Code Line Allocation with Storage Constraints
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Solution Overview
Problem
In on-demand service environments, efficiently assigning code lines to clusters with storage and other constraints is challenging due to shared infrastructure, varying resource capacities, and limitations in storage and processing power, leading to inefficient resource utilization and complex allocation scenarios.
Innovation Solution
A method that involves determining available and required resource capacities for clusters and code lines, sorting them, and allocating code lines based on their resource demands to optimize resource allocation across clusters, ensuring no cluster exceeds its capacity and allowing for partial allocation of code lines when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If code lines are allocated to clusters in a development environment using the same infrastructure as production, then testing can be performed separately from production, but resource utilization efficiency deteriorates because development environment consumes resources that could be used for production work
Solution Approach 1:
The patent segments the allocation process by dividing code lines into different priority categories (production-related vs. non-production) and allocating them to different clusters or time slots. This allows the system to maintain separate testing capabilities while preserving production resources for their primary function.
Solution Approach 2:
The patent implements dynamic resource allocation where cluster assignments are not fixed but adapt based on production workload conditions. When production resources are available, non-production code lines can utilize them; when production needs resources, the system dynamically reassigns or delays non-production testing to prevent resource conflicts.
2Adaptability or versatility
If clusters have non-uniform storage capacity and processing power, then resource specialization is possible, but allocation complexity increases due to varying constraints across clusters
Solution Approach 1:
The patent applies local quality by matching specific code line characteristics with corresponding cluster capabilities. Code lines with specific resource requirements (storage-intensive or CPU-intensive) are allocated to clusters with matching specialized capacities, optimizing performance while the systematic matching process manages the complexity of non-uniform resources.
Solution Approach 2:
The patent transforms the allocation problem by changing parameters from absolute resource amounts to relative compatibility metrics. Instead of comparing fixed resource quantities, the system evaluates match quality between code line requirements and cluster capabilities, simplifying the allocation decision process across heterogeneous clusters.
3Productivity
If code lines are allocated without considering storage constraints, then allocation speed is faster, but storage capacity constraints are violated leading to resource overutilization
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the storage requirements of each code line and the available capacity of each cluster before allocation begins. This preprocessing step enables the allocation algorithm to make decisions based on pre-evaluated compatibility, maintaining fast allocation speed while ensuring storage constraints are never violated.
Data Source
AI summary
In accordance with disclosed embodiments, there are provided methods, systems, and apparatuses for assigning code lines to clusters with storage and other constraints in an on-demand service environment including, for example, receiving as input, a plurality of code lines for test within a host organization; determining available resource capacity for each of a plurality of clusters within the host organization; determining required resource capacity for each of the plurality of code lines for test within the host organization; sorting the plurality of clusters according to the determined available resource capacity for each; sorting the plurality of code lines according to the determined required resource capacity for each; and allocating the plurality of code lines amongst the plurality of clusters based on the sorting of the plurality of clusters and based further on the sorting of the plurality of code lines. Other related embodiments are disclosed.