Abstract
Cloud Warehouses have been exploiting CPU-FPGA environments to accelerate multi-tenant applications to achieve scalability and maximize resource utilization. In this scenario, kernels are sent to CPU and FPGA concurrently, considering available resources and workload characteristics, which are highly variant. Therefore, we propose a multi-objective optimization strategy to improve resource provisioning in CPU-FPGA environments. It is based on the Genetic Multidimensional Knapsack solution and can be tuned to minimize makespan or energy. Our strategy provides similar results as the optimal Exhaustive Search, but with feasible execution time, while presenting 77% energy savings with 39% lower makespan than the commonly-used First-Fit strategy.
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