# 4.1 Computing Power and Resource Layer

The computing power and resource layer constitutes the underlying support of **GamePad**'s operational infrastructure, responsible for providing continuous and stable computing power for the decentralized finance system during its operational phase. As DeFi protocols gradually introduce automated strategies, AI-driven risk control, cross-market decision-making, and long-term operating agents, computing resources are no longer just auxiliary conditions in the development or analysis phase, but a fundamental element throughout the protocol's lifecycle.

In terms of load type, this layer mainly handles two types of long-term computing needs:

* Continuous inference workloads from strategy calculations, risk assessments, and market analysis include price forecasting, position management, liquidation decisions, and dynamic parameter adjustments;
* The continuous decision-making workload generated by the resident execution of AI agents includes tasks such as automated market making, arbitrage execution, risk hedging, and governance parameter tuning.

The two types of workloads differ significantly in resource configuration, latency sensitivity, and scheduling objectives: Policy and risk control computation emphasizes low-latency response and stable throughput to ensure timely decision-making; agent-based persistent execution focuses more on session persistence, state maintenance, and predictable resource allocation to support long-term financial activities. The core design goal of the computing power and resource layer is to explicitly model these differences and achieve schedulable, isolated, and scalable long-term computing power supply within a unified resource system.

**Resource Model**

**GamePad** standardizes and abstracts underlying computing resources, introducing unified resource descriptors to characterize the capabilities of heterogeneous GPU and CPU nodes. The resource descriptors cover the following key dimensions:

* GPU computing power and memory specifications (capacity, bandwidth, computing power level)
* CPU core count, memory size, and instruction set characteristics
* Network bandwidth, latency and stability metrics
* Local and remote storage throughput
* Node availability, health status, and historical stability

Through this abstraction mechanism, different types of financial computing tasks can express their operational constraints and resource preferences based on the same set of interfaces. For example, high-frequency strategies require low latency and stable throughput; risk control models rely on persistent GPU memory and state caching; and long-term agents require continuous computing power quotas and session persistence. This provides clear input for subsequent scheduling strategies, capacity planning, and elastic management.

**Resource organization methods**

The computing power and resource layer adopts a resource pooling architecture, which unifies distributed computing power from different sources and of different specifications into an allocable resource pool, and operates in a control plane-execution plane separation manner:

* The control plane is responsible for resource registration, capacity view maintenance, task admission and scheduling decisions;
* The execution plane consists of distributed computing nodes, which are responsible for executing actual computing tasks, isolating resources, and transmitting operational metrics.

By continuously maintaining a global capacity view, the system can perceive the computing power supply status in real time; through access control and scheduling decisions, different types of financial computing tasks can obtain predictable resource allocation behavior in a unified queue system; through the indicator feedback mechanism, the system forms an operational closed loop oriented towards elastic scaling and fault recovery.

**Scheduling strategies for DeFi scenarios**

In strategy computation and risk assessment scenarios, the computing layer prioritizes low latency and decision determinism. For highly sensitive tasks such as price fluctuations, liquidation triggers, and risk threshold calculations, the system uses stable resource allocation and priority scheduling to avoid decision lags or execution deviations caused by resource fluctuations.

In scenarios where AI agents are constantly running, computing power and resources are shifting towards a session-based, long-term resource provisioning model. Agents typically involve model loading, state maintenance, and historical context accumulation. The system reduces cold start costs through model hot loading, weight caching, and session binding mechanisms, and ensures predictable performance for long-term execution through stable quotas and isolation strategies.

In terms of elastic design, the computing power and resource layer combine capacity planning, dynamic scaling and fault recovery mechanisms: Capacity planning establishes assumptions about resource redundancy and load distribution; dynamic scaling adjusts based on indicators such as queue length, latency, and memory pressure; fault recovery brings the uncertainty of the distributed environment to a manageable range through node health checks, task migration and session rebinding.

Through the above mechanism, the platform can continuously provide a stable computing foundation for the upper-level intelligent execution environment under the realities of market fluctuations, strategy-intensive execution, and resource heterogeneity.