Benchmarking IoT Workloads on Edge Platforms

Edge computing is transforming how IoT data is processed by handling tasks locally rather than in the cloud. This shift is crucial for managing latency, bandwidth, and privacy concerns. However, benchmarking these systems is complex due to diverse hardware, limited resources, and varying real-time requirements. Here's what you need to know:

• Key Metrics: Power consumption, CPU/memory usage, latency, and start-up times are critical for evaluating edge platforms.
• Challenges: Limited device capabilities, network variability, and distributed systems require tailored testing approaches.
• Tools: Frameworks like EdgeFaaSBench and ComB help measure performance across different devices and workloads.
• Lightweight Methods: Containerisation and micro-benchmarks enable cost-effective testing without complex setups.
• Dataset Selection: Using accurate, varied datasets ensures relevant results for IoT applications like smart cities or industrial systems.

Smart Mapping:Optimizing Compute Across Cloud and Edge in IoT Networks - Peer Stritzinger |Code BEAM

Core Concepts and Challenges in IoT Edge Benchmarking

Benchmarking IoT workloads on edge platforms is a unique challenge compared to cloud environments. Unlike the cloud, which benefits from abundant resources and a uniform infrastructure, edge platforms operate with limited CPU power, memory, and storage. This fundamental difference requires a tailored approach to benchmarking [1].

Another layer of complexity comes from the real-time processing needs of many IoT applications. Edge platforms often have to meet strict latency requirements and make decisions instantly - conditions that differ significantly from cloud environments, where minor delays are often acceptable [1].

Hardware diversity adds to the challenge. Edge devices can range from ARM-based Raspberry Pi units to x86-based systems, each with varying computational capabilities and hardware accelerators [1][2]. This variety means results from one device type might not apply to another, making context-specific benchmarking essential.

Benchmarking Metrics for Edge Computing

Edge computing relies on specialised metrics that reflect the unique constraints of operating in resource-limited environments. These metrics go beyond the standard performance indicators used for cloud platforms.

• Power consumption: For edge devices, power efficiency is a top priority, especially since many operate on batteries or under strict energy constraints [1].
• Resource utilisation: Metrics like CPU usage, memory consumption, disk I/O, and network bandwidth are critical. Unlike the cloud, where resources can scale as needed, edge devices operate within fixed hardware limits, making efficient resource management crucial [1].
• Network latency and response times: Metrics like the makespan, which measures total execution time, are vital for determining if edge devices can meet the real-time demands of IoT applications [1].
• Cold and warm start times: These measure how quickly edge functions initialise and respond to requests, which directly impacts application responsiveness and user experience. This is especially relevant in serverless edge computing [1].

These metrics are designed to address the unique challenges of edge computing, ensuring that real-world constraints are taken into account.

Edge IoT Workload Challenges

The practical challenges of managing edge workloads highlight the need for customised benchmarking methods.

One of the biggest hurdles is the limited computational power of edge devices. Systems like the Raspberry Pi 4B or Jetson Nano must handle complex tasks with far less processing capability than cloud servers [1].

Network variability is another significant issue. Unlike the stable, high-bandwidth connections common in the cloud, edge devices often deal with inconsistent connectivity and unpredictable network conditions. Using traditional benchmarking techniques in such scenarios can lead to inaccurate performance assessments [1].

There’s also the challenge of orchestration overhead in distributed edge environments. Communication between devices can become a major bottleneck, especially when compared to cloud setups where inter-service communication is less impactful [2].

To address these challenges, specialised benchmarking tools like EdgeFaaSBench and ComB have been developed. These frameworks are designed specifically for edge environments, offering metrics and insights tailored to the unique constraints of edge computing. By using these tools, organisations can make better decisions about their edge computing strategies, recognising that traditional cloud benchmarks often fall short in these scenarios [1][2].

Benchmarking Frameworks and Tools for IoT Workloads

To tackle the challenges of benchmarking IoT edge workloads, modern frameworks and tools have stepped up with solutions tailored to the unique demands of edge computing. These systems go beyond traditional cloud benchmarks, addressing the specific constraints of IoT devices and edge platforms. Let’s dive into some of the key approaches that make edge benchmarking more effective.

Multi-Tier Benchmarking Systems

Edge computing often spans multiple layers, from IoT sensors at the edge to cloud infrastructure at the core. A great example of this approach is EdgeFaaSBench, which integrates IoT sensors, edge-level processing, and cloud offloading to provide an end-to-end view of performance across all tiers [1].

This framework doesn't just focus on one part of the system - it evaluates performance at every stage, helping organisations pinpoint bottlenecks that might be missed in single-layer tests. It supports 14 serverless workloads for both micro- and application-level benchmarking. Built on Apache OpenFaaS and Docker Swarm, it works seamlessly across different hardware setups [1].

Another standout is EdgeBench, designed to compare edge computing platforms like Amazon AWS Greengrass and Microsoft Azure IoT Edge. By using detailed performance metrics, it helps organisations understand the practical differences between platforms, aiding in better decision-making [3][4].

Container-Based Benchmarking Methods

Containerisation has become a game-changer for standardising benchmarking across various edge devices. Docker-based methods bundle workloads and their dependencies, ensuring consistent results whether you're testing on a Raspberry Pi 4B or an NVIDIA Jetson Nano [1][2][3].

A good example of this approach is ComB (Combination Benchmark), which uses a microservice-based Multi-Object Tracking pipeline. By containerising each service, ComB allows developers to tweak, replace, or expand individual components without disrupting the entire system [2].

This modularity also makes it easy to reconfigure tests. Teams can quickly adjust workloads or resource allocations without needing to rebuild the entire testing environment. Tools like Docker Swarm and Kubernetes further simplify orchestration, offering the scalability needed for large-scale benchmarking projects [1][2].

Real-Time Monitoring and Data Collection

Effective benchmarking isn’t just about running tests - it’s about monitoring performance in real time. Modern frameworks now include tools to track key metrics like CPU usage, memory consumption, network I/O, and disk activity during tests [1][2].

For instance, EdgeFaaSBench captures a wide range of metrics, from system-level resource use to application-specific response times. It even tracks serverless-specific metrics like cold and warm start times, revealing how concurrent executions affect performance [1].

Advanced frameworks go even further, measuring variables like CPU frequency, power consumption, and thermal behaviour. This kind of data is especially valuable for battery-powered edge devices, as it helps organisations balance performance with energy efficiency [5].

For businesses looking to optimise their edge computing setups, these insights are crucial. By linking performance metrics to specific workloads, organisations can identify the best configurations for different scenarios, ultimately improving resource use and cutting costs across their edge deployments.

Performance Metrics and Testing Methods

Evaluating performance in IoT edge workloads demands precision and practicality. Unlike traditional cloud environments, edge setups come with unique challenges - limited resources, energy constraints, and the need for real-time processing. This means the metrics you choose must reflect actual deployment scenarios and provide actionable insights. These metrics form the backbone of any effective performance evaluation.

Key Metrics for IoT Workloads

Execution time is the fundamental metric for benchmarking edge IoT systems. Tools like EdgeFaaSBench measure both system-level metrics (CPU, memory, network, disk I/O) and application-specific metrics (response times, concurrency effects) to provide a comprehensive view [1].

Energy efficiency is another critical benchmark, especially for deployments where energy consumption is a concern. This metric goes beyond measuring power usage, focusing instead on how effectively resources are utilised per unit of energy consumed. Hardware energy monitors and profiling tools are invaluable for gathering accurate data across a range of devices [1][2].

Cold and warm start times are particularly important for serverless edge environments. Cold starts measure the time an application takes to handle its first request, while warm starts track the response time for subsequent requests. Both directly impact the responsiveness of IoT systems. Scalability metrics, on the other hand, assess how well platforms maintain performance as workload demands grow. This involves monitoring response times and throughput while gradually increasing concurrent workloads or connected devices. Maintaining performance under these conditions is crucial for real-world scenarios [1][3].

For example, a 2022 study using EdgeFaaSBench evaluated image classification and object detection workloads on Raspberry Pi 4B and Jetson Nano devices. The study measured metrics like system utilisation and start times, revealing that Jetson Nano outperformed Raspberry Pi 4B in GPU-accelerated tasks, achieving up to 40% faster response times for image classification workloads [1].

Multi-Objective Optimisation Testing

Performance evaluation doesn’t stop at individual metrics. Multi-objective optimisation testing takes it a step further by addressing the trade-offs between latency, cost, performance, and battery life. Using methods like hypervolume metrics, this approach assesses solution quality across multiple dimensions [2].

This kind of testing is especially useful for organisations aiming to balance performance with operational costs. It helps pinpoint configurations that strike the right balance between performance, energy use, and expenses [2]. Hypervolume metrics, in particular, provide a standardised way to compare solutions when objectives conflict, helping developers make informed decisions.

A practical example of this is the ComB framework, which was tested in April 2022 using a distributed video analytics pipeline. Deployed on devices like Raspberry Pi and Nvidia Jetson units, ComB demonstrated how modular microservice workloads can mimic real-world edge application performance. CPU profiling showed minimal overhead and strong scalability [2].

Resource allocation analysis is another essential aspect of testing. By monitoring how workloads use resources under varying conditions, you can identify the best ways to distribute resources while maintaining performance and minimising waste. Combining micro-benchmarks for specific resources with application-level benchmarks provides a holistic view of resource utilisation [1][2].

Modern benchmarking methods emphasise testing under realistic conditions. For instance, EdgeBench compared AWS Greengrass and Azure IoT Edge in November 2018 for speech-to-text and image recognition workloads. Both platforms delivered similar execution times, but Azure IoT Edge proved better at scaling container-based deployments, while Greengrass excelled in Lambda function cold starts [3][7]. These approaches complement traditional metrics, offering a more comprehensive understanding of IoT edge performance.

For businesses looking to optimise their edge deployments, these metrics and testing methods are invaluable. By aligning specific metrics with workload characteristics, organisations can identify configurations that maximise both performance and cost-efficiency. This data-driven approach not only enhances operational efficiency but also improves financial outcomes. UK companies can also turn to Hokstad Consulting for customised strategies to optimise edge deployments and reduce operational costs.

Workload Analysis and Dataset Selection

Choosing the right workloads and datasets is a critical step when testing IoT performance metrics. Without using data that reflects real-world conditions, even the most advanced benchmarks can produce results that don't hold up in practical scenarios. These initial decisions directly shape the accuracy and reliability of performance testing.

Workload analysis involves studying how applications behave, examining computational and communication patterns, and matching these to the hardware's capabilities. This process captures both low-level and application-level metrics, providing a comprehensive look at system performance [2][6][1]. To achieve this, it's essential to collect real-world workload traces that mirror actual operational behaviours, including data rates and resource usage, from active IoT systems [6].

The diversity of edge devices further complicates matters. For instance, a workload that runs efficiently on a Jetson Nano might behave very differently on a Raspberry Pi 4B due to differences in hardware architecture. Understanding these variations is key to developing benchmarks that reflect the heterogeneous nature of edge computing environments [1][2].

Simulating IoT Application Scenarios

To create realistic IoT scenarios, datasets must accurately represent the specific data flows and processing needs of targeted environments. For example, smart cities and industrial IoT deployments require datasets that account for varying data rates and include both structured and unstructured data [2].

Existing frameworks already provide a range of workloads, from micro-benchmarks to application-level scenarios. These often include tasks like image classification and object detection, which are common in real-world IoT applications [1].

A great example is the ComB framework, which features a distributed video analytics pipeline. Designed as a series of microservices, this workload reflects neural network-based edge applications commonly used in areas like surveillance, traffic management, and retail analytics. Its modular setup allows researchers to tweak the benchmark for different use cases while maintaining the core characteristics of distributed video processing [2].

Microservice-based pipelines have gained traction because they mimic the distributed and diverse nature of modern edge systems. By combining different services, these pipelines enable benchmarking frameworks to replicate complex application scenarios [2].

Dataset Quality and Accuracy Requirements

Once realistic scenarios are established, the focus shifts to dataset quality. High-quality datasets are essential for producing reliable benchmarking results. Poor-quality data can lead to skewed metrics that fail to reflect how edge platforms perform under real-world conditions [6].

When selecting datasets, several factors must be considered. First, relevance is critical - a dataset designed for smart city traffic monitoring won't work for industrial predictive maintenance. Scalability is another key factor, as datasets must support both lightweight tasks and more demanding computational operations [6].

Data diversity is also important. Datasets should include a range of data types, such as images, sensor readings, and time series, as well as varying data volumes and velocities. The presence of labelled data or ground truth is essential for validation, ensuring benchmarks can assess edge platforms across a wide array of IoT workloads [2][6].

One standout example is the MOTChallenge dataset, widely used for benchmarking video analytics in multi-object tracking scenarios. This dataset includes well-labelled video sequences that closely resemble real-world surveillance applications, providing credibility and consistency in benchmarking [2][6].

Lastly, scalability and adaptability are vital to maintaining long-term relevance. Datasets should be modular and easy to update as IoT technologies advance. Comprehensive metadata further enhances their usability across different hardware and software setups [2][6].

For organisations deploying IoT edge solutions, selecting the right datasets is a crucial factor in achieving success. Aligning benchmarking datasets with actual operational needs helps businesses make better decisions when choosing and configuring edge platforms. In the UK, companies aiming to optimise their IoT edge deployments might consider working with experts like Hokstad Consulting, which specialises in cloud and edge infrastructure optimisation.

