glongh / awesome-edge-computing

A curated list of awesome edge computing, including Frameworks, Simulators, Tools, etc.

• Awesome edge computing
• Simulators
• Frameworks
  • Engine
  • Networks
• Test (data, benchmark)
• Tools
• Applications
• Edge-AI frameworks
• Prototype proposed in researches

Awesome edge computing

Simulators

• CloudSim: CloudSim goal is to provide a generalized and extensible simulation framework that enables modeling, simulation, and experimentation of emerging Cloud computing infrastructures and application services, allowing its users to focus on specific system design issues that they want to investigate, without getting concerned about the low level details related to Cloud-based infrastructures and services.

• CloudSimSDN: An SDN extension of CloudSim project to simulate SDN features in the context of a cloud data center. CloudSimSDN supports calculating power consumption by both hosts and switches. For instance, network-aware VM placement policies can be evaluated using CloudSimSDN.

• CloudSimPy: CloudSimPy is a data centric task schedule framework. It is bases on SimPy, a process-based discrete-event simulation framework based on standard Python. The scientific computing, deep learning, and machine learning ecology of the Python language is more complete than other programming languages. CloudSimPy works well with deep learning frameworks with Python support (such as TensorFlow, PyTorch) The combination helps to study resource management methods based on machine learning or deep learning.

• CFN: Computing First Networking that based on Named Data Networking (NDN).

• Cooja: Cooja Simulator is a network simulator specifically designed for Wireless Sensor Networks.

• CORE:The Common Open Research Emulator (CORE) is a tool for emulating networks on one or more machines. You can connect these emulated networks to live networks. CORE consists of a GUI for drawing topologies of lightweight virtual machines, and Python modules for scripting network emulation.

• EdgeCloudSim: EdgeCloudSim: An Environment for Performance Evaluation of Edge Computing Systems.

• EdgeSim:An open-source simulator of edge computing and caching. This simulator system contains three models: Content, Base Station and User.

• EmuFog: EmuFog helps to test fog computing applications more efficiently. Instead of actual deploying large network topologies with your application to test, EmuFog helps to generate networks that can be emulated easily with MaxiNet, a distributed version of the popular Mininet. This provides more realistic results than simulations and is cheaper and faster than real deployments. As an input EmuFog supports generated topologies from BRITE or measured real world topologies from Caida. In those networks EmuFog places fog nodes efficiently based on user defined constrains such as network latency thresholds or resource constraints. Applications for clients and fog nodes can be anything shipped in a Docker container.

• Fogbed: Fogbed is a framework that extends the Mininet emulator to create fog testbeds in virtualized environments. Using a desktop approach, Fogbed enables the deployment of virtual fog nodes as Docker containers under different network configurations. The Fogbed API provides functionality to add, connect and remove containers dynamically from the network topology. These features allow for the emulation of real world cloud and fog infrastructures in which it is possible to start and stop compute instances at any point in time. Also, it is possible to change at runtime resource limitations for a container, such as CPU time and memory available.

• FogNetSim++: FogNetSim++ extends OMNeT++, which is a well known framework for building network simulators, to model all these aspects. Moreover, it includes popular communication protocols for simulation, such as TCP, UDP, MQTT, and CoAP. Furthermore, FogNetSim++ models several other aspects, such as energy consumption, pricing, mobility, and handoff mechanisms.

• FogTorchPI: FogTorchΠ is an open source prototype, developed in Java, based on a model for Fog computing infrastructures and applications. 1), It takes into account non-functional parameters within the model (i.e., hardware, software, latency and bandwidth) to determine, compare and contrast different eligible deployments of a given application over a Fog infrastructure. 2), In the case of hardware capabilities, it considers CPU cores, RAM and storage available at a given node or required by a given software component. 3), Software capabilities are represented by a list of software names (operating system, programming languages, frameworks etc). 4), It considers latency, and both download and upload bandwidths as QoS attributes. Latency is measured in milliseconds (ms), while bandwidth is given in Megabits per second (Mbps). Note: Outputs of FogTorchPI can be the input of iFogSim.

• IoTSim-Edge: IoTSim captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim-Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices.

