Cloud computing is becoming a major trend for delivering and accessing infrastructure on demand via the network. Meanwhile, the usage of FPGAs (Field Programmable Gate Arrays) for computation acceleration has made significant inroads into multiple application domains due to their ability to achieve high throughput and predictable latency, while providing programmability, low power consumption and time-to-value. Many types of workloads, e.g. databases, big data analytics, and high performance computing, can be and have been accelerated by FPGAs. As more and more workloads are being deployed in the cloud, it is appropriate to consider how to make FPGAs and their capabilities available in the cloud. However, such integration is nontrivial due to issues related to FPGA resource abstraction and sharing, compatibility with applications and accelerator logics, and security, among others. In this paper, a general framework for integrating FPGAs into the cloud is proposed and a prototype of the framework is implemented based on OpenStack, Linux-KVM and Xilinx FPGAs. The prototype enables isolation between multiple processes in multiple VMs, precise quantitative acceleration resource allocation, and priority-based workload scheduling. Experimental results demonstrate the effectiveness of this prototype, an acceptable overhead, and good scalability when hosting multiple VMs and processes.

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The Internet of Things (IOT) has been widely applied in many fields, while the IOT data are always large volume, update frequently and inherently multi-dimensional, these characteristics bring big challenges to the traditional DBMSs. The traditional DBMSs have rich functionality and can deal with multi-attributes access efficiently, they can not scale good enough to deal with large volume data and can not support high insert throughput. The cloud-based database systems have good scalability, but they don't support multidimensional access natively.In order to deal with the large volume of IOT data, we propose an update and query efficient index framework (UQE-Index) based on key-value store that can support high insert throughput and provide efficient multi-dimensional query simultaneously.We implemented a prototype based on HBase and did comprehensive experiments to test our solution's scalability and efficiency.

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