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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. 

Multi-sensor IoT devices can gather different types of data by executing different sensing activities or tasks. Therefore, IoT applications are also becoming more complex in order to process multiple data types and provide a targeted response to the monitored phenomena. However, IoT devices which are usually resource-constrained still face energy challenges since using each of these sensors has an energy cost. Therefore, energy-efficient solutions are needed to extend the device lifetime while balancing the sensing data requirements of the IoT application. Cooperative monitoring is one approach for managing energy and involves reducing the duplication of sensing tasks between neighboring IoT devices. Setting up cooperative monitoring is a scheduling problem and is challenging in a distributed environment with resource-constrained IoT devices. In this work, we present our Distributed Token and Tier-based task Scheduler (DTTS) for a multi-sensor IoT network. Our algorithm divides the monitoring period (5 min epochs) into a set of non-overlapping intervals called tiers and determines the start deadlines for the task at each IoT device. Then to minimize temporal sensing overlap, DTTS distributes task executions throughout the epoch and uses tokens to share minimal information between IoT devices. Tasks with earlier start deadlines are scheduled in earlier tiers while tasks with later start deadlines are scheduled in later tiers. Evaluating our algorithm against a simple round-robin scheduler shows that the DTTS algorithm always schedules tasks before their start deadline expires. 

Multi-sensor IoT devices enable the monitoring of different phenomena using a single device. Often deployed over large areas, these devices have to depend on batteries and renewable energy sources for power. Therefore, efficient energy management solutions that maximize device lifetime and information utility are important. We present SEMA, a smart energy management solution for IoT applications that uses a Model Predictive Control (MPC) approach to optimize IoT energy use and maximize information utility by dynamically determining task values to be used by the IoT device’s sensors. Our solution uses the current device battery state, predicted available solar energy over the short-term, and task energy and utility models to meet the device energy goals while providing sufficient monitoring data to the IoT applications. To avoid the need for executing the MPC optimization at a centralized sink (from which the task values are downloaded to the SEMA devices), we propose SEMA-Approximation (SEMA-A), which uses an efficient MPC Approximation that is simple enough to be run on the IoT device itself. SEMA-A decomposes the MPC optimization problem into two levels: an energy allocation problem across the time epochs, and task-dependent sensor scheduling problem, and finds efficient algorithms for solving both problems. Experimental results show that SEMA is able to adapt the task values based on the available energy, and that SEMA-A closely approximates SEMA in sensing performance. 

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. 

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.