• IoTSim-Osmosis: IoTSim-Osmosis is a simulation framework that supports the testing and validation of osmotic computing applications. In particular, it enables a unified modelling and simulation of complex IoT applications over heterogeneous edge-cloud SDN-aware environments.DOI:10.1016/j.sysarc.2020.101956

• MobFogSim: An extension which can support for Mobility of iFogSim. MobFogSim can model more generalised aspects related to device mobility and VM/container migration in the fog, e.g., user position and speed, connection handoff, migration policies and strategies, to name a few. More details can be found in MobFogSim: Simulation of Mobility and Migration for Fog Computing.

• Mini-NDN:Mini-NDN is a lightweight networking emulation tool that enables testing, experimentation, and research on the NDN platform. It was initially based on Mini-CCNx which was a fork of Mininet. Mini-NDN uses the NDN libraries, NFD, NLSR, and tools released by the NDN project to emulate an NDN network on a single system.

• Mininet-WiFi: Emulator for Software-Defined Wireless Networks.

• ndnSIM: The ndnSIM 2.0 is NS-3 module that implements Named Data Networking (NDN) communication model, the clean slate Internet design.

• NFaaS: Enabling to migrate functions closer to the user in ICN environment without a global view of the network.

• iFogSim: The iFogSim Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments.

• lightMANO: A lightweight distributed service orchestrator designed for resource constrained multi-access edge computing environments.

• Packet Tracer: Packet Tracer is a cross-platform visual simulation tool designed by Cisco Systems that allows users to create network topologies and imitate modern computer networks. The software allows users to simulate the configuration of Cisco routers and switches using a simulated command line interface. Packet Tracer makes use of a drag and drop user interface, allowing users to add and remove simulated network devices as they see fit.

• pFogSim: pFogSim (/p/-fôg-/sɪm/) is a play off iFogSim; A simulator made to handle large-scale FOG networks with the HAFA Puddle Strategy to help evaluate the potential advantages/disadvantages within user-customizable scenarios.

• PureEdgeSim: a new simulator based on CloudSim Plus that is designed to simulate Cloud, Edge, and Mist computing environments. It allows to evaluate the performance of resources management strategies in terms of network usage, latency, resources utilization, energy consumption, etc. and enables the simulation of several scenarios such as the Internet of Things (IoT), connected vehicles/VANETs/MANET, Mist computing environments (peer-to peer networks such as mobile devices Cloud), and mobile Edge computing.

• RECAP Simulator Framework: The RECAP Simulation Framework is an open source simulation framework designed to support experimentation of infrastructure with different description models for workload, user distribution, network topology, and (physical and virtual) resource placement. It is able to simulate different use cases including Virtual Network Functions (VNFs), Elasticsearch, smart cities, and virtual Content Delivery Networks (vCDN). The output of the RECAP Simulation Framework depends on the use case, but in general, it is a set of metrics predefined by the user such as bandwidth, resource consumption (CPU, memory, storage), network delay, energy consumption, active number of VMs, cache hits and cache misses.

• SimMobility: SimMobility is an integrated mobility simulation platform that comprehensively simulates Future Mobility scenarios by integrating long, medium, and short-term travel behavior. Various mobility-sensitive behavioral models are integrated within the state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. The platform simulates the effects of a portfolio of technology, policy and investment options under alternative future scenarios. SimMobility encompasses the modeling of millions of agents, including pedestrians, drivers, phones, traffic lights, GPS, cars, buses, and trains, from second-by-second to year-by-year simulations and across countries.

• SimpleIoTSimulator: SimpleIoTSimulator® is an easy to use, IoT Sensor/device simulator that quickly creates test environments made up of thousands of sensors and gateways, all on just one computer. SimpleIoTSimulator supports many of the common IoT protocols. They include: MQTT, MQTT-SN, MQTT-Broker, CoAP, HTTP/s client, HTTP/s server, Modbus over TCP, BACnet/IP server, LoRa Gateway, LoRa Device.

• SLEIPNIR: SLEIPNIR is a DAG scheduling simulator focused on mobile cloud/edge/iot infrastructures.

• Step-ONE: Simulated Testbed for Edge-Fog Processes based on the Opportunistic Network Environment simulator.

• Veins: The open source vehicular network simulation framework.

• Veins LTE: Veins LTE is a simulator for heterogeneous vehicular networks. It provides fine-grained simulation of vehicular networks based on IEEE 802.11p and TE.

• VirtFogSim: VirtFogSim is a MATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). Check the paper link for more details.

