Pub Date : 2013-06-01DOI: 10.1109/SERVICES.2013.55
Gueyoung Jung, Tridib Mukherjee, Shruti Kunde, Hyunjoo Kim, Naveen Sharma, Frank Goetz
The proliferation of cloud computing can imply a barrier to cloud users. When deploying their complex workloads into clouds, cloud users are typically overwhelmed by too many technical choices. Moreover, underlying technologies and pricing mechanisms of clouds vary and are not transparent to them. Consequently, it is hard for cloud users to capture the monetary and performance implications of their workload deployments. This paper introduces a cloud recommendation platform, referred to as Cloud Advisor. It allows cloud users to explore various cloud configurations recommended based on user preferences such as budget, performance expectation, and energy saving for given workload. Then, it allows cloud users to compare offered price and performance with other clouds' offerings for the workload. By providing transparent comparisons, it can also support cloud provider to develop a competitive pricing strategy such as price reduction driven by energy efficiency. We have applied the proposed platform for recommendation from a real data center and some external clouds.
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Pub Date : 2013-05-08DOI: 10.1109/SERVICES.2013.30
W. Jaradat, A. Dearle, A. Barker
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.
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