Parallel Service Deployment via Task List Parsing
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Solution Overview
Problem
Traditional service deployment in virtualized operator environments is time-consuming and resource-intensive due to sequential execution of task lists, which hampers efficient deployment of multiple services in cloud-based computing clusters.
Innovation Solution
The method involves parsing a service specification to obtain task lists and launching multiple executors to execute these lists in parallel, taking into account dependency relationships between services to determine an optimal execution plan, thereby enabling partial parallel execution of tasks and reducing deployment time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequential execution of task lists is used for service deployment, then deployment order and dependency management are ensured, but deployment time consumption increases
Solution Approach 1:
The patent segments the deployment process by dividing task lists into independent executable units and assigning different executors to handle different service deployments simultaneously. This allows the system to maintain dependency management through structured task organization while enabling parallel execution of independent services, thereby resolving the contradiction between reliable dependency management and deployment time efficiency.
Solution Approach 2:
The patent implements dynamic execution planning that determines the execution order of task lists based on service dependency relationships. The system dynamically adjusts which services can be deployed in parallel versus which must be sequential, optimizing deployment time while ensuring dependencies are respected. This dynamic approach allows the system to adapt the execution strategy based on the specific dependency structure of the services being deployed.
2Productivity
If multiple executors are launched to execute task lists in parallel, then deployment efficiency increases, but system complexity and resource management difficulty increase
Solution Approach 1:
The patent introduces a reconciliation process as an intermediary layer between the deployment specification and multiple executors. This reconciliation process parses the service deployment specification, determines dependency relationships, and coordinates task list execution across multiple executors. By centralizing the coordination logic in the reconciliation process, the system can manage multiple parallel executors without proportionally increasing overall system complexity, as the intermediary handles the complexity of coordinating parallel execution.
3Loss of time
If parallel deployment of services is implemented, then deployment time decreases, but resource utilization and coordination overhead increase
Solution Approach 1:
The patent implements partial parallelization by analyzing service dependency relationships and deploying only those services that can be executed in parallel, while maintaining sequential execution for dependent services. This partial parallel approach optimizes resource utilization by launching executors only when independent tasks are available, avoiding the excessive resource consumption that would result from forcing parallel execution of all services regardless of dependencies. The system performs excessive action only where safe and beneficial, maintaining time efficiency without unnecessary resource overhead.
Data Source
AI summary
Embodiments of the present disclosure relate to deployment of a plurality of services. In an embodiment, a computer-implemented method is disclosed. According to the method, one or more processors receive a deployment event for a plurality of services in a computing cluster. One or more processors perform a reconciliation process in a virtualized operator environment for deployment of the plurality of services. The reconciliation process comprises obtaining, by the one or more processors, a plurality of task lists for the deployment of the plurality of services by parsing a service specification associated with the deployment event, and deploying, by the one or more processors, the plurality of services into the computing cluster by launching a plurality of executors to execute tasks in the plurality of task lists at least partially in parallel. In other embodiments, a system and a computer program product are disclosed.