DevEdgeOps Defined
Mon, 25 Sep 2023 07:30:48 -0000
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What is a DevEdgeOps Platform?
As the demand for edge computing continues to grow, organizations are seeking comprehensive solutions that streamline the development and operational processes in edge environments. This has led to the emergence of DevEdgeOps platforms, with specialized processes and tools. Including frameworks designed to support the unique requirements of developing, deploying, and managing applications in edge computing architectures.
Edge Operations Shift Left
Shift Left refers to the practice of moving activities that were traditionally performed mostly at production stage to an earlier in the development stage. It is often applied in software development and DevOps to integrate testing, security, and other considerations earlier in the development lifecycle. Similarly, in the world of edge computing, we are moving operational tasks to an earlier stage, just like how we did with Shift Left in DevOps. We call this new idea, DevEdgeOps.
A DevEdgeOps platform facilitates collaboration between developers and operations teams, addressing challenges like network connectivity, security, scalability, and edge deployment management.
In this blog post, we introduce edge computing, its use cases, and architecture. We explore DevEdgeOps platforms, discussing their features and impact on edge computing development and operations.
Introduction to Edge Computing
Edge computing is a distributed computing paradigm that brings data processing and analysis closer to the source of data generation, rather than relying on centralized cloud or datacenter resources. It aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, privacy concerns, and the need for real-time decision-making.
To learn more about the use cases, unique challenges, typical architecture and taxonomy refer to my previous post: Edge Computing in the Age of AI: An Overview
DevEdgeOps
DevEdgeOps is a term that combines elements of development (DevOps) and edge computing. It refers to the practices, methodologies, and tools used for managing and deploying applications in edge computing environments while leveraging the principles of DevOps. In other words, it aims to enable efficient development, deployment, and management of applications in edge computing environments, combining the agility and automation of DevOps with the unique requirements of edge deployments.
DevEdgeOps Platform
A DevEdgeOps platform provides developers and operations teams with a unified environment for managing the entire lifecycle of edge applications, from development and testing to deployment and monitoring. These platforms typically combine essential DevOps practices with features specific to edge computing, allowing organizations to build, deploy, and manage edge applications efficiently.
Key Features of DevEdgeOps Platforms
- Centralized edge application management—DevEdgeOps platforms provide centralized management capabilities for edge applications. They offer dashboards, interfaces, and APIs that allow operations teams to monitor the health, performance, and status of edge deployments in real-time. These platforms may also include features for configuration management, remote troubleshooting, and log analysis, enabling efficient management of distributed edge nodes.
- Integration with edge infrastructure—DevEdgeOps platforms often integrate with edge infrastructure components such as edge gateways, edge servers, or cloud-based edge computing services. This integration simplifies the deployment process by providing seamless connectivity between the development platform and the edge environment, facilitating the deployment and scaling of edge applications.
- Edge-aware development tools—DevEdgeOps platforms offer development tools tailored for edge computing. These tools assist developers in optimizing their applications for edge environments, providing features such as code editors, debuggers, simulators, and testing frameworks specifically designed for edge scenarios.
- CI/CD pipelines for edge deployments—DevEdgeOps platforms enable the automation of continuous integration and deployment processes for edge applications. They provide pre-configured pipelines and templates that consider the unique requirements of edge environments, including packaging applications for different edge devices, managing software updates, and orchestrating deployments to distributed edge nodes.
- Edge simulation and testing capabilities—DevEdgeOps platforms often include simulation and testing features that help developers validate the functionality and performance of edge applications in various scenarios. These features simulate edge-specific conditions such as low-bandwidth networks, intermittent connectivity, and edge device failures, allowing developers to identify and address potential issues proactively.
Final Words
The emergence of new edge use cases that combine cloud-native infrastructure and AI introduces an increased operational complexity and demands more advanced application lifecycle management. Traditional management approaches may no longer be sustainable or efficient in addressing these challenges.
