What is Edge Observability - A Guide to Modern System Monitoring

Edge observability lets you monitor and understand system behavior at edge locations in real-time. It extends traditional monitoring capabilities to the network edge—where your applications interact directly with users and devices. Unlike centralized monitoring, edge observability focuses on collecting and analyzing telemetry data at distributed edge locations.

Understanding Edge Observability

Edge computing moves processing and data storage closer to where data originates. This shift requires a new approach to monitoring.

Edge observability combines three key telemetry types:

• Metrics: Numerical measurements of system performance (like CPU utilization, memory usage, or response times). Metrics provide a quick overview of health and performance and help detect real-time anomalies.
• Logs: Records of events, errors, and transactions that occur at the edge. Logs capture detailed information on each step of a process, enabling deeper troubleshooting and audit trails for events that might impact performance.
• Traces: End-to-end tracking of requests as they move across services and devices. Traces are essential for understanding dependencies, identifying bottlenecks, and following data flow across distributed systems.

Key Distributed Environments for Edge Observability

Traditional monitoring focuses on centralized data centers, while edge observability extends monitoring to distributed edge locations like:

• Fog Computing Environments: Fog computing extends cloud capabilities to the edge, enabling local data processing and storage. Observability in fog computing ensures the health and performance of distributed edge nodes, tracks data movement across the network, and identifies network bottlenecks or failures that could disrupt real-time applications.
• IoT Gateways: IoT gateways serve as intermediaries between IoT devices and cloud or edge platforms, aggregating and processing data. Observability at the edge tracks device health, data flow, and potential failures at the gateway level, ensuring reliable device connectivity and data integrity across large IoT networks.
• Kubernetes at the Edge: Kubernetes at the edge manages containerized applications across distributed edge nodes. Observability tools monitor the health of containers, services, and clusters, ensuring optimal performance, scalability, and quick recovery in case of failures across edge deployments.
• Edge-Enabled Cloud Services: Edge-enabled cloud services extend traditional cloud computing closer to the edge, providing localized processing. Observability tools track resource utilization, application performance, and the interaction between edge nodes and the cloud, ensuring seamless operation and minimal latency in hybrid cloud-edge environments.
• Distributed Database and Data Pipeline Systems: Distributed databases and data pipelines handle large-scale real-time data processing across edge locations. Observability ensures data consistency, availability, and performance by monitoring replication status, pipeline health, and the flow of data from edge devices to processing systems.
• Content Delivery Nodes (CDN): Observing content delivery at the edge helps ensure seamless, low-latency content access for end-users, particularly in video streaming and web applications, where user experience relies on fast load times.
• Network Edge Platforms: Network edge platforms manage services and applications at the network's edge, including SD-WAN and 5G technologies. Observability tools monitor network traffic, connectivity health, and security, ensuring low-latency communication, optimal resource allocation, and protection against potential threats in real-time applications.
• Multi-Access Edge Computing (MEC): MEC brings computation and storage closer to the end-user, often leveraging 5G networks. Edge observability in MEC involves monitoring the performance of edge nodes, network slices, and real-time applications, ensuring low-latency and high-quality service for critical applications like autonomous vehicles and smart cities.

Traditional v/s Edge Observability

Traditional observability focuses on centralized systems monitoring, whereas edge observability extends this to decentralized environments, ensuring insights closer to the source.

A comparison of traditional observability versus edge observability is shown in the table below.

Core Components of Edge Observability

Edge observability involves monitoring and analyzing performance data from distributed edge environments. These environments require a different approach to tracking system health, user interactions, and the flow of requests, as edge devices are often remote and have limited resources. Let's break down the core components:

1. Distributed Tracing

Distributed tracing allows you to track the journey of a request as it moves through various services, including those at the edge. At the edge, each service or device (like IoT sensors, mobile network servers, or POS systems) processes a portion of the data. Tracing helps to understand the end-to-end flow and time taken across each stage.

Let’s see how we can propagate trace context in a distributed system through the following code snippet:

# Example trace context propagation
@tracer.start_span('edge_transaction')
def process_at_edge():
    # Process data locally
    edge_result = local_processing()

    # Forward trace context
    context = tracer.get_current_span().context
    send_to_central(edge_result, context)

When a request is processed at the edge, a span (@tracer.start_span('edge_transaction')) is started to represent the transaction's processing. This trace context is then forwarded to the central system to maintain visibility over the request's entire lifecycle.

In the above code snippet,

• local_processing() refers to the data handling done on the edge device, while
• send_to_central() propagates the trace context to the central system, ensuring end-to-end visibility from edge to cloud.

2. Metrics Collection

Metrics collection at the edge is crucial for measuring performance and resource usage locally, without sending large amounts of data back to the central system. Metrics such as CPU usage, memory consumption, and request latency help in understanding how each edge device is performing.

# Example edge metrics configuration
metrics:
  collection:
    interval: 10s
    edge_points:
      - cpu_usage
      - memory_usage
      - request_latency
    aggregation: local

The YAML configuration sets a collection interval (10s) for key metrics such as cpu_usage, memory_usage, and request_latency, with local aggregation to prevent network overload and provide timely insights at the edge. Aggregation helps reduce network overload by combining data before sending, minimizing the volume of transmitted information while still providing timely insights at the edge.

In this setup, the edge device gathers metrics every 10 seconds, performing aggregation locally to reduce network load and optimize edge resource use. This approach is especially valuable in bandwidth-constrained environments.

3. Log Management

Log management at the edge involves capturing detailed logs of events, errors, or transactions that happen on edge devices. Logs are essential for troubleshooting and understanding system behaviors at the local level, especially when devices are remote or experience network issues.

The Python example configures a logger to handle local storage, log compression, error-level thresholds, and retention.

# Example edge logging
logger.config({
    'local_storage': True,
    'compression': True,
    'forward_threshold': 'ERROR',
    'retention_days': 7
})

In this example,

local_storage: True enables logs to be stored directly on the device, reducing the need for continuous network communication.

compression: True saves storage by compressing log files.

forward_threshold: 'ERROR' ensures that only error-level logs are sent to the central system, highlighting critical issues.

retention_days: 7 sets the local log retention period to seven days before deletion.

Why Edge Observability Matters

Edge observability provides critical benefits:

• Faster Response: It allows data to be processed and monitored locally at the network's edge (close to data source), which means there’s minimal latency. Since it reduces the time data needs to travel to and from a central server, edge observability supports immediate actions and critical decision-making in time-sensitive scenarios
• Cost Reduction: Edge devices can process data locally, reducing expensive data transfers to the cloud, especially for high-bandwidth tasks like video streaming and IoT applications.
• Enhanced Performance: By processing data locally, edge devices reduce the load on central infrastructure, ensuring smoother operation even with limited connectivity.
• Optimized Bandwidth Usage: Observability reduces unnecessary data transmission by processing and filtering data locally, ensuring only essential insights are sent to the central system, which helps optimize bandwidth, especially in remote or limited connectivity environments.
• Enhanced Data Privacy and Security: By keeping sensitive data at the edge and only sending necessary information to central systems, observability reduces the risk of data exposure and enhances security, ensuring compliance with privacy regulations.
• Scalability Across Distributed Environments: Observability enables scalable monitoring in distributed environments by processing data locally at the edge, allowing systems to grow without overloading centralized infrastructure.

Challenges in Edge Environments

Edge environments present unique monitoring challenges such as:

Implementing Edge Observability

Implementing edge observability involves setting up the necessary infrastructure, defining key performance indicators, and establishing monitoring protocols to ensure visibility into the health and performance of distributed edge environments.

The following steps are crucial for successfully adopting edge observability:

1. Setup Infrastructure

This first step focuses on establishing the fundamental architecture for monitoring at the edge.

• Deploy Collectors at Edge Locations: Edge collectors are lightweight agents or services deployed on edge devices to capture telemetry data. After gathering data locally, these collectors either send it to a central system or process it on-site, depending on the needs.

  For example, you could deploy a collector on each edge node to monitor CPU and memory usage, log specific events, and trace requests as they flow through the edge system.

• Configure Data Retention Policies: Given the limited storage capacity of edge devices, data retention policies are important to ensure that only relevant data is stored locally for the appropriate amount of time. It's crucial to determine how long data should be kept (e.g., logs from the past 7 days) before it is either archived or deleted.

• Establish Secure Communication Channels: Secure protocols (e.g., TLS) should transmit data between edge devices and central systems to protect sensitive data like logs and metrics from unauthorized access. Encrypted channels can also be used to prevent data breaches by safeguarding traces and logs sent to the central observability system

2. Define Key Metrics

Once the infrastructure is set up, you need to define which metrics will be monitored at the edge to ensure the system's health and performance.

• System Health Indicators: These metrics give your insight into the overall health of edge devices. This includes CPU and memory usage, disk space, and network availability. Monitoring these metrics helps detect potential issues before they affect system performance. A high CPU usage rate may signal that a device is overloaded and may need additional resources or optimization.
• Application Performance Metrics: For edge applications, you'll want to monitor things like response time, error rates, and throughput. These metrics help you assess how well edge applications are performing under load and if they are meeting user expectations. If an IoT sensor application is reporting unusually high response times, this could indicate that there’s a bottleneck in the data processing pipeline.
• Business-Specific Key Performance Indicator (KPIs): Depending on the use case, edge observability might also include tracking business-specific metrics like transaction volume, user engagement, or uptime for critical services. These KPIs can help ensure that edge systems are delivering value and meeting organizational objectives. For an example, in a retail environment, KPIs could include transaction success rates or customer wait times, which directly impact business performance.

