Performance Evaluation of Automated Multi-Service Deployment in Edge-Cloud Environments with the CODECO Toolkit

This paper evaluates the open-source CODECO toolkit, demonstrating that it significantly reduces manual intervention and maintains competitive performance compared to baseline Kubernetes workflows for automating multi-service deployments across heterogeneous Edge-Cloud environments.

The Gist

Imagine you are the manager of a massive, high-tech delivery company. Your goal is to get packages (data and applications) from a central warehouse (the Cloud) to thousands of local shops (Edge devices like smart cameras, robots, or sensors) as fast as possible.

In the past, managing this fleet was a nightmare. You had to manually tell every single truck where to go, check if the roads were clear, and ensure the trucks had enough fuel. If a road was blocked, you had to reroute everything by hand. This is what managing computer systems used to be like.

This paper introduces a new, smart manager called CODECO and tests how well it works compared to the old, manual way of doing things.

The Problem: The "Manual" Traffic Jam

Currently, most companies use a system called Kubernetes (or K8s) to manage their digital "trucks" (called containers/microservices). Think of Kubernetes as a very good, but somewhat rigid, traffic controller. It can park trucks in spots, but it doesn't really "think" about the bigger picture. It doesn't know if a specific road is congested, if a truck is running out of battery, or if a shop needs a package right now versus later.

In the complex world of Edge-Cloud computing (where data is processed both in the cloud and on local devices), this rigidity causes problems. Setting up these systems usually requires a team of engineers to spend hours manually configuring servers, installing software, and connecting networks. It's like building a house brick-by-brick by hand every time you want to move to a new city.

The Solution: The "Cognitive" Manager (CODECO)

Enter CODECO. The authors describe it as a "cognitive" (thinking) toolkit. Instead of just following a list of rules, CODECO is like a smart GPS for your entire delivery fleet.

It has five special "brains" working together:

1. The Dispatcher (ACM): Takes your order and figures out the best way to set up the system.
2. The Route Planner (SWM): Decides exactly which truck should go to which shop, not just based on distance, but based on traffic, fuel levels, and urgency.
3. The Weather Forecaster (PDLC): Uses AI to predict problems before they happen (e.g., "That road will be jammed in 10 minutes, let's reroute now").
4. The Road Crew (NetMA): Manages the actual connections (networks) to ensure the roads are open and secure.
5. The Scribe (MDM): Keeps a detailed log of everything that happens so you can learn from it later.

The Experiment: Race Day!

The researchers didn't just talk about CODECO; they put it to the test. They set up a "race" between the Old Way (Vanilla Kubernetes) and the New Way (CODECO) across different types of "cities":

• Big Cities: Powerful servers in data centers.
• Small Towns: Raspberry Pis (tiny, cheap computers) acting as edge devices.
• Real-world Scenarios: They tested it with real use cases like monitoring traffic in a smart city, managing energy grids, and controlling autonomous robots in a factory.

They measured three main things:

1. How much human effort was needed? (Did we have to type commands manually, or did the robot do it?)
2. How long did it take to get the system running? (Deployment time).
3. How much "fuel" (CPU, memory, and energy) did it use?

The Results: The Smart Manager Wins (Mostly)

Here is what they found, translated into everyday terms:

1. The "Human Effort" Score: A Huge Win

• The Old Way: Setting up a cluster required about 19 manual steps just to get the servers ready, and 42 steps to install the software. It was like assembling IKEA furniture without the instructions.
• The CODECO Way: It automated almost everything. The human effort dropped by 75% to 90%. You basically just tell the system what you want, and it builds the house for you.
• Analogy: It's the difference between hand-painting a mural (Old Way) and using a 3D printer that knows exactly what to print (CODECO).

2. The "Speed" Score: A Slight Trade-off

• The Catch: Because CODECO is "thinking" so hard to find the perfect route, it takes a tiny bit longer to get the trucks moving initially.
• The Reality: For simple tasks, it was only a few seconds slower. For complex tasks (like the "Bookinfo" app, which is a standard test for these systems), it was still very fast (under 15 seconds).
• Analogy: It's like a taxi driver who takes 30 seconds longer to check the map and traffic report before leaving, but then drives the most efficient route, saving you time later. The initial delay is worth the smarter routing.

3. The "Fuel" Score: Heavier, but Manageable

• The Cost: Because CODECO has all these extra "brains" (AI, monitoring, planning), it uses more computer memory (RAM) and a little bit more electricity.
• The Verdict: The extra energy cost was small (about 5-10% more). The researchers concluded that the "fuel" cost is a small price to pay for the massive reduction in human work and the smarter management of resources.
• Analogy: It's like driving a slightly heavier, smarter car that gets 5% worse gas mileage but saves you from getting lost and ensures you never run out of gas in the wrong place.

The Bottom Line

The paper proves that CODECO is a game-changer for managing complex, distributed computer systems.

• For Humans: It saves a massive amount of time and frustration by automating the boring, difficult setup work.
• For the System: It makes the network smarter, more adaptable, and ready for the future, even if it uses a little extra memory to do so.

In short, the researchers built a "self-driving" system for cloud computing. It's not perfect (it's a bit heavier and slightly slower to start), but it drives itself, knows the traffic, and gets the job done with far less human help than before. This is a crucial step toward making our smart cities, factories, and energy grids run smoothly on their own.

Technical Summary

Here is a detailed technical summary of the paper "Performance Evaluation of Automated Multi-Service Deployment in Edge-Cloud Environments with the CODECO Toolkit."

