Distributed Data Management

Transcription

1 Distributed Data Management Christoph Lofi José Pinto Christian Nieke Institut für Informationssysteme Technische Universität Braunschweig

2 14.0 The Cloud 14.1 Cloud beyond Storage 14.2 Computing as a Service SaaS PaaS IaaS Distributed Data Management Christoph Lofi IfIS TU Braunschweig 2

3 14.1 The Cloud Distributed Data Management Christoph Lofi IfIS TU Braunschweig 3

4 14.1 The Cloud The term cloud computing is often seen as a successor of client-server architectures Often used as synonym for centralized on-demand pay-what-you-use provisioning of general computation resources e.g. compared to utility providers like electric power grids or water supply Computing as a commodity Cloud is used as a metaphor for the Internet Users or applications just use computation resources provided in the internet instead using local hardware or software Distributed Data Management Christoph Lofi IfIS TU Braunschweig 4

5 14.1 The Cloud Computation resources can mean a lot of things: Dynamic access to raw metal Raw storage space or CPU time Fully operational server are provided by the cloud Low-level services and platforms e.g. runtime platforms like Jave JRE» User can run application directly on cloud platform» No own servers or platform software needed e.g. abstracted storage space like space within a database or a file system» This is what we did in the last weeks! Distributed Data Management Christoph Lofi IfIS TU Braunschweig 5

6 14.1The Cloud Software services i.e. some functionalities required by user software is provided by the cloud» Used via web service remote procedure calls» e.g. delegate a the rendering of a map in a user applciarion to Google Maps Full software functionality e.g. rented web applications replacing traditional server or desktop applications» e.g. rent CRM software online from SalesForce, use Google apps instead of MS Office, etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 6

7 14.1The Cloud Underlying base problem Successfully running IT departments and IT infrastructure can be very difficult and expensive for companies High fixed costs Acquiring and paying competent IT staff Competent is often very hard to get Buying and maintaining servers Correctly hosting hardware Proper power and cooling facilities, network connections, server racks, etc. Buying and maintaining software Distributed Data Management Christoph Lofi IfIS TU Braunschweig 7

8 14.1The Cloud Load and Utilization Issues How much hardware resources are required by each application and / or service? How to handle scaling issues? What happens if demand increases or declines? How to handle spike loads? Digg Effect Traditional data centers are notoriously underutilized, often idle 85% of the time Over provisioning for future growth or spikes Insufficient capacity planning and sizing Improper understanding of scalability requirements etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 8

9 14.1The Cloud Cloud computing centrally unifies computation resources and provides them on-demand Degree of centralization and provision may differ Centralize hardware within a department? A company? A number of companies? Globally? Provide resources only oneself? To some partners? To anybody? How to compensate resource for resource usage? Provide resources by a rental model (e.g. monthly fee)? Provide resources metered on what-is-used basis (e.g. similar to electricity or water?) Provide resources for free? Distributed Data Management Christoph Lofi IfIS TU Braunschweig 9

10 14.1 The Cloud Usually, three types of clouds are distinguished Public Cloud Private Cloud Hybrid Cloud Distributed Data Management Christoph Lofi IfIS TU Braunschweig 10

11 14.1 The Cloud Public Cloud Traditional cloud computing Services and resources are offered via the internet to anybody willing to pay for them User just pays for services, usually no acquisition, administration or maintenance of hardware / software necessary Services usually provided by off-site 3 rd party providers Open for use by general public Exist beyond firewall, fully hosted and managed by the vendor Customers are individuals, corporations and others e.g. Amazon's Web Services and Google AppEngine Offers startups and SMB s quick setup, scalability, flexibility and automated management. Pay as you go model helps startups to start small and go big Security and compliance? Reliability and privacy concerns hinder the adoption of cloud Amazon S3 services were down for 6 hours in 2010 What will Amazon do with all the data? Distributed Data Management Christoph Lofi IfIS TU Braunschweig 11

12 14.1 The Cloud Private Cloud Cloud computing hardware are within the premises of a company behind the cooperate firewall Resources are only provided internally for various departments Private clouds are still fully bought, build, and maintained by the company using it But usually not exclusive to single departments! Still, costs could be prohibitive and may by far exceed that of public clouds Fine grained control over resources More secure as they are internal to organization Schedule and reshuffle resources based on business demands Ideal for apps requiring tight security and regulatory concerns Development requires hardware investments and in-house expertise Distributed Data Management Christoph Lofi IfIS TU Braunschweig 12

