Orchestration Control Plane DoS Protection via Dynamic Request Metering
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Solution Overview
Problem
Existing orchestration systems for managing containerized microservices applications are vulnerable to Denial of Service (DoS) attacks due to the inability of current protection mechanisms to effectively manage and prioritize API server requests, leading to potential overload and excessive response times.
Innovation Solution
The implementation of a dynamic and proactive system that meters requests to the orchestration system's control plane and API server, determining a threshold request rate using machine-learning and AI techniques, and invoking policy actions such as discarding excess requests, reassigning priority, or alerting security platforms to prevent DoS events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If basic API Priority and Fairness mechanisms are implemented, then request prioritization is improved, but the system remains vulnerable to DoS attacks and abnormal events
Solution Approach 1:
The system performs preliminary actions by establishing baseline request rate metrics through machine learning before DoS attacks occur. This baseline is used to detect abnormal request patterns and trigger protective policies in advance, allowing the system to prepare defense mechanisms before being overwhelmed by malicious traffic
Solution Approach 2:
The system implements continuous feedback loops that monitor request rates, compare them against dynamically updated baselines, and automatically adjust prioritization policies. This feedback mechanism enables real-time detection of DoS attacks and automatic response through policy invocation, improving reliability while maintaining ease of operation
2Reliability
If request metering and dynamic policy invocation are implemented, then protection against DoS events is improved, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where machine learning models automatically establish baselines and detect anomalies without manual configuration. The system autonomously invokes protective policies based on detected threats, reducing the need for complex manual management while maintaining high reliability against DoS events
Solution Approach 2:
The system dynamically changes operational parameters such as request rate thresholds and policy priorities based on real-time conditions. By adjusting these parameters adaptively rather than using fixed complex configurations, the system achieves robust DoS protection while keeping the overall architecture manageable
3Reliability
If aggressive request filtering is applied, then DoS prevention is improved, but legitimate traffic may be blocked
Solution Approach 1:
The system applies different quality levels of filtering to different request streams. By analyzing request patterns locally and comparing them against established baselines, the system selectively applies aggressive filtering only to abnormal requests while allowing legitimate traffic to pass through with minimal interference, thus maintaining both DoS prevention and productivity
Data Source
AI summary
Techniques for intelligently, dynamically, and proactively protecting orchestration system control planes and/or their application programming interface (API) servers against Denial of Service (DoS) and other abnormal events, whether intentional or unintentional. The techniques may include determining, based at least in part on metering requests to a control plane associated with an orchestration system for managing containerized microservices applications, a threshold request rate associated with invoking a policy action for preventing a denial-of-service (DoS) event. The techniques may also include determining that a rate in which the requests are received at the control plane meets or exceeds the threshold request rate. Based at least in part on the rate meeting or exceeding the threshold request rate, the policy action may be invoked to prevent the DoS event.