Automated Service Level Management in Cloud Computing
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Solution Overview
Problem
Cloud computing environments face challenges in efficiently managing application service levels due to varying workload and resource availability, making it difficult to guarantee performance, scalability, and availability while maintaining cost-effectiveness.
Innovation Solution
The implementation of automated service level management using deployment descriptors, which involves receiving service level parameter values, creating deployment descriptors, identifying suitable application servers, and dynamically migrating applications to optimize resource allocation and meet SLA targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated deployment and migration is implemented, then productivity and resource utilization improve, but device complexity and system management complexity increase
Solution Approach 1:
The patent introduces an automation controller as an intermediary component that mediates between the deployment descriptor inputs and the application servers. This controller orchestrates the automated deployment and migration processes, managing the complexity internally while presenting a simplified interface to users, thereby improving productivity without proportionally increasing observable system complexity.
Solution Approach 2:
The system enables self-service automation where the deployment descriptor automatically contains all necessary configuration information, and the automation controller uses this descriptor to autonomously perform deployment decisions and actions without requiring manual intervention. This self-service mechanism improves deployment efficiency while containing complexity within the automated system rather than requiring complex human-operated procedures.
2Reliability
If service level parameters are strictly enforced, then reliability and service quality improve, but adaptability and cost-effectiveness deteriorate
Solution Approach 1:
The patent implements dynamic resource allocation where the automation controller continuously monitors service level parameter compliance and dynamically adjusts application deployment and migration decisions. When service levels are met, resources can be reallocated flexibly; when service levels approach thresholds, the system automatically adjusts to maintain reliability. This dynamic approach allows the system to adapt resource allocation in real-time, maintaining both reliability and flexibility rather than being locked into static configurations.
Solution Approach 2:
The system changes operational parameters dynamically based on current system state. The deployment descriptor contains target service level parameters, and the automation controller adjusts deployment decisions by changing parameters such as application server selection, resource allocation levels, and migration timing. This parameter-based control enables the system to maintain service level guarantees while adapting to varying workloads and resource availability, preventing rigid enforcement that would reduce flexibility.
3Ease of manufacture
If manual infrastructure deployment is used, then device complexity is lower, but productivity and time to market deteriorate
Solution Approach 1:
The patent applies preliminary action by requiring the deployment descriptor to be prepared in advance with all necessary configuration information, service level parameters, and application metadata. This preliminary preparation of the descriptor allows the automation controller to execute rapid automated deployment without requiring complex real-time decision-making or manual configuration during the actual deployment process, thereby increasing deployment speed while keeping the automation logic manageable.
Solution Approach 2:
The system replaces manual mechanical deployment processes with automated computational processes. Instead of manually configuring application servers and deploying applications, the automation controller uses the deployment descriptor to automatically perform these tasks through software-based orchestration. This substitution of manual operations with automated software control significantly increases deployment productivity while the complexity is managed through structured descriptor-based input rather than complex procedural programming.
Data Source
AI summary
Automated service level management of applications can include automated deployment, monitoring, forecasting, and/or predicting based on a plurality of service levels comprising application level, application server platform level, virtual machine level, and/or infrastructure level, and optimizations at multiple levels using a plurality of techniques including automated dynamic application migration. Automated deployment of applications in a cloud computing environment using deployment descriptors comprises receiving values for service level parameters for an application, creating a deployment descriptor based on the parameters, identifying application servers that satisfy the deployment descriptors, and deploying the application to the identified application servers. Automated dynamic migration of applications in a cloud computing environment comprises deciding to migrate an application, obtaining application resource requirements, identifying application server candidates, selecting an application server from one of the candidates, and migrating the application.