Cloud providers require high-throughput traffic to test the effectiveness of congestion control (CC) configurations (i.e., CC algorithm selection and their parameter settings) in networks. A network tester capable of evaluating CC configurations needs to fulfill the following requirements: (R1) Capable of generating traffic with CC behaviors. (R2) Ability to customize CC algorithms. (R3) High throughput CC traffic generation. However, existing network testers fail to meet these requirements simultaneously. The paper presents Marlin, a novel high-throughput network tester designed for CC evaluation. Marlin leverages a high-throughput, low-programmability device to amplify the traffic generated by a low-throughput, high-programmability device. The low-throughput device is responsible for complex computational tasks, such as running CC and flow scheduling algorithms, and communicates with the high-throughput device at a high frequency using small packets to instruct it to generate high-throughput traffic with CC behaviors. This hybrid approach allows for customizable, high-throughput CC testing. Our experiments demonstrate that Marlin can accurately emulate CC behaviors and replicate real-world scenarios. Marlin can generate 1.2 Tbps of CC traffic using a single programmable switch pipeline and one 100 Gbps port of an FPGA NIC, supporting up to 65,536 concurrent flows.

The goal of the next-generation mobile communication system is higher data-rates, lower latency, and higher energy-efficient performance, which bring about the demands for fast beam tracking in time-varying mobile communication. With the development of large-scale antenna array technology, highly directional beams can be formed with limited radio frequency chains. However, traditional exhaustive searching scheme has unacceptable overhead that leads to great challenges for applying to mobile millimeter-wave environments. Fast beam tracking scheme therefore has been recognized as a key technology in millimeter wave communication. To address this issue, this paper proposes a data-driven multi-armed beam tracking scheme to select the beamforming/combining vectors that achieve the target quality of service based on the real-time measurement, rather than the prior knowledge such as channel and user mobility information in beamforming design. To further speed up the beam tracking process, multi-armed beam is created to sample multiple spatial directions simultaneously. Simulation results show that the proposed data-driven multi-armed beam tracking method could achieve fast beam tracking performance with high resolution and reduced training overhead.

The healthcare industry faces challenges regarding the security and efficiency of data management for patient health records (PHRs). We propose SafePHR: a secure and efficient medical data service with application aware deduplication in fog-to-multicloud encrypted storage. It introduces a fog-to-multicloud cooperative storage model, which combines low-latency and high-safety local fog with unlimited capacity and built-in disaster recovery in remote multi-cloud to enhance eHealth data management. It also provides an application aware secure deduplication scheme to improve the data traffic and space efficiency of cloud storage for encrypted PHRs using variants of convergent encryption. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a fault-tolerant coding scheme. Compared with the state-of-the-art of cloud-assisted eHealth systems, our experiments demonstrate that SafePHR can ensure data confidentiality of deduplication, achieve a competitive low cloud storage space overhead, and improve system performance with high fault-tolerance capability.

Mobile Ad hoc Networks (MANETs) is a decentralized network where mobile nodes in the network communicate with each other over wireless links without any infrastructure or central controller. With the latest development of unmanned aerial vehicles (UAVs), the development of network technology for multi-cooperative UAVs has become crucial. Routing in a Flying Ad hoc Networks (FANETs) is a challenging problem. However, traditional MANET routing protocols often only consider the current connection state when establishing a route and selecting the next hop. In high mobility FANETs, these methods are possible not optimal or even wrong. Meanwhile, FANET has certain advantages. For example, most UAVs are equipped with Global Position System (GPS) or similar devices that can provide their own spatial location information. Routing decisions can be optimized and adjusted with this available information. In this paper, based on the Optimized Link State Routing (OLSR) protocol, we propose a Link Duration OLSR (LD-OLSR) protocol. The proposed protocol uses the situation information of the nodes in three dimensions to predict the duration of the link to optimize the establishment of the backbone network. The simulation results show that the algorithm effectively improves the packet delivery rate and reduces the delay of packets.

In this article, we investigate resource allocation with edge computing in Internet-of-Things (IoT) networks via machine learning approaches. Edge computing is playing a promising role in IoT networks by providing computing capabilities close to users. However, the massive number of users in IoT networks requires sufficient spectrum resource to transmit their computation tasks to an edge server, while the IoT users were developed to have more powerful computation ability recently, which makes it possible for them to execute some tasks locally. Then, the design of computation task offloading policies for such IoT edge computing systems remains challenging. In this article, centralized user clustering is explored to group the IoT users into different clusters according to users' priorities. The cluster with the highest priority is assigned to offload computation tasks and executed at the edge server, while the lowest priority cluster executes computation tasks locally. For the other clusters, the design of distributed task offloading policies for the IoT users is modeled by a Markov decision process, where each IoT user is considered as an agent which makes a series of decisions on task offloading by minimizing the system cost based on the environment dynamics. To deal with the curse of high dimensionality, we use a deep Q-network to learn the optimal policy in which deep neural network is used to approximate the Q-function in Q-learning. Simulations show that users are grouped into clusters with optimal number of clusters. Moreover, our proposed computation offloading algorithm outperforms the other baseline schemes under the same system costs.

Public cloud storage can provide customers with unlimited capacity and built-in disaster recovery by exploiting IT resources in data center but suffers high latency and security risk. The emergency of fog computing can mitigate data security risk by leveraging local private server with low latency. We introduce F2MC: a Fog-to-MultiCloud hybrid storage service that combines local fog computing with remote cloud computing to enhance the quality of service (QoS) of data management. It provides an application aware data reduction scheme to improve the data traffic and space efficiency of cloud storage by exploiting deduplication and compression. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a secure fault-tolerant coding. Compared with the state-of-the-art of multicloud storage systems, our experiments demonstrate that F2MC can achieve the highest performance with the same fault-tolerance capability, and a competitive low cloud cost due to its low cloud storage space overheads.

The increasingly extensive availability of location- acquisition technologies (such as GPS and GSM networks) and mobile computing techniques have generated a lot of spatial-temporal trajectory data which represents the mobility of diversification of moving objects such as people, vehicles, and animals. This brings new opportunities to understand the movement behaviors of moving objects in mobile social networks, which can become valuable for location-based services (LBS). However, the explosive growth of users' trajectory data has brought a lot of troubles to online real-time processing. In this paper, we focus on this direction and develop a complete data-driven framework involving real-time user mobility pattern modeling and a novel user similarity measurement based on both spatial and temporal information. Through experimental evaluation, it is verified that the proposed mobility pattern modeling method and similarity measurement can deliver excellent performance.