addressing software complexity at the edge

Addressing Software Complexity at the Edge

Smart Computing is a distributed computing platform which streamlines running business logic on a global computing infrastructure that extends from data centers to connected devices.

At Taubyte, we’ve built the next generation computing platform! With a self-operating platform that extends from data centers to the far Edge, where connected devices, like IoT Gateways, are prevalent, we enable the true potential of the global computing infrastructure catalyzed by 5G and IoT technologies.

Company Profile

Introduction 04

Edge Computing 07

Why Edge Matters for IoT 08

Silver Lining 10

What is Serverless? 11

Internet of Things (IoT) and Software Challenges 12

The Solution! 13

Conclusion 14

Use Cases 15

Get in Touch 16

Table of Content

Annual recurring expense for the traditional DevOps approach.

$2.35M

Typical cost to implement DevOps for a product line valued at $100M.

$5.6MAccording to DORA (DevOps Research and Assessment), a product line valued at $100M can cost about $5.6M to implement initially.1 This includes the cost for on-boarding, tools, and engineering salaries to transform the existing product line to a DevOps culture. Much of this figure, $2.35M, will be an annual recurring expense with the lion’s share going to automation tools.

During the last decade the Cloud grew and became the backend of most software-based technologies and products. Huge computing and storage capacity is now aggregated in regional data centers, and is offered as a service. As of 2020, the largest Cloud service provider has about 100 data centers in 25 regions worldwide. Running software, making sure it’s uptodate, and managing its infrastructure on the Cloud evolved to be a highly complex task over the years, ultimately giving rise to its automation, also known as DevOps!

Introduction

1. Forecasting the Value of DevOps Transformations - Measuring ROI of DevOps

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Taubyte - Addressing Software Complexity at the Edge

Edge Computing actually extends to connected devices, which can add, yet another layer of complexity. In fact, per “Security Today”2, in 2019, the number of active IoT3 devices on the web is nearing 27 billion, and is currently growing at a rate of about 127 new devices per second. The number of devices for each Edge data center location can easily be in the millions.

A forecast from International Data Corporation (IDC) estimates that there will be “41.6 billion connected IoT devices ... generating 79.4 zettabytes (ZB) of data in 2025”.

With Edge Computing, which pushes computing beyond the geographical and hardware limits of the Cloud, building momentum and expanding over tens of thousands of data centers, including Telco central offices and also container-sized data centers which are popping up all over the United States alone, the cost of DevOps will grow exponentially in the next decade.

With this ubiquity of IoT devices and data centers in proximity, the task of building and shipping software for Edge Computing is becoming increasingly complex to the point that the DevOps approach itself needs automation.

2. An Industry-leading, security products magazine.3. In this document IoT collectively represents IoT and IIoT; where, IIoT represents “industrial” IoT.

With this growth forecast along with the rising complexity in software development and deployment, it will be necessary to ease the onboarding of data processing and storage needs of smart devices to the internet.

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A centralized Cloud architecture might seem to be the answer to this problem as it offers agility with unlimited horizontal scalability and elasticity. However, it is not conducive to IoT applications as smart devices’ communication medium is mostly metered and expected to fail often. Some of them even have real-time performance requirements; attributes that the Cloud does not adequately address.

A decentralized Edge architecture, on the other hand, can address these shortcomings. The reason is that IoT applications have characteristics that require implementation at the Edge; telemetry data is transient, data streams can be high-volume, and data may pose privacy concerns. According to a report published by Topio Networks in 2020, 30% of the market segment has a high requirement for Edge Computing.

The Edge also has a smaller and more manageable attack surface. For example, consider a DDoS (Distributed Denial-of-Service) attack. In the case of the Cloud, hundreds of millions of connections will be focused at a single point (e.g., the 2019 sustained DDoS attack on AWS lasted about 8 hours where the US East Coast was severely hit). In retrospect, for the Edge, those attacks will be distributed across tens of thousands of locations, and can be handled by modest equipment and software.

Most importantly, because these devices are scattered all over the globe and also in orbit, their software development and deployment complexity will be proportional to that of the computing model and platform they rely on. Imagine having a global presence for your application based on the characteristics just described above. How do you plan on managing software updates and infrastructure changes? This will push the entire DevOps culture into hyperscale territory, at which point the traditional CI/CD agile methodologies will exhibit lackluster performance.

