Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, unpredictability and instability in service capacity often incur operational and infrastr...
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Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, unpredictability and instability in service capacity often incur operational and infrastructural costs. In this paper, we seek to characterize optimal distributedalgorithms that maximize the predictability, stability, or both when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes both the stationary mean and variance of the service capacity subject to strict demand and deadline requirements. For more general settings, we characterize the minimal-variance distributed policies with soft demand requirements, soft deadline requirements, or both. The performance of the optimal distributed policies is compared with that of the optimal centralized policy by deriving closed-form bounds and by testing centralized and distributedalgorithms using real data from the Caltech electrical vehicle charging facility and many pieces of synthetic data from different arrival distributions. Moreover, we derive the Pareto-optimality condition for distributed policies that balance the variance and mean square of the service capacity. Finally, we discuss a scalable partially centralized algorithm that uses centralized information to boost performance and a method to deal with missing information on service requirements.
Driven by ever-increasing vehicular intelligent computation-intensive and delay-sensitive services, this paper in-vestigates the computing offloading in unmanned aerial vehicle (UAV)-assisted vehicular networks. Due t...
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Driven by ever-increasing vehicular intelligent computation-intensive and delay-sensitive services, this paper in-vestigates the computing offloading in unmanned aerial vehicle (UAV)-assisted vehicular networks. Due to the limited onboard energy and computational resources of the mobile entities (i.e., the vehicles and the UAV), it is significant to explore the collab-orative computation among the vehicles, the UAV, and the ter-restrial computing servers for improving energy efficiency (EE) while trading off the service delay. Unlike existing work in the literature that is based on offline settings with a global view, an onlinedistributed mechanism is proposed to cope with the spatial and temporal variations of the networks. Specifically, upon the arriving tasks and the real-time channel conditions, mobile entities adaptively decide about the task offloading and computational resources allocation in parallel. Moreover, the UAV also designs its trajectory with the residual battery capacity taken into account. Theoretical analysis shows that the developed approach can achieve the EE-delay tradeoff as [O(1/V ), O (V )] with V being a control parameter, and can strike a flexible balance between them by tuning V. Numerical results verify the theoretical analysis and reveal that the performance gain can be obtained over conventional methods in the EE performance.
In this brief, we study the problem of onlinedistributed seeking for first-order Nash equilibria of nonconvex noncooperative games with multiple clusters. In this game, each cluster is composed of multiple players, w...
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In this brief, we study the problem of onlinedistributed seeking for first-order Nash equilibria of nonconvex noncooperative games with multiple clusters. In this game, each cluster is composed of multiple players, whose goals are to cooperatively minimize the sum of time-varying cost functions in their own cluster. Each player can only have access to its own cost function and action set, and can only communicate with its immediate neighbors in the same cluster through a time-varying graph. Moreover, cost functions cannot be known by players in advance. Unlike existing studies on noncooperative games, the cost function we consider is nonconvex. To address this challenge, an onlinedistributed dual averaging algorithm is proposed. Interestingly enough, the performance of the algorithm is measured by regrets involving the first-order Nash equilibrium condition, and the sublinearity of the regrets is achieved under the proposed algorithm. The validity of the results is illustrated by a numerical example.
The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interfe...
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The deployment of large-scale UAV clusters for cluster advantages will lead to high competition and excessive congestion of spectrum resources, which results in mutual interference. This paper investigates the interference-aware online spectrum access problem for multicluster Flying Ad-Hoc Network (FANET) under different network topologies, i.e., the locations of UAV clusters are varying. First, the problem is formulated as a data-assisted multistage channel access game with the goal of mitigating interference of all UAV clusters and decreasing channel switching cost during each slot. As we prove that the game is an exact potential game that guarantees at least one pure-strategy Nash equilibrium and interference-aware online channel preserving based concurrent best response (IOCPCBR) algorithm, an online distributed algorithm is proposed to achieve the desirable solution. Finally, the simulation results demonstrate the validity and effectiveness of the multistage channel access game as well as IOCPCBR algorithm.
The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compa...
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The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users' arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs' supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users' arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.
With increasing the presence of co- and tri-generating units, energy hub operators are encouraged to optimally schedule the available energy resources in an economic way. This scheduling needs to be run in an online m...
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With increasing the presence of co- and tri-generating units, energy hub operators are encouraged to optimally schedule the available energy resources in an economic way. This scheduling needs to be run in an online manner due to the uncertainties in energy prices and demands. In this paper, the real-time scheduling problem of energy hubs is formulated in a dynamic pricing market. The energy hubs interaction is modeled as an exact potential game to optimize each energy hub's payments to the electricity and gas utilities, as well as the customers' satisfaction from energy consumption. The potential game approach enables us to study the existence and uniqueness of the Nash equilibrium and to design an online distributed algorithm to achieve that equilibrium. Simulations results show that the proposed algorithm can increase the energy hubs' average payoff by 18.8%. Furthermore, energy service companies can improve the technical performance of energy networks by reducing the peak-to-average ratio in the electricity and natural gas by 27% and 7%, respectively. When compared with a centralized approach with the objective of social welfare, the proposed algorithm has a significantly lower running time at the cost of lower social welfare.
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