Workflow as a Service: An Approach to Workflow Farming Reginald Cushing , Adam Belloum, Vladimir Korkhov, Dmitry Vasyunin, Marian Bubak, Carole Leguy Institute for Informatics University of Amsterdam 3 rd International Workshop on Emerging Computational Methods for the Life Sciences 18 th June 2012
Outline ● Scientific Workflows ● Farming Concepts ● Workflow as a Service (WfaaS) ● System overview – Task Harnessing – Messaging ● Application Use Case ● Results ● Conclusions
Scientific Workflows ● Composing experiments from reusable modules ● Vertexes represent computation ● Edges represent data dependency and data communication ● Modules/Tasks communicate through channels represented by ports ● Workflow engines distribute workload onto resources such as grids and clouds ● Modules run in parallel thus achieving better throughput
Farming Concepts ● Many scientific applications require a parameter space study a.k.a parameter sweep ● In workflows parameter sweeps can be achieved by running multiple identical workflows with different parameter inputs ● Cons: Every instance of a workflow has to be submitted to distributed resources where queue waiting times play significant role on throughput
Farming Concepts Parameters organized on message queues Task
Farming Concepts Parameters organized on message queues Task processes data sequentially Task
Farming Concepts Parameters organized on message queues Task processes data sequentially Task
Farming Concepts Parameters organized on message queues Task processes data sequentially Task
Farming Concepts Parameters organized on message queues Task processes data sequentially Task Task Adding more tasks Task increases message consumption rate Challenge: How many tasks to create? Too many - tasks get stuck on queues. Too few - optimal performance not achieved
Workflow as a Service Workflow execution is persistent i.e. it runs, process data and does ● NOT terminate but wait for more data An active workflow instance can process multiple parameters ● Make better usage of computing resources ● A parameter space can be partitioned amongst a pool of active ● workflow instances (a farm of workflows) A workflow acts as a service by accepting requests to process data ● with given parameters – Request 1: data A , parameters {p1,p2,...} – Request 2: data A , parameters {k1,k2,...} Multiple WfaaS processing requests form a farm of workflows ●
System Overview Loosely coupled modules revolving around a message Queues
Enactment Engine Dataflow engine ( top-level scheduler) based on Freefluo § engine Models workflows as dataflow graphs Vertices are tasks while edges are dependencies (data Tasks have ports to simulate data channels Dataflow model dictates that only tasks which have input are scheduled for execution. § http://freefluo.sourceforge.net
Message Broker Message broker plays a pivotal role in the system Message queues act as a data buffer Communicating tasks are time decoupled Through queue sharing we can achieve scaling Tasks communicate through messaging where messages contain references to actual data
Submission System Pluggable schedulers ( bottom- level ) for task match-making Submitters (drivers) abstract actual resources such as cluster, grid, cloud Scheduler matches a task to a submitter Submitter does actual task/job submission
Task Harnessing Task harness is a late binding , pilot- job mechanism A pilot-job (harness) is submitted which will pull the actual job The harness separates data transport from scientific logic Better control of tasks
Task Auto-Scaling Messages between tasks are monitored Size of queued data and mean data processing time are used to calculate task load Auto-scaling replicates a particular task to ameliorate the task load Replicated tasks ( clones ) partition data by sharing same input message queues
Parameter Mapping ● One to one mapping: each parameter is mapped to one workflow instance ● Generates many workflow instances which end up stuck on queues waiting execution ● High scheduling overhead, high concurrency ● Many to one mapping: all parameters are mapped to the same workflow instance ● Only one workflow to schedule, takes long to process all the parameter space ● Low scheduling overhead, Low concurrency ● Many to many: parameter space is partitioned amongst a farm of workflows ● A number of workflows scheduled which accelerates processing ● Low scheduling overhead, high concurrency
Task harnessing ● WfaaS is enabled through task harnessing ● A harness is a caretaker code that runs alongside the module on the resource/worker node ● It implements a plugin architecture ● Modules are dynamically loaded at runtime ● Data communication to and from the module is taken care of by the harness ● The harness invokes the module with new requests of data processing ● The harness is akin to a container while the module is akin to a service ● The harness enables asynchronous module execution as communication is done through messaging
Messaging ● In WfaaS modules communicate through messaging ● Message queues allow multiple instances of modules to share the same input space ● Through message queues, data is partitioned amongst modules ● Messaging circumvents the need to co-allocate resources ● A pull model implies that each module can process data at its own pace ● Once a module has finished processing data it asks for more (pull)
Application Use Case ● Biomedical study for which 3000 runs were required to perform global sensitivity analysis ● Patient-specific simulation includes many parameters based on data measured in-vivo ● Arterial tree model geometry and representation of model parameters constrained to uncertainties ● Parameters: flow velocity, brachial, radial, ulnar radii. Length of brachial, radial, ulnar. etc
Results ● Left: WfaaS 100 simulations takes around 3h:15min ● Right: Non WfaaS 100 simulations take 5h:15min ● The WfaaS approach, each workflow instance performs multiple simulations which drastically reduces queue waiting times ● The non-WFaaS approach generates 100 workflow instances with most of them getting stuck on job queues ● In both cases worklows were competing for 28 worker nodes
Conclusions ● WfaaS is an ideal approach to large parametric studies ● WfaaS reduces common scheduling overhead associated with queue waiting times ● WfaaS is achieved through task harnessing whereby caretaker routines can invoke the task multiple times ● A farm of wokflows can progress at its own pace through a parameter pulling mechanisim
Further Information ● WSVLAM workflow management system – http://staff.science.uva.nl/~gvlam/wsvlam/ ● Computational Sciences at University of Amsterdam – http://uva.computationalscience.nl ● COMMIT – http://www.commit-nl.nl/new
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