The answer to this challenge is a smart approach when moving Cloud services to the Edge, and at Taubyte we’re making that happen. Taubyte is simplifying the development of distributed software as well as automating the entire DevOps process. The challenges of partitioning, caching, replicating, and resilience are all handled seamlessly through our platform. And, as the Taubyte stack exhibits architectural independence, it can be embedded in any hardware platform and operate with integrity at the Edge. Which, by the way, due to its close proximity to the smart devices, localizes security concerns.

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MOBILE EDGEis somewhat a nebulous term as it combines Infrastructure Edge, Device Edge, and Network Slicing capabilities adjusted for particular use cases, like in-vehicle entertainment, real-time autonomous vehicle control, or cellular vehicle-to-everything communication (C-V2X). These applications, due to their mobile nature, require high-bandwidth, low-latency, and seamless reliability for proper functioning. Network Slicing uses a common physical hardware to run multiple virtual networks for different apps and services.

DEVICE EDGEis on the end user side of the last-mile network; typically, this is reliant on an IoT Gateway to collect and process data from smart devices. Compute, storage, and network resources at this Edge can be provisioned with elasticity. Since, the computing unit (e.g., Industrial Computer, IoT Gateway, etc.) is on-premises near the client devices, latency and data transport costs are virtually eliminated. Data privacy and security concerns are also maintained the same way as it’s done at the Infrastructure Edge.

INFRASTRUCTURE EDGEis deployed on the operator side of the last-mile network. Compute, storage, and network resources at this Edge can be provisioned with elasticity. Since the servers are in close proximity to the client devices, latency and data transport costs are drastically reduced. Data privacy and security concerns are also maintained, as subscribers can programmatically control what data is sent to other tiers.

Unlike Cloud Computing which has a centralized architecture with their servers located at large regional data centers, Edge Computing, due to its decentralized architecture, generally has computing resources at smaller data centers in varied locations distributed throughout metropolitan areas; additionally, the devices themselves can often participate as Edge nodes. Edge Computing can be divided into three categories: Infrastructure Edge, Device Edge, Mobile Edge

Edge Computing


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01Telemetry data from IoT devices are highly repetitive. Consider, for example, a temperature sensor configured to report temperature readings every 5 or 10 seconds, and the readings do not vary much for hours, days, or even weeks for that matter. Sensors can be used in rooms to help with heating and cooling, in process plants to monitor temperature of fluids in a pipeline, and in many other applications. Although, it is imperative that the temperature is within a range, transmitting each reading to the centralized location is overkill; especially when you accumulate and aggregate the data from all the sensors that are transmitting every few seconds. On the other hand, if there are anomalies in the readings, measures will have to be taken to report it and correct it, such as turning off the air conditioning or adjusting the motor speed. This requires an immediate real-time response, which can be automated, based on analysis being conducted close to the sensors. This sort of transient telemetry data applies to all kinds measurement characteristics that need monitoring and control to ensure quality and reliability in the product or service. Industrial and commercial applications can employ sensors for telemetry on level, pressure, humidity, temperature, viscosity, vibration, altitude, flow rate, and many other physical attributes.

Transient Nature of Data

Transient Nature of Data

Privacy Rights

Government Regulations

Limited Bandwidth

High Latency

Today’s technological advancements make it possible for devices to be connected on the internet while adding value to products by offering new and/or improved capabilities. As machine-to-machine communications become more common in the internet-of-things, the synergy potential will be great. As the nascent Edge Computing market matures we need to consider the characteristics of data and their limiting factors in the overall strategy.

Why Edge Matters for IoT

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04Smart sensors have virtually unlimited applications, as indicated in the foregoing, which can potentially generate a significant number of events with associated large data volumes. Sending such data to a central location can severely impact an application’s ability to respond to events in real-time. As implied in the introductory paragraph, the number of IoT devices in the field is low or moderate today, but this number is rapidly climbing and is expected to penetrate the market to record highs by middle of this decade and beyond; hence the issue around latency will become prominent in this decade if Edge Computing is side-stepped. The answer to this burgeoning problem is to bring compute power in proximity to the sensors through Edge data centers and IoT Gateways, where automated responses can be virtually instantaneous with local analysis. Furthermore, with 5G roll outs the subject latency problem will experience compounding effects as 5G will enable IoT devices to transmit even larger amounts of data, thereby enticing customers to capture more data.

