This paper presents the Maximum Effective Reduction (MER) algorithm, which optimizes the resource efficiency of a workflow schedule generated by any particular scheduling algorithm. The inefficiency in resource usage of workflow execution/schedule is not only in the number of resources used, but also the actual amount of "used" resource time, including idle time between any two task executions sourced from data dependencies. MER trades the minimal makespan increase for the maximal resource usage reduction by consolidating tasks with the exploitation of resource inefficiency in the original workflow schedule. Our evaluation using traces from four real-world scientific workflow applications shows that the rate of resource usage reduction far ...
Many fields of modern science require huge amounts of computation, and workflows are a very popular ...
Abstract—Large computational problems may often be mod-elled using multiple scientific workflows wit...
Data-intensive workflows stage large amounts of data in and out of compute resources. The data stagi...
AbstractThis paper presents the Maximum Effective Reduction (MER) algorithm, which optimizes the res...
Resource abundance is apparent in today's multicore era. Workflow applications common in science and...
UnrestrictedThe development of grid and workflow technologies has enabled complex, loosely coupled s...
This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algor...
This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algor...
This paper shows through a case study the potential for optimizing resource allocation in business p...
Many functions in today’s society are immensely dependent on data. Data drives everything from busin...
With the up rise of fourth paradigm, that is discovery of science over a prolonged period of time, s...
While in the past scheduling algorithms were almost exclusively targeted at optimizing applications'...
In attempts to exploit a diverse set of resources in grids efficiently, numerous assays in resource ...
The rise in demand for cloud resources (network, hardware and software) requires cost effective scie...
Abstract—Large computational problems may often be mod-elled using multiple scientific workflows wit...
Many fields of modern science require huge amounts of computation, and workflows are a very popular ...
Abstract—Large computational problems may often be mod-elled using multiple scientific workflows wit...
Data-intensive workflows stage large amounts of data in and out of compute resources. The data stagi...
AbstractThis paper presents the Maximum Effective Reduction (MER) algorithm, which optimizes the res...
Resource abundance is apparent in today's multicore era. Workflow applications common in science and...
UnrestrictedThe development of grid and workflow technologies has enabled complex, loosely coupled s...
This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algor...
This paper presents an algorithm called Failure-Aware Workflow Scheduling (FAWS). The proposed algor...
This paper shows through a case study the potential for optimizing resource allocation in business p...
Many functions in today’s society are immensely dependent on data. Data drives everything from busin...
With the up rise of fourth paradigm, that is discovery of science over a prolonged period of time, s...
While in the past scheduling algorithms were almost exclusively targeted at optimizing applications'...
In attempts to exploit a diverse set of resources in grids efficiently, numerous assays in resource ...
The rise in demand for cloud resources (network, hardware and software) requires cost effective scie...
Abstract—Large computational problems may often be mod-elled using multiple scientific workflows wit...
Many fields of modern science require huge amounts of computation, and workflows are a very popular ...
Abstract—Large computational problems may often be mod-elled using multiple scientific workflows wit...
Data-intensive workflows stage large amounts of data in and out of compute resources. The data stagi...