网络实验室:为依赖设备的硬件实验迁移到混合云解决方案

N. Koike
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引用次数: 5

摘要

FPGA设计实验网络实验室在混合云环境下将实际实验室与远程实验室无缝结合。混合云由支持设备相关服务的本地私有云和支持实验室其他服务的公共云组成。本地私有云允许通过使用Web服务并发使用设备相关的任务,例如FPGA运行的服务。公共云的弹性特性适合于应对动态变化的学生实验用法。公共云中的服务器数量可以根据学生同时访问的数量进行扩展或缩小。昂贵的许可CAD工具,如Verilog-HDL合成工具,分配给有限数量的公有云和私有云服务器,在公有云和私有云服务器上实现Verilog-HDL合成Web服务,实现这些工具的远程共享。对于远程实验室案例,在公共云中分配学生虚拟机,并远程发布FPGA运行服务和Verilog-HDL合成服务。现有的教师文件/数据库和教育支持系统作为粘合剂,将本地云和公共云结合在一起,以最大限度地减少它们之间的数据传输,并在远程和实际实验室之间执行学生VM迁移。网络实验室实现了实验室平台的高效利用,并且可以降低TCO(总拥有成本),因为公共云可以应对动态变化的实验负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cyber laboratory: Migration to the hybrid cloud solution for device dependent hardware experiments
Cyber laboratory for FPGA design experiments combines the actual laboratory and the remote laboratory in a seamless way in a hybrid cloud environment. The hybrid cloud consists of on premise private cloud to support device dependent services and a public cloud to support rest of the laboratory services. The on premise private cloud allows concurrent use of device dependent tasks, such as FPGA run services, by making use of the Web services. The elastic nature of the public cloud is suitable for coping with dynamically changing student experimental usages. The number of servers in the public cloud can be scale out or shrink according to the number of simultaneous students accesses. Expensive license CAD tools, such as Verilog-HDL synthesis tools, are allocated for a limited number of the servers in the public cloud and the private cloud, where Verilog-HDL synthesis Web services are realized to achieve remote sharing of these tools. For remote laboratory case, student virtual machines are allocated in the public cloud and remotely issue the FPGA run services and the Verilog-HDL Synthesis services. The existing faculty file/database and education support system serves as an adhesive agent to combine the on premise cloud and the public cloud to minimize the data transfer among them and to perform student VM migrations between remote and actual laboratories. The Cyber Laboratory achieves an efficient use of laboratory platforms and can reduce TCO (total cost of ownership), as the public cloud can cope with the dynamically changing experimental loads.
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