Cloud Computing. Up until now

Transcription

1 Cloud Computing Lectures 16 and 17 Cloud Execution and Monitoring Up until now Introduction Definition of Cloud Computing Grid Computing Content Distribution Networks Cycle-Sharing Distributed Scheduling Map Reduce Cloud Storage 1

2 Outline Cloud Execution Platforms and Models: Google App Engine (+ a little programming model for the lab) Amazon EC2 Microsoft Azure Cloud Monitoring: Microsoft Azure: Fabric Controller AWS: CloudWatch, AutoScale, Elastic Load Balancing Google App Engine 2

3 Google Google provides a large set of services: Google Search Google Image Search Google News Gmail Google Scholar Google Book Search Google Maps Google Talk Google Reader Google Analytics Google Calendar Picasa Web Albums Google Docs Google Wave Plus many acquisitions: Youtube, Blogger, Orkut, Google Infra-structure Most services use a set of common infrastructural cloud services: GFS: Google File System BigTable MapReduce These services are not openly available. (Partly justifying Hadoop). 3

4 Google App Engine Google s cloud app programming framework/platform. All apps are web-based and written in Java or Python. Google App Engine only processes HTTP(S) requests. Application configuration is quite simple: There is limited access to performance tuning. The framework is designed for scalability: The goal is to reduce interactions so as not to limit number of apps, requests per second, storage space The APIs are as simple as possible. The cloud infra-structure is almost completely hidden from the programmer and the users. Google App Engine Architecture 4

5 Front-end Identifies the application based on the URL. Separates requests for static and dynamic content. May redirect the request to Google s authentication page. Applications are configured using two XML files: web.xml: index of the site: servlets, jsp, authentication, appengine-web.xml: web app config: URLs, static content path, sessions, services, Execution Environment Applications execute within a sandbox. It is not possible to: Process a request for more than 10 seconds: This is a problem: Accessing the Datastoreis slow. Write to files. Open sockets. Create threads or processes. Make system calls. 5

6 APIs Google App Engine Apps can call GAE sapis for: DataStore, Datastore access. Images, perform image manipulation. Memcache, memory cache management. Mail, sending s. URL Fetch, access other web apps. Users, integration with Google s account/login system. Memory Cache A distributed memory cache (memcache) allows for a faster access to stored data. Data are stored as <key,value> pair: MemcacheService memcache = MemcacheServiceFactory.getMemcacheService(); memcache.put("headlines", headlines); headlines = (List<String>) memcache.get("headlines"); memcache.delete("headlines"); A data item can be an instance of any serializableclass. The memcacheisn t stable. Memcacheentries can be eliminated by the runtime. Memcachecan be configured to store data for limited time. Each entry can have up to 1 MB. 6

7 GAE Static File Servers They serve the static content of the websites. By default, everything outside the application s war file WEB-INF folder. The extensions that are static/dynamic are configurable. Integration with the Google Accounts System The Google accounts system can be used to authenticate users. The programmer configures the login in the web.xml file: <security-constraint> <web-resource-collection> <url-pattern>/profile/*</url-pattern> </web-resource-collection> <auth-constraint> <role-name>*</role-name> </auth-constraint> </security-constraint> 7

8 Integration with the Google Accounts System In the application code, authentication can be performed and the user s id used: if (request.getuserprincipal()!= null) { response.getwriter().println("<p>hello, " + request.getuserprincipal().getname() + "! You can <a href=\"" + userservice.createlogouturl(thisurl) + "\">sign out</a>.</p>"); } else { response.getwriter().println("<p>please <a href=\"" + userservice.createloginurl(thisurl) + "\">sign in</a>.</p>"); } Mail GAE apps may send and receive messages. Sending uses a simple API: Message msg = new MimeMessage(session); msg.setfrom(new InternetAddress("admin@example.com", "Example.com Admin")); msg.addrecipient(message.recipienttype.to, new InternetAddress("user@example.com", "Mr. User")); msg.setsubject("your Example.com account has been activated"); msg.settext(msgbody); Transport.send(msg); 8

9 Mail Each application owns a Mail reception is configured in the web.xml configuration file: <servlet> <servlet-name>mailhandler</servlet-name> <servletclass>mailhandlerservlet</servlet-class> </servlet> <servlet-mapping> <servlet-name>mailhandler</servlet-name> <urlpattern>/_ah/mail/x@myapp.appspotmail.com< /url-pattern> <servlet-mapping> Administration Console 9

10 Scalability Scalability is achieved at three levels. The number of frontend and application servers is scaled automatically by Google App Engine. Web apps are kept in memory. Web requests are sent to the server(s) where the application is already running. Applications are replaced using a LRU (least recently used) policy. Static variables must not contain the application state. Only configurations, temporary values, etc... Storage scales automatically based on the Datastore/BigTable/Google File System. SLA Payment is independent from the number of servers. It s charged on CPU time, data bandwidth, data storage and number of calls to the Mail API. 10

