Resources Capacity Demand Unused resources Time Static data center 31

32 Risks of underutilization Even if load can be correctly anticipated Provisioning for peak load wastes resources (shaded area) during no-peak times 32

33 Under-provisioning I Potential revenue from users not served (shaded area) is sacrificed. Resources Capacity Demand Time (days) 33

34 Under-provisioning II Some users desert the site permanently after experiencing poor service This attrition and possible negative press result in a permanent loss of a portion of the revenue stream. Resources Capacity Demand Time (days) 34

35 New Scenarios Demand for a service varies with time E.g. temporal peak loads Demand is unknown in advance Animoto traffic doubled every 12 hours for 3 days when released as Facebook plug-in Scaled from 50 to >3500 servers...then scaled back down Preform batch analytics using cost associativity E.g. using 1000 machines for one hour =$= using one machine for 1000 hours 35

36 Cloud Computing Infrastructures EC2 CPU 1 Memory 1.7G Disk 160G Unit G 850G G 1690G 2 Price $0.125 $0.50 $1.00 S3 Storage Transfer Requests $0.150 per GB $0.100 per GB $0.01 per 10,000 GET vcloud 36

37 Example: Deploying an online service vcloud 37 Challenge 1: Selecting appropriate providers and resources

38 Service deployed with one provider 38

39 Service gains popularity Challenge 2: Adapting to changes in application load 39

40 Provider outage Challenge 3: Responding to provider outage 40

41 SERVICE MODELS AND PROVIDERS 41

42 Classifying Clouds Instruction set VM (Amazon EC2) Managed runtime VM (Microsoft Azure) Framework VM (Google AppEngine) Tradeoff: flexibility/portability vs. build-in functionality Lower-level, Less managed Higher-level, More managed EC2 Azure AppEngine Force.com 42

43 Three Service Models Infrastructure as a Service (IaaS): basic compute and storage resources On-demand servers Amazon EC2, VMWare vcloud Platform as a Service (PaaS): cloud application infrastructure On-demand application-hosting environment E.g. Google AppEngine, Windows Azure, Salesforce.com Software as a Service (SaaS): cloud applications On-demand applications E.g. Gmail, Office

44 Service Model Overview 44

45 INTERCONNECT NETWORKS 45

46 Interconnection Networks Clustering means communication The more CPUs the higher the communication Each processor performs 1/N of the overall work But each one consumes some I/O for input some for output and some for cross processor data Need to keep scaling the I/O by 1/N even though more I/O is produced If not : Ahmdal slaw predicts that I/O will become the bottleneck The technologies built to support these requirements are known as Interconnection Networks 46

47 Network Topology Properties Direct vs. Indirect Direct: No intermediate nodes - connections are only between end-nodes/hosts Indirect: Communication between each two nodes is done through a switch In direct networks nodes often have very few ports (2,3,4, ) Low port networks are also called low-radix networks Elements (e.g. switches) in indirect networks often have higher port numbers (16,32,64,128,...) High port networks are also called high-radix networks 47

48 Network Topology Properties Diameter: the maximum (over all pairs of nodes) of the shortest path between a given pair of nodes The more hops the bigger the latency The more hops the more congestion in the network Bisectional Bandwidth Sum of the bandwidths of the minimum set of channels which, if removed, partition the network into two equal sets of nodes Fault Tolerance Minimal number of links to disconnect a node from any other node Path redundancy Multiple paths between source/destination nodes Affects reliability, bandwidth, etc. 48

49 Fully connected graph Consists of a single switch connecting all inputs to all outputs Diameter = 1 Node Degree = N-1 Fault tolerance Loss of the switch -> loss of the network Loss of a link -> loss of one node Cost? bus => O(N), but BW is O(1) crossbar => O(N2) for BW O(N) VLSI technology determines switch degree VLSI chip density => no. of nodes fully connected by a switch 49

50 Linear arrays and rings Linear Array Torus Torus arranged to use short wires Linear Array Diameter? Average Distance? Bisection bandwidth? Torus? Unidirectional links? Bidirectional links? Easily laid out to use uniformly short wires Distance and cost grow O(N) Aggregated bandwidth O(1) 50

51 Multidimensional Meshes and Torus d-dimensional array n = k d-1 X...X k O nodes described by d-vector of coordinates (i d-1,..., i O ) d-dimensional k-ary mesh: N = k d k = d N described by d-vector of radix k coordinate d-dimensional k-ary torus (or k-ary d-cube) 51

52 d-dimensional (n0,n1 )-size mesh 52

53 d-dimensional k-ary Torus k = {} k= {2} k= {2, 3} k= {2, 2, 2} 53

54 Hyper Cubes / d-dimensional Cube 54

55 Properties Routing relative distance: R = (b d-1 - a d-1,..., b 0 - a 0 ) traverse ri = b i - a i hops in each dimension dimension-order routing Average Distance Wire Length? d x 2k/3 for mesh dk/2 for cube Degree? Bisection bandwidth? Partitioning? k d-1 bidirectional links Physical layout? 2D laid out O(N) space in a plane with short wires higher dimension? 55

56 Real World 2D mesh 1824 node Paragon: 16 x 114 array 56

57 Embedding in two dimensions Embed multiple logical dimension in one physical dimension using long wires 57

58 Next week More on network topologies Core-aggregation-access layered data center architecture Data center network virtualization 58

59 Reference Parallel Computer Architecture: A Hardware/Software Approach David E. Culler and Jaswinder Pal Singh 59