Global Data Service Framework for Database Load Balancing
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Solution Overview
Problem
Managing multiple replicas of data across heterogeneous distributed systems is cumbersome due to differences in hardware, operating systems, and lack of shared storage, leading to inefficient workload management and load balancing.
Innovation Solution
A global data service framework that automatically selects the best database server instances for clients based on advisory messages from the Global Data Service (GDS) framework, considering load, performance, and replication, allowing for proactive and predictive load balancing and failover across diverse database environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If database replicas are maintained across heterogeneous distributed systems, then data availability and business continuity are improved, but workload management complexity increases
Solution Approach 1:
The patent introduces a service layer that acts as an intermediary between clients and distributed database replicas. This service layer abstracts the complexity of managing heterogeneous replicas across different systems, providing a unified interface for workload management while maintaining data availability across the distributed environment.
Solution Approach 2:
The service layer is designed to work with multiple types of database systems and replication configurations simultaneously. It provides universal workload management capabilities that can handle different hardware architectures, operating systems, and database types through a common interface, reducing the complexity of managing diverse distributed systems.
2Speed
If multiple distributed replicas are maintained across different systems, then performance optimization for local clients is improved, but load balancing efficiency deteriorates
Solution Approach 1:
The service layer implements location-aware routing that directs client requests to geographically or network-proximally optimal database replicas. This ensures local clients access nearby replicas for fast response times while the service layer maintains an overview of all replica loads to balance workloads efficiently across the distributed system.
Solution Approach 2:
The system implements monitoring and feedback mechanisms that track the performance and load status of distributed replicas. This feedback information is used by the service layer to dynamically adjust load balancing decisions, optimizing both local client access speed and overall system-wide load distribution efficiency.
3Adaptability or versatility
If heterogeneous database systems are used in distributed environment, then adaptability to different platforms is improved, but service consistency worsens
Solution Approach 1:
The service layer acts as a mediator that translates and standardizes requests and responses between clients and diverse database systems. It maintains service consistency by enforcing common protocols and data formats while allowing the underlying heterogeneous systems to operate with their native capabilities, thus preserving platform adaptability.
Data Source
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AI summary
Techniques are provided to allow users to define a global service that is offered across multiple replicated databases. Database clients connect to and use a global service just as they do with regular services on a single database today. Upon receiving a connection request, a collection of components, referred to collectively as the Global Data Service framework (GDS framework), automatically chooses the best database server instances to which to connect a client. Once those connections have been established, the clients determine which database server instance, of those database server instances to which they are connected, to send requests to based, at least in part, on advisory messages sent to the clients by the GDS framework.