SMO↔︎Hop3 Optimisation Integration¶

This note explores how the optimisation features of the SMO align with the H3NI project’s goals and Key Performance Indicators (KPIs), and discusss additional optimization dimensions that could be introduced, particularly in light of H3NI’s objectives.

Relating SMO’s Optimization to H3NI Project Goals & KPIs¶

The H3NI project aims to integrate its Hop3 platform with SMO, enhancing orchestration capabilities. SMO’s existing optimization features provide a strong starting point for achieving H3NI’s objectives and meeting its KPIs.

H3NI Key Objectives for Integration¶

Dynamic Scaling & Elasticity:
- SMO’s Contribution: The decide_replicas optimization in SMO is directly responsible for dynamic scaling. It adjusts replicas (r_current) based on real-time request_rates (from Prometheus) and the service capacity model (alpha, beta). The “elasticity response time” KPI for Hop3 would be influenced by how quickly SMO’s scaling loop detects changes, solves the optimization, and instructs Karmada to scale.
- Hop3 Integration: Hop3 can leverage this. Instead of Hop3 implementing its own complex scaling logic across multiple clusters, it could:
  - Feed SMO the necessary service-specific parameters (alpha, beta, CPU limits, max replicas) if these are determined by Hop3 or the application developer.
  - Expose SMO’s scaling decisions through Hop3’s user interface.
  - Ensure SMO has access to the correct Prometheus metrics for Hop3-managed applications.
- Predictive Scaling (H3NI): SMO’s current model is reactive (based on current request rates). To achieve predictive scaling, the input request_rates to decide_replicas would need to come from an ML model’s forecast. Hop3 could integrate an ML forecasting module that provides these predicted rates to SMO.
Energy Optimization:
- SMO’s Contribution (Potential):
  - The decide_placement objective w_dep * cp.sum(x) could be adapted. If w_dep was not a uniform weight but rather represented the energy cost or PUE (Power Usage Effectiveness) of deploying a service on a particular cluster e, then minimizing this term would inherently favor energy-efficient clusters.
  - Similarly, the cluster_capacities constraint could be augmented with an energy budget per cluster or a preference for renewable-powered nodes by adding terms to the objective function or new constraints.
  - The decide_replicas objective w_util * cp.sum(...) (minimizing resource utilization) indirectly contributes to energy efficiency, as fewer active resources generally mean less power.
- Hop3 Integration: Hop3 could provide SMO with the necessary metadata about clusters:
  - Energy source (renewable/grid).
  - Current energy cost or carbon intensity.
  - Power consumption characteristics of nodes/clusters.
    SMO’s optimization models would then need to be extended to incorporate these factors into their objective functions or constraints.
Interoperability:
- SMO’s Contribution: SMO already has REST APIs (for HDAGs, clusters, NFVCL).
- Hop3 Integration:
  - Hop3 would primarily interact with SMO’s /project/<project>/graphs endpoint to deploy applications (HDAGs). Hop3 would translate its application definitions into SMO’s HDAG descriptor format.
  - To incorporate SMO’s resource optimization, Hop3 would essentially delegate the placement and scaling decisions for HDAG components to SMO. Hop3 would need to provide the application’s structural information (services, dependencies, resource needs) and potentially the performance model parameters (alpha, beta) to SMO.
  - Python-based plugins in Hop3 could act as clients to the SMO API.
Usability Enhancements:
- SMO’s Contribution: SMO abstracts multi-cluster complexity.
- Hop3 Integration: Hop3 can provide a user-friendly interface (UI/CLI) that front-ends SMO. Users interact with Hop3’s familiar workflows, and Hop3, in the background, translates these actions into SMO API calls.
  - For example, a Hop3 “deploy application” command could gather necessary info, construct an HDAG descriptor, and POST it to SMO.
  - Hop3 could display status and metrics sourced from SMO (which in turn gets them from Karmada/Prometheus).
  - Backward Compatibility (GitOps): If Hop3 uses GitOps, the “desired state” in Git could be an HDAG descriptor. Hop3’s GitOps controller would then reconcile this with SMO.
Open Source Commitment: SMO’s codebase shows it’s built with open-source components. Hop3’s contributions (e.g., plugins, UI enhancements for SMO interaction, potential extensions to SMO’s optimization models) would also be open source.