13 14.1 The Cloud Hybrid Cloud Both private and public cloud services or even non-cloud services are used or offered simultaneously State-of-art for most companies relying on cloud technology Distributed Data Management Christoph Lofi IfIS TU Braunschweig 13

14 14.1 The Cloud Properties promised by Cloud computing Agility Resources are quickly available when needed Costs i.e. servers must not be ordered and build, software doesn t need to be configured and installed, etc. Capital expenditure is converted to operational expenditure Independence Services are available everywhere and for any device Distributed Data Management Christoph Lofi IfIS TU Braunschweig 14

15 14.1 The Cloud Multi-tenancy Resources are shared by larger pool of users Resources can be centralized which reduces the costs Load distribution of users differs Peak loads can usually be distributed Overall utilization and efficiency of resources is better Reliability Most cloud services promise durable and reliable resources due to distribution and replication Scalability If a user needs more resources or performance, it can easily provisioned Distributed Data Management Christoph Lofi IfIS TU Braunschweig 15

16 14.1The Cloud Low maintenance Cloud services or applications are not installed on user s machines, but maintained centrally by specialized staff Transparency and metering Costs for computation resources are directly visible and transparent Pay-what-you-use models Cloud computing generally promises to be beneficial for fast growing startups, SMBs and enterprises alike. Cost effective solutions to key business demands Improved overall efficiency Distributed Data Management Christoph Lofi IfIS TU Braunschweig 16

17 14.1The Cloud The cloud heavily encourages a self-service model Users can simply request the resources they need from cloudscaling.com Distributed Data Management Christoph Lofi IfIS TU Braunschweig 17

18 14.2 XaaS Anything-as-a-Service XaaS= X as a service In general, cloud providers offer any computation resources as a service In the long run, all computation needs of a company should be modeled, provided and used as a service e.g. in Amazon s private and public cloud infrastructures: everything is a service! Distributed Data Management Christoph Lofi IfIS TU Braunschweig 18

19 14.2 XaaS Services provide a strictly defined functionality with certain guarantees Service description and service-level agreement (SLA) Services description explains what is offered by the service SLA further clarifies the provisioning guarantees Often: performance, latency, reliability, availability, etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 19

20 14.2 XaaS Usually, three main resources may be offered as a service Software as a Service SaaS Platform as a Service PaaS Infrastructure as a Service IaaS Client Application Platform Infrastructure Server Distributed Data Management Christoph Lofi IfIS TU Braunschweig 20

21 14.2 XaaS Application Services (services on demand) Gmail, GoogleCalender Payroll, HR, CRM, etc Sugar CRM, IBM Lotus Live Platform Services (resources on demand) Middleware, Integration, Messaging, Information, connectivity etc Amazon AWS, Boomi, CastIron, Google AppEngine Infrastructure as services (physical assets as services) IBM Blue House, VMWare Cloud Edition, Amazon EC2, Microsoft Azure Platform, Distributed Data Management Christoph Lofi IfIS TU Braunschweig 21

22 14.2 XaaS Individuals Corporations Non-Commercial? CLOUD Cloud Middle Ware Storage Provisioning OS Provisioning Network Provisioning Service(apps) Provisioning SLA(monitor), Security, Billing, Payment Resources Services Storage Network OS Distributed Data Management Christoph Lofi IfIS TU Braunschweig 22

23 14.2 IaaS Infrastructure as a Service (IaaS) Provides raw computation infrastructure, i.e. usually a virtual server e.g. see hardware virtualization (VMWare & co.) Successor to dedicated server rental For the user, a virtual server is similar to a real server Has CPU cores, main memory, hard disc space, etc. Usually provided as self-service raw machine User is responsible for installing and maintaining applications like e.g. operating system, databases or server software User does not need to buy, host or maintain the actual hardware Distributed Data Management Christoph Lofi IfIS TU Braunschweig 23

24 14.2 IaaS The IaaS provider can host multiple virtual servers on a single, real machine Often, virtual severs per real server Virtualization is used to abstract server hardware for virtual servers Virtual system also often called virtual machines (neutral term) or appliances (usually suggesting preinstalled OS and software) Virtualization of hardware is usually handled by a socalled hypervisor, e.g. Xen, KVM, VMWare, HyperV, Distributed Data Management Christoph Lofi IfIS TU Braunschweig 24

25 1 many #appliances 14.2 IaaS In short, IaaS is virtualization on multiple hardware machines Normal Server 1 machine with one OS Traditional virtualization 1 machine hosting multiple virtual servers Distributed Application 1 appliance running on multiple machines IaaS Multiple machines running multiple virtual servers Dynamic load balancing between machines Traditional virtualization Normal server IaaS Distributed Appliance 1 #machines many Distributed Data Management Christoph Lofi IfIS TU Braunschweig 25