11 Amazon EC2: Elastic Compute Cloud Elastic Compute Cloud On demand server (instances) provision service. Uses disk images created by the users and then transferred, or pre-built by Amazon (AMI, Amazon Machine Images, max. 10GB). Low granularity payments. Access via Web Services (and languagespecific APIs on top of the WS). 11

12 Instances Instances are virtual machines running a particular image. CPU performance is measured using EC2 Compute Unit (empirical unit designed by Amazon) 1.0 to 1.2 GHz AMD Opteron. The virtual machines do not match the physical machines but have clearly defined characteristics: Small: 32-bit, 1 ECU, 1,7GB RAM, 160 GB HD temporary. Large: 64-bit, 2x2 ECU, 7,5 GB RAM, 850 GB HD temp. XL: 64-bit, 4x2 ECU, 15 GB RAM, 1690 GB HD temp. Other instances: micro, high memory, cluster,... Examples of AMIs OSs: Red Hat Linux, Windows Server 2003, OpenSolaris, Ubuntu, Debian DBs: DB2, SQL Server, mysql High performance computing: Hadoop, Condor, OpenMPI Web Servers: Apache, IIS And many more Beware of user submitted AMIs 12

13 EC2: WS Access Several WS protocols are available: REST: Each resource has a specific URI and the HTTP operations HTTP represent specific resource manipulations: GET: read the resource s description; PUT: change the resource; POST: create sub-resources; DELETE: eliminate the resource; SOAP: HTTP+XML In SOAP, HTTP is just a container for an XML message that encapsulates the complete description of the operation. EC2: Use There are many more interfaces to AWS services: Several language APIs: Java, C#, Python, Command line tools Browser extensions (e.g. ElasticFoxfor Mozilla Firefox) Web App: Amazon Management Console 13

14 Amazon Management Console EC2: Example The use of EC2 requires creating a pair of public/private keys and associating them with the virtual machine: CreateKeyPair: creates a pair of public/private keys. DescribeKeyPairs: list the users keys. DeleteKeyPair: eliminates a pair of keys. 14

15 EC2: Example To start instances: RunInstances ImageId: id of the AMI to be started. MinCount: minimum number of instances. MaxCount: expected number of instances. KeyName: key to access the instance. SecurityGroup: list of security groups (network address based ACLs). InstanceType: S, L, XL. EC2: Example Once the instances are started, DescribeInstances describes their state: ImageId; State: pending, running, shuttingdown, terminated; Index in the list of instances; Public and private (inside AWS) DNS name; 15

16 Amazon Management Console Amazon Management Console 16

17 Amazon Management Console Elastic Block Store 1GB upto 1 TB Instances: IP addresses Each instance has a public <IP address, DNS name> pair and a private one. IP addresses are dynamic and require that a dynamic DNS service be used. Delays in the update of DNS names may appear to be faults. Alternatively, one may add a front-end server with a fixed address outside AWS. 17

18 Security Groups Security Groupsare groups of networks rules to control the access to EC2 instances. They only limit inbound traffic. Outbound traffic is unrestricted. There are IP rules and group rules: IP rules limit inbound traffic based on: Protocol Destination port Source IP Security Groups (2) Group rules limit access from other EC2 instances. Group rules limit access by port, protocol and security group. The security group is just a label that can be added/removed from each instance. Each instance can be part of several security groups. Each security group can have up to 100 security rules. 18

19 Security Groups: Operations CreateSecurityGroup DeleteSecurityGroup DescribeSecurityGroup AuthorizeSecurityGroupIngress GroupName: security group name IpProtocol: tcp, udp, icmp FromPort: first port of the allowed range ToPort: last port of the allowed range CidrIP: network mask of source addresses RevokeSecurityGroupIngress: similar parameters to AuthorizeSecurityGroupIngress. Availability vs. Performance The cloud may not be fast but it s available. EC2 (100 sec. execution) vs. National Center for Supercomputing Applications(25 sec. execution). Graph shows time to guarantee 32 nodes. 19

20 Microsoft Azure Windows Azure Flexible application hosting platform. Transparent application management: Just provide code and service model. Includes large-scale storage. IsbeingusedbyMicrosoft for itsowncloudservices (Microsoft Live). 20

21 Azure: Goal Properties (Similar to other platforms.) High availability. Performance. Scalability. Resource sharing. Familiar programming models. Hide system administration: Manage service (not servers), manage data (not databases). Windows Azure 21

22 Windows Azure Nodes: Windows HPC Server Each node is running Windows Server HPC. Windows HPC Server is Windows Server with: Cluster manager: organizes nodes into head nodesand compute nodes. Performs software updates. Scheduler: starts jobs in compute nodes. Cluster monitor: gathers information about the cluster s status. Active replication of the head node. MPI: message passing interface for concurrent programming. 22

