Dependency-Aware Dataflow Server Queuing
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Solution Overview
Problem
Existing dataflow computing systems struggle to efficiently process work requests due to a lack of awareness about dependencies among work requests, leading to inefficient serialization and underutilization of resources in virtualized and cloud environments.
Innovation Solution
A dependency-aware (DA) reconfigurable dataflow system (RDS) that includes a DA server capable of processing work requests concurrently based on identified dependencies. The DA server interacts with work requesters to receive and record dependencies, and schedules work requests for execution by RDS resources, optimizing parallelization and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If work requests are processed in a traditional dataflow system without dependency awareness, then the system structure remains simple, but resource utilization is underutilized and processing efficiency deteriorates due to unnecessary serialization
Solution Approach 1:
The system segments work requests into different categories based on their dependency characteristics (independent requests, requests with output dependencies, requests with input dependencies). This segmentation allows the DA server to apply different processing strategies to different segments, enabling parallel processing of independent requests while maintaining proper ordering for dependent requests, thus improving productivity without overwhelming system complexity
Solution Approach 2:
The DA server acts as an intermediary between work requesters and RDS resources. It receives work requests, analyzes their dependency relationships, and makes intelligent scheduling decisions. This intermediary layer adds complexity but enables the system to optimize resource utilization and processing efficiency by coordinating request execution based on dependency awareness
2Productivity
If the system processes all work requests concurrently without considering dependencies, then resource utilization improves, but processing correctness deteriorates due to violation of dependency constraints
Solution Approach 1:
The system dynamically adjusts the degree of parallelization based on dependency analysis. For work requests with no dependencies or only output dependencies, the system allows full concurrent execution to maximize resource utilization. For requests with input dependencies, the system dynamically determines execution timing to maintain correctness. This dynamic approach balances productivity and reliability adaptively
Solution Approach 2:
The system applies different quality levels of dependency handling to different work requests based on their specific needs. Independent requests receive immediate processing with highest priority, requests with output dependencies are processed after their prerequisites complete, and requests with input dependencies are processed only when their required outputs are available. This localized quality approach ensures correctness where needed while maximizing parallelization where possible
3Reliability
If the system serializes all work request processing to ensure correctness, then processing reliability is maintained, but productivity deteriorates due to loss of parallelization opportunities
Solution Approach 1:
The system applies partial parallelization rather than full serialization or full parallelization. It processes work requests in parallel to the extent that dependency constraints allow, while maintaining serial processing only where strictly necessary to ensure correctness. This partial action approach recovers much of the lost productivity from full serialization without compromising reliability
Data Source
AI summary
Techniques and systems disclosed herein may relate to a server processing work requests of a work requester. For example, the work requester may generate a plurality of work requests comprising functions of an application to be executed by a dependency-aware (DA) server, determine processing dependencies associated with the work requests, communicate, to the DA server, the processing dependencies, and enqueue the work requests on a dependent queue and a conditioned dependent queue based on processing dependencies associated with the DA server. The DA server may then select one of the work requests from the dependent queue or the conditioned dependent queue for processing and processing the selected work request.