Cloud Computing + Workflows

1. Cloud Computing + Workflows Anushri Khandekar

2. Cloud Computing • Delivering applications or services in on-demand environment • Hundreds of thousands of users / applications • Systems should be fast, secure and available • Intelligent infrastructure: • Transparency • Scalability • Monitoring • Security • All services and associated data

3. Workflows • Operational aspect of a work procedure: • how tasks are structured, • who performs them, • what their relative order is, • how they are synchronized, • how information flows to support the tasks and • how tasks are being tracked.

7. Workflow Management • An activity is a discrete step in a business process (workflow). • Activities range from calling a remote service to perform a task, e.g. calculating taxes, performing currency conversions, looking up inventory, to custom-defined services. • Activities are orchestrated together in a workflow in BizTalk using XOML (eXtensible Object Markup Language). • Other languages BPEL, ebXML, XPDL etc.

8. Workflows in Cloud • Microsoft allows hosting of Biztalk activities in a cloud at biztalk labs. • Developers integrate those cloud hosted activities into a BizTalk workflow (orchestration) by calling them as they would any other web-based service or hosted activity. • “Service orchestration” – business process is modeled using workflows • Invokes Internet Service Bus and perform HTTP request • Language used XOML • Main task – First create a workflow instance and start it

9. Transparency • “Actual Implementation” of services obscured • Another version of virtualization • Transparent load-balancing and application delivery • Solution to be automated and integrated in workflow process • Example: • A service running with a single server, more users join in hence additional servers required, transparency allows integration without interrupting the service running or reconfiguration.

10. Scalability • Scale up and build “mega data centers” • Not transparent – Need configuration or re-architecting • Potential of interrupting services is huge • Ability to transparently scale the service infrastructure and the solution • On-demand, real time scaling • Control node – provides dynamic application scalability • Integration with virtualization solution or orchestration with workflow process to manage provisioning

11. Intelligent Monitoring • Control node – intelligent monitoring capabilities • Server overwhelming or application performance affected by network conditions – behavior outside accepted norms • More than knowing when a service in trouble what action should be taken • Example – application responding slowly, adjust application requests add more server if required • Detect and participate in the provisioning of new instance

12. Capacity Management • From buckets to rivers • Constrained set of resources – predict peak usage and have in-house data centre to manage them • Unlimited computing power with cloud – How IT departments properly manage this river? • Constraint on new model • Not upper limit of computing power but speed at which new services can be provisioned and put into production • Scaling up means: • Initiate new system, transfer data, connect existing system, test combined system, manage complete life cycle

13. Capacity Management • Traditional life cycle stages: • Modeling, provisioning, monitoring, maintaining, and modifying. • Important here – “Maintaining” & “Modifying” • Elastic means provisioning and de-provisioning • Is it right time to add an IT asset or get rid of an asset? • Economic benefits rely on when to stop using an asset • Utilize the cloud for additional capacity when it is apparent your own data centre can't handle the load and it is cost-prohibitive to invest in additional servers and infrastructure to increase capacity

14. Problem Statement • Efficient management of workflows in a cloud environment to allow fast scaling up and scaling down • Storing scalability/ compressibility options for every node in the workflow • Input events and output events of every node in workflow • Mechanism to integrate new scaled model of web service in original cloud workflow

15. Proposed Idea

16. Workflow Management • Workflow management important – heavy workflow of traditional waterfall approaches with smallest detail will slow down the use of cloud computing • Separate main workflow from details of mechanism required to scale any activity node • Have efficient way of storing this information

18. Workflow Management • Workflow Main • Has the cloud structure with each web service as an activity node • Workflow Shadow • Has sub-workflows for other options for each activity nodes • Workflows – Online or Offline. • Online – running and executing at a particular time • Offline – workflows in passive state waiting for an event to trigger them