Lightweight Benchmarking Implementation

When it comes to evaluating edge platforms, lightweight benchmarking stands out as a resource-friendly alternative to traditional methods. Edge devices often operate under strict resource and budget constraints, making elaborate benchmarking setups impractical. Lightweight approaches tackle this challenge by delivering valuable insights through simpler, more cost-effective methods that don’t require extensive infrastructure or complex environments.

The core idea here is efficiency without compromise. Instead of relying on expensive hardware setups or intricate testing systems, lightweight benchmarking employs techniques like simulation, micro-benchmarks, and containerisation. These methods allow businesses - whether startups or large enterprises - to conduct meaningful performance evaluations without breaking the bank.

Low-Cost Benchmarking for Edge Devices

Techniques such as simulation-based and micro-benchmarking replicate real-world IoT workloads to assess CPU, memory, network, and disk I/O performance. Importantly, this is done without overwhelming the limited hardware capabilities of edge devices [1][2].

For instance, in 2022, researchers used EdgeFaaSBench on devices like the Raspberry Pi 4B and Jetson Nano to evaluate system utilisation, response times, and cold/warm start times across 14 serverless workloads. The results highlighted clear performance differences between hardware types and workload categories, proving that even budget-friendly devices can deliver meaningful benchmarking results [1].

Open-source benchmarking tools play a significant role in keeping costs down. By eliminating licensing fees and offering modular frameworks, these tools allow organisations to customise their setups, selecting only the components they need. This not only reduces resource consumption but also simplifies the overall process [1][2].

While the diversity of hardware remains a challenge, modern frameworks like ComB and EdgeFaaSBench are designed to adapt. Their configurable testing options make them versatile enough to handle a variety of edge devices [1][2]. Additionally, container-based solutions bring another layer of efficiency to the table.

Benefits of Container-Based Testing

Containers provide isolated, reproducible environments that can be quickly deployed and dismantled, cutting down on setup time [1].

Take the ComB framework as an example. In April 2022, researchers ran a video analytics workload, involving multiple microservices, on a heterogeneous edge testbed that included Raspberry Pi and Nvidia Jetson devices. The profiling revealed minimal overhead, with the majority of resources dedicated to workload execution [2].

The isolation offered by containers ensures accurate measurements by running each workload in its own environment. This eliminates resource conflicts that might otherwise distort results. Whether testing on a Raspberry Pi, Jetson Nano, or an industrial edge gateway, containers provide consistent execution across platforms, making it easier to compare different edge solutions.

For organisations aiming to optimise their edge deployments, container-based benchmarking presents a practical, efficient option. With its low overhead, quick deployment, and reliable results, this approach is particularly appealing to businesses working under tight budgets or timelines. Companies like Hokstad Consulting have integrated these methods into their DevOps pipelines to validate updates and maximise resource efficiency.

Additionally, the lightweight nature of container-based benchmarks allows for performance evaluations in live production environments without causing disruptions [2].

Conclusion and Key Takeaways

Evaluating IoT workloads on edge platforms is essential for fine-tuning edge deployments. By systematically analysing performance, organisations can make smarter choices about hardware, resource management, and deployment strategies tailored to various edge environments.

Key metrics like system utilisation (CPU, memory, network, and disk I/O), application response times, and cold/warm start performance for serverless functions lay the groundwork for deeper benchmarking efforts. These metrics reveal how different edge platforms manage the demands of IoT applications, whether it's industrial automation or smart city systems [1][3].

Modern benchmarking frameworks are rising to meet the challenges of edge computing. Tools like EdgeFaaSBench and ComB show that even budget-friendly devices can provide meaningful performance insights [1][2]. Comparing frameworks highlights that while platforms may perform similarly under standard tests, their underlying technologies can significantly impact the developer experience.

Lightweight benchmarking methods offer a practical solution for environments with limited resources. This approach ensures that even smaller-scale deployments can benefit from performance evaluations tailored to IoT and edge computing needs.

For UK businesses aiming to optimise their edge infrastructure, combining systematic benchmarking with expert advice on cloud cost management can lead to major operational gains. Companies such as Hokstad Consulting incorporate these benchmarking practices into their DevOps services, helping organisations improve resource efficiency and cut deployment costs.

Regular benchmarking allows organisations to balance critical goals like performance, energy efficiency, and cost [1][2]. As edge hardware and application needs evolve, businesses that adopt flexible and scalable benchmarking strategies will be better equipped to make informed decisions, achieving improved performance and smarter resource use.

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