1. Problem Statement

The shift from centralized cloud architectures to Edge-Cloud-IoT (CEI) continua has introduced significant operational challenges. While Kubernetes (K8s) is the standard for orchestrating microservices, managing multi-service applications across heterogeneous, distributed environments (mixing ARM/x86, varying network conditions, and resource-constrained devices) remains difficult.

• Key Challenges: Automating deployment and lifecycle management without excessive manual intervention; maintaining performance and low latency; handling dynamic workloads; and integrating intelligence (AI/ML) for context-aware decision-making.
• Gap: Existing solutions often lack a unified, lightweight framework that bridges the gap between standard K8s capabilities and the specific needs of cognitive, decentralized Edge-Cloud orchestration.

2. Methodology

The authors conducted a quantitative performance evaluation of the CODECO (Cognitive Decentralised Edge Cloud Orchestration) toolkit, an open-source framework designed to extend K8s with cognitive, policy-driven control.

• Experimental Framework (CODEF): The study utilized the CODECO Experimentation Framework (CODEF), a modular, open-source tool that automates the entire experimental workflow (node allocation, cluster deployment, application setup, and result visualization) to ensure reproducibility and minimize human error.
• Infrastructure: Experiments were conducted across six heterogeneous environments (INF1–INF6), including:
  • Local servers (Intel/AMD).
  • Edge devices (Raspberry Pi 4/5, NVIDIA Jetson AGX Orin).
  • Cloud/VM environments (OpenStack, AWS).
  • Different K8s distributions: Vanilla K8s, k3s (lightweight), and KinD (Kubernetes in Docker).
• Workloads: Three application scenarios were tested to evaluate scalability and complexity:
  1. Batch Pods: Minimal "pause" pods (10–150) to test raw initialization speed.
  2. Dummy App: A simple Frontend-Backend microservice to test service-level orchestration.
  3. Bookinfo: A complex, industry-standard microservice benchmark suite (Istio/Bookinfo) to test realistic multi-service deployment.
• Baseline: CODECO performance was compared against Vanilla K8s (manual deployment) to isolate the overhead introduced by the toolkit.

3. Key Contributions

The paper makes the following specific contributions:

1. Quantitative Evaluation: A rigorous assessment of CODECO using three Key Performance Indicators (KPIs): Deployment Time, Manual Intervention Percentage (MIP), and Resource Footprint (CPU, Memory, Energy).
2. Multi-Infrastructure Study: A comprehensive analysis spanning diverse hardware (ARM vs. x86), edge constraints, and K8s flavors, validating the toolkit's adaptability.
3. Reproducibility: All implementation details, configuration scripts, and results are open-sourced, adhering to IETF Benchmarking (BMWG) guidelines.
4. Automation Metrics: Introduction of a specific metric (MIP) to quantify the reduction in human effort required for cluster and service provisioning.

4. Key Results

A. Automation and Manual Intervention (MIP)

CODECO and the CODEF framework significantly reduced manual effort compared to baseline K8s:

• Cluster Deployment: Reduced manual actions by 74.7% (from 19 actions to 3).
• K8s Installation: Reduced manual actions by 90.5% (from 42 actions to 4).
• Service Deployment: Reduced manual actions by 81.8% (from 11 actions to 2).
• Conclusion: CODECO effectively automates the "boring" infrastructure setup, allowing operators to focus on policy and intent.

B. Deployment and Lifecycle Latency

• Pod Startup: CODECO introduces a modest overhead compared to Vanilla K8s.
  • For 150 pods, readiness times increased by roughly 15–50% depending on the hardware (e.g., +5.7s on AWS, +12% on RPi).
  • In some optimized configurations (INF4), CODECO matched or slightly outperformed K8s due to scheduling optimizations.
• Service Startup (Bookinfo): Overhead was more noticeable for complex apps (e.g., 11s vs 3.5s on AWS), but absolute times remained under 15 seconds, which is acceptable for Edge-Cloud scenarios.
• Scaling: The NetMA (Network Management and Adaptation) component was identified as the primary scaling bottleneck, consuming 34–64% of installation time as cluster size increased, due to network path configuration overhead.

C. Resource Footprint (CPU, Memory, Energy)

• CPU: In idle states, CODECO increased CPU usage by +7.23% (on VMs) and +4.4% (on KinD) compared to baseline K8s.
• Memory: This was the most significant overhead. CODECO increased memory consumption by +4.57 GB (VMs) and +11.42 GB (KinD, 20 nodes) due to the additional orchestration components (ACM, PDLC, SWM, etc.).
• Energy: Energy overhead was relatively low, estimated at +4.7% (VMs) and +10.7% (KinD) in idle states.

D. Heterogeneous Performance

• CODECO demonstrated stable operation across all platforms, including resource-constrained Raspberry Pi clusters (INF5).
• While RPi nodes showed the highest absolute latency (due to hardware limits), CODECO maintained stable operation and, in some teardown scenarios, performed comparably to or better than baseline K8s.

5. Significance and Conclusion

The study validates CODECO as a viable solution for automating multi-service deployments in complex Edge-Cloud environments.

• Trade-off Analysis: The paper demonstrates that the resource overhead (primarily memory) and latency increase introduced by CODECO are acceptable given the substantial gains in automation, policy-driven management, and cross-layer intelligence (network/energy awareness).
• Scalability: The toolkit scales effectively, though network management (NetMA) remains a critical area for optimization in large-scale distributed clusters.
• Future Directions: The authors plan to extend evaluation to federated multi-cluster environments and incorporate more dynamic workload patterns (bursty traffic) and fault injection scenarios.

Final Verdict: CODECO successfully bridges the gap between standard K8s and the cognitive needs of Edge-Cloud computing, offering a robust, automated framework that significantly reduces human intervention while maintaining competitive performance across diverse hardware architectures.