Dynamic Workload Dispatching for Cloud Identity Management
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Solution Overview
Problem
Current identity management systems face challenges in providing secure and unified access across diverse cloud environments, including unauthorized access, inconsistent security between on-premise and cloud environments, and inadequate security measures for non-employee users like customers and partners.
Innovation Solution
A cloud-scale Identity and Access Management (IAM) platform based on a multi-tenant, microservices architecture that dynamically dispatches workloads across heterogeneous cloud services, ensuring sufficient quotas and processing loads are met before execution, and provides secure access through identity cloud services that integrate with both public and private clouds, supporting hybrid deployments and various user types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If the system executes transactions using metered cloud services, then cost control is improved, but transaction execution may be delayed when quotas are insufficient
Solution Approach 1:
The system dynamically adjusts workload dispatching based on real-time quota availability of metered cloud services. When quotas are sufficient, transactions are executed immediately to optimize cost control. When quotas are insufficient, the system dynamically switches to alternative execution strategies or delays non-critical transactions, thereby balancing cost control with transaction execution timeliness.
Solution Approach 2:
The system performs preliminary checks on metered cloud service quotas before dispatching transactions. By anticipating quota availability and pre-planning workload execution schedules, the system可以避免unexpected execution delays while maintaining cost control through informed decision-making about when to execute transactions.
2Reliability
If the system monitors and waits for sufficient cloud service quotas, then service quality is improved, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism that continuously monitors cloud service quota availability and adjusts workload dispatching decisions accordingly. This feedback loop ensures that transactions are only executed when sufficient quotas are available, maintaining high service quality. The feedback mechanism is implemented through standardized API calls and decision logic that, while adding some complexity, provide automated and reliable quota management.
Solution Approach 2:
The system employs a universal workload dispatching framework that can handle multiple cloud services (metered and non-metered) through a common monitoring and decision-making mechanism. This multi-functional approach consolidates quota management across different service types into a single system, preventing exponential growth in complexity while maintaining comprehensive service quality control.
3Adaptability or versatility
If the system dynamically dispatches workloads across heterogeneous services, then adaptability is improved, but processing time increases
Solution Approach 1:
The system segments workloads into different categories (e.g., critical vs. non-critical transactions) and assigns them to different cloud services based on their requirements. This segmentation allows the system to maintain high adaptability by routing different workload types to appropriate services while minimizing processing time for critical transactions that require immediate execution regardless of quota availability.
Solution Approach 2:
The system changes operational parameters such as quota thresholds, service selection criteria, and execution timing based on workload characteristics and current cloud service status. By dynamically adjusting these parameters, the system optimizes the balance between adaptability and processing speed, ensuring that workload dispatching remains flexible while avoiding unnecessary delays.
Data Source
AI summary
A system for executing a workload that includes a plurality of transactions for a first time slot determines whether a metered cloud service has a sufficient quota of operations available to execute respective metered transactions. For the first time slot, the system determines whether a non-metered cloud service has a sufficient processing load to execute respective non-metered transactions. The system executes the plurality of transactions during the first time slot when each metered cloud service has the sufficient quota and each non-metered cloud service has the sufficient processing load. Further, the system waits to execute the plurality of transactions of the workload during a time slot subsequent to the first time slot when any of the metered cloud services does not have the sufficient quota or any of the non-metered cloud services does not have a sufficient processing load.