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This paper studies a cloud datacenter (DC) consisting of two types of tasks with different priority levels. While non-priority tasks generally request the use of a single virtual machine (VM), priority tasks may utilize multiple available VMs to accelerate processing. We focus on determining whether to accept or reject non-priority tasks to maximize overall system benefits. By formulating the problem as a stochastic dynamic program, it is verified that the best approach for handling nonpriority tasks adheres to a control-limit framework. Both experimental outcomes and numerical evaluations highlight the efficacy of the proposed method, leading to the identification of the optimal threshold. The key contribution of this paper is the development of a stochastic dynamic program for DC resource management and the explicit derivation of an optimal control-limit policy. Both value iteration and linear programming methods are utilized to solve optimization problems. These results offer essential understanding for assessing the performance of various DC models, optimizing both rewards and resources efficiently. 

In the construction industry, the advent of teleoperation and robotic technologies is revolutionizing traditional recruitment prac-tices, introducing new criteria for identifying qualified workers. This evolution presents significant challenges for employers aiming to recruitworkers who can maximize organizational utility. Although contract theory offers a promising solution to these challenges, its inherent self-disclosure property could inadvertently lead to privacy breaches, such as revealing gender-related information. Such disclosure risk mightintensify existing biases, notably gender bias, within the sector. To this end, we proposed deep reinforcement learning (DRL)-based contracttheory. Firstly, the trained DRL model will produce unpredictable contract bundles, restricting employers’ access to workers’ privacy. Sub-sequently, to ensure employers adopt DRL-based contract theory, we utilized blockchain to supervise contract bundle generation. Finally,given that the DRL models are homogenous among employers, we integrated transfer learning to reduce unnecessary overhead. Simulationexperiments conducted using US labor force statistical data demonstrated that our work can effectively mitigate potential gender bias byaugmenting the contract selection rights for female workers from 72.73% and 60% to 96.97% and 95% in comparison with traditionalcontract theory while maximizing employers’ utility. In addition, with the integration of transfer learning, the training overhead ofDRL-based contract theory can decrease by 50%. The meaning and significance of the results lie in the innovative integration of contracttheory, deep reinforcement learning, and transfer learning into the recruitment framework, significantly advancing the body of knowledge inunbiased workforce development. DOI: 10.1061/JCEMD4.COENG-15330. © 2024 American Society of Civil Engineers. 

Cloud computing (CC), often necessitates dynamic adjustments due to its inherently fluid nature. In this paper, we introduce a novel dynamic task scheduling model that incorporates reward and holding cost considerations, leveraging the Continuous-Time Markov Decision Process (CTMDP) framework in heterogeneous CC systems. The primary goal of this model is to maximize the overall system reward for the Cloud Service Provider. By solving the Bellman Optimality Equation using the value-iteration method, we can derive an optimal scheduling policy for the dynamic task scheduling model. Additionally, to enhance its practicality in real-world scenarios, we incorporate a model-free reinforcement learning algorithm to obtain the optimal policy for our proposed model without requiring explicit knowledge of the system environment. Simulation results demonstrate that our proposed model outperforms two common static scheduling methods. 

This paper investigates a hybrid service system with a cloud server and an in-house server. We consider two different scenarios: a hybrid service system with orbit space and a hybrid service system without orbit space. In the hybrid service system with orbit space, customers who fail to enter the cloud server can choose to join the in-house subsystem or to enter an orbit space and retry the cloud server. An admission control mechanism based on queue-length limitation is adopted to adjust whether the cloud service resources are open to customers. When the cloud server cannot be accessed immediately, some customers send their jobs to the in-house subsystem, while others (called opportunists) try to send their jobs to the cloud server again. We obtain the optimal queue-length limitation for a given retrial rate. The service provider and customers are different stakeholders, and their market forces are also different. Therefore, it is more realistic to explore the game relationship between them by using dynamic game theory. We can also explore the joint optimums of the queue-length limitation and the retrial rate in the framework of the Stackelberg game. Finally, by comparing with the hybrid service system without orbit space, we discuss the significance of the existence of orbit space, and gain management insights. It is found that the existence of opportunists may benefit the service provider, although they significantly harm social interests, regardless of whether they are cooperative or non-cooperative; therefore, opportunists are encouraged in some situations. Numerical analysis shows that adding a retrial orbit to a hybrid cloud service system with certain input parameters may even more than triple the service provider’s revenue.