Edge-Cloud Disaggregated Architecture

• Edge-Cloud Disaggregated Architecture is a design paradigm that separates latency-critical processing at distributed edge nodes from heavy data processing in centralized cloud data centers.
• It leverages lightweight containerization and WebAssembly to rapidly deploy applications across heterogeneous hardware while minimizing startup overhead.
• Dynamic orchestration with federated learning and energy-aware resource management ensures enhanced performance, privacy, and fault tolerance in modern distributed systems.

Edge-Cloud Disaggregated Architecture refers to the system-level design paradigm where computational, storage, orchestration, and monitoring resources are intentionally divided and distributed between physically proximate “edge” sites (such as micro-datacenters, gateways, and embedded devices) and centralized “cloud” data centers. Unlike monolithic architectures, edge-cloud disaggregation enables strict latency, privacy, scalability, and reliability targets by placing real-time or latency-critical computation close to users/devices while relegating resource-intensive operations (training, long-term analytics, feature storage) to cloud nodes. Fundamental to this approach are dynamic orchestration, robust containerization, advanced multi-tier monitoring, federated privacy mechanisms, and energy-aware resource management. Contemporary implementations leverage lightweight containers (notably WebAssembly), Kubernetes-based orchestration, heuristic resource sharing, geo-distributed databases, and advanced scheduling policies for task migration and load balancing across highly heterogeneous hardware and network conditions.

1. System Architecture: Distributed Fabric and Disaggregation

Modern edge-cloud disaggregated architectures employ a physically and logically distributed fabric comprising edge nodes, cloud nodes, and a centralized orchestration plane (Marsh et al., 2022).

• Edge nodes (micro-datacenters): These are geographically distributed, often single-rack deployments co-located with 5G base stations or industrial floors. Each has limited compute (multi-core servers), small local storage, battery-based power provisioning, and interfaces for on-site power generation. Edge nodes are optimized for hosting ultra-low-latency, real-time workloads encapsulated in lightweight containers or WebAssembly modules.
• Cloud nodes (regional/central data centers): These provide high-capacity compute for AI training, massive feature-store management (e.g., RonDB), and serve as master repositories for model versions and large datasets.
• Centralized orchestration plane: Logical control, often physically distributed, performs slice-level resource allocation, shared-protection heuristics for backup compute/connectivity, auto-scaling based on learned performance models, and federated learning for privacy-preserving AI updates.
• Interconnection and data synchronization: Edge nodes are connected to the cloud via low-latency backhaul (5G or dedicated fiber). Workloads are disaggregated; latency-sensitive inference tasks run at the edge, and heavyweight training/storage persist at the cloud. Feature Stores are geo-distributed, with RonDB key-value replication for synchronizing hot feature vectors between edge and cloud.

2. Performance, Slicing, and Power Models

Performance and resource management in edge-cloud architectures rely on closed-form analytical models defining latency, resource slicing, and energy.

• Latency decomposition: End-to-end latency is expressed as $$L_{\mathrm{E2E}} = L_{\mathrm{edge\_proc}} + L_{\mathrm{backhaul}} + L_{\mathrm{cloud\_proc}}$$, isolating each contributor for optimization (Marsh et al., 2022).
• Resource-slice allocation: Per-edge CPU resources $R_{\mathrm{edge}}$ are partitioned by share $\alpha_i$ per slice, with allocated container CPU $$C^{\mathrm{alloc}}_i = \alpha_i \times R_{\mathrm{edge}}$$ and $\sum_i \alpha_i \le 1$.
• Power management: Edge site $j$ power model: $$P^{\mathrm{site}}_j(t) = \sum_{i \in \mathrm{VMs}} P^{\mathrm{comp}}_{i,j}(t) + P^{\mathrm{comm}}_j(t)$$; total $$P_\mathrm{total}(t) = \sum_j P^{\mathrm{site}}_j(t)$$.
• Optimization for power-cost: Across time horizon $T$, the policy seeks $$\min_{u(t)} \sum_{t=1}^T [p_\mathrm{grid}(t) P_\mathrm{grid}(t) - \pi_\mathrm{sell}(t) P_\mathrm{batt\_dis}(t)]$$ under battery/grid constraints.

These models guide dynamic slice scaling, load shifting (UPS battery during peak hours), and offer formal basis for incentive alignment with grid operations.

3. Lightweight Containerization, WebAssembly, and Edge Compute

Edge nodes require minimal-footprint virtualization for efficient task deployment and rapid scale.

• WebAssembly runtimes: WASM modules are typically <1 MB and execute in sandboxed environments with no OS dependencies, enabling startup in tens of milliseconds and memory overheads of only a few MB per module (Marsh et al., 2022).
• Portability: One WASM binary can run on microcontrollers, ARM SoCs, and x86 servers, supporting "write once, run anywhere".
• Runtime overhead: WASM eliminates heavy kernel namespace setup and multiplexes dozens of modules over a shared runtime per node.
• SynergAI integration: Architecture-aware inference serving across heterogeneous edge-cloud resources is executed by SynergAI, which uses an offline Configuration Dictionary (mapping optimal threads/power modes to QPS per model/worker) and an online priority scheduler to allocate AI inference jobs, achieving a 2.4× reduction in QoS violations versus state-of-the-art (Stathopoulou et al., 12 Sep 2025).

