Summary In postdisaster rescue scenario, unmanned aerial vehicles (UAVs) are effective tools to help ground users in disaster areas to transmit rescue‐critical data to the relief center in time due to their flexible mobilities and fast deployment. However, how UAVs choose disaster areas to take part in traffic offloading with limited bandwidth resources is challenging. This article investigates UAVs' access selection and disaster areas' bandwidth allocation scheme by jointly optimizing network throughput, bandwidth and UAVs' energy cost. The UAVs form coalitions to participate in traffic offloading cooperatively, where each UAV selects a disaster area independently. Specifically, the bandwidth resources are dynamically allocated to all disaster areas when a new coalition partition forms. The UAVs adjust their access choices for disaster areas when the bandwidth allocation changes. When no UAVs migrate to other disaster areas, the network achieves a stable state. The UAVs fly to the final selected disaster area and provide traffic offloading services. To resolve traffic offloading competitions yet enable cooperations among UAVs, we address the UAVs' access selection issue by coalition formation game. A gradient projection method is then proposed to allocate bandwidth resources which maximizes the benefit of the network. We demonstrate that the UAVs' access selection and disaster areas' bandwidth allocation algorithms are convergent. The simulation results demonstrate that our proposed cooperation order is better than the pareto and selfish orders, thereby increasing the benefit of the network.

The energy harvesting of unmanned aerial vehicle (UAV) has been researched extensively in recent years. However, the existing energy harvesting between the base station and UAVs does not consider the information asymmetry factor, which means the base station provides the radio frequency (RF) energy for UAVs in the context of UAVs’ partial private information. In order to maximize the base station’s utility or payoff, it is crucial for the base station to motivate more UAVs to harvest RF energy. In the paper, we propose an effective incentive energy harvesting mechanism in UAV communication network, which is a challenging problem since there exist interest conflicts that the base station and UAVs are rational individuals who maximize their utilities. Our objective is to make the base station’s utility maximum via balancing the tradeoff between transmit power cost and charged price benefit, while incentivizing UAVs to purchase transmit power. We design a series of optimal energy harvesting contract with different price discounts targeting different types of UAVs by contract theory. Owing to information asymmetry, we analyze two different information scenarios: complete and incomplete information. We suppose the base station knows each UAV’s type in complete information, then we analyze the practical case that the base station is aware of incomplete information of UAV’s private information. The base station aims to maximize its utility by providing contract. The UAVs choose the contract meeting the individual rationality (IR) and incentive compatibility (IC) rules while maximizing their utilities. Our simulation shows that the energy harvesting mechanism maximizes the base station’s utility and stimulates UAVs to purchase RF energy transmit power in different scenarios. Compared with other methods, our proposed optimal contract can improve the utility of the base station while maximizing the utility of UAVs.

Linux Control Groups, i.e., cgroups, are the key building blocks to enable operating-system-level containerization. The cgroups mechanism partitions processes into hierarchical groups and applies different controllers to manage system resources, including CPU, memory, block I/O, etc. Newly spawned child processes automatically copy cgroups attributes from their parents to enforce resource control. Unfortunately, inherited cgroups confinement via process creation does not always guarantee consistent and fair resource accounting. In this paper, we devise a set of exploiting strategies to generate out-of-band</>workloads via de-associating processes from their original process groups. The system resources consumed by such workloads will not be charged to the appropriate cgroups. To further demonstrate the feasibility, we present five case studies within Docker containers to demonstrate how to break the resource rein of cgroups in realistic scenarios. Even worse, by exploiting those cgroups' insufficiencies in a multi-tenant container environment, an adversarial container is able to greatly amplify the amount of consumed resources, significantly slow-down other containers on the same host, and gain extra unfair advantages on the system resources. We conduct extensive experiments on both a local testbed and an Amazon EC2 cloud dedicated server. The experimental results demonstrate that a container can consume system resources (e.g., CPU) as much as $200\times$ of its limit, and reduce both computing and I/O performance of particular workloads in other co-resident containers by 95%.

Network virtualization provides a promising tool to allow multiple virtual networks (VNs) to run on a shared substrate network (SN) simultaneously. VN embedding (VNE) is one of the key technologies of network virtualization. The main goal of VNE is to effectively map VN requests to the SN, which is efficiently utilizes the network resources. The emergence of software defined networks provides a platform for network virtualization to be used and promoted. In a real environment, the resource requirements of tenants are generally different. A single VN mapping algorithm can not effectively handle the multi-demand problem of tenants. We propose a self-adaptive VNE algorithm. VN requests are divided into different types by an adaptive algorithm, we use an integer linear programming formulation to solve VNE problem. This paper considers three different types of VN requests. Type 1 VN requests for high bandwidth requirements, type 2 VN requests for low latency requirements, and type 3 VN requests for high bandwidth requirements and latency requirements. The simulation results show that the virtual network embedding algorithm proposed in this paper can make full use of the SN resources and improve the overall revenue, while effectively dealing with the multi-demand problem of tenants.