Desmet, Stein

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Ghent University Academic Bibliography

Optimal Deployment of Workflows in DistributedEnvironmentsStein DesmetSupervisor(s): Filip De Turck, Bruno VolckaertI. INTRODUCTIONOver the last few years Web Services andService Oriented Architecture (SOA) in gen-eral have rapidly gained in popularity, due topromises of increased flexibility and reusabil-ity. Services expose functionality over the net-work, using well-defined standards to estab-lish service description, service discovery andservice invocation. Service Oriented Architec-tures subsequently combine these services intohigher-level services with new functionality. Inthis way, business processes can be easily im-plemented and automated using existing func-tionality. The automated business processes areoften referred to as workflows.However, an important but often unad-dressed aspect in Service Oriented Architec-tures is the optimal allocation of the services tothe available resources (Figure 1). Optimal re-source usage is not only vital to scalability andeliminating bottlenecks, but also to the Quality-of-Service (QoS) that can be provided. Smartresource selection may result in improved reli-ability or faster execution times.This is certainly true for media related en-vironments, such as for example broadcast-ing companies [1]. Media services like videoprocessing or video analyzing are computa-tionally very intensive, often extremely long-running and exchange considerable amountsof data, making intelligent service and re-source allocation of vital importance to imple-—————————————————–S. Desmet is with the Department of InformationTechnology, Ghent University (UGent), Gent, Bel-gium. E-mail: Stein.Desmet@intec.ugent.be .menting business processes. With the currentshift from Standard-Definition towards High-Definition video material, this becomes evenmore stringent, as file sizes and execution timesare easily quadrupled.II. STATIC APPROACHDetermining this optimal selection of ser-vices is obviously non-trivial. This problemis related to both the mapping problem andthe scheduling problem. Services need to bemapped to resources, and scheduling may benecessary in order to meet deadlines or to as-sure mutually exclusive use of a particular re-source. Both the scheduling and the mappingproblem are known to be NP-hard.Techniques such as Integer Linear Program-ming (ILP) [2] exist, which are able to find thebest solution, but these are not usable in prac-tice, due to excessive running times. In otherwords, it is necessary to develop heuristics suchas Line Scheduling that are able to find a a goodsolution in a reasonable amount of time. Notethat techniques such as ILP are still very use-full, as they can be used to gauge how closea solution from a heuristic approaches the bestsolution.Media related SOAs add to the complex-ity of the problem. Services are long run-ning, but deadlines for workflows are often ex-tremely important. For example, a broadcastingcompany will definitely want to air its showson time. Services exchange large amounts ofdata, but this may happen by prefetching or bystreaming the data. Both cases pose other re-quirements and demand a different approach.Figure 1: The problem of allocating of workflows to resources.Data storage and replication is often importantas well. These are but a few examples to illus-trate the complexity of media SOAs.III. DYNAMIC APPROACHNaturally, a given service allocation won’tremain optimal forever. A company is not astatic environment. It is quite possible and evenlikely that typical invocation patterns of ser-vices change over time, changes in the networkoccur, new QoS demands need to be met or newworkflows are added. The altered situation ob-viously requires a new allocation, as the cur-rent solution will no longer be optimal. Ideally,finding this new assignment is done as fast aspossible. A poor service allocation is detrimen-tal to the company, as are long downtimes dueto implementing the new allocation. Static al-gorithms can take time to find a new solution,and often cannot start from an existing situa-tion It is therefore useful to develop dynamicoptimisation algorithms that can handle contin-uously changing search spaces, so that optimalresource usage and QoS are guaranteed at alltimes.IV. DIMENSIONINGWhen designing heuristics to find an opti-mal service allocation, it is also usefull to adaptthem in order to allow performing dimension-ing analyses. A dimensioning analysis canshow the impact of removing or adding a re-source, or can show where potential problemssuch as bottlencks are located. Such an analy-sis can also be used to determine the maximumload an infrastructure can handle.V. CONCLUSIONSWeb Services and Service Oriented Archi-tectures are becoming increasingly popular.Finding an optimal allocation of services andworkflows to the available resources can yieldsubstantial benefits to the host of the work-flows. These benefits include improvements inthe Quality-of-Service and more intelligent re-source usage.Several static optimization heuristics havebeen implemented for two different use cases[3]. Both use cases are situated in a me-dia related context, in an intra- and intercom-pany context. Extensive Integer Linear Pro-gramming algorithms have been implementedas well. Preliminary results clearly show animprovement in resource usage, scalability andQuality-of-Service over random or ad-hoc ser-vice allocations.REFERENCES[1] D. Van der Weken, S. Van Assche, D. Clabaut,S. Desmet, and B. Volckaert, “Automating work-flows with service oriented media applications,”Services, IEEE Congress on, vol. 0, pp. 507–514,2009.[2] H. Karloff, Linear programming, BirkhauserBoston Inc., Cambridge, MA, USA, 1991.[3] S. Desmet, W. De Cock, S. Van Assche, D. Vander Weken, B. Muylaert, D. Clabaut, and K. De-marest, “Geisha work package 5 – service ori-ented architecture for media production,” Tech.Rep., IBBT, 2009.

Optimal deployment of workflows in distributed environments

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Optimal deployment of workflows in distributed environments

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