• YAFS(Yet Another Fog Simulator): YAFS is a simulator tool based on Python of architectures such as: Fog Computing ecosystems for several analysis regarding with the placement of resources, cost deployment, network design, ... IoT environments are the most evident fact of this type of architecture. The highlights points of YAFS are:

  • Dinamyc topology: entities and network links can be created or removed along the simulation.
  • Dinamyc creation of messages sources: sensors can generate messages from different point access along the simulation.
  • And for hence, the placement allocation algorithm and the orchestration algorithm, that are extended by the user, can run along the simulation.
  • The topology of the network is based on Complex Network theory. Thus, the algorithms can obtain more valuable indicators from topological features.
  • The results are stored in a raw format in a nosql database. The simpler the format, the easier it is to perform any type of statistics.

Frameworks

Engine

• Apache Edgent(incubating): Apache Edgent is a programming model and micro-kernel style runtime that can be embedded in gateways and small footprint edge devices enabling local, real-time, analytics on the continuous streams of data coming from equipment, vehicles, systems, appliances, devices and sensors of all kinds (for example, Raspberry Pis or smart phones). Working in conjunction with centralized analytic systems, Apache Edgent provides efficient and timely analytics across the whole IoT ecosystem: from the center to the edge.

• Baetyl: Baetyl is an open edge computing framework of Linux Foundation Edge that extends cloud computing, data and service seamlessly to edge devices. It can provide temporary offline, low-latency computing services include device connection, message routing, remote synchronization, function computing, video capture, AI inference, status reporting, configuration ota etc.

• Distributed Strom: Distributed Storm is an extension of Apache Storm that supports the execution of distributed, adaptive, and QoS-aware scheduling algorithms. It introduces some key components on each worker node that enhance the system with adaptation capabilities, relying on a MAPE (Monitor, Analyze, Plan, and Execute) feedback loop periodically executed. Specifically, the newly introduced components are: the AdaptiveSchedulers, the QoSMonitors, and the WorkerMonitors. Logics of a Bolt can be regarded as an edge server processes the data.

• ENORM: The ENORM framework primarily addresses the deployment and load balancing challenges on individual edge nodes. ENORM operates in a three-tier environment, but a master controller does not control the edge nodes. Instead, it is assumed that they are visible to cloud servers that may want to make use of the edge. The framework allows for partitioning a cloud server and offloading it to edge nodes for improving the overall QoS of the application.

• FogFlow: FogFlow is an IoT edge computing framework to automatically orchestrate dynamic data processing flows over cloud and edges driven by context, including system context on the available system resources from all layers, data context on the registered metadata of all available data entities, and also usage context on the expected QoS defined by users.

• k3OS: k3OS is purpose-built to simplify Kubernetes operations in low-resource computing environments. Installs fast. Boots faster. Managed through Kubernetes.

• KubeEdge:KubeEdge is an open source system extending native containerized application orchestration and device management to hosts at the Edge. It is built upon Kubernetes and provides core infrastructure support for networking, application deployment and metadata synchronization between cloud and edge. It also supports MQTT and allows developers to author custom logic and enable resource constrained device communication at the Edge. KubeEdge consists of a cloud part and an edge part.

• OCI: a framework that enables network operators with the ability to open up their edge facilities to Application Service Providers, by offering edge computing. As an example, consider a third-party Application Service Provider selling an IoT device (say, a home thermostat or security camera). Whenever that device shows up at the edge of the network, the Open Carrier Interface framework starts the Application Service Provider implemented edge software at the network operator’s Central Offices, which supports the IoT device with edge processing. Thus, even an early-stage Application Service Provider, who has few financial or physical resources, can offer the same level of edge support as giant companies, and therefore can compete with them on an even footing. read more.

• OpenStack++: is a framework developed by Carnegie Mellon University Pittsburgh for providing VM-based cloudlet platform on regular x86 computers for mobile application offloading. A set of new mobile computing applications that build upon OpenStack++ and leverage its support for cloudlets is also supported.

• WSO2-IoT Server: An extension of the popular open-source enterprise service-oriented integration platform WSO2 server that consists of certain IoT-related mechanisms, such as connecting a broad range of common IoT devices with the cloud using standard protocols such as MQTT and XMPP. Further, WSO2–IoT server includes the embedded Siddhi 3.0 component that allows the system to deploy real-time streaming processes in embedded devices. In other words, WSO2–IoT server provides the FEC computing capability to outer-edge devices.

Networks

• Awesome-pcaptools:A collection of tools developed by other researchers in the Computer Science area to process network traces.

