This page has only limited features, please log in for full access.

Unclaimed
Hongfang Yu
Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Article
Published: 09 April 2021 in Cluster Computing
Reads 0
Downloads 0

Network function virtualization (NFV) has gained prominence in next-generation cloud computing, such as the fog-based radio access network, due to their ability to support better QoS in network service provision. However, most of the current service function chain (SFC) deployment researches do not consider the Security-Service-Level-Agreement (SSLA) in the deployment solution. Therefore, in this work, we introduce the SSLA into SFC deployment to defend attacks. Firstly, we formulate the SSLA guaranteed SFC deployment problem by using linear programming. Then, we propose the Maximal-security SFC deployment algorithm (MS) to maximize the security of the SFC deployment. However, the MS algorithm results in a high deployment cost. To reduce the deployment cost, we propose the Minimal-cost and SSLA-guaranteed SFC deployment algorithm (MCSG) to minimize the deployment while satisfying the SSLA. In order to reduce the blocking ratio caused by MCSG, the Minimal-cost and SSLA-guaranteed SFC deployment algorithm with feedback adjustment (MCSG-FA) is proposed. Finally, we evaluate our proposed algorithms through simulations. The simulation results show that the blocking ratio and the deployment cost of our algorithms are better than that of the existing algorithm when meeting the SSLAs.

ACS Style

Dongcheng Zhao; Long Luo; Hongfang Yu; Victor Chang; Rajkumar Buyya; Gang Sun. Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks. Cluster Computing 2021, 1 -16.

AMA Style

Dongcheng Zhao, Long Luo, Hongfang Yu, Victor Chang, Rajkumar Buyya, Gang Sun. Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks. Cluster Computing. 2021; ():1-16.

Chicago/Turabian Style

Dongcheng Zhao; Long Luo; Hongfang Yu; Victor Chang; Rajkumar Buyya; Gang Sun. 2021. "Security-SLA-guaranteed service function chain deployment in cloud-fog computing networks." Cluster Computing , no. : 1-16.

Conference paper
Published: 01 April 2021 in Blockchain Technology and Innovations in Business Processes
Reads 0
Downloads 0

Machine learning requires accessing all dataset to train a high-quality model. Due to the data regulations and privacy concerns, the dataset of different data centers cannot be collected into one data center. It is unavoidable to conduct distributed training across multiple data centers. However, state-of-the-art distributed learning algorithms suffer from high communication cost due to the low-speed, highly heterogeneous wide area network connecting the data centers. In this paper, we propose a novel network-aware decentralized distributed training algorithm, namely NAD-PSGD, to overcome the problem. NAD-PSGD can enable worker nodes to mainly use high-speed links to exchange information and thus significantly reduce communication cost. Through our experiment on Amazon clouds and testbed cluster, NAD-PSGD can reduce the convergence training time by up to 42.8 and 66.9%, in comparison with advanced algorithms AD-PSGD and Allreduce-SGD, respectively.

ACS Style

Pan Zhou; Gang Sun; Hongfang Yu; Victor Chang. Network-Aware Distributed Machine Learning Over Wide Area Network. Blockchain Technology and Innovations in Business Processes 2021, 55 -62.

AMA Style

Pan Zhou, Gang Sun, Hongfang Yu, Victor Chang. Network-Aware Distributed Machine Learning Over Wide Area Network. Blockchain Technology and Innovations in Business Processes. 2021; ():55-62.

Chicago/Turabian Style

Pan Zhou; Gang Sun; Hongfang Yu; Victor Chang. 2021. "Network-Aware Distributed Machine Learning Over Wide Area Network." Blockchain Technology and Innovations in Business Processes , no. : 55-62.

Journal article
Published: 02 February 2021 in IEEE Transactions on Intelligent Transportation Systems
Reads 0
Downloads 0

With the rapid development of the Internet of vehicles (IoV), routing in vehicular ad hoc networks (VANETs) has become a popular research topic. Due to the features of the dynamic network structure, constraints of road topology and variable states of vehicle nodes, VANET routing protocols face many challenges, including intermittent connectivity, large delay and high communication overhead. Location-based geographic routing is the most suitable method for VANETs, and such routing performs well on paths with an appropriate vehicle density and network load. We propose an intersection-based V2X routing protocol that includes a learning routing strategy based on historical traffic flows via Q-learning and monitoring real-time network status. The hierarchical routing protocol consists of two parts: a multidimensional Q-table, which is established to select the optimal road segments for packet forwarding at intersections; and an improved greedy strategy, which is implemented to select the optimal relays on paths. The monitoring models can detect network load and adjust routing decisions in a timely manner to prevent network congestion. This method minimizes the communication overhead and latency and ensures reliable transmission of packets. We compare our algorithm with three benchmark algorithms in an extensive simulation. The results show that our algorithm outperforms the existing methods in terms of network performance, including packet delivery ratio, end-to-end delay, and communication overhead.

ACS Style

Long Luo; Li Sheng; Hongfang Yu; Gang Sun. Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -14.

AMA Style

Long Luo, Li Sheng, Hongfang Yu, Gang Sun. Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-14.

Chicago/Turabian Style

Long Luo; Li Sheng; Hongfang Yu; Gang Sun. 2021. "Intersection-Based V2X Routing via Reinforcement Learning in Vehicular Ad Hoc Networks." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-14.

Journal article
Published: 08 January 2021 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Blockchain is a promising emerging technology that is envisioned to play a key role in establishing secure and reliable Internet of Things (IoT) ecosystems without the involvement of any third party. Hyperledger Fabric, a permissioned blockchain system that can yield high throughput and low consensus delay, has shown its capability in enhancing security and privacy protection for delay-sensitive Internet of Things (IoT) services. The literature however has not considered the conflicting transaction problem which may substantially limit the system performance and degrade QoS for the end users. In this paper, we propose CATP-Fabric, a new blockchain system to address the conflicting transaction problem by reducing the number of potentially conflicting transactions with less overhead. First, the transactions within a block are divided into different groups to facilitate parallel transaction processing. Then, CATP-Fabric filters stale transactions and prioritizes the read-only transactions in each group to eliminate unnecessary overhead. Finally, we formulate the selection of aborting transactions in CATP-Fabric as a binary integer programming problem and develop a low-complexity optimization algorithm to minimize the number of aborted transactions. Illustrative results show that our proposed CATP-Fabric blockchain system achieves high throughput of successful transactions while maintaining a lower aborting transaction rate compared to the benchmark blockchain systems.

ACS Style

Xiaoqiong Xu; Xiaonan Wang; Zonghang Li; Hongfang Yu; Gang Sun; Sabita Maharjan; Yan Zhang. Mitigating Conflicting Transactions in Hyperledger Fabric-Permissioned Blockchain for Delay-Sensitive IoT Applications. IEEE Internet of Things Journal 2021, 8, 10596 -10607.

AMA Style

Xiaoqiong Xu, Xiaonan Wang, Zonghang Li, Hongfang Yu, Gang Sun, Sabita Maharjan, Yan Zhang. Mitigating Conflicting Transactions in Hyperledger Fabric-Permissioned Blockchain for Delay-Sensitive IoT Applications. IEEE Internet of Things Journal. 2021; 8 (13):10596-10607.

Chicago/Turabian Style

Xiaoqiong Xu; Xiaonan Wang; Zonