Abstract
Crowdsourcing system, which utilizes many workers to process computer-complexity tasks, has become an effective platform in today’s online labor markets. In a crowdsourcing system, maximizing the total utility is one key design goal. This goal is extremely hard because a computer-complexity task can be a multi-dimensional large-scale task that contains thousands or millions of atomic tasks. In online situation, we need to consider both the varying service of workers and future unknown task arrivals. As we know, none of the previous work considers a multi-dimensional large-scale task assignment for utility maximization. In this paper, an online framework is proposed to solve this optimization problem by running atomic tasks in parallel on workers. To estimate worker service rates, we consider each varying worker as an arm for a multi-armed bandit in crowdsourcing system. We design the online scheduling algorithm from a bandit perspective by Online Convex Optimization (OCO) techniques. We prove that our designed algorithm can yield a sublinear regret bound. Finally, we show that our designed algorithm is better than the baseline algorithms by nearly 10% for the total utility achieved.
Highlights
Crowdsourcing system has become more and more popular for workers to perform computer-complexity tasks
(multi-dimensional large-scale crowdsourcing task) to maximize total utility, which are assigned to multiple workers and run in parallel [5]
We propose a new online Multi-Armed Bandits (MAB) framework to address multi-dimensional large-scale tasks for maximizing total utility with the variability service of workers
Summary
Crowdsourcing system has become more and more popular for workers to perform computer-complexity tasks. (multi-dimensional large-scale crowdsourcing task) to maximize total utility, which are assigned to multiple workers and run in parallel [5]. In this paper, maximizing total utility is one key design goal, which is a hard optimization problem in an online manner The hardness of this optimization problem comes from four aspects: (1) It is challenging to assign all L-tasks to workers under worker’s capacity constraints and task’s deadline constraints [6] (2) A task assignment decision is an integer programming problem [7]. To maximize the total utility, a practical task assignment plan should meet the following four requirements: online manner task assignments, multi-dimensional tasks, concave utility functions, and the varying service of workers. In [13], the scheme satisfies online manner task assignments and concave utility functions.
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