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Cloud-native microservice applications use different communication paradigms to network microservices, including both synchronous and asynchronous I/O for exchanging data. Existing solutions depend on kernel-based networking, incurring significant overheads. The interdependence between microservices for these applications involves considerable communication, including contention between multiple concurrent flows or user sessions. In this paper, we design X-IO, a high-performance unified I/O interface that is built on top of shared memory processing with lock-free producer/consumer rings, eliminating kernel networking overheads and contention. X-IO offers a feature-rich interface. X-IO’s zero-copy interface supports building provides truly zero-copy data transfers between microservices, achieving high performance. X-IO also provides a POSIX-like socket interface using HTTP/REST API to achieve seamless porting of microservices to X-IO, without any change to the application code. X-IO supports concurrent connections for microservices that require distinct user sessions operating in parallel. Our preliminary experimental results show that X-IO’s zero-copy interfaces achieve 2.8x-4.1x performance improvement compared to kernel-based interfaces. Its socket interfaces outperform kernel TCP sockets and achieve performance close to UNIX-domain sockets. The HTTP/REST APIs in X-IO perform 1.4 x-2.3 x better than kernel-based alternatives with concurrent connections. 

The 5G user plane function (UPF) is a critical inter-connection point between the data network and cellular network infrastructure. It governs the packet processing performance of the 5G core network. UPFs also need to be flexible to support several key control plane operations. Existing UPFs typically run on general-purpose CPUs, but have limited performance because of the overheads of host-based forwarding. We design Synergy, a novel 5G UPF running on SmartNICs that provides high throughput and low latency. It also supports monitoring functionality to gather critical data on user sessions for the prediction and optimization of handovers during user mobility. The SmartNIC UPF efficiently buffers data packets during handover and paging events by using a two-level flow-state access mechanism. This enables maintaining flow-state for a very large number of flows, thus providing very low latency for control and data planes and high throughput packet forwarding. Mobility prediction can reduce the handover delay by pre-populating state in the UPF and other core NFs. Synergy performs handover predictions based on an existing recurrent neural network model. Synergy's mobility predictor helps us achieve 2.32× lower average handover latency. Buffering in the SmartNIC, rather than the host, during paging and handover events reduces packet loss rate by at least 2.04×. Compared to previous approaches to building programmable switch-based UPFs, Synergy speeds up control plane operations such as handovers because of the low P4-programming latency leveraging tight coupling between SmartNIC and host. 

Cellular network control procedures (e.g., mobility, idle-active transition to conserve energy) directly influence data plane behavior, impacting user-experienced delay. Recognizing this control-data plane interdependence, L25GC re-architects the 5G Core (5GC) network, and its processing, to reduce latency of control plane operations and their impact on the data plane. Exploiting shared memory, L25GC eliminates message serialization and HTTP processing overheads, while being 3GPP-standards compliant. We improve data plane processing by factoring the functions to avoid control-data plane interference, and using scalable, flow-level packet classifiers for forwarding-rule lookups. Utilizing buffers at the 5GC, L25GC implements paging, and an intelligent handover scheme avoiding 3GPP's hairpin routing, and data loss caused by limited buffering at 5G base stations, reduces delay and unnecessary message processing. L25GC's integrated failure resiliency transparently recovers from failures of 5GC software network functions and hardware much faster than 3GPP's reattach recovery procedure. L25GC is built based on free5GC, an open-source kernel-based 5GC implementation. L25GC reduces event completion time by ~50% for several control plane events and improves data packet latency (due to improved control plane communication) by ~2×, during paging and handover events, compared to free5GC. L25GC's design is general, although current implementation supports a limited number of user sessions. 

Serverless computing promises an efficient, low-cost compute capability in cloud environments. However, existing solutions, epitomized by open-source platforms such as Knative, include heavyweight components that undermine this goal of serverless computing. Additionally, such serverless platforms lack dataplane optimizations to achieve efficient, high-performance function chains that facilitate the popular microservices development paradigm. Their use of unnecessarily complex and duplicate capabilities for building function chains severely degrades performance. 'Cold-start' latency is another deterrent. We describe SPRIGHT, a lightweight, high-performance, responsive serverless framework. SPRIGHT exploits shared memory processing and dramatically improves the scalability of the dataplane by avoiding unnecessary protocol processing and serialization-deserialization overheads. SPRIGHT extensively leverages event-driven processing with the extended Berkeley Packet Filter (eBPF). We creatively use eBPF's socket message mechanism to support shared memory processing, with overheads being strictly load-proportional. Compared to constantly-running, polling-based DPDK, SPRIGHT achieves the same dataplane performance with 10× less CPU usage under realistic workloads. Additionally, eBPF benefits SPRIGHT, by replacing heavyweight serverless components, allowing us to keep functions 'warm' with negligible penalty. Our preliminary experimental results show that SPRIGHT achieves an order of magnitude improvement in throughput and latency compared to Knative, while substantially reducing CPU usage, and obviates the need for 'cold-start'. 

