With the increasing attraction of unmanned aerial vehicles (UAVs) in civil, public, and military areas, multi-UAV-base applications are foreseen as an important trend for edge computing scenarios. However, due to UAVs’ limited energy supplies and their continuous increase in the number of sensors, energy efficiency is a critical issue in multi-UAV systems. We believe that the fusion of edge computing and cloud computing can provide effective support for energy savings. This paper presents an energy-efficient edge cloud architecture called RESERVE for intelligent multi-UAV. Under RESERVE, we study the energy-efficient computation offloading decision-making problem in a decentralized manner. The problem is formulated as a three-layer game in which the discretionary approach to reaching Nash Equilibrium is presented. Based on the proposed game, we design decentralized algorithms for two different cases. The algorithms can both achieve Nash Equilibrium. Furthermore, we propose a decentralized computation offloading mechanism and analyze the performance of the game by its efficiency ratio. Evaluation results demonstrate that the proposed game methods achieve more than 30% extra energy consumption reduction compared with the state-of-the-art decentralized algorithm and less than 10% performance loss relative to the centralized solution. The prototype framework we have developed proves the concept we propose.

Currently, the virtualization technologies for cloud computing infrastructures supporting extra devices, such as GPU, require additional development and refinement. This requirement is particularly evident in the area of resource sharing and allocation under some performance constraints, like the quality of service (QoS) guarantee, in light of the closed GPU platform. This deficiency significantly limits the applicability range of the cloud platform, which aims to support the efficient and fluent execution of business and academic workloads. This paper introduces gQoS, an adaptive virtualized GPU resource capacity sharing system under the QoS target, which can share and allocate the virtualized GPU resource among workloads adaptively, guaranteeing the QoS level with stability and accuracy. We evaluate the workloads and compare our gQoS strategy with other allocation strategies. The experiments show that our strategy guarantees much better accuracy and stability in QoS control and that the total GPU resource utilization under gQoS can be rewarded with at most a 25.85 percent reduction compared with other strategies.

Content service is a type of Internet cloud service that provides end-users plentiful contents. To ensure high performance for content delivering, content service utilizes a technology known as content multihoming: contents are generated from multiple geographically distributed data centers and delivered by multiple distributed content distribution networks (CDNs). The electricity costs for data centers and the usage costs for CDNs are major contributors to the contents service cost. As electricity prices vary across data centers and usage costs vary across CDNs, scheduling data centers and CDNs has a tremendous consequence for optimizing content service cost. In this paper, we propose a novel framework named Cost Optimization for Internet Content Multihoming (COMIC). COMIC dynamically balances end-users' loads among data centers and CDNs so as to minimize the content service cost. Using real-life electricity prices and CDN traces, the experiments demonstrate that COMIC effectively reduces the content service cost by more than 20 percent.

System-level mixed-criticality design aims at reducing production cost and enhancing resource efficiency. This paper studies the technology of integrating mixed-criticality avionics traffics for Avionics Full-Duplex Switched Ethernet (AFDX) network, which can transmit both critical and non-critical traffics. These two traffics have different QoS requirements, such as low latency for critical traffics and high bandwidth for non-critical traffics. We use system-level compositional scheduling to integrate mixed-criticality traffics into one network to enhance the scalability of AFDX network. In the architecture of the proposed compositional scheduling, critical traffics are scheduled by bandwidth allocation gap-based scheduler, and non-critical traffics by Round Robin manner. To estimate the delay bound meeting requirements of applications, end-to-end delay for both critical and non-critical traffics are analyzed by using network calculus. Finally, a true time-based simulation of AFDX networks is conducted to verify the effectiveness of the proposed approach.