Embedded Persistent Queue for Distributed Task Balancing
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Solution Overview
Problem
Existing distributed application management systems fail to effectively balance tasks across nodes in data centers, leading to potential imbalances and inefficiencies due to the lack of consideration for executing or executed tasks within the datacenter.
Innovation Solution
A management service utilizing an embedded persistent queue framework that optimizes task distribution by monitoring node resource data and task execution data, employing scheduling rules and algorithms to dynamically adjust task assignments and capacities based on actual resource usage, ensuring balanced workload distribution across nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If tasks are distributed by dividing sets of hardware or domain entities within a management plane, then task distribution is simplified, but task balance across the datacenter becomes unbalanced and sensitive to perturbations in cluster membership
Solution Approach 1:
The patent implements feedback mechanisms where the management service continuously monitors task execution data and node resource data, then adjusts task distribution decisions based on this feedback. This closed-loop control system observes actual task completion and resource usage patterns, dynamically adapting to maintain balanced task distribution across nodes despite cluster membership changes or perturbations.
Solution Approach 2:
The system enables nodes to self-regulate their task loads by monitoring their own resource usage and task execution performance. Each node can independently assess its capacity and adjust its task acceptance behavior, allowing the distributed system to self-balance without centralized micromanagement, thereby improving both simplicity and stability.
2Ease of manufacture
If existing methods of task distribution are used, then implementation is straightforward, but other tasks for distributed applications executing or have executed within the datacenter are not considered
Solution Approach 1:
The management service performs multiple functions: it acts as a task distributor, a performance monitor, a resource analyzer, and an optimization engine all in one system. By integrating these functions, the service can consider multiple task types and execution contexts simultaneously, adapting task distribution decisions to account for diverse workloads while maintaining a unified implementation approach.
Solution Approach 2:
The system collects and analyzes task execution data and node resource data in advance before making task distribution decisions. By preliminarily gathering information about current task loads, resource availability, and historical performance, the management service can make informed distribution decisions that optimize overall system efficiency rather than reacting to immediate demands alone.
3Productivity
If dynamic task distribution optimization is implemented, then task balance and data center efficiency improve, but system complexity increases due to monitoring and adjustment mechanisms
Solution Approach 1:
The patent combines task distribution management, performance monitoring, resource analysis, and optimization functions into a single integrated management service. By merging these previously separate functions, the system reduces overall architectural complexity while maintaining the capabilities needed for dynamic optimization, as the unified service can share data structures and processing logic across all functions.
Data Source
AI summary
Various aspects are disclosed for distributed application management using an embedded persistent queue framework. In some aspects, task execution data is monitored from a plurality of task execution engines. A task request is identified. The task request can include a task and a Boolean predicate for task assignment. The task is assigned to a task execution engine embedded in a distributed application process if the Boolean predicate is true, and a capacity of the task execution engine is sufficient to execute the task. The task is enqueued in a persistent queue. The task is retrieved from the persistent queue and executed.