H3NI Evaluation Criteria (KPIs)¶

Reduction in deployment time for distributed applications:
- The SMO itself automates many steps. Hop3’s integration, by providing a streamlined interface to the SMO, could further reduce the manual effort and cognitive load associated with deployment, effectively reducing end-to-end deployment time for the user. The actual backend deployment time depends on Helm, Karmada, and network speed, which SMO/Hop3 don’t directly control but orchestrate.
Improvement in resource utilization and energy efficiency:
- The SMO’s decide_replicas directly aims to optimize resource utilization (w_util term) by scaling to meet demand without over-provisioning. This inherently improves resource utilization and contributes to energy efficiency.
- The SMO’s decide_placement can be extended (as discussed above) to explicitly consider energy efficiency (e.g., by favoring clusters with lower PUE or renewable energy). Hop3 would need to supply this cluster metadata to the SMO.
- H3NI’s KPI Target: “Achieve at least 15% energy efficiency improvement” – This would require modifying SMO’s placement/scaling objectives to directly include energy metrics and demonstrating the impact.
- H3NI’s KPI Target: “Demonstrate a 10% reduction in operational costs” – Improved resource utilization from SMO’s scaling directly translates to lower operational costs (less compute needed).
Scalability across cloud-edge-IoT environments:
- The SMO, via Karmada, is designed for multi-cluster deployments. The NFVCL integration further allows it to span into different infrastructure environments (like OpenStack).
- H3NI’s KPI Target: “Achieve horizontal scalability to 50 nodes” – This is more about the underlying Karmada/Kubernetes scalability. SMO/Hop3 would orchestrate applications on such a scaled environment. The test would be whether the SMO’s algorithms can efficiently manage placement/scaling decisions across a large number of clusters/nodes.
- H3NI’s KPI Target: “Maintain 99.99% uptime” – This is primarily a function of the application’s design, Kubernetes’ resilience, and Karmada’s capabilities. SMO contributes by enabling robust deployment patterns, but uptime itself is a broader concern.
- H3NI’s KPI Target: “Ensure elasticity response time of 5 seconds” – This depends on:
  1. Prometheus metric collection interval.
  2. The SMO’s SCALING_INTERVAL.
  3. Time taken by CVXPY to solve the optimization.
  4. Time taken by the SMO to call Karmada API.
  5. Time taken by Karmada and Kubernetes to scale the deployment (e.g., pod startup time).
    Achieving 5 seconds end-to-end is ambitious and would require careful tuning of all these stages.

How Hop3 Integration Can Leverage/Enhance the SMO’s Optimization¶

The collaboration between Hop3 and SMO can create a more powerful and user-centric orchestration solution:

Provide Richer Input to Optimizers:
- Hop3 can be the source of more detailed application profiles, including more accurate service performance models (alpha, beta values for scaling) and perhaps even application-specific dependency information that can refine SMO’s placement d[i-1] constraint.
- For energy optimization, Hop3 can gather and provide cluster energy characteristics (source, PUE, real-time carbon intensity) to SMO.
Abstract SMO’s Complexity:
- Hop3 users shouldn’t need to craft complex HDAG YAMLs. Hop3 can generate these from simpler Hop3 application definitions.
- Hop3 can provide a UI to tune SMO’s optimization weights (w_dep, w_re, w_util, w_trans) if desired, or offer pre-set profiles (e.g., “cost-optimized,” “stability-focused,” “energy-focused”).
Extend Optimization Models (Potentially):
- If H3NI identifies optimization goals not currently in SMO (e.g., a very specific cost model, data locality constraints), the integration could involve contributing extensions to SMO’s CVXPY models or developing Hop3-specific pre/post-processing logic around SMO’s calls.
Feedback Loop:
- Hop3 could potentially provide feedback to SMO about the actual performance or energy consumption post-deployment, which could be used to refine SMO’s models over time (though this is more advanced, leaning towards adaptive optimization).

Additional Optimisation Features¶

Here are other optimization areas that could be implemented in SMO, related to H3NI’s objectives:

1. Energy-Aware Optimization¶

To directly meet H3NI’s energy efficiency KPIs, SMO’s optimization logic needs to treat energy as a first-class concern:

