Request Token Encoding for Distributed Load Scheduling
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Solution Overview
Problem
In distributed computing systems, managing load-shedding when resources are overloaded is challenging, as existing methods do not effectively leverage information about past requests to optimize scheduling, leading to undesirable scenarios like throttling existing jobs or impairing customer experience.
Innovation Solution
The system uses a lightweight, distributed approach by encoding prior request data into a token that is passed with subsequent requests, allowing for intelligent scheduling decisions based on recent user behavior, such as request frequency and resource consumption, to prioritize requests and avoid overloading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional scheduling methods are used in distributed computing systems, then system simplicity is maintained, but resource allocation fairness deteriorates during high load periods
Solution Approach 1:
The system performs preliminary actions by encoding prior request performance information into tokens that are included with subsequent requests. This allows the scheduling system to make informed decisions about resource allocation fairness during high load periods without requiring complex real-time analysis, as the performance history is pre-packaged in the token.
Solution Approach 2:
The patent introduces a token as an intermediary that carries performance information between requests. This token acts as a mediator that conveys historical performance data to the scheduling system, enabling fair resource allocation without direct complex communication between components.
2Measurement precision
If prior request information is collected and analyzed centrally, then scheduling accuracy improves, but system latency increases
Solution Approach 1:
The patent extracts performance information from prior requests and encapsulates it in a token that travels with subsequent requests. This extraction approach allows scheduling decisions to be made using pre-collected information without requiring centralized real-time analysis, thereby maintaining accuracy while reducing latency.
Solution Approach 2:
Each request carries its own performance history token, enabling self-service scheduling decisions at the request level. This eliminates the need for centralized collection and analysis of performance data, as each request brings its own historical context for immediate scheduling decisions.
3Ease of operation
If existing jobs are prioritized during high load, then customer experience improves, but system throughput decreases
Solution Approach 1:
The scheduling system dynamically adjusts priority based on the performance information contained in tokens from prior requests. During high load periods, the system can dynamically favor existing jobs with established performance history while still processing new requests, creating a dynamic balance between customer experience and system throughput.
Data Source
AI summary
A network-based service may schedule requests received from clients according to information regarding prior requests from the client included the requests. When a request is received at a network-based service, information regarding the request, and possibly other prior requests, may be included in response for the request sent to the client. The client may then include that information in a subsequent request to the network-based service. An evaluation of the information may determine a scheduling decision for the request and the request may then be processed according to the scheduling decision.