Commvault Systems, Inc. (20240296099). SCALABLE JOB MANAGER SERVICE FOR DATA MANAGEMENT-AS-A-SERVICE (DMaaS) simplified abstract
Contents
SCALABLE JOB MANAGER SERVICE FOR DATA MANAGEMENT-AS-A-SERVICE (DMaaS)
Organization Name
Inventor(s)
Hemant Mishra of Englishtown NJ (US)
Amey Vijaykumar Karandikar of Marlboro NJ (US)
Sergio J. Bonilla of Champions Gate FL (US)
SCALABLE JOB MANAGER SERVICE FOR DATA MANAGEMENT-AS-A-SERVICE (DMaaS) - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296099 titled 'SCALABLE JOB MANAGER SERVICE FOR DATA MANAGEMENT-AS-A-SERVICE (DMaaS)
The patent application describes an improved system architecture for scaling deployments of Data Management-as-a-Service (DMAAS) in a cloud computing environment. The system distributes "job manager" functionality to prevent performance bottlenecks that can slow down core data protection operations at scale.
- Job manager functionality is distributed across multiple machines or compute resources, acting as local job managers.
- Components scale horizontally to carry out job management responsibilities for storage management operations such as backup, auxiliary copy, and archive jobs.
- Distributed components operate as local job managers, handling storage management jobs from start to finish without centralized control.
- Local job managers maintain, collect, and store job metadata locally.
Potential Applications: - Cloud computing environments - Data storage management systems - Data protection operations
Problems Solved: - Performance bottlenecks in large-scale data storage management systems - Slowdowns in core data protection operations at scale
Benefits: - Improved scalability of DMAAS deployments - Enhanced performance in storage management operations - Efficient job management across distributed components
Commercial Applications: Title: Enhanced Scalability for Data Management-as-a-Service (DMAAS) in Cloud Environments This technology can be utilized by cloud service providers, data management companies, and organizations with large-scale data storage needs. It can improve the efficiency and scalability of data management operations in cloud environments, leading to better performance and cost-effectiveness.
Prior Art: Prior art related to this technology may include research papers, patents, or industry publications on distributed job management systems, cloud computing architectures, and data storage management solutions.
Frequently Updated Research: Researchers may be exploring ways to further optimize job manager distribution in DMAAS deployments, enhance the fault tolerance of distributed components, or improve the security of job metadata storage in cloud environments.
Questions about the technology: 1. How does the distributed job manager system in DMAAS improve performance and scalability compared to traditional centralized job management systems? 2. What are the key challenges in implementing and maintaining a distributed job manager infrastructure for storage management operations in cloud environments?
Original Abstract Submitted
an improved system architecture for scaling deployments of data management-as-a-service (dmaas) distributes “job manager” functionality so that a large-scale data storage management system may be deployed in a cloud computing environment as dmaas without experiencing performance bottlenecks that slow down core data protection operations at scale. the disclosed solution creates an infrastructure where job manager features run on any number of distinct machines or compute resources that act as local job managers. in the illustrative dmaas, any number of components that scale horizontally (“the distributed components”) carry job management responsibility for storage management operations such as backup jobs, auxiliary copy jobs, archive jobs, etc. these distributed components are configured to perform locally as job managers and to see each storage management job through from beginning to end without centralized control. these distributed components or local job managers also maintain, collect, and locally store job metadata.
