Cognitive Containers for Self-Managed Cloud Services
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Solution Overview
Problem
The increasing complexity of managing services and security in virtualized and cloud-based environments, particularly due to the lack of separation between control and data paths in computing networks, leads to vulnerabilities and scalability issues in resource management across multiple datacenters and clouds.
Innovation Solution
The introduction of a cognitive container model that provides self-awareness and self-management through a meta-model capturing application intent, using parallel Turing machines and a signaling overlay network for dynamic fault, configuration, accounting, performance, and security management, decoupling service management from underlying resource management systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If layers of orchestration and management are added to manage distributed systems and applications, then service management capability is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service through cognitive containers that autonomously manage their own execution contexts, resource requirements, and state transitions. The computational elements themselves perform management functions rather than requiring external orchestration layers, thereby improving service management capability while reducing system complexity.
Solution Approach 2:
The patent segments the monolithic management system into distributed cognitive containers, each independently managing its own computational context. This segmentation eliminates the need for centralized orchestration layers while maintaining comprehensive service management capability across the distributed system.
2Reliability
If virtual machines are used to improve resiliency and provide live migration capabilities, then service reliability is improved, but management burden increases
Solution Approach 1:
Cognitive containers implement self-service by autonomously managing their own state, resource allocation, and migration capabilities. Each container independently tracks its execution context and can self-manage relocation across computational elements without requiring complex external orchestration, thereby maintaining reliability while reducing management burden.
Solution Approach 2:
The patent employs dynamic cognitive containers that can adapt their execution context and resource requirements in real-time. This dynamic nature enables flexible live migration and load balancing operations that maintain service reliability while simplifying management through automated, context-aware decisions rather than static configurations.
3Reliability
If server-centric operating system security with network-centric security is implemented, then security coverage is improved, but resource sharing complexity increases
Solution Approach 1:
The patent merges server-centric and network-centric security models into a unified cognitive container security framework. The container security model integrates both computational and network security concerns into a single coherent approach, providing comprehensive security coverage while eliminating the complexity of managing separate security systems and their resource sharing interactions.
4Ease of operation
If more resource administration and operational controls are introduced to manage increasing datacenter complexity, then control capability is improved, but system scalability deteriorates
Solution Approach 1:
Cognitive containers implement self-service by autonomously managing their own resource requirements and operational parameters. This self-management capability provides comprehensive control over computational resources while maintaining scalability, as each container independently adapts to changing conditions without requiring additional centralized control mechanisms.
Solution Approach 2:
The patent creates universal cognitive containers that can execute any computational element with any resource profile. This multi-functionality provides comprehensive operational control while maintaining system scalability, as the same container infrastructure can accommodate diverse workloads without requiring specialized management systems for each type of computation.
Data Source
AI summary
A cognitive container includes a set of managers for monitoring and controlling a computational element based on context, constraints and computing resources available to that computational element. Collectively, the set of managers may be regarded as a service regulator that specifies the algorithm context, constraints, connections, communication abstractions and control commands which are used to monitor and control the algorithm execution at run-time. The computational element is the algorithm executable module that can be loaded and run. The managers may communicate with external agents using a signaling channel that is separate from a data path used by the computational element for inputs and outputs, thereby providing external agents the ability to influence the computation in progress.