# PHEFT: Pessimistic Image Processing Workflow Scheduling for DSP Clusters
## 7 Conclusions  
Effective image and signal processing workflow management requires the efficient allocation of tasks to limited resources. In this paper, we presented allocation strategies that took into account both  
Figure 6: \(cpw\) performance profile, \(\tau=[1\ldots 1.2]\). (**a**) Montage; (**b**) Ligo.  
Figure 7: Mean performance profile over all metrics and test cases, \(\tau=[1\ldots 1.2]\).  
infrastructure information and workflow properties. We conducted a comprehensive performance evaluation study of six workflow scheduling strategies using simulation. We analyzed strategies that included task labeling, prioritization, resource selection, and DSP-cluster scheduling.  
To provide effective guidance in choosing the best strategy, we performed a joint analysis of three metrics (makespan, mean critical path waiting time, and critical path slowdown) according to a degradation methodology and multi-criteria analysis, assuming the equal importance of each metric.  
Our goal was to find a robust and well-performing strategy under all test cases, with the expectation that it would also perform well under other conditions, for example, with different cluster configurations and workloads.  
Our study resulted in several contributions:  
1. We examined overall DSP-cluster performance based on real image and signal processing data, considering Ligo and Montage applications;
2. We took into account communication latency, which is a major factor in DSP scheduling performance;
3. We showed that efficient job allocation depends not only on application properties and constraints but also on the nature of the infrastructure. To this end, we examined three configurations of DSP-clusters.  
We found that an appropriate distribution of jobs over the clusters using a pessimistic approach had a higher performance than an allocation of jobs based on an optimistic one.  
There were two differences to PHEFT strategy, compared to its original HEFT version. First, the data transfer cost within a workflow was set to maximal values for a given infrastructure to support pessimistic scenarios. All data transmissions were assumed to be made between different integrated modules and different DSPs to obtain the worst data transmission scenario with the maximal data rate coefficient.  
Second, PHEFT had reduced time complexity compared to HEFT. It did not need to consider every combination of DSPs, where the two given tasks were executed, and did not need to take into account the data transfer rate between the two nodes to calculate a rank value (upward rank) based on mean computation and communication costs. Low complexity is important for industrial signal processing systems and real-time processing.  
We conclude that for practical purposes, the scheduler PHEFT can improve the performance of workflow scheduling on DSP clusters. Although, more comprehensive algorithms can be adopted.  
All authors contributed to the analysis of the problem, designing algorithms, performing the experiments, analysis of data, and writing the paper.  
**Acknowledgments:** This work was partially supported by RFBR, project No. 18-07-01224-a.  
**Conflicts of Interest:** The authors declare no conflict of interest.