In my previous post, How the Edge Breaks DevOps, I referred to the unique challenges that the edge introduces and the need for a generic platform that will abstract the complexity associated with those. In this blog, I introduced DevEdgeOps platforms that combine essential DevOps practices with features specific to edge computing. I also described the set of features that are expected to be part of this new platform category. By embracing these approaches, organizations can effectively manage operational complexity and fully harness the potential of edge computing and AI.
Related Blog Posts
Edge Computing in the Age of AI: An Overview
Wed, 27 Sep 2023 05:19:01 -0000
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Introduction to Edge Computing
Edge computing is a distributed computing paradigm that brings data processing and analysis closer to the source of data generation, rather than relying on centralized cloud or datacenter resources. It aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, privacy concerns, and the need for real-time decision-making.
Edge Computing Use Cases
Edge computing finds applications across various industries, including manufacturing, transportation, healthcare, retail, agriculture, and digital cities. It empowers real-time monitoring, control, and optimization of processes. This enables efficient data analysis and decision-making at the edge as it complements cloud computing by providing a distributed computing infrastructure.
Here are some common examples:
- Industrial internet of things (IIoT)—Edge computing enables real-time monitoring, control, and optimization of industrial processes. It can be used for predictive maintenance, quality control, energy management, and overall operational efficiency improvements.
- Digital cities—Edge computing supports the development of intelligent and connected urban environments. It can be utilized for traffic management, smart lighting, waste management, public safety, and environmental monitoring.
- Autonomous vehicles—Edge computing plays a vital role in autonomous vehicle technology. By processing sensor data locally, edge computing enables real-time decision-making, reducing reliance on cloud connectivity and ensuring quick response times for safe navigation.
- Healthcare—Edge computing helps in remote patient monitoring, telemedicine, and real-time health data analysis. It enables faster diagnosis, personalized treatment, and improved patient outcomes.
- Retail—Edge computing is used in retail for inventory management, personalized marketing, loss prevention, and in-store analytics. It enables real-time data processing for optimizing supply chains, improving customer experiences, and implementing dynamic pricing strategies.
- Energy management—Edge computing can be employed in smart grids to monitor energy consumption, optimize distribution, and detect anomalies. It enables efficient energy management, load balancing, and integration of renewable energy sources.
- Surveillance and security—Edge computing enhances video surveillance systems by enabling local video analysis, object recognition, and real-time threat detection. It reduces bandwidth requirements and enables faster response times for security incidents.
- Agriculture—Edge computing is utilized in precision farming for monitoring and optimizing crop conditions. It enables the analysis of sensor data related to soil moisture, weather conditions, and crop health, allowing farmers to make informed decisions regarding irrigation, fertilization, and pest control.
These are just a few examples, and the applications of edge computing continue to expand as technology advances. The key idea is to process data closer to its source, reducing latency, improving reliability, and enabling real-time decision-making for time-sensitive applications.
The Challenges with Edge Computing
Edge computing brings numerous benefits, but it also presents a set of challenges that organizations need to address. The following image highlights some common challenges associated with edge computing:
Edge Computing Architecture Overview
The following diagram represents a typical edge computing architecture and its associated taxonomy.
A typical edge computing architecture consists of several components working together to enable data processing and analysis at the edge. Here are the key elements you would find in such an architecture:
- Edge devices—These are the devices deployed at the network edge, such as sensors, IoT devices, gateways, or edge servers. They collect and generate data from various sources and act as the first point of data processing.
- Edge gateway—An edge gateway is a device that acts as an intermediary between edge devices and the rest of the architecture. It aggregates and filters data from multiple devices, performs initial pre-processing, and ensures secure communication with other components.
- Edge computing infrastructure—This includes edge servers or edge nodes deployed at the edge locations. These servers have computational power, storage, and networking capabilities. They are responsible for running edge applications and processing data locally.
- Edge software stack—The edge software stack consists of various software components installed on edge devices and servers. It typically includes operating systems, containerization technologies (such as Docker or Kubernetes), and edge computing frameworks for deploying and managing edge applications.