23 Azure Fabric Controller Control VM VM VM VM WS08 Hypervisor Control Agent Services Out-of-band communication: hardware control Load Balancing Switches Fabric Controller (high availability) In-band communication: software control WS08 A node can be a physical machine or a VM in the Hyper-V hypervisor. Microsoft s hypervisor. Hyper-V Based on Xenand supports Xen sos versions (paravirtualization). Compared to VMWare: Based on a micro-kernel design: no inclusion of third party code for better security. Open virtual disk specification. Free with Windows Server. 23

24 Monitoring in the Cloud Monitoring in Cloud Crucial due to the scale. Goals: Detect faulty/failed nodes and applications. Detect overloaded application. Detect load imbalances. Correct these problems by: Issuing commands to the nodes. Restarting nodes. Moving applications. Reporting problems. Most secretive part of commercial cloud platforms. 24

25 Azure Fabric Controller Azure Fabric Controller Manages all the hardware. Talks to a specific daemon on each worker node. Distributes services among the available processors. Monitors the system and adapts to the hardware needs. Monitors inactive hardware. Starts instances of application. Keeps applications running by reacting to faults. Maintains a state machine on how to lead each node from the current state to its goal state. If it fails, applications are restarted on other nodes. 25

26 Azure Fabric Controller Thecontrollerisa 5 to 7 nodecluster: State is replicated passively. A newleaderiselectedincase ofa fault. Applications do not fail if the controller fails. Client Node FC Agent Primary FC Node Secondary FC Node Secondary FC Node FC Core Object Model FC Core Object Model FC Core Object Model Uncommitted Committed Committed Committed Replication system Disk Disk Disk Monitoring in AWS 26

27 Amazon CloudWatch Elastic Compute Cloud s monitoring mechanism. Goal: allow users and services to check the status of AWS running nodes. Interface: set of application probing Web Services. Data gathered by node hypervisor and aggregated at CloudWatch nodes. AWS CloudWatch:Operations ListMetrics, lists metrics available to each client: <ListMetricsResponse> <ListMetricsResult> <Metrics> <member> <MeasureName>NetworkIn</MeasureName> <Namespace>AWS/EC2</Namespace> </member>... 27

28 Amazon CloudWatch Definitions of provided measures: Measure: measuring events (e.g. a disk access). Are aggregated per minute. Unit: unit in which a measure is expressed. Metric: Criteria for measure aggregation (time or bytes). Statistic: a metric s characteristic: min, max, average, sum. Namespace: name of the AWS area that generated the metric. Dimension: aggregation scale (by instance, datacenter or AutoScaling group). AWS CloudWatch: Operations GetMetricStatistics, parameters: MeasureName: a measure name returned by ListMetrics. StartTime, EndTime: date/time of the first and last values requested. Dimension: ImageId, AutoScalingGroupName, InstanceId, InstanceType. Returns a set of DataPoints. 28

29 Amazon CloudWatch AutoScaling Auto Scaling allows automatically scaling Amazon EC2 according to user-specified conditions. With Auto Scaling, the set of instances can be automatically adjusted to demand in order to minimize costs. 29

30 Auto Scaling: Example Creating a launch configuration: as-create-launch-config MyLaunchConfiguration --image-id myami - -instance-type m1.small Creating an Auto Scaling group: as-create-auto-scaling-group MyAutoScalingGroup --launchconfiguration MyLaunchConfiguration -- availability-zones us-east-1a --min-size 2 --max-size 20 --load-balancers MyLoadBalancer Auto Scaling: Example Creating activation conditions: as-create-or-update-trigger MyTrigger --autoscaling-group MyAutoScalingGroup --namespace "AWS/EC2" --measure CPUUtilization --statistic Average --dimensions "AutoScalingGroupN ame=myautoscalinggroup" --period 60 --lowerthreshold 40 --upper-threshold 80 --lowerbreach-increment "-1" --upper-breach-increment "1" --breach-duration 600 Check activation conditions: as-describe-triggers MyAutoScalingGroup (resposta) TRIGGER MyTrigger MyAutoScalingGroup NoData AWS/EC2 CPUUtilization Average 60 30

31 Auto Scaling: Open Issues Will AWS respond in time (EC2 startup time = 10 min. for 99,9% of the calls)? Are we reacting to real traffic or to a DoS attack (denial of service)? Is it going to be profitable to react to the traffic increase? Is Auto Scaling a crutch for bad planning? Elastic Load Balancing AWS Load balancer. Redirects inbound requests to a set of nodes. Supports HTTP, HTTPS, TCP, SSL requests. Supports grouping all of a session s requests: Groups requests using load balancer or app cookies. Monitors node s status. Removes faulty nodes from load sharing group. 31

32 Next Time... Programming for the Cloud 32