High Latency

03Camera feeds, like telemetry data, can also generate repetitive data for sufficiently long time before something happens. The main difference is that video surveillance generates data in much greater volume. Imagine a gated entrance to a remote site where tanker trucks arrive for pickups and deposits. The site may be vacant for a very long time, and will yield nothing interesting for the object recognition algorithm to process during this dormancy; perhaps an animal will be detected or debris and scrap may be identified from wind gusts. Placing a Device Edge node in proximity to the camera will provide the needed intelligence to filter out mundane images and streamline the data for deeper processing or long term storage at the Edge data centers. The example just mentioned is a single use case, but there are thousands of video surveillance applications, and these are often fitted with multiple cameras. Without using a Device Edge node to manage the aggregate data streams, the amount of video footage will overwhelm the available bandwidth for transferring the raw data out of locality.

Limited Bandwidth

02Data from IoT devices can help to identify people, but also infringe on their privacy rights and government regulations. To circumvent this, a Digital Twin (DT) can be used to obscure individuals while still representing their virtual placement at a location. Consider a traffic situation where pedestrians are monitored at venues such as amusement parks, airports, city streets, etc. Using data to identify individuals can pose legal problems; object detection and recognition can be applied to the data streams from video surveillance to place individuals within a DT of the associated venue, and any monitoring of that space can occur virtually within the DT. In the context of privacy it is crucial to process the camera feed locally at the Edge, only forwarding the DT outside the locality. The original data might be held at the Edge for a legal period of time before being deleted.

Privacy Rights & Government Regulations

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With the ability to provision computing resources, up or down, based on need, businesses have been prompted to adopt Cloud services during the last decade. The Cloud also accelerates go-to-market as capital is not expended on hardware; furthermore, human resources are not allocated to address hardware upgrades and maintenance. Hence, businesses can focus on their core competencies. One contemporary concern about the Cloud, though most data is encrypted, is privacy. Since Edge Computing is closer to the source of data, the sensitive part can be programmatically controlled for processing and/or storage at the Edge; thereby, abiding privacy laws and reducing the risk of leaking or misuse of private data.

A centralized Cloud-based architecture is not practical for applications requiring local data creation and consumption, regulatory constraints, high-volume data transfers, and low latency. In such cases, Edge Computing might be a better fit. There are two primary application types worth mentioning here: Edge-Enhanced Applications, Edge-Native Applications

Will the Edge replace the Cloud? If centralized architectures are still around, probably not! At Taubyte we believe that with the democratization of distributed architectures, the seperation line between the Cloud and the Edge will eventually fade giving birth to a global computing platform.

Silver Lining

EDGE-ENHANCED APPLICATIONSimprove performance by handling a subset of functionalities at the Edge. Excess latency will not cause any failures on these applications. For example, image transfers or bulk data processing will succeed on both the Edge and the Cloud. In fact, Cloud applications, if serverless, can be migrated to the Edge with little to no change to leverage these benefits.

EDGE-NATIVE APPLICATIONSare practical to operate at the Edge only. They process large volumes of data locally before forwarding the result to remote locations to be stored and/or further processed, enabling real-time decision making, addressing privacy concerns, and managing data sovereignty issues where raw data must remain in the locality in which they were generated. Consider a video surveillance application. Raw video data can be denatured before being sent out of the locality, while encrypted originals are retained at the Edge for legal reasons. Another use case is where data is analyzed at the Edge and only extracted information or filtered data is forwarded out. Not only does this reduce the cost of storage and bandwidth for migrating the content, but it maintains privacy and data sovereignty initiatives. Another criteria for an Edge-Native Application is excess latency. For example, real-time tasks for supporting an autonomous vehicle will result in catastrophic failures if the required data processing has to travel long distances to reach the processing point.

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Serverless or FaaS (Function-as-a-Service) is a software architecture approach where the underlying operational infrastructure related to servers, virtual machines, and/or containers is abstracted by the service provider. Thus, letting developers focus on business logic rather than on software deployment concerns like provisioning, utilization, scalability, fault tolerance, and monitoring.

From this perspective, a traditional codebase is broken up into individual functions that are executed independently. In essence, serverless/FaaS relies on an “event-driven” application structure that spins up servers on demand. As a result, resources are only allocated when these events are triggered.

