1. C LAUDIO A A RDAGNA, E RNESTO D AMIANI, F ULVIO F RATE, D AVIDE R EBECCANI Universita degli Studi di Milano, Italy M ARCO U GHETTI Telecom Italia, TILab, Italy 13 th November, 2012. Presented By, Chidambara Nadig. 2012 IEEE 5 TH I NTERNATIONAL C ONFERENCE ON C LOUD C OMPUTING

2. Abstract Platform as a Service is a cloud based approach where enterprises have little to do with the underlying cloud infrastructure. Installing, configuring and managing the underlying middleware, operating system and hardware is done by the cloud provider. Thus, Scalability becomes an important factor to decide, The capabilities of each virtual resource in the cloud. The number of resources in the cloud. This paper presents few Scalability Patterns for PAAS infrastructure and a method to automatically manage scalability. 2Scalability Patterns for Platform-as-a-Service

3. IAAS – Infrastructure as a Service. PAAS – Platform as a Service. SAAS – Software as a Service. 3Scalability Patterns for Platform-as-a-Service IAAS, PAAS and SAAS

4. Scalability Patterns for Platform-as-a-Service4

5. Levels of Abstraction in Cloud Services. Scalability Patterns for Platform-as-a-Service5

6. Some examples Scalability Patterns for Platform-as-a-Service6

7. Actions on Resource Scaling Vertical Scaling – Scale Up – Additional Resources are added to a single machine when the load increases. The resources can be either physical resources added to a server, or virtual resources dynamically assigned to a virtual machine or its applications. Horizontal Scaling – Scale Out – New machines are added to the system providing more software and hardware resources. Scale Down – Releasing Resources when they are not necessary. Scalability Patterns for Platform-as-a-Service7

8. Scalability Patterns 1. Single Platform Pattern (SPP) 2. Shared Platform Pattern (ShPP) 3. Clustered Platform Pattern (CPP) 4. Multiple Shared Platform Patter (MShPP) 5. Multiple Clustered Platform Patter (MCPP) Scalability Patterns for Platform-as-a-Service8

9. Single Platform Pattern (SPP) Each customer is given a complete virtual machine with a platform installed on it. SPP is single tenant scenario. Resource Utilization is scarce. Scalability is low because the number of virtual machines and platforms is linear in the number of customers. Scalability Patterns for Platform-as-a-Service9

10. Shared Platform Pattern (ShPP) A Multitenant scenario. One platform is installed on a set of virtual machines and is shared by multiple tenants. Each tenant has a right to manage a portion of the platform and deploy their services on it independently. Performance of the platform is maintained by up-scaling and down- scaling the resources assigned to the virtual machine. Scalability Patterns for Platform-as-a-Service10

11. Whenever the load increases – degrading the performance metrics of the platform – RAM, CPU, or bandwidth can be increased. On the other hand, when the load decreases, resources can be freed and made available to other processes in the architecture. In ShPP resources are shared and therefore need to be managed to ensure security and isolation among tenants. Provides High utilization. However, ShPP doesn’t provide linear scalability increase due to increased overheads for resource management. Scalability Patterns for Platform-as-a-Service11

12. Clustered Platform Pattern (CPP) A single platform is deployed supporting clustering and is shared by all tenants. Multiple instances of the platform components can be deployed in different machines of the cluster. Similar to ShPP, CPP manages shared resources preserving security and isolation among tenants. This pattern also implements load balancing, PAAS monitoring, and elastic auto-scaling. Scalability Patterns for Platform-as-a-Service12

13. CPP provides high resource utilization, since the machines in the cluster are shared among different tenants. CPP provides some scalability as system resources can be incrementally extended. The Clustered Platform Pattern also promises high reliability and availability due to increased redundancy. Scalability Patterns for Platform-as-a-Service13

14. Multiple Shared Platform Pattern (MShPP) MShPP is an extension of ShPP. Initially a single Shared Platform is deployed. Upon an increase in the load, additional resources (CPU, RAM or bandwidth) are assigned to maintain the performance metrics. In case additional resources are not sufficient, a new platform is deployed and a part of the existing tenants are migrated to the new platform along with the resources they own. Scalability Patterns for Platform-as-a-Service14

15. When the load decreases, the additional platforms can be removed causing the tenants to migrate back to the available platforms. MShPP has lower manageability than ShPP owing to the fact that tenants have to be migrated from one platform to another when a new platform is deployed. MShPP provides high resource utilization. Its scalability depends on the specific scenario and number of deployed platforms.  In the worst case, when all tenants experienced a traffic peak, a platform is deployed for each tenant and therefore scalability of MShPP is equivalent to the one of SPP.  In the average case, MShPP provides high scalability. Scalability Patterns for Platform-as-a-Service15