With the commercialization and deployment of 5G, efforts are beginning to explore the design of the next generation of cellular networks, called 6G. New and constantly evolving use cases continue to place performance demands, especially for low latency communications, as these are still challenges for the 3GPP-specified 5G design, and will have to be met by the 6G design. Therefore, it is helpful to re-examine several aspects of the current cellular network’s design and implementation.Based on our understanding of the 5G cellular network specifications, we explore different implementation options for a dis-aggregated 5G core and their performance implications. To improve the data plane performance, we consider advanced packet classification mechanisms to support fast packet processing in the User Plane Function (UPF), to improve the poor performance and scalability of the current design based on linked lists. Importantly, we implement the UPF function on a SmartNIC for forwarding and tunneling. The SmartNIC provides the fastpath for device traffic, while more complex functions of buffering and processing flows that suffer a miss on the SmartNIC P4 tables are processed by the host-based UPF. Compared to an efficient DPDK-based host UPF, the SmartNIC UPF increases the throughput for 64 Byte packets by almost 2×. Furthermore, we lower the packet forwarding latency by 3.75× by using the SmartNIC. In addition, we propose a novel context-level QoS mechanism that dynamically updates the Packet Detection Rule priority and resource allocation of a flow based on the user context. By combining our innovations, we can achieve low latency and high throughput that will help us evolve to the next generation 6G cellular networks. 

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to software-based components. However, L2/L3 network functions (NFs) are being implemented on Network Function Virtualization (NFV) platforms that extensively exploit kernel-bypass technology. They often use DPDK for zero-copy delivery and high performance. On the other hand, L4/L7 middleboxes, which usually require full network protocol stack support, take advantage of a full-fledged kernel-based system with a greater emphasis on functionality. Thus, L2/L3 NFs and middleboxes continue to be handled by distinct platforms on different nodes.This paper proposes MiddleNet that seeks to overcome this dichotomy by developing a unified network resident function framework that supports L2/L3 NFs and L4/L7 middleboxes. MiddleNet supports function chains that are essential in both NFV and middlebox environments. MiddleNet uses DPDK for zero-copy packet delivery without interrupt-based processing, to enable the ‘bump-in-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities provided by the extended Berkeley Packet Filter (eBPF) and seamlessly integrates it with shared memory for high-performance communication in L4/L7 middlebox function chains. The overheads for MiddleNet are strictly load-proportional, without needing the dedicated CPU cores of DPDK-based approaches. MiddleNet supports flow-dependent packet processing by leveraging Single Root I/O Virtualization (SR-IOV) to dynamically select packet processing needed (Layer 2 to Layer 7). Our experimental results show that MiddleNet can achieve high performance in such a unified environment. 

Despite advances in network security, attacks targeting mission critical systems and applications remain a significant problem for network and datacenter providers. Existing telemetry platforms detect volumetric attacks at terabit scales using approximation techniques and coarse grain analysis. However, the prevalence of low and slow attacks that require very little bandwidth, makes flow-state tracking critical to overall attack mitigation. Traffic queries deployed on network switches are often limited by hardware constraints, preventing them from carrying out flow tracking features required to detect stealthy attacks. Such attacks can go undetected in the midst of high traffic volumes. We design SmartWatch, a novel flow state tracking and flow logging system at line rate, using SmartNICs to optimize performance and simultaneously detect a number of stealthy attacks. SmartWatch leverages advances in switch based network telemetry platforms to process the bulk of the traffic and only forward suspicious traffic subsets to the SmartNIC. The programmable network switches perform coarse-grained traffic analysis while the SmartNIC conducts the finer-grained analysis which involves additional processing of the packet as a 'bump-in-the-wire'. A control loop between the SmartNIC and programmable switch tunes the queries performed in the switch to direct the most appropriate traffic subset to the SmartNIC. SmartWatch's cooperative monitoring approach yields 2.39 times better detection rate compared to existing platforms deployed on programmable switches. SmartWatch can detect covert timing channels and perform website fingerprinting more efficiently compared to standalone programmable switch solutions, relieving switch memory and control-plane processor resources. Compared to host-based approaches, SmartWatch can reduce the packet processing latency by 72.32%. 

Ensuring high scalability (elastic scale-out and consolidation), as well as high availability (failure resiliency) are critical in encouraging adoption of software-based network functions (NFs). In recent years, two paradigms have evolved in terms of the way the NFs manage their state - namely the Stateful (state is coupled with the NF instance) and a Stateless (state is externalized to a datastore) manner. These two paradigms present unique challenges and opportunities for ensuring high scalability and high availability of NFs and NF chains. In this work, we assess the impact on ensuring the correctness of NF state including the implications of non-determinism in packet processing, and carefully analyze and present the benefits and disadvantages of the two state management paradigms. We leverage OpenNetVM and Redis in-memory datastore to implement both state management paradigms and empirically compare the two. Although the stateless paradigm is desirable for elastic scaling, our experimental results show that, even at line-rate packet processing (10 Gbps), stateful NFs can achieve chain-level failover across servers in a LAN incurring less than 10% performance. The state-of-the-art stateless counterparts incur severe throughput penalties. We observe 30-85% overhead on normal processing, depending on the mode of state updated to the externalized datastore.