- Edge analytics and AI—Edge analytics involves running data analysis and machine learning algorithms at the edge. This enables real-time insights and decision-making without relying on a centralized cloud infrastructure. Edge AI refers to the deployment of artificial intelligence algorithms and models at the edge for local inference and decision-making. The next section: Edge Inferencing describes the main use case in this regard.
- Connectivity—Edge computing architectures rely on connectivity technologies to transfer data between edge devices, edge servers, and other components. This can include wired and wireless networks, such as Ethernet, Wi-Fi, cellular networks, or even specialized protocols for IoT devices.
- Cloud or centralized infrastructure—While edge computing emphasizes local processing, there is often a connection to a centralized cloud or data center for certain tasks. This connection allows for remote management, data storage, more resource-intensive processing, or long-term analytics. Those resources are often broken down into two tiers – near and far edge:
- Far edge: Far edge refers to computing infrastructure and resources that are located close to the edge devices or sensors generating the data. It involves placing computational power and storage capabilities in proximity to where the data is produced. Far edge computing enables real-time or low-latency processing of data, reducing the need for transmitting all the data to a centralized cloud or datacenter.
- Near edge: Near edge, sometimes referred to as the "cloud edge" or "remote edge" describes computing infrastructure and resources that are positioned farther away from the edge devices. In the near edge model, data is typically collected and pre-processed at the edge, and then transmitted to a more centralized location, such as a cloud or datacenter for further analysis, storage, or long-term processing.
- Management and orchestration—To effectively manage the edge computing infrastructure, there is a need for centralized management and orchestration tools. These tools handle tasks like provisioning, monitoring, configuration management, software updates, and security management for the edge devices and servers.
It is important to note that while the components and the configurations of edge solution may differ, the overall objective remains the same: to process and analyze data at the edge to achieve real-time insights, reduced latency, improved efficiency, and better overall performance.
Edge Inferencing
Data growth driven by data intensive applications and ubiquitous sensors to enable real time insight is growing three times faster than access network. This drives data processing at the edge to keep up with the pace and reduce cloud cost and latency. IDC estimates that by 2027 62% of enterprises data will be processed at the edge!
Inference at the edge is a technique that enables data-gathering from devices to provide actionable intelligence using AI techniques rather than relying solely on cloud-based servers or data centers. It involves installing an edge server with an integrated AI accelerator (or a dedicated AI gateway device) close to the source of data, which results in much faster response time.1 This technique improves performance by reducing the time from input data to inference insight, and reduces the dependency on network connectivity, ultimately improving the business bottom line.2 Inference at the edge also improves security as the large dataset do not have to be transferred to the cloud.3
Final Notes
Edge computing in the age of AI marks a significant paradigm shift in how data is processed, and insights are generated. By bringing AI to the edge, we can unlock real-time decision-making, improve efficiency, and enable innovations across various industries. While challenges exist, advancements in hardware, software, and security are paving the way for a future where intelligent edge devices are an integral part of our interconnected world.
It is expected that Inferencing market alone will overtake training with highest growth at the edge – necessitating competition in data center, near edge, and far edge.
For more information on how edge-inferencing works, refer to the next post on this regard: Inferencing at the Edge
1 https://steatite-embedded.co.uk/what-is-ai-inference-at-the-edge/
2 https://www.storagereview.com/review/edge-inferencing-is-getting-serious-thanks-to-new-hardware
Unlocking the Power of AI-Assisted DevEdgeOps Automation
Wed, 27 Mar 2024 18:13:00 -0000
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In today's digital landscape, the expansion of edge computing has transformed how data is processed and managed. However, with this evolution comes the challenge of managing and maintaining numerous edge deployments efficiently. DevEdgeOps is a shift-left approach that moves operational tasks to an earlier stage. This approach facilitates collaboration between IT and OT streamlining edge operations. By integrating AI-assisted techniques into DevEdgeOps practices, organizations can unlock stacks of benefits, ranging from increased productivity to improved operational efficiency.
