Policy-Based Queue Controller for Multi-Tenant Cloud Workload Scheduling
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Solution Overview
Problem
Multi-tenant cloud computing systems face challenges in scheduling and prioritizing workloads due to simultaneous requests from multiple tenants, lacking the ability to handle requests simultaneously and manage incoming requests during maintenance activities effectively.
Innovation Solution
A policy-based queue controller is implemented to analyze work request attributes and apply configured policies, determining the status and scheduling of workloads across systems, work engines, and data centers, ensuring timely and efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a multi-tenant cloud computing system receives simultaneous work requests from multiple tenants, then the system can provide on-demand resources to each tenant, but the system lacks the ability to handle requests simultaneously and determine prioritization
Solution Approach 1:
The patent introduces a queue controller as an intermediary component between incoming work requests and the execution systems. This queue controller receives all work requests, analyzes their attributes against configured policies, determines prioritization status, and schedules requests for execution. The queue controller acts as a mediator that manages the complexity of simultaneous requests from multiple tenants, allowing the system to maintain on-demand resource provision while implementing robust scheduling control.
2Ease of operation
If the system processes work requests without policy-based control, then the processing is simpler, but the system cannot determine if requests can be executed or need to wait during maintenance activities
Solution Approach 1:
The patent implements preliminary action by having the queue controller analyze work request attributes against configured policies before scheduling execution. The controller determines in advance whether a request can be executed immediately or needs to be queued during maintenance activities. This preliminary analysis ensures reliable workload execution by preventing inappropriate requests from being processed during maintenance windows, while maintaining simple request processing through automated policy-based decision making.
3Reliability
If the system implements robust control over workload scheduling and prioritization, then the system can manage requests effectively during maintenance, but the system complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the workload management function into distinct components: the queue controller handles scheduling and prioritization, while individual execution systems handle actual workload processing. The queue controller segments the request analysis process by examining specific attributes (workflow type, systems affected, tenant information, job type) separately and applying policies to each attribute independently. This segmentation reduces overall system complexity by modularizing the robust control mechanisms.
4Reliability
If the system queues work requests for execution based on policy status, then the system can ensure timely processing according to policies, but the request processing time increases
Solution Approach 1:
The patent implements dynamics by making the queue controller adaptable to changing conditions. The controller continuously monitors system state, maintenance activity status, and policy configurations to dynamically adjust request scheduling decisions. When policies allow immediate execution, the controller processes requests quickly; when maintenance activities or policy constraints require queuing, the controller appropriately delays requests. This dynamic approach ensures policy-compliant execution while minimizing unnecessary processing delays.
Data Source
AI summary
Novel tools and techniques for controlling workloads in a multi-tenant environment. Some such tools provide a queue controller that can control workflow processing across systems, work (provisioning engines, computing clusters, and/or physical data centers. In an aspect, a queue controller can determine the status of each work request based on one or more attributes, such as the workflow type, the systems affected by (and/or involved with) the workflow, information about the tenant requesting the workflow, the job type, and/or the like. In another aspect, a queue controller can be policy-based, such that policies can be configured for one or more of these attributes, and the attribute(s) of an individual request can be analyzed against one or more applicable policies to determine the status of the request. Based on this status, the requested work can be scheduled.