26 14.2 IaaS Hypervisor is responsible for allocating available resources to VMs Dispatch VMs to machines Relocate VM to balance load Distribute resources Network adaptors, logical discs, RAM, CPU cores, etc Distributed Data Management Christoph Lofi IfIS TU Braunschweig 26

27 14.2 IaaS Usually, virtual machines offered by IaaS infrastructures cannot grow arbitrarily big Usually capped by actual server size or a smaller server group Really big applications are usually deployed in socalled Pods Similar to database shards Group of machines running one or multiple appliances Machines within a Pod are very tightly networked Distributed Data Management Christoph Lofi IfIS TU Braunschweig 27

28 14.2 IaaS i.e. each Pod is a full copy of given virtual machines with full OS and application installed Usually, there are multiple copies of a given Pod (and its VMs) Each Pod is responsible for a disjoint part of the whole workload Pods are usually scattered across availability zones (e.g. data centers or a certain rack) Physically separated, usually with own power / network, etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 28

29 14.2 IaaS IaaS Pods from CloudScaling.com Distributed Data Management Christoph Lofi IfIS TU Braunschweig 29

30 14.2 IaaS Simplified Pod example: Googl Multiple Pods, each Pod running on multiple machines with a full and independent installation of Gmail software Load balancer decides during user log-in which Pod will handle the user session Users are distributed across Pods Pods are flexible by using shared GFS file system Distributed Data Management Christoph Lofi IfIS TU Braunschweig 30

31 14.2 IaaS Mission critical applications should be designed such that they run in multiple availability zones on multiple Pods Cloud control system (CCS) responsible for distribution and replication Distributed Data Management Christoph Lofi IfIS TU Braunschweig 31

32 14.2 IaaS Pod Architectures Each pod consists of multiple machines with mainboards, CPUs, and main memory Question: where to put secondary storage? Usually, three options Storage area network (SAN) Direct attached storage (DAS) Network attached storage (NAS) or. Storage Service! (e.g. GFS & co.) Distributed Data Management Christoph Lofi IfIS TU Braunschweig 32

33 14.2 IaaS SAN Pods Individual servers don t have own secondary storage Storage area network provides shared hard disks storage for all machines of a Pod Pro All machines have access to the same data Allows for dynamic load balancing or migration of appliances e.g. VMware vmotion Con Very very expensive Higher latency than direct attached storage Distributed Data Management Christoph Lofi IfIS TU Braunschweig 33

34 14.2 IaaS SAN Pods Distributed Data Management Christoph Lofi IfIS TU Braunschweig 34

35 14.2 IaaS DAS Pods Each server has its own set of hard drives Accessing data from other servers may be difficult Pro Cheap Low latency for accessing local data Con Usually, no shared data access Usually, difficult to live-migrate appliances (due to no shared data) But: by using clever storage abstractions, common problems can be circumvented Use distributed file system or a distributed data store! e.g. Apache S3 & SimpleDB, Google GFS & BigTable, Apache HBase & HFS, etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 35

36 14.2 IaaS DAS Pods Distributed Data Management Christoph Lofi IfIS TU Braunschweig 36

37 14.2 Amazon EC2 IaaS example: Amazon EC2 Elastic Compute Cloud is one of the core service of the Amazon Cloud Infrastructure Public IaaS Cloud Customers may rent virtual servers hosted at Amazons Data Centers Can freely install OS and applications as needed Virtual servers are offered in different sizes and are paid by CPU usage Basic storage is offered within the VM, but usually additional storage services are used by application which cost extra e.g. S3, SimpleDB, or Dynamo DB Distributed Data Management Christoph Lofi IfIS TU Braunschweig 37

38 14.2 Amazon EC2 Example: t2.micro 1.0 GB memory 1 vcpu units 1 virtual core 1 vcpu is roughly one 2.5 GHz Xeon core No dedicated storage Has to use AWS network storage Burstable performance: 6 CPU credits per hour 1 CPU credit = 1 minute full CPU performance Costs $0.013 per hour $9,30 per month Usually many users start will the small instance, also heavily used for testing From July 2010 Distributed Data Management Christoph Lofi IfIS TU Braunschweig 38

39 14.2 Amazon EC2 Example: m3.xlarge 15 GB memory 4 vcpu units Total of 13 ECU (Elastic Compute Units) 1 ECU is roughly equal to 1.5GHz Xeon core 80 GB instance storage on SSD More storage via AWS Costs $0.28 per hour $201 per month Distributed Data Management Christoph Lofi IfIS TU Braunschweig 39