16. Multiple Clustered Platform Pattern (MCPP) MCPP is an extension of CPP. At initialization time, a single, shared, multi-tenant platform supporting clustering is deployed. Upon an increase in the load, additional resources (i.e., machines in the cluster) are added to maintain the performance level. In case the extended cluster is not sufficient to manage the new load, a new platform supporting clustering is deployed, and a part of the existing tenants are migrated to the new clustered platform together with the services they own. Scalability Patterns for Platform-as-a-Service16

17. When the load decreases, the additional platforms can be removed causing the tenants to migrate back to the available platforms. MCPP has the lowest manageability among patterns. MCPP usually provides high utilization of resources, although utilization may decrease in case of multiple platform deployments. Promises  High Scalability.  High Availability.  High Reliability. Scalability Patterns for Platform-as-a-Service17

18. Overview Scalability Patterns for Platform-as-a-Service18

19. Performance Measurement 1. Performance Metrics at the Platform Level Total Count (TC) – Number of messages forwarded to a given end point. If the metrics exceeds a known threshold, the performance could be affected and an alarm is raised. Fault Count (FC) – Number of messages that resulted in a fault while being forwarded to the end point. Minimum Time (MinT) Maximum Time (MaxT) Average Time (AveT) Time Taken to send a request to an end point and receive a response. Scalability Patterns for Platform-as-a-Service19

20. 2. Performance Metrics at the Host Level CPU Load (CL) – CPU Utilization on host and guest systems. High values of CL in a Virtual Machine signifies a problem in the fulfillment of request messages backlog. Memory Occupancy (MO) – Memory Utilization on host and guest systems. Services that require a huge amount of data may require substantial portions of memory at the detriment of other services. Network Utilization (NU) – Utilization of the network bandwidth. High values of NU may suggest re-allocation of external resources to manage a peak of requests. Host Availability (HA) – Number of virtual machines available and accessible through the network. The falling of the HA under a pre-defined threshold indicates the new for new machines. Scalability Patterns for Platform-as-a-Service20

21. Performance Monitoring Based on the certain measurements of the Performance metrics, certain alarms are raised. Two Categories of Alarms: 1. Message Alarm – A message alarm is raised when: System is not able to manage the message queue efficiently. Average message delivery time is above a preset threshold. The difference between the maximum and minimum message time is above a preset threshold. 2. Processing Alarm – A process alarm is raised when service execution may involve high execution time or a lot of resources. Scalability Patterns for Platform-as-a-Service21

22. Alarm Rules Scalability Patterns for Platform-as-a-Service22 HIGH and LOW thresholds in the above table can be defined on the basis of previous experimental tests and/or expert knowledge.

23. Alarm-driven selection of scalability patterns Scalability Patterns for Platform-as-a-Service23 The initial node ∗ represents the basic installation scenario in which different tenants share the same platform with default configurations.

24. The two-fold Monitoring Approach Upon an increase in the load that raises a message alarm, the algorithm moves to node ShPP and applies a ShPP pattern If a processing alarm is raised, the algorithm moves to node CPP and applies a CPP pattern. When the ShPP pattern is not sufficient to solve further alarms, it moves to node MShPP in case of message alarms or to node CPP in case of processing alarms. The algorithm moves from CPP to MCPP for both types of alarms, while it moves from MShPP to MCCP in case of processing alarms. Scalability Patterns for Platform-as-a-Service24

25. Experimental Setting WSO2 Platform is a cloud-deployable, Java-based service-oriented platform. A WSO2 platform with default configurations is used as the experimental environment. A realistic scenario is simulated where concurrent requests come from different clients. Each test case starts with 20 active clients sending SOAP (Simple Object Access Protocol) requests, which ramp up to a maximum of 100 clients. All test cases have a duration of 60 seconds. Load Varying is done by increasing the requests per second (rps) from 10rps to 500 rps. Scalability Patterns for Platform-as-a-Service25

26. Baseline Measurement without security Scalability Patterns for Platform-as-a-Service26 RT – Response Time (in a logarithmic scale) TPS – Transactions per Second rps – Requests per second

27. Baseline Measurement with security Scalability Patterns for Platform-as-a-Service27 RT – Response Time (in a logarithmic scale) TPS – Transactions per Second rps – Requests per second

28. Performance of ShPP without security Scalability Patterns for Platform-as-a-Service28

29. Performance of ShPP with security Scalability Patterns for Platform-as-a-Service29

30. Performance of CPP without security Scalability Patterns for Platform-as-a-Service30

31. Performance of CPP with security Scalability Patterns for Platform-as-a-Service31

32. Comment.. Result 1 – Security causes a substantial decrease in the performance of a SOA deployed on the cloud. Result 2 – ShPP results in a performance gain both on TPS and RT with respect to the baseline. Result 3 – CPP provides a further improvement with respect to ShPP. Scalability Patterns for Platform-as-a-Service32

33. THANK YOU! Scalability Patterns for Platform-as-a-Service33