• EdgeNet: edgeNet is a distributed edge cloud, in the family of PlanetLab, GENI, Canada’s SAVI infrastructure, Japan’s JGN-X, Germany’s G-Lab, and PlanetLab Europe. It is a modern distributed edge cloud, incorporating advances in Cloud technologies over the past few years.

• MinNet: Mininet creates a realistic virtual network, running real kernel, switch and application code, on a single machine (VM, cloud or native), in seconds, with a single command:sudo mn. Mininet is also a great way to develop, share, and experiment with OpenFlow and Software-Defined Networking systems.

• MobEmu: An opportunistic network emulator that can run a user-created routing or dissemination algorithm on a desired mobility trace or synthetic model.

• Mosquitto: Eclipse Mosquitto is an open source (EPL/EDL licensed) message broker that implements the MQTT protocol versions 5.0, 3.1.1 and 3.1. Mosquitto is lightweight and is suitable for use on all devices from low power single board computers to full servers. The MQTT protocol provides a lightweight method of carrying out messaging using a publish/subscribe model. This makes it suitable for Internet of Things messaging such as with low power sensors or mobile devices such as phones, embedded computers or microcontrollers. The Mosquitto project also provides a C library for implementing MQTT clients, and the very popular mosquitto_pub and mosquitto_sub command line MQTT clients.

• Naming Data Network Platform: NDN is an entirely new architecture, but one whose design principles are derived from the successes of today’s Internet, reflecting our understanding of the strengths and limitations of the current Internet architecture, and one that can be rolled out through incremental deployment over the current operational Internet.

• Node-RED: Node-RED is a programming tool for wiring together hardware devices, APIs and online services in new and interesting ways. It provides a browser-based editor that makes it easy to wire together flows using the wide range of nodes in the palette that can be deployed to its runtime in a single-click.

• OMNeT++: OMNeT++ is a public-source, component-based, modular and open-architecture simulation environment with strong GUI support and an embeddable simulation kernel. Its primary application area is the simulation of communication networks, but it has been successfully used in other areas like the simulation of IT systems, queueing networks, hardware architectures and business processes as well.

• Open vSwitch: is a production quality, multilayer virtual switch licensed under the open source Apache 2.0 license. It is designed to enable massive network automation through programmatic extension, while still supporting standard management interfaces and protocols (e.g. NetFlow, sFlow, IPFIX, RSPAN, CLI, LACP, 802.1ag). In addition, it is designed to support distribution across multiple physical servers similar to VMware's vNetwork distributed vswitch or Cisco's Nexus 1000V.

• POX: POX is a networking software platform written in Python. POX started life as an OpenFlow controller, but can now also function as an OpenFlow switch, and can be useful for writing networking software in general. An useful link of how to simulate a SDN can check the link: http://www.brianlinkletter.com/using-the-pox-sdn-controller/

• VerneMQ: VerneMQ is a high-performance, distributed MQTT message broker. It scales horizontally and vertically on commodity hardware to support a high number of concurrent publishers and consumers while maintaining low latency and fault tolerance. VerneMQ is the reliable message hub for your IoT platform or smart products.

• Wonder Shaper: Wonder Shaper is a script that allows the user to limit the bandwidth of one or more network adapters. It does so by using iproute's tc command, but greatly simplifies its operation.

Test (data, benchmark)

• The CAIDA Anonymized Internet Traces 2015 Dataset: CAIDA's passive traces dataset contains traces collected from high-speed monitors on a commercial backbone link. The data collection started in April 2008 and ended in January 2019. These data are useful for research on the characteristics of Internet traffic, including application breakdown, security events, geographic and topological distribution, flow volume and duration.

• CloudSuite: CloudSuite is a benchmark suite for cloud services. The third release consists of eight applications that have been selected based on their popularity in today’s datacenters. The benchmarks are based on real-world software stacks and represent real-world setups.

• DeFog: DeFog, a first Fog benchmarking suite to: (i) alleviate the burden of Fog benchmarking by using a standard methodology, and (ii) facilitate the understanding of the target platform by collecting a catalogue of relevant metrics for a set of benchmarks.

• Edge AIBench: Edge AIBench is a benchmark suite for end-to-end edge computing including four typical application scenarios: ICU Patient Monitor, Surveillance Camera, Smart Home, and Autonomous Vehicle, which consider the complexity of all edge computing AI scenarios. In addition, Edge AIBench provides an end-to-end application benchmarking framework, including train, validate and inference stages.

