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Version: development

Adaptive Service Protection

Overview​

Adaptive service protection leverages closed-loop feedback of service health telemetry to dynamically adjust the rate of requests processed by a service. This adjustment is managed by Aperture Agents, which provide a virtual request queue at the service's entry point.

The queue adjusts the rate of requests in real-time based on the service's health, effectively mitigating potential service disruptions and maintaining optimal performance under varying load conditions. This strategic management of service load not only maximizes infrastructure utilization and service uptime, but also ensures the fair admission of requests into the service based on the priority and weight of each request.

Service overloads can result from a wide variety of failure scenarios, such as cascading failures where a subset of service instances cause a wider outage, or service slowdowns that result in failure at dependent services. Metastable failures, where a system remains in a degraded state long after the original failure condition has passed, can also lead to service overloads. In such complex failure scenarios, Aperture's load scheduling feature offers a reliable safeguard, ensuring that your system maintains optimal performance and uptime.

flowchart LR classDef Controller fill:#F8773D,stroke:#000000,stroke-width:2px; classDef Agent fill:#56AE89,stroke:#000000,stroke-width:2px; classDef Signal stroke:#EFEEED,stroke-width:1px; classDef Service fill:#56AE89,stroke:#000000,stroke-width:1px; HS("Health Signals") --> Controller class HS Signal CS("Confirmation Signals") --> Controller class CS Signal subgraph "Controller" policy("Service Protection Policy") end Controller -- "Adjust load multiplier" --> Agent class Controller Controller subgraph " " Client -- "Incoming requests" --> Agent class Client Service subgraph "Agent" subgraph "Req Queue" packets("[] [] []") class packets Service end end Agent -- "req/s" --> Service class Agent Agent class Service Service end

The diagram illustrates the working of a load scheduling policy. The policy is evaluated at the controller, which analyzes health signals in real-time. Based on these metrics, it calculates a load multiplier, which is relayed to the agents. The agents then adjust the rate of requests locally based on the load multiplier applied to the recent rate of requests.

note

Aperture facilitates the observation of health signals from various services. For instance, adaptive service protection can also be implemented based on the health observation of an upstream service in relation to a downstream service.

Example Scenario​

Consider the scenario of an e-commerce platform during a major sale event. To handle the increased traffic, the load scheduling policy will monitor the health of the service to detect overloads. The request rate will be adjusted in case the service begins to deteriorate. This prevents service overloads, which reduce good throughput and lead to cascading failures. Load scheduling policy ensures smooth service operation and a consistent user experience for a successful sales event.