40 14.2 Amazon EC2 Example: i2.8xlarge 244 GB of memory 32 vcpu Total of 104 ECU units 6400 GB of instance storage on SSD Costs $6.82 per hour $4910 per month Distributed Data Management Christoph Lofi IfIS TU Braunschweig 40

41 14.2 Amazon EC2 Rough Estimations (Oct 2009) Roughly 40,000 servers Uses standard server racks with 16 machines per rack Mostly packed with 2U dual-socket Quad-Core Intel Xeons Roughly matches the High-Mem Quad XL instance Uses around 8 500GB Raid-0 disks Target cost around $2500 per machine in average 75% of the machines are US, the remainder in Europe and Asia Amazon aims at a utilization rate of 75% Very rough guesses state that Amazon may earn $25,264 per hour with EC2! From Oct 2009 Distributed Data Management Christoph Lofi IfIS TU Braunschweig 41

42 14.2 PaaS Platform as a Service (PaaS) Provides software platforms on demand e.g. runtime engines (JavaVM,.Net Runtime, etc.), storage systems (distributed file system, or databases), web services, communication services, etc. PaaS systems are usually used to develop and host web applications or web services User applications run on the provided platform In contrast to IaaS, no installation and maintenance of operation system and server applications necessary Centrally managed and maintained Services or runtimes are directly usable Distributed Data Management Christoph Lofi IfIS TU Braunschweig 42

43 14.2 Google AppEngine Google AppEngine provides users a managed Phyton or Java Runtime Web applications can be directly hosted in AppEngine Just upload you WAR file and you are done Users are billed by resource usage Some free resources provided everyday 1 GB in- and out traffic, 6.5 hours CPU, 500 MB storage overall Resource Unit Unit cost Outgoing Bandwidth GB $0.12 Incoming Bandwidth GB $0.10 CPU Time CPU hours $0.10 Stored Data GB / month $0.15 Recipients ed recipients $ Distributed Data Management Christoph Lofi IfIS TU Braunschweig 43

44 14.2 Google AppEngine Each application can access system resources up to a fixed maximum AppEngine is not fully scalable! AppEngine max values (2010) CPU: 1730 hours CPU per day; 72 minutes CPU per minute Data in or out: 1 TB per day; 10 GB per minute Request: 43M web service calls per day, 30K calls per minute Data storage: no limit (uses BigTable which can scale in size!!) Distributed Data Management Christoph Lofi IfIS TU Braunschweig 44

45 14.2 Amazon SimpleDB Amazon Simple DB is data storage system roughly similar to Google BigTable Simple table-centric database engine SimpleDB is directly ready to use No user configuration or administration Accessible via web service SimpleDB is highly available, uses flexible schemas, and eventual consistency Similar to HBase or BigTable Distributed Data Management Christoph Lofi IfIS TU Braunschweig 45

46 14.2 Amazon Simple DB Any application may use SimpleDB for data storage Simple web service provided to interact with Simple DB Create or delete a table (called domain) Put and delete rows Query for rows Users pay for storage, data transfer, and computation time 25 hours computation time (for querying) are free per month Later: $0.154 per machine hour in 2009 Later: $0.140 per machine hour in GB of data transfer is free per month Later: $0.15 per GB in 2009 Later: $0.12 per GB in Gb of data storage is free per month Later: $0.28 per GB in 2009 Later: $0.25 per GB in 2014 Distributed Data Management Christoph Lofi IfIS TU Braunschweig 46

47 14.2 SaaS Software as a Service (SaaS) Full applications are offered on-demand User just need to consume the software; no installation or maintenance necessary All administrative and maintenance tasks are performed by the Cloud provider e.g. hosting physical hardware, maintaining platforms, maintaining software, dealing with security, scalability, etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 47

48 14.2 SalesForce Salesforce.com On-Demand CRM software Customer-Relationship-Management Cooperation with Google Apps in early summer Provides simple online services for Customer database Lead management Call center Customer portal Knowledge Bases Collaboration environments Etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 48

49 14.2 SalesForce Distributed Data Management Christoph Lofi IfIS TU Braunschweig 49

50 14.2 SalesForce Distributed Data Management Christoph Lofi IfIS TU Braunschweig 50

51 14.2 SalesForce Bills per month and user, based on edition Distributed Data Management Christoph Lofi IfIS TU Braunschweig 51