Current SMO: Placement considers CPU/GPU; scaling considers CPU utilization. Energy is not a direct factor in the current CVXPY objective functions.
Potential SMO/H3NI Optimization:
- Energy-Aware Placement:
  - Objective: Minimize overall energy consumption or carbon footprint, or prioritize placement on clusters powered by renewable energy.
  - Inputs (from H3NI/SMO): Cluster energy profiles (PUE, energy source, real-time carbon intensity), service power consumption models (if available, or estimated based on CPU/GPU usage).
  - CVXPY Model Changes: Modify the placement objective function. Instead of/in addition to w_dep * cp.sum(x), use terms like sum(energy_cost[e] * x[s,e]) or add constraints like “total carbon footprint < threshold.”
- Energy-Aware Scaling:
  - Objective: Scale services to meet demand while minimizing energy. This might involve consolidating workloads onto fewer, more efficiently utilized nodes/clusters, even if it means slightly higher CPU utilization on those active nodes (up to a point).
  - Inputs: Similar to energy-aware placement, plus real-time power draw if available from nodes.
  - CVXPY Model Changes: Add energy consumption terms to the scaling objective function. Could also influence the alpha/beta service capacity models if they can be energy-aware.
- Dynamic Power State Management (Advanced): If infrastructure supports it (e.g., turning nodes on/off), an optimization could decide to power down underutilized clusters/nodes during low-demand periods.

2. Network-Aware Optimization (Latency, Bandwidth, Cost)¶

For hyper-distributed applications, network performance and cost are critical:

Current SMO: Considers connectivity via connectionPoints and serviceImportClusters (implying Submariner setup), but doesn’t explicitly optimize for network characteristics during placement.
Potential SMO/H3NI Optimization:
- Latency-Sensitive Placement:
  - Objective: Place communicating services in clusters with low inter-cluster latency or place services close to their end-users/data sources.
  - Inputs: Inter-cluster latency matrix, user/data source locations, service latency requirements (new intent type).
  - CVXPY Model Changes: Add terms to the placement objective penalizing high-latency links between dependent services, or add constraints ensuring latency SLAs are met.
- Bandwidth-Aware Placement/Scaling:
  - Objective: Avoid network congestion by placing high-bandwidth services on clusters with ample network capacity or by distributing traffic.
  - Inputs: Cluster network capacity, service bandwidth consumption profiles.
  - CVXPY Model Changes: Add network capacity constraints to clusters; potentially influence scaling decisions if high replica counts strain network links.
- Network Cost Optimization:
  - Objective: Minimize data transfer costs between clusters or out to the internet, especially relevant in public cloud environments.
  - Inputs: Data transfer cost matrix between clusters/regions.
  - CVXPY Model Changes: Add data transfer costs to the placement objective function, favoring co-location of chatty services or placement in low-cost regions.

3. Cost Optimization (Financial)¶

While resource optimization leads to cost savings, direct financial cost modeling can yield further benefits, directly addressing H3NI’s operational cost reduction KPI:

Current SMO: Indirectly affects cost by optimizing resource utilization. But actual costs (prices) are not taken in to account in the model
Potential SMO/H3NI Optimization:
- Cost-Aware Placement & Scaling:
  - Objective: Minimize the overall financial cost of running the HDAG.
  - Inputs: Per-cluster/per-resource (CPU, RAM, GPU, storage, network egress) cost models. These could be complex, considering on-demand vs. reserved instances, spot pricing, etc.
  - CVXPY Model Changes: Incorporate financial costs directly into the objective functions for both placement and scaling. This could involve trade-offs (e.g., a slightly less performant placement might be significantly cheaper).
  - H3NI KPI: “Demonstrate a 10% reduction in operational costs” – This optimization directly targets such KPIs.

4. Data Locality Optimization¶

Crucial for data-intensive applications and AI/ML workflows (an H3NI focus):

Current SMO: No explicit data locality considerations.
Potential SMO/H3NI Optimization:
- Data-Gravity Aware Placement:
  - Objective: Place compute services close to the data they process to minimize data movement, reduce latency, and potentially lower costs.
  - Inputs: Location of large datasets, service data access patterns/requirements (new intent type).
  - CVXPY Model Changes: Add terms to the placement objective penalizing distance between a service and its required data, or constraints to ensure co-location with specific data stores. This is highly relevant for AI/ML workflows (H3NI goal).