3. Set Baselines

Establishing baselines for what is considered "normal" helps to identify anomalies and performance degradation early on.

• Monitor Normal Patterns: By tracking historical data for a period, you can establish baseline patterns for the metrics you've defined. This helps you recognize what typical performance looks like for each edge device, application, or service. To take an example, if you monitor memory usage over the course of a week, you may find that most devices consume 50% of their memory. This gives you a baseline for comparison in case of spikes.
• Establish Alert Thresholds: Once baselines are defined, it’s crucial to set up alerting mechanisms. Alerts should trigger when metric values deviate significantly from the baseline, such as CPU usage consistently exceeding 90%). Setting up thresholds based on normal patterns will help you catch issues early and minimize downtime.
• Define Performance Targets: To ensure optimal performance, it's important to set performance targets that align with your business objectives. These targets can be based on things like:
  • Response Time: The time it takes for a system to respond to a request (e.g., requests should be processed in under 100ms).
  • Uptime: The percentage of time a system is operational and available (e.g., 99.9% availability).
  • Resource Utilization: The amount of system resources, such as CPU or memory, being used (e.g., CPU usage should stay below 75%).
  As an example, in a content delivery system, the target might be that content load times should be under 3 seconds for 95% of users.

Strategic Approaches for Effective Edge Monitoring

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Getting Started with SigNoz for Edge Monitoring

SigNoz provides comprehensive edge observability features:

1. Install SigNoz: Make sure that SigNoz is installed on your system.

   SigNoz cloud is the easiest way to run SigNoz. Sign up for a free account and get 30 days of unlimited access to all features.

   

   You can also install and self-host SigNoz yourself since it is open-source. With 19,000+ GitHub stars, open-source SigNoz is loved by developers. Find the instructions to self-host SigNoz.

2. Configure edge collectors: After installing SigNoz, the next step is configuring edge collectors. These collectors gather telemetry data from your edge devices and send it to SigNoz. Below is an example configuration for two edge devices:

collectors:
  edge:
    enabled: true
    endpoints:
      - name: retail-pos
        url: <http://pos-system:4317>
      - name: warehouse-iot
        url: <http://iot-gateway:4317>

The enabled: true flag activates edge collectors, while the endpoints section defines edge devices—such as retail POS systems or IoT gateways—by specifying each device's name and url for telemetry data collection. Additional edge devices can be added to the configuration as needed, each with a unique name and url.

1. Create custom dashboards for edge metrics: SigNoz allows you to visualize the performance of your edge systems through custom dashboards. To get the most out of your edge observability, you can create dashboards that display metrics specific to your edge devices.

   For example, you can create dashboards to monitor:

   • IoT sensor data (e.g., temperature, humidity)
   • POS system health (e.g., transaction volume, error rates)
   • Network performance (e.g., latency, packet loss)

   These dashboards will give you a real-time view of the health and performance of your edge systems, making it easier to detect issues and optimize operations.

2. Set up automated alerts: To ensure you are always informed of potential issues, you can configure SigNoz to automatically send alerts when predefined conditions are met. These alerts could be based on metrics like:

   • High CPU usage
   • Increased error rates
   • Network latency

   For example, you can set a threshold for CPU usage at 85% for more than 5 minutes, and SigNoz will trigger an alert if this condition is met. You can configure alerts to be sent via email, Slack, or other communication channels.

Key Takeaways

• Edge observability extends monitoring to distributed locations, providing real-time insights into remote devices and environments that are otherwise hard to access or manage centrally.
• Implementation requires balanced data collection strategies, ensuring that data is processed locally at the edge while only critical or aggregated information is sent to the central system to avoid bandwidth overload.
• Real-time monitoring enables quick issue resolution, allowing teams to detect and respond to performance issues or failures instantly, minimizing downtime and ensuring continuous operations.
• Proper tooling automates complex monitoring tasks, using advanced software to streamline data collection, anomaly detection, and alerting, which reduces manual effort and enhances overall system efficiency.

FAQs

What's the difference between edge observability and traditional monitoring?

Edge observability focuses on distributed locations and local processing, while traditional monitoring centralizes data collection and analysis.

How do you handle data storage in edge observability?

Use local storage with compression and selective forwarding based on importance and available bandwidth.

What metrics should I monitor in edge computing?

Monitor system metrics (CPU, memory, network), application metrics (latency, errors), and business KPIs specific to your edge use case.

How does edge observability impact system performance?

Proper implementation has minimal impact through efficient sampling and local processing, while providing crucial insights for optimization.