4. Orchestration, Auto-Scaling, and Privacy-Preserving ML

Disaggregated architectures depend on coordinated, federated orchestration:

• Slice provisioning: Shared-backup pools for compute/connectivity drastically reduce blocking probability under constrained edge resources by an order of magnitude (Marsh et al., 2022).
• Auto-scaling: Transfer-learning-enabled performance models analyze edge telemetry to predict KPIs, with local edge scaling decisions guided by policies from the orchestration tier.
• Federated learning: Edge sites train models on local sensitive data, exchanging only parameter updates. The central source aggregates these updates, refining global models without raw data transfer.
• SynergAI’s online scheduling: For each inference job $j$, urgency is computed as $$U_j = T_\mathrm{Remaining,j} - T_\mathrm{Estimated,j,w^*}$$, ranking jobs to minimize deadline violation rates (Stathopoulou et al., 12 Sep 2025).

5. Monitoring, Failure Detection, and Data Management

Monitoring and managing distributed failures with minimal telemetry overhead is central:

• Unsupervised feature selection: Reduces SNMP/Prometheus probes by 50–70% via minimal metric subset selection that maintains model accuracy, minimizing CPU/bandwidth drain (Marsh et al., 2022).
• Hierarchical failure detection: Periodic low-frequency heartbeat plus event-driven tracing for high-risk components; local escalation to the orchestrator on threshold breaches.
• Geo-distributed feature stores: RonDB synchronizes routine health and telemetry data across edge and cloud, supporting aggregation and rapid failure recovery.
• Data flow and reduction: In stream-management architectures, fog nodes handle sorting, cleaning, and deduplication—removing up to 59.4% of redundant data before cloud ingestion (Hernandez et al., 2017).

6. Energy Optimization, Scalability, and Fault Tolerance

Scalable architectures prioritize both operational cost reduction and resilience:

• Power management: Battery-backed load shifting at the edge, dynamic price signals, and minimum battery sizing formulas; edge sites bid flexibility into local grid markets (Marsh et al., 2022).
• Horizontal scaling: Edge/fog clusters managed by Kubernetes (or custom fog managers) shard tasks and coordinate resource distribution, confirmed to yield up to 40% reductions in execution time and energy (see ABEONA’s empirical findings, which use cost functions $\Delta C_{i\to j}(t)$ for migration decisions) (Rocha et al., 2019).
• Fault tolerance: Shared backup pools, local health checks, and adaptive load balancing (e.g., Armada switches user connections to next best edge agent in case of node failure with latency spikes $<10$ ms) (Huang et al., 2021).
• Auto-scaling methods: Triggered by region-wise user count histogram, tasks are spawned or removed proportionally, respecting maximum region capacity and maintaining load balancing.

7. Application Domains and Future Directions

Disaggregated edge-cloud architectures are adopted for:

• 5G networks and smart manufacturing: Reduced application latency, scalable small datacenter deployment, privacy via federated learning (Marsh et al., 2022).
• AI inference orchestration: SynergAI demonstrates robust placement, architecture-aware configuration, and tail-latency reduction for heterogeneous online scheduling (Stathopoulou et al., 12 Sep 2025).
• Generative AI service delivery: Synergistic deployment of big cloud models and small edge models, enabling privacy-preserving and adaptive GenAI services; BAIM compression ratio $r_t$ achieves $\sim$0.2, and FID improvements after edge/cloud collaboration (Tian et al., 3 Jan 2024).
• Stream data management and IoMT: Edge-fog-cloud solutions eliminate redundant traffic and improve data quality for moving things networks, validated by a 59% reduction in uplinked tuples (Hernandez et al., 2017).

Emerging directions include asynchronous federated aggregation, integration with disaggregated memory fabrics, incentive-aligned energy management, and multi-objective policy optimization for cross-layer resource orchestration.

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Summary Table: Key Traits of Edge-Cloud Disaggregated Architectures

Feature	Architecture-Specific Realization	Source
Low-latency compute	Edge-hosted WASM modules, slice provisioning	(Marsh et al., 2022)
Scalable orchestration	Central orchestrator, shared backup pools	(Marsh et al., 2022)
Privacy-preserving ML	Federated learning, transfer-enabled scaling	(Marsh et al., 2022)
Energy-efficient operation	Battery-backed UPS, load-shifting, grid bids	(Marsh et al., 2022)
Architecture-aware AI serving	Offline/online scheduling, QPS maximization	(Stathopoulou et al., 12 Sep 2025)
Data reduction/quality	Edge/fog cleaning, stream database	(Hernandez et al., 2017)
Fault tolerance & monitoring	Unsupervised metric selection, event tracing	(Marsh et al., 2022)

These core elements form the operational, analytical, and experimental foundation for current and next-generation edge-cloud disaggregated systems.