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, a lightweight, high-performance, responsive serverless framework. exploits shared memory processing and dramatically improves the scalability of the dataplane by avoiding unnecessary protocol processing and serialization-deserialization overheads. 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, achieves the same dataplane performance with 10$\times$less CPU usage under realistic workloads. Additionally, eBPF benefits, by replacing heavyweight serverless components, allowing us to keep functions ‘warm’ with negligible penalty. Our preliminary experimental results show that 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’.

Traditional network resident functions (e.g., firewalls, network address translation) and middleboxes (caches, load balancers) have moved from purpose-built appliances to softwarebased 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 have a greater emphasis on functionality, take advantage of a full-fledged kernelbased system. L2/L3 NFs and L4/L7 middleboxes continue to be handled by distinct platforms on different nodes. This paper proposes MiddleNet that develops 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 the Data Plane Development Kit (DPDK) library for zero-copy packet delivery without interrupt-based processing, to enable the ’bumpin-the-wire’ L2/L3 processing performance required of NFV. To support L4/L7 middlebox functionality, MiddleNet utilizes a consolidated, kernel-based protocol stack for processing, avoiding a dedicated protocol stack for each function. MiddleNet fully exploits the event-driven capabilities of 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 in L4/L7 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 the packet processing needed (Layers 2 -7). Our experimental results show that MiddleNet achieves high performance in such a unified environment.

This paper demonstrates a new dimension in packet programming and processing by leveraging New IP technology since applications are sensitive to different types of network constraints. For instance, emerging industry operations, manufacturing, and autonomics are limited by the stochastic quality of services and inflexible address structures. Instead, they require efficiency and deterministic networks. In this paper, we propose a programmable data plane for New IP packet processing and show how network headers could evolve in the future. We demonstrate the implementation of New IP stack to encompass three goals: (1) address customization: applications and routers can forward packets between hosts with different address formats. (2) design an end-to-end model to meet service delivery guarantees: routers implement various in-network New IP contracts as described by the applications. (3) Rapid experimentation of the New IP components. With New IP, developers can describe packet processing functionalities without depending on the specifics of the underlying hardware. Our implementation of New IP stack uses the existing toolsets and capabilities of the Linux platform, such as eXpress Data Path (XDP) and Traffic Control (TC) subsystem. It consists of an end-to-end solution with a new network stack on the host side and a corresponding packet processing and forwarding engine on the network. It is validated using Network Stack Tester (NeST), a lightweight tool built on Linux network namespaces.

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.

Softwarized network resident functions have been extensively used to replace purpose-built appliances. However, there is a lack of alternatives for richer network resident functionality with a seamless combination of L2/L3 Network Function Virtualization (NFV) and L4/L7 middleboxes.We propose MiddleNet, a unified L2/L3 NFV and L4/L7 middlebox framework. MiddleNet uses DPDK in L2/L3 NFV to achieve high-performance, zero-copy packet delivery. MiddleNet exploits the event-driven capabilities of extended Berkeley Packet Filter (eBPF) to build up lightweight L4/L7 middleboxes with load-proportional overheads. MiddleNet constructs complex L2/L3 NF and L4/L7 middlebox function chains with low overhead using shared memory communication. With the integration of Single Root I/O Virtualization (SR-IOV), MiddleNet supports dynamically selecting packet processing layers (L2 to L7) based on the flow. In this demo, we show MiddleNet’s operation.

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.

This paper proposes a method to test the sobriety of an individual using infrared images of the persons eyes, face, hand, and facial profile. The database we used consisted of images of forty different individuals. The process is broken down into two main stages. In the first stage, the data set was divided according to body part and each one was run through its own Convolutional Neural Network (CNN). We then tested the resulting network against a validation data set. The results obtained gave us an indication of which body parts were better suited for identifying signs of drunken state and sobriety. In the second stage, we took the weights of CNN giving best validation accuracy from the first stage. We then grouped the body parts according to the person they belong to. The body parts were fed together into a CNN using the weights obtained in the first stage. The result for each body part was passed to a simple back-propagation neural network (BPNN) to get final results. We tried to identify the most optimal configuration of neural networks for each stage of the process. The results we obtained showed that facial profile images tend to give very good indications of sobriety. The results also showed that combining the results of multiple body parts using a simple BPNN gives a higher accuracy than that of individual ones.