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Cost and Accuracy Aware Scientific Workflow
Composition for Service-Oriented
Environments
ABSTRACT:
Large-scale scientific data analysis projects have catalyzed service-based
work flow management systems. We present an approach for integrating
user preferences on completion time and workflow accuracy in a workflow
composition system. The relationship between workflow execution time and
the accuracy of results is exploited by our workflow system. Specifically,
our system is equipped with a way for users to define cost models on service
completion time and error propagation (prevalent in many scientific and
data analysis applications). Together with these models and an ontology for
describing web service and data dependences, our system plans service
based workflows to answer high-level queries. Our system was evaluated
under a real service-based environment against user constraints on time,
accuracy, and network bandwidth variations. In the worst case in our
experiments, we observed an average deviation of 14.3 percent below the
desired time constraints, which suggests that our system is time-
conservative. Within varying network bandwidth environments, we can
also meet time constraints through sampling, and only a 12.4 percent
deviation below time expectations are observed on average. We further
show that, though negotiating with services’ error models, our system is

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capable of planning data reduction measures (e.g., sampling) directly
within workflow plans to achieve the desired accuracy.
EXISTING SYSTEM:
We present an approach for integrating user preferences on completion
time and workflow accuracy in a workflow composition system. The
relationship between workflow execution time and the accuracy of results is
exploited by our workflow system. Our system was evaluated under a real
service-based environment against user constraints on time, accuracy, and
network bandwidth variations. In the worst case in our experiments, we
observed an average deviation of 14.3 percent below the desired time
constraints, which suggests that our system is time-conservative. Within
varying network bandwidth environments, we can also meet time
constraints through sampling, and only a 12.4 percent deviation below time
expectations are observed on average
PROPOSED SYSTEM:
Two cost functions are proposed for propagating workflow execution time
and accumulated errors, respectively. We proposed and implemented an

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architectural reference for scientific workflow systems. In this paper, they
identified several key functional differences between scientific and business
workflow managers. In this paper, we considered algorithms to manipulate
only one for simplicity and leave multiple tunable accuracy/time
parameters for future work. Another area is service scheduling on
distributed heterogeneous resources. The problem, which is inherently NP
hard, has received much recent attention in academia. While many
heuristics have been developed to address this issue, integrating novel
features such as partial workflow caching and migration adds complexity
to the problem.
CONCLUSION:
This paper reports an approach toward enabling time and accuracy
constraints in service workflow composition through methods for modeling
application-specific error and execution times. We evaluated our system in
many dimensions, and overall, our results show that the inclusion of such
cost models contributes insignificantly to the overall execution time of our
workflow composition algorithm, and in fact, can reduce its overall time
through pruning unlikely candidates at an early stage. We also showed that
our adaptive accuracy parameter adjustment is effective for suggesting
relevant values for dynamically reducing the size of data.