2 research outputs found
A Dataflow Language for Decentralised Orchestration of Web Service Workflows
Orchestrating centralised service-oriented workflows presents significant
scalability challenges that include: the consumption of network bandwidth,
degradation of performance, and single points of failure. This paper presents a
high-level dataflow specification language that attempts to address these
scalability challenges. This language provides simple abstractions for
orchestrating large-scale web service workflows, and separates between the
workflow logic and its execution. It is based on a data-driven model that
permits parallelism to improve the workflow performance. We provide a
decentralised architecture that allows the computation logic to be moved
"closer" to services involved in the workflow. This is achieved through
partitioning the workflow specification into smaller fragments that may be sent
to remote orchestration services for execution. The orchestration services rely
on proxies that exploit connectivity to services in the workflow. These proxies
perform service invocations and compositions on behalf of the orchestration
services, and carry out data collection, retrieval, and mediation tasks. The
evaluation of our architecture implementation concludes that our decentralised
approach reduces the execution time of workflows, and scales accordingly with
the increasing size of data sets.Comment: To appear in Proceedings of the IEEE 2013 7th International Workshop
on Scientific Workflows, in conjunction with IEEE SERVICES 201
Workflow Partitioning and Deployment on the Cloud using Orchestra
Orchestrating service-oriented workflows is typically based on a design model
that routes both data and control through a single point - the centralised
workflow engine. This causes scalability problems that include the unnecessary
consumption of the network bandwidth, high latency in transmitting data between
the services, and performance bottlenecks. These problems are highly prominent
when orchestrating workflows that are composed from services dispersed across
distant geographical locations. This paper presents a novel workflow
partitioning approach, which attempts to improve the scalability of
orchestrating large-scale workflows. It permits the workflow computation to be
moved towards the services providing the data in order to garner optimal
performance results. This is achieved by decomposing the workflow into smaller
sub workflows for parallel execution, and determining the most appropriate
network locations to which these sub workflows are transmitted and subsequently
executed. This paper demonstrates the efficiency of our approach using a set of
experimental workflows that are orchestrated over Amazon EC2 and across several
geographic network regions.Comment: To appear in Proceedings of the IEEE/ACM 7th International Conference
on Utility and Cloud Computing (UCC 2014