Task Mapping via Precondition Exclusivity
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Solution Overview
Problem
Existing task mapping techniques in distributed systems rely on workload estimates, which can be inaccurate and inefficient, leading to suboptimal allocation of computational tasks to resources.
Innovation Solution
A method that determines the mutual exclusivity of task preconditions to map tasks to the same or different computational locations, reducing overhead by co-locating mutually exclusive tasks and optimizing resource allocation without relying on workload estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tasks are assigned to computational locations using traditional workload estimates, then task allocation can be performed, but the allocation is inaccurate and inefficient due to unreliable workload predictions
Solution Approach 1:
The patent extracts the workload estimation component from the task allocation process and replaces it with precondition-based mutual exclusivity analysis. By removing the unreliable workload estimate dependency, the system achieves accurate task allocation based solely on precondition compatibility between tasks and computational locations.
Solution Approach 2:
The patent introduces precondition mutual exclusivity analysis as an intermediary mechanism between tasks and computational locations. Instead of directly using workload estimates for allocation, the system evaluates whether task preconditions are mutually exclusive with location constraints, providing a more reliable basis for allocation decisions.
2Reliability
If tasks are mapped to different computational locations to handle potential conflicts, then task execution isolation is improved, but overhead increases due to frequent state save and restore operations
Solution Approach 1:
The patent performs preliminary analysis of precondition mutual exclusivity before task allocation. By determining in advance whether tasks have conflicting preconditions, the system can proactively assign compatible tasks to the same computational location, avoiding unnecessary state save and restore operations while maintaining execution isolation where needed.
Solution Approach 2:
The patent changes the allocation decision parameter from workload estimates to precondition mutual exclusivity status. This parameter change enables the system to identify tasks that can safely share computational locations without requiring frequent state saves and restores, thereby reducing overhead while maintaining reliability.
3Productivity
If computational resources are allocated to maximize task parallelism, then throughput is improved, but resource utilization decreases when tasks have conflicting preconditions
Solution Approach 1:
The patent applies local quality by allowing different computational locations to have different task assignments based on their specific precondition compatibility profiles. Tasks with compatible preconditions are localized to the same computational location to improve resource utilization, while tasks with conflicting preconditions are assigned to different locations to maintain throughput.
Data Source
AI summary
Computational tasks are mapped with computational locations in a distributed system such as a cloud computing environment. Mapping does not rely on workload estimates. Instead, tasks whose prerequisite tasks or other preconditions are determined to be mutually exclusive are co-located, while other tasks are mapped to different locations than one another. Locations are servers, processor cores, virtual machines, applications, or computational processes, for example. Mutual exclusivity may be determined by detecting that preconditions require different values of a shared variable in order to be satisfied, for example, or determining that preconditions correspond to different branches of a conditional programming statement. A satisfiability engine may also provide a satisfiability determination. Co-located tasks may also be batched, for improved execution performance. Co-location based on mutual exclusivity may result in fewer operations to save and restore task state, fewer cache misses, greater co-allocation of computational resources by the tasks, and easier debugging.