5. Resilience and Fault Tolerance Optimization¶

Beyond basic Kubernetes/Karmada failover:

Current SMO: Relies on Kubernetes/Karmada for basic fault tolerance (e.g., restarting pods). Placement ensures each service is on one cluster.
Potential SMO/H3NI Optimization:
- Availability Zone / Region-Aware Placement (for High Availability):
  - Objective: Distribute replicas of critical services across different fault domains (availability zones, regions, or even different Karmada member clusters acting as distinct failure domains) to ensure high availability.
  - Inputs: Cluster fault domain information, service HA requirements (new intent type, e.g., “deploy N replicas across M fault domains”).
  - CVXPY Model Changes: Modify placement constraints. Instead of one cluster per service, it might be “at least N distinct fault domains for service X.” This makes the placement problem more complex (multi-instance placement).
- Dynamic Failover Orchestration (Advanced):
  - Objective: If a cluster/service fails, automatically re-deploy/scale up affected services on healthy clusters according to predefined policies.
  - Inputs: Real-time cluster/service health, failover policies.
  - Logic: This might involve a reactive “re-placement” strategy triggered by failure alerts.

6. Optimization for Specific Workload Types (e.g., AI/ML)¶

H3NI’s interest in AI/ML workflows suggests a need for specialized optimization:

Current SMO: GPU awareness in placement.
Potential SMO/H3NI Optimization:
- AI/ML Training Job Placement:
  - Objective: Optimize for factors like GPU type, inter-GPU communication bandwidth (e.g., NVLink), access to large datasets, and job completion time.
  - Inputs: Detailed GPU specifications per cluster, dataset locations, specific requirements of AI training frameworks.
  - CVXPY Model Changes: More granular resource constraints and potentially different objective functions tailored for training jobs.
- AI/ML Inference Service Placement/Scaling:
  - Objective: Optimize for low latency, high throughput, and cost-effectiveness for inference endpoints.
  - Inputs: Inference request patterns, latency SLAs.
  - CVXPY Model Changes: Scaling models might need to be very responsive; placement might prioritize edge locations.

7. Compute Offloading / Live Migration Strategy Optimization¶

These H3NI goals represent advanced dynamic workload management:

Current SMO: No direct support.
Potential SMO/H3NI Optimization:
- Offloading Decision Optimization:
  - Objective: Decide when and what to offload from (e.g.) an edge device/cluster to a more powerful central cluster. Factors could include edge resource constraints, current load, energy cost, processing time benefits.
  - Inputs: Edge device capabilities/load, central cluster availability/cost, application offloadability characteristics.
  - Logic: This would likely be a new type of strategy plugin, not just a modification of placement/scaling. It might involve a cost-benefit analysis.
- Live Migration Path Optimization:
  - Objective: If live migration is supported by the underlying platform (a big if for general Kubernetes workloads across clusters, easier within a single virtualized environment), optimize the target selection and timing to minimize disruption and cost.
  - Logic: Similar to offloading, a specialized strategy.

How to Implement in SMO (Leveraging Pluggability¶

(See SMO - Potential Improvements and SMO↔︎Hop3 Improvement Plan for more detailed discussion).

For most of these new optimization types, the “Pluggable Strategies” improvement for the SMO would be the key enabler:

New Intent Types: SMO’s HDAG descriptor schema and intent parsing logic would need to be extended to recognize new requirements (e.g., latency SLAs, energy preferences, data locality needs).
New Metadata: SMO’s Cluster model (and potentially Service model) would need to store new types of information (e.g., cluster PUE, inter-cluster latency, data store locations). H3NI/Hop3 could be responsible for populating this metadata.
New Strategy Plugins: H3NI (or others) would implement new PlacementStrategy or ScalingStrategy plugins (or entirely new strategy types like OffloadingStrategy) that:
- Consume the new intents and metadata.
- Define new CVXPY models (or other decision logic) with objective functions and constraints tailored to the specific optimization goal (energy, network, cost, etc.).
SMO Core Invokes Plugins: SMO’s core workflow would select and invoke the appropriate strategy plugin based on configuration or the specified intents.

By adopting a pluggable architecture, SMO can become a versatile platform where various optimization goals, including those critical for H3NI’s use cases and KPIs, can be implemented and experimented with as distinct, manageable modules.

Conclusion¶

The SMO’s existing optimization framework for placement and scaling provides a strong foundation for H3NI to achieve its goals. The key will be:

Effective Data Exchange: Hop3 needs to provide SMO with the necessary application and infrastructure metadata.
API Integration: Hop3 needs to seamlessly call SMO APIs to trigger deployment, placement, and scaling.
Model Augmentation (if needed for KPIs): Specifically for direct energy optimization KPIs, SMO’s current models would need to be explicitly extended to include energy as a primary objective or constraint, using data provided by Hop3.
UI/UX: Hop3 will provide the user-friendly layer on top of SMO’s powerful but potentially complex backend.

Page last modified: 2025-05-08 11:32:02