Abstract: To find the optimal deployment of allocating the maximum number of virtual Field Programmable Gate Array (vFPGA) in the minimum number of Field Programmable Gate Array (FPGA) in reconfigurable cryptographic resource pool, the traditional Ant Colony Optimization (ACO) algorithm was optimized, and a vFPGA deployment strategy based on optimized ACO algorithm with considering FPGAs’ characteristics and actual requirements was proposed. Firstly, the load balancing among FPGAs was achieved by giving ants the ability of perceiving resource status, at the same time, the frequent migration of vFPGAs was avoided. Secondly, the free space was designed to effectively reduce the Service Level Agreement (SLA) conflicts caused by dynamical demand change of tenants. Finally, CloudSim toolkit was extended to evaluate the performance of the proposed strategy through simulations on synthetic workflows. Simulation results show that the proposed strategy can reduce the usage number of FPGAs by improving the resource utilization under the promise of guaranteeing the system service quality.

Key words: cloud computing, Field Programmable Gate Array (FPGA) virtualization, Virtual FPGA Placement (VFP), Ant Colony Optimization (ACO) algorithm, partial reconfiguration

摘要： 针对可重构密码资源池中，如何在最少的现场可编程门阵列（FPGA）上部署虚拟FPGA （vFPGA）的问题，结合FPGA的工作特点和应用场景的需求，在传统蚁群算法的基础上进行了优化，提出了一个基于蚁群优化（ACO）算法的vFPGA部署策略。首先，通过赋予蚂蚁资源状态感知的能力实现各个FPGA之间的负载均衡，同时避免频繁的vFPGA迁移；其次，设计预留空间，有效减少因为租户需求动态变化带来的服务等级协议（SLA）冲突；最后，对CloudSim进行功能扩展，使用合成的工作流进行仿真实验，对该策略性能进行评估。实验结果表明，所提策略可以在保证系统服务质量的前提下，提高FPGA资源利用率，减少FPGA使用量。

关键词: 云计算, 现场可编程门阵列虚拟化, 虚拟现场可编程门阵列部署, 蚁群优化算法, 局部可重构