This thesis presents a reservation-based dynamic scheduling approach for deadline-constrained moldable jobs. The goal is to maximize the service provider’s profit. This is an emerging research topic as HPC as a Service [1] becomes a future trend for high performance computing, but not yet receives enough research attention.
The proposed approach is actually a scheduling framework involving three important scheduling and allocation issues, including waiting queue sequencing, temporal resource allocation, and spatial resource allocation. To deal with this emerging research topic, we first defined the profit model, charge model and penalty model to be used in performance evaluation.
Then, we proposed several policies for the above three issues, including EDF, SJF, LIF for waiting queue sequencing; ASAP, ALAP for temporal resource allocation, and AFAP, AMAP for spatial resource allocation.
We conducted a series of simulation experiments to evaluate our reservation-based dynamic scheduling approach. The experimental results show that our approach can effectively increase the revenue of HPCaaS providers for both types of charge models, achieving 7% to 85% increase in different scenarios compared to moldable EDF [35] and the Algorithm 3 in [27]. However, the experimental results also indicate that careful selection of scheduling and allocation policies is essential since the best combination usually depends on the types of deadline and charge models. For example, for soft-deadline jobs, ASAP + AFAP + SJF is the best for the sequential-runtime charge model, while ASAP + AMAP + SJF achieves the largest profit for the parallel-runtime charge model. For jobs of hard deadline, AFAP + ALAP + EDF performs best for the sequential-runtime charge model, and AMAP + ALAP + LIF leads to the
largest revenue for the parallel-runtime charge model. As HPCaaS is an emerging model, the characteristics of jobs, penalty model, charge model, and profit model are all evolving.
Therefore, there will be many new research issues on parallel job scheduling to explore in the future.
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