Take for example, a process flow that needs monitoring and control on an oil and gas pipeline. Typically, a differential pressure instrument is used for this purpose. An IoT Gateway that supports industry protocols like Modbus or Fieldbus is needed to handle telemetry data from the pressure instrument. In this case, events from the instrument are handled in a serverless fashion where the events are passed as parameters to processing functions. In this approach, the service provider offers abstraction interfaces to those industrial protocols saving the developers the burden of implementing them. Companies across industries have adopted serverless as major parts of their architecture. In turn, they have experienced significant cost reductions as well as ease of development and maintenance.

What is Serverless?

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Internet of Things (IoT) and Software Challenges

The vision behind the Internet of Things is for the inter-network of smart devices connected and communicating with each other to drive efficiencies and to assist in executive decision making through analytics.

One of the differentiators for IoT applications compared to other contenders at the Edge is that smart devices have hardware variety; i.e., they lack standardization and abstraction. Some designs may be for hostile environments, and others could be for office settings. Applications could also be for real-time data communications requiring seamless reliability. Beyond this is the number of SKUs for each product family could also vary, along with their geographical dispersion, which could vary as well. Building software for such diversity is a challenge.

Smart devices are distributed at global scale, and offer emergence of new and innovative capabilities. As these devices proliferate in this decade, we see obstacles in the centralized approaches to software development and deployment using traditional DevOps strategies. Some typical questions arise: How do the distributed parts communicate? How do you route traffic? How do you update software?

You might think, with DevOps, managing systems and applications manually is a thing of the past, thanks to automation through code. But at the scale of the Edge, this won’t work as the number of use cases will grow exponentially.

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Writes and certifies data transactions in an immutuable

database. Like all our services, it is serverless and natively ditributed.

Ledger

Stores, replicates, deduplicates, and self-authenticates objects

and telemetry data. Distributed throughout our network, it has the ability to collect and make

data available at the edge.

Storage

Stores data in a document-based distributed database. Serverless

and dynamically scable, it provides a MongoDB-compatible

API.

NoSQL Database

Stores Key-Value enteries in a dynamically sharded and replicated global database.

Key-Value Database

Enables distributed applications, systems, and services to

transparently communicate with each other. Supports Modbus,

Fieldbus, MQTT, etc.

Message Broker

Runs business logic compiled into Web Assembly. That could be sequential execution or parallel,

as well as running on a single node, or distributed on multiple

ones.

Function Execution

equation for our customers, so they can focus on their business logic.

Our platform has the ability to extend to the far Edge which includes IoT Gateways and devices, forming an overlay peer-to-peer network of Taubyte-enabled nodes, on top of which business logic, alongside the computing services it requires, is provisioned intelligently.

Each Taubyte-enabled node, depending on hardware capabilities and resource limitations, has the ability to make available all or a subset of the following services: Function Execution, Message Broker, Key-Value Database, NoSQL Database, Storage, Ledger

To solve the problem we need an intelligent system that self-operates. Basically a NoOps computing platform. This is a new category we defined at Taubyte, we call it Smart Computing!

Smart Computing is a distributed computing platform which streamlines running business logic on a global computing infrastructure that extends from data centers to connected devices.

At Taubyte our mission is to enable the true potential of the global computing infrastructure catalyzed by the Edge. Being the Smart Computing provider, we offer a fully serverless experience where Ops are not part of the

Solution!

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As the Edge and 5G technologies penetrate the market, key characteristics of data that influence their adoption need to be observed for maintaining quality standards. Namely, as mentioned in the forgoing, transient nature of data along with privacy concerns, bandwidth issues, and latency problems. Most of this will comprise Edge-Native Applications; however, there may be some gravity to migrate Edge-Enhanced Applications to the Edge. Ultimately, paving the way to merging the Cloud with the Edge.

With this rapid growth of Edge infrastructure and IoT, full automation of any form of operation is essential, challenging the centralized Cloud approaches and any form of automation that worked for it. Therefore, the cost of implementing DevOps for Edge Computing is significant, and because this type of computing is more widespread it will be far more expensive to put into effect than implementing it in the Cloud.

Smart Computing is the best approach as it eliminates the need for operations overhead. In addition, to ease the development, deployment, and routing concerns, Taubyte platform also has the ability to work offline with seamless recuperation so you can focus on developing business logic.

Conclusion

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Oil & Gas

Safety & SecuritySmart Cities

Farming & AgricultureLogistics & Supply Chain

Use Cases

Smart Manufacturing

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addressing software complexity at the edge

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