“The automation process using infrastructure as code (IaC) is complex, especially when it comes to highly distributed edge environments. It must be balanced with the requirements of edge operating environments which are different than IT. Gen AI and Copilot-based edge automation development tools can reduce the development process of that automation code and help to meet the requirements of edge operations workloads.” says Nati Shalom, Fellow at Dell NativeEdge, introducing the topic in his blog Edge-AI trends in 2024.
In a recent study by McKinsey, it indicates a potential improvement of up to 56 percent in productivity. DevEdgeOps advocates for reducing that complexity using a shift-left approach where production issues can be identified earlier during the development phase.
Figure 1. The benefits of using Gen AI as a coding assistant (Copilot) (Source: McKinsey)
Simplifying Complex Tasks with Automation
One of the primary advantages of leveraging AI in DevEdgeOps is the ability to automate and optimize complex tasks associated with edge operations. Traditional methods of managing edge environments often involve manual interventions, which are time consuming and error prone. AI-powered automation tools can streamline these processes by intelligently analyzing data patterns, predicting potential issues, and automating corrective actions. This reduces the burden on IT teams, minimizes the risk of downtime, and improves system reliability.
A case in point was the recent winner of the Dell Hackathon, Rachel Shalom, from the NativeEdge team. In her article DevOps Made Easy with Gen AI, she explores the integration of Gen AI into DevOps practices, simplifying and optimizing the development process. By leveraging Gen AI's capabilities, developers can automate tasks such as code generation, testing, and deployment, reducing time-to-market and enhancing efficiency. Through Rachel’s real-world example, she illustrates how Gen AI streamlines DevOps workflows, empowers teams to focus on innovation, and fosters collaboration between development and operations teams.
Regarding data and modeling insights, Rachel writes “You might be wondering, why not use Copilot or a commercial GPT for queries right off the bat? We gave that a shot, but it fell short of our specific need to generate configuration files. This was mainly because our proprietary internal data was not familiar to the model, leading us to the necessity of fine-tuning with a private GPT-3.5.”
A Proactive Approach to Edge Management
AI-assisted DevEdgeOps enables organizations to adopt a proactive approach to edge management. By leveraging predictive analytics and machine learning algorithms, businesses can anticipate and prevent potential issues before they escalate into critical failures. This proactive approach enhances system resilience and enables organizations to allocate resources more effectively, which optimizes operational costs.
Rapid Development and Deployment
AI-driven DevEdgeOps facilitates rapid development and deployment of edge applications. Traditional development processes often struggle to keep pace with the dynamic nature of edge computing, resulting in delays and inefficiencies. By harnessing Gen AI capabilities such as Copilot-based development tools, organizations can accelerate the development life cycle, reduce time-to-market, and stay ahead of the competition. This enhances agility and allows businesses to capitalize on emerging opportunities more effectively.
Reducing Costs with Automation
In addition to operational benefits, AI-assisted DevEdgeOps can also drive significant cost savings for organizations. The McKinsey study referenced earlier highlights the potential for a 56 percent improvement in productivity through the adoption of AI-driven automation. By automating repetitive tasks, optimizing resource utilization, and minimizing downtime, businesses can achieve substantial cost reductions while maximizing the return on investment (ROI) of their edge investments.
Furthermore, AI-assisted DevEdgeOps fosters innovation by empowering organizations to focus on value-added activities rather than repetitive mundane operational tasks. By automating routine maintenance, troubleshooting, and provisioning activities, IT teams can devote more time and resources to innovation-driven initiatives that drive business growth and competitive advantage. This enhances organizational agility and fosters a culture of continuous improvement and innovation.
Conclusion
The benefits of automating edge operations with AI-assisted DevEdgeOps are undeniable. By leveraging AI capabilities to streamline processes, enhance proactive management, accelerate development, and drive cost savings, organizations can unlock the full potential of their edge deployments. As the digital landscape continues to evolve, embracing AI-driven automation in DevEdgeOps will be essential for organizations looking to stay competitive, agile, and resilient in the face of the ever-changing demands.