Distributed Service Framework with Peer Entity Load Balancing
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Solution Overview
Problem
Existing distributed service frameworks face challenges in achieving high performance, scalability, and robustness in executing services across a distributed system, particularly in managing workload and resource allocation effectively.
Innovation Solution
A distributed service framework that utilizes peer entities with worker modules to process tasks represented as tuples, employing tuple handling and load handling modules to dynamically allocate and deallocate resources based on workload thresholds and availability, ensuring efficient task routing and load balancing without centralized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If dynamic resource allocation and deallocation is implemented based on workload thresholds, then system scalability and adaptability are improved, but device complexity increases due to additional management overhead
Solution Approach 1:
The system implements self-service through autonomous peer entities that independently monitor their own workloads and make decisions about resource allocation and deallocation. Each peer entity uses local thresholds and forwarding tables to automatically manage resources without requiring centralized control, thereby achieving adaptability while limiting the complexity increase to local decision-making logic rather than system-wide management overhead.
2Reliability
If centralized control is used for resource management, then system coordination is simplified, but system robustness decreases due to single point of failure
Solution Approach 1:
The system segments centralized control into distributed peer entities, each independently managing local resources. This segmentation eliminates the single point of failure characteristic of centralized control while maintaining coordination through shared forwarding tables and threshold-based decision logic. The segmentation principle directly addresses robustness by ensuring that failures in one peer entity do not propagate system-wide.
3Productivity
If static resource allocation is used, then system simplicity is maintained, but system performance deteriorates under varying workload conditions
Solution Approach 1:
The system transitions from static to dynamic resource allocation by implementing peer entities that can add, remove, and reallocate resources based on real-time workload monitoring. Each peer entity dynamically adjusts its forwarding table and resource distribution according to current conditions, enabling performance optimization under varying workloads. The dynamics principle is implemented through continuous monitoring loops and conditional allocation decisions that respond to workload changes.
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AI summary
A system for executing services in a distributed way, the execution of each service involving the processing of a plurality of tasks of predetermined types, each task type to be processed being represented by a corresponding type of tuples of data relating to the task type, wherein: the system comprises a plurality of peer entities; each peer entity is adapted to store and execute a plurality of worker modules, each worker module being adapted to process at least one task type; each peer entity comprises tuple handling modules that, for a tuple of a type T, are adapted: a) to check if at least one of the plurality of the worker modules comprised in the peer entity is adapted to process the task represented by said tuple, b) in the positive case of a), to take in charge the processing of the task represented by said tuple, c) in the negative case of a), to execute a forwarding procedure for forwarding said tuple to another peer entity of the plurality of peer entities of the system.