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Mario Baldi

Publications and source records attributed to Mario Baldi.

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A Protocol-Independent Transport Architecture

The network transport layer is increasingly implemented in the NIC hardware to meet the performance demands of modern workloads, but this has made it difficult to evolve or deploy new transport protocols. Existing approaches either fix protocol logic in the data-path or build protocol-specific assumptions into the architecture that limit the range of protocols that can be supported on a single hardware substrate. We present PITA, a protocol-independent transport architecture that enables full data-path programmability while sustaining line-rate performance. PITA eliminates protocol-specific assumptions by structuring the data-path around a uniform abstraction over events, state, and instructions, and rethinks core components, including scheduling, packet generation, and data reassembly, to operate on this abstraction. We evaluate PITA along key dimensions reflecting the goals of its protocol-agnostic datapath design. Specifically, we show that PITA supports diverse protocol semantics by showing it can implement TCP and \roce on the same data path and preserve their distinct end-to-end behavior. Through targeted microbenchmarks and synthesis on Alveo U250 cards, we show that PITA's redesigned components sustain high performance under demanding conditions, with modest hardware overhead and meeting timing at 250MHz.

cs.NI

A Target-Agnostic Protocol-Independent Interface for the Transport Layer

Transport protocols continue to evolve to meet the demands of new applications, workloads, and network environments, yet implementing and evolving transport protocols remains difficult and costly. High-performance transport stacks tightly interweave protocol behavior with system-level mechanisms such as packet I/O, memory management, and concurrency control, resulting in large code bases where protocol logic is scattered and hard to modify -- an issue exacerbated by modern heterogeneous execution environments. This paper introduces transport programs, a target-independent abstraction that precisely and centrally captures a transport protocol's reactions to relevant transport events using abstract instructions for key transport operations such as data reassembly, packet generation and scheduling, and timer manipulation, while leaving execution strategy and low-level mechanisms to the target. We show that transport programs can express a diverse set of transport protocols, be efficiently realized on targets built over DPDK and Linux XDP, achieve performance comparable to hand-optimized implementations, and enable protocol changes and portability across targets without modifying underlying infrastructure.

cs.NI

A Primer on RecoNIC: RDMA-enabled Compute Offloading on SmartNIC

Today's data centers consist of thousands of network-connected hosts, each with CPUs and accelerators such as GPUs and FPGAs. These hosts also contain network interface cards (NICs), operating at speeds of 100Gb/s or higher, that are used to communicate with each other. We propose RecoNIC, an FPGA-based RDMA-enabled SmartNIC platform that is designed for compute acceleration while minimizing the overhead associated with data copies (in CPU-centric accelerator systems) by bringing network data as close to computation as possible. Since RDMA is the defacto transport-layer protocol for improved communication in data center workloads, RecoNIC includes an RDMA offload engine for high throughput and low latency data transfers. Developers have the flexibility to design their accelerators using RTL, HLS or Vitis Networking P4 within the RecoNIC's programmable compute blocks. These compute blocks can access host memory as well as memory in remote peers through the RDMA offload engine. Furthermore, the RDMA offload engine is shared by both the host and compute blocks, which makes RecoNIC a very flexible platform. Lastly, we have open-sourced RecoNIC for the research community to enable experimentation with RDMA-based applications and use-cases.

cs.DC

NEMA: Automatic Integration of Large Network Management Databases

Network management, whether for malfunction analysis, failure prediction, performance monitoring and improvement, generally involves large amounts of data from different sources. To effectively integrate and manage these sources, automatically finding semantic matches among their schemas or ontologies is crucial. Existing approaches on database matching mainly fall into two categories. One focuses on the schema-level matching based on schema properties such as field names, data types, constraints and schema structures. Network management databases contain massive tables (e.g., network products, incidents, security alert and logs) from different departments and groups with nonuniform field names and schema characteristics. It is not reliable to match them by those schema properties. The other category is based on the instance-level matching using general string similarity techniques, which are not applicable for the matching of large network management databases. In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection. We design matching metrics and scores for both numerical and non-numerical fields and propose algorithms for matching these fields. The effectiveness and efficiency of NEMA are evaluated by conducting experiments based on ground truth field pairs in large network management databases. Our measurement on large databases with 1,458 fields, each of which contains over 10 million records, reveals that the accuracies of NEMA are up to 95%. It achieves 2%-10% higher accuracy and 5x-14x speedup over baseline methods.

cs.DB

CrowdSurf: Empowering Informed Choices in the Web

When surfing the Internet, individuals leak personal and corporate information to third parties whose (legitimate or not) businesses revolve around the value of collected data. The implications are serious, from a person unwillingly exposing private information to an unknown third party, to a company unable to manage the flow of its information to the outside world. The point is that individuals and companies are more and more kept out of the loop when it comes to control private data. With the goal of empowering informed choices in information leakage through the Internet, we propose CROWDSURF, a system for comprehensive and collaborative auditing of data that flows to Internet services. Similarly to open-source efforts, we enable users to contribute in building awareness and control over privacy and communication vulnerabilities. CROWDSURF provides the core infrastructure and algorithms to let individuals and enterprises regain control on the information exposed on the web. We advocate CROWDSURF as a data processing layer positioned right below HTTP in the host protocol stack. This enables the inspection of clear-text data even when HTTPS is deployed and the application of processing rules that are customizable to fit any need. Preliminary results obtained executing a prototype implementation on ISP traffic traces demonstrate the feasibility of CROWDSURF.

cs.CY