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In recent years, Field Programmable Gate Arrays (FPGAs) have gained prominence in cloud computing data centers, driven by their capacity to offload compute-intensive tasks and contribute to the ongoing trend of data center disaggregation, as well as their ability to be directly connected to the network. While FPGAs offer numerous advantages, they also pose challenges in terms of configuration, programmability, and monitoring, particularly in the absence of an operating system with essential features like the TCP/IP networking stack. This paper introduces an In-band Network Telemetry (INT) approach based on the P4 language for FPGA data plane programming. The goal is to facilitate monitoring and network performance analysis by providing one-way packet delay information. The approach is demonstrated in the Open Cloud Testbed (OCT) and FABRIC testbeds, both offering open access to the research community with greater FPGA availability than commercial clouds. The workflow enables researchers to create custom P4 programs and bitstreams for installation on FPGAs. The paper presents a multi-step approach allowing experimentation within the New England Research Cloud (NERC), testing in OCT, and final deployment in FABRIC, well-suited for one-way delay measurements due to synchronized clocks via GPS time signals. Contributions include the provision of a P4 workflow for FPGAs in a research cloud, a novel FPGA clock-based INT approach, and a comprehensive evaluation through simulation and experiments in the Open Cloud and FABRIC testbeds. 

This paper presents a framework for cloud users who wish to specify their experiments in the P4 language and map them to FPGAs in the Open Cloud Testbed (OCT). OCT consists of P4-enabled FPGA nodes that are directly connected to the network via 100 gigabit Ethernet connections, and which support runtime reconfiguration. Cloud users can quickly prototype and deploy their P4 applications through our framework, which provides the necessary infrastructure including a network interface shell for the P4 logic. We have provided several examples using this framework that demonstrate designs running at the 100 GbE line rate with the support of runtime reconfiguration for P4 functions. By combining an existing network interface shell and P4 toolchain on FPGAs, we offer a framework that enables users to rapidly execute their P4 experiments in real time on FPGAs. 

Many organizations maintain and operate large shared computing clusters, since they can substantially reduce computing costs by leveraging statistical multiplexing to amortize it across all users. Importantly, such shared clusters are generally not free to use, but have an internal pricing model that funds their operation. Since employees at many large organizations, especially Universities, have some budgetary autonomy over purchase decisions, internal shared clusters are increasingly competing for users with cloud platforms, which may offer lower costs and better performance. As a result, many organizations are shifting their shared clusters to operate on cloud resources. This paper empirically analyzes the user incentives for shared cloud clusters under two different pricing models using an 8-year job trace from a large shared cluster for a large University system. Our analysis shows that, with either pricing model, a large fraction of users have little financial incentive to participate in a shared cloud cluster compared to directly acquiring resources from a cloud platform. While shared cloud clusters can provide some limited reductions in cost by leveraging reserved instances at a discount, due to bursty workloads, realizing these reductions generally requires imposing long job waiting times, which for many users are likely not worth the cost reduction. In particular, we show that, assuming users defect from the shared cluster if their wait time is greater than 15x their average job runtime, over 80% of the users would defect, which increases the price of the remaining users such that it eliminates any incentive to participate in a shared cluster. Thus, while shared cloud clusters may provide users other benefits, their financial incentives are weak. 

This paper presents a framework for cloud users who wish to specify their experiments in the P4 language and map them to FPGAs in the Open Cloud Testbed (OCT). OCT consists of P4-enabled FPGA nodes that are directly connected to the network via 100 gigabit Ethernet connections, and which support runtime reconfiguration. Cloud users can quickly prototype and deploy their P4 applications through our framework, which provides the necessary infrastructure including a network interface shell for the P4 logic. We have provided several examples using this framework that demonstrate designs running at the 100 GbE line rate with the support of runtime reconfiguration for P4 functions. By combining an existing network interface shell and P4 toolchain on FPGAs, we offer a framework that enables users to rapidly execute their P4 experiments in real time on FPGAs. 

This paper presents a framework for cloud users who wish to specify their experiments in the P4 language and map them to FPGAs in the Open Cloud Testbed (OCT). OCT consists of P4-enabled FPGA nodes that are directly connected to the network via 100 gigabit Ethernet connections, and which support runtime reconfiguration. Cloud users can quickly prototype and deploy their P4 applications through our framework, which provides the necessary infrastructure including a network interface shell for the P4 logic. We have provided several examples using this framework that demonstrate designs running at the 100 GbE line rate with the support of runtime reconfiguration for P4 functions. By combining an existing network interface shell and P4 toolchain on FPGAs, we offer a framework that enables users to rapidly execute their P4 experiments in real time on FPGAs.