• EUA Datasets: This repository maintains a set of EUA datasets which we collected from real-world data sources. The datasets are publicly released to facilitate research in Edge Computing. All the data is in Australia region which contains edge server locations and user location.

• MLPerf Inference Benchmark Suite: MLPerf Inference is a benchmark suite for measuring how fast systems can run models in a variety of deployment scenarios.

• networkX: NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields.

• Neural Network Accelerator Comparison: A comparison of AI hardware accelerators among different platform.

• Rocketfuel: Traceroutes were sourced from 800 vantage points hosted by nearly 300 traceroute web servers. They started by mapping 10 ISPs, in Europe, Australia and the United States. In that process, they constructed a database of over 50 thousand IP addresses representing 45 thousand routers in 537 POPs connected by 80 thousand links.

• tinyMLPerf Deep Learning Benchmarks for Embedded Devices: The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devices typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power.

Tools

• netem : netem provides Network Emulation functionality for testing protocols by emulating the properties of wide area networks. The current version emulates variable delay, loss, duplication and re-ordering.

• GeoLite2: Mapping source IPv4 addresses to geo-graphical locations.

Applications

• Edge Courier: An application for solving the whole-file-sync problem which needs high bandwidth in the cloud.

• Eman's Edge Computing System For AI Applications: Highly accurate AI applications, particularly for Computer Vision requires extensive memory and computational power. Eman's Edge Computing System orchestrates resource limited devices that requires using resource heavy AI algorithms so that a system can still achieve ideal system performances. This is done through Edge's monitoring modules that determines how to adjust the system so the system can operate efficiently based on its environments conditions.

Edge-AI frameworks

• Apache TVM: Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend.

• BerryNet: This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network.

• BranchyNet: Fast inference via early exiting from deep neural networks. The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence.

• DDNN: distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. Due to its distributed nature, DDNNs enhance data privacy and system fault tolerance for DNN applications. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span.

• Distiller: Distiller is an open-source Python package for neural network compression research. It provides a PyTorch environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.

• FATE:FATE (Federated AI Technology Enabler) is an open-source project initiated by Webank's AI Department to provide a secure computing framework to support the federated AI ecosystem. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). It supports federated learning architectures and secure computation of various machine learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning.

• nn-Meter:nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. The key idea is dividing a whole model inference into kernels, i.e., the execution units of fused operators on a device, and conduct kernel-level prediction.

• NVIDIA TensorRT: NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high throughput for deep learning inference applications.

• Once for All: Train One Network and Specialize it for Efficient Deployment.

• Paddle-Lite: Paddle Lite is an updated version of Paddle-Mobile, an open-open source deep learning framework designed to make it easy to perform inference on mobile, embeded, and IoT devices. It is compatible with PaddlePaddle and pre-trained models from other sources.

• TensorFlow Lite: TensorFlow Lite is an open-source deep learning framework to run TensorFlow models on-device. If you are new to TensorFlow Lite, we recommend that you first explore the pre-trained models and run the example apps below on a real device to see what TensorFlow Lite can do.

Prototype proposed in researches

Machine Learning

• DeepThings: A framework for locally distributed and adaptive CNN inference in resource-constrained IoT edge clusters. DeepThings mainly consists of: 1). A Fused Tile Partitioning (FTP) method for dividing convolutional layers into independently distributable tasks. FTP fuses layers and partitions them vertically in a grid fashion, which largely reduces communication and task migration overhead. 2). A distributed work stealing runtime system for IoT clusters to adaptively distribute FTP partitions in dynamic application scenarios.

• FedProx: FedProx aims to solve two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity).

• KitNET: KitNET is an online, unsupervised, and efficient anomaly detector. A Kitsune, in Japanese folklore, is a mythical fox-like creature that has a number of tails, can mimic different forms, and whose strength increases with experience. Similarly, Kit-NET has an ensemble of small neural networks (autoencoders), which are trained to mimic (reconstruct) network traffic patterns, and whose performance incrementally improves overtime.

• ns3-gym: OpenAI Gym is a toolkit for reinforcement learning (RL) widely used in research. The network simulator ns–3 is the de-facto standard for academic and industry studies in the areas of networking protocols and communication technologies. ns3-gym is a framework that integrates both OpenAI Gym and ns-3 in order to encourage usage of RL in networking research.