52 14.2 Google Apps Google Apps Provides standard office application on-demand i.e. Targeting at the lower-end of the customer base of Microsoft Office MS counters with Office 365 Google Apps provides & Groupware Spreadsheets Documents Presentations Online Forms Drawings etc. Distributed Data Management Christoph Lofi IfIS TU Braunschweig 52

53 14.2 Google Apps Distributed Data Management Christoph Lofi IfIS TU Braunschweig 53

54 Grid Computing at CERN Christian Nieke CERN IT-DSS-DT IfIS Braunschweig 54

55 CERN European Organization for Nuclear Research Running the Large Hadron Collider (LHC) Proton-Proton collider to create short lived exotic particles Grid Computing at CERN - Christian Nieke 55

56 CERN European Organization for Nuclear Research 21/07/2015 Grid Computing at CERN - Christian Nieke 56

57 Data Taken by Experiment Detectors 21/07/2015 Grid Computing at CERN - Christian Nieke 57

58 Reconstruction Turn RAW data into physics events 21/07/2015 Grid Computing at CERN - Christian Nieke 58

59 Reconstruction 21/07/2015 Grid Computing at CERN - Christian Nieke 59

60 Tracks Easy? Grid Computing at CERN - Christian Nieke 60

61 RAW Data Not THAT easy actually 21/07/2015 Grid Computing at CERN - Christian Nieke 61

62 Simulation Comparing to the model Simulated architecture of the detector Very complex, high precision Every sensor, wall and bolt, with their density and material properties Up to 10 µm precision Monte-Carlo Simulation Create random particle decays Based on probability according to standard model Simulate sensor responses Grid Computing at CERN - Christian Nieke 62

63 Data Acquisition Grid Computing at CERN - Christian Nieke 63

64 Processing in the Grid Distributed Tier Architecture Tier-0 (CERN): Data recording Initial data reconstruction Data distribution Tier-1 (12 centres + Russia): Permanent storage Re-processing Analysis Tier-2 (~140 centres): Simulation End-user analysis ~ 160 sites, 35 countries cores 200 PB of storage 2 Million jobs/day 10 Gbps links Grid Computing at CERN - Christian Nieke 64

65 Processing Event Data Embarrassingly Parallel One event = one collision of bunches of protons The next event is independent One event is about 8MB We have a lot of very tiny packages of data Easy to distribute to several (virtual) machines Grid Computing at CERN - Christian Nieke 65

66 Batch Processing Simple Approach for a simple problem Multicore hypervisors run one virtual machine per core Scheduler starts a pilot job Load image of OS, libraries, configurations(remote storage, security tokens) Load shared data sets (e.g. detector geometry, several GB) Fetch job requests, load specific data, run job, repeat Grid Computing at CERN - Christian Nieke 66

67 Why not a Cloud Provider? Under consideration, but: Network storage services Security Specific configurations Payment for transfers into / out of the cloud Costs CERN is still cheaper than Amazon (non-profit) But the gap is closing Political reasons Site provide resources to buy into the CERN cooperation Grid Computing at CERN - Christian Nieke 67

68 Storage Traditional Approach Archiving on tape Low cost (medium, energy, fault tolerance) Online data in distributed disk storage EOS distributed file system Large namespace (500GB in memory) Security and authentication Interfaces to FUSE, http, WebDAV, Based on xroot transport protocol for redirection, failover, localityawareness, Object Stores coming up Grid Computing at CERN - Christian Nieke 68

69 Accounting What do you pay? CERN (and other sites) provide computing resources to the experiments Payment per CPU second But not every CPU second is worth the same! CPU seconds are scaled by performance of the computing node Grid Computing at CERN - Christian Nieke 69

70 Benchmarking Active Benchmark: HepSpec06 Intended to represent typical experiment workload Expensive to perform ~8 hours on every machine Requires empty hypervisor Test once at commissioning Benchmark is sometimes not up to date with actual configuration Grid Computing at CERN - Christian Nieke 70

71 Benchmarking Passive Benchmark Use actual workload as benchmark For free from existing logs Can be repeated at any time BUT: Requires minimal amount of observed jobs Cold start problem Grid Computing at CERN - Christian Nieke 71

72 Summary Simple in Theory Embarrassingly parallel problem Mature technologies Tapes, disks, virtual machines, But the Devil is in the Details Accounting Politics Security Grid Computing at CERN - Christian Nieke 72

73 Next Semester Deductive Databases Information Retrieval Seminar: Linked Open Data Distributed Data Management Christoph Lofi IfIS TU Braunschweig 73

74 Distributed Data Management Thank you for your attention! Distributed Data Management Christoph Lofi IfIS TU Braunschweig 74