19. Activity Node

20. Scalability Options • Considering transparency, two ways to scale a workflow • Scale an activity node • Addition of new activity node • More tricky, dynamic, according to environment • Scale an activity node • When? – store criteria • Example, for a web server if load increases above a threshold, expand • How? – again as a workflow • Example, store all the steps to be done in order to expand, configure and connect the node back to original workflow

21. Cloudbursting vs Bursting the Cloud • Cloudbursting is to allow the cloud to act as overflow resources in the event your own infrastructure becomes overloaded • Critical tasks (revenue generating) in own datacentre • Bursting in the cloud is applied to resources such as servers, application servers, application delivery systems, and other infrastructure required to provide on-demand computing environments

22. Bursting the cloud • Automate the cloud's data centre • Requires more than simple workflow systems • on-demand control and management over all devices in the delivery chain • from the storage to the application and web servers to the load-balancers and acceleration offerings that deliver the applications to end-users • “Data centre orchestration” – many moving parts and pieces be coordinated in order to perform a highly complex set of tasks

23. Hadoop As a Service • Automated installation and provisioning • Research Questions: • How to support multi-tenancy with QoS differentiation • How to optimize workflows across users with fluctuating capacity requirements • Key features: • On-demand creation • Dynamic resource flexing

24. Differentiated Hadoop services Problem: • More important jobs should preempt less important jobs • Time critical jobs need to meet deadlines • Test jobs need no stringent QoS guarantees • How to get users to truthfully reveal their resource requirements?

25. Differentiated Hadoop services • Approach • Market-based resource allocator, Tycoon • Continuous bidding (of spending rates) for resource capacity • Proportional allocation • Allocation materialized as VM • Users can evaluate and select providers based on cost/benefit metrics (best value for money) • Gives incentive to users to be judicial about capacity requests and time to submit

26. Economic workflow optimization • Assumption: • Not all subtasks need maximum capacity at all times • Approach: • Automatically rescale the capacity as needed to optimize the cost/benefit ratio of the workflow as a whole • Opportunity: • Application scalability profile not perfectly linear

27. Optimization strategies • Node Priority P: Some nodes more performance critical than others S: Boost spending on critical nodes (e.g. master funding boost) • Workflow Priority: P: Some workflows more performance critical than others (although they look the same to the system) S: Declare relative priority of workflows and split budget accordingly • Job Priority: P: Some stages of a workflow are more i/o intensive, others more cpu intensive S: Boost resource spending during resource-intense stages of workflow • Bottleneck Mitigation: P: During map/reduce synch up some nodes may be bottlenecks S: Redistribute funds to active bottlenecks

28. Optimization strategies • Best Response: P: When other users place competing bids, optimal configuration/allocation might change S: Find game theoretical best response bids continuously to maximize utility • Risk: P: Some users are more risk averse than others (can tolerate less fluctuations) S: Bid on nodes based on predicted guarantee to deliver a QoS level

29. Managing Resources • Includes clear policies on • who to admit • how to arbitrate among competing requests • what resource capacity may be requested over what time frames • Isolated Datacentre • Reset, reboot, power up, power down, get status • Bias towards large and short experiments • Site coordination required, e.g. accounting

30. XOML • Original Cloud Activities • CloudHttpSend • CloudHttpReceive • CloudIfElse • CloudSequence • Activity node – details should be stored with this • CloudServiceBusSend • CloudDelay • CloudWhile

31. Microsoft Azure

32. Citrix Cloud Centre • XenServer Cloud Edition – a complete, cloud-ready virtual infrastructure • NetScaler – to load balance, speed access to backend VMs and dynamically provision workloads. • "There's more to providing [cloud computing] than simply providing a flat virtual infrastructure. You want to have workflows, you want SLAs, you want to be able to automate and move things around, and that's essentially what Citrix is bringing to the table -- a full suite of tools to do all of that." • James Staten • Citrix WANScaler and Citrix Workflow Studio • “Single Automated Cohesive system”

33. Conclusions • “Workflow management matters because much of the benefits of cloud computing comes from the speed and ease with which IT resources can be created and put into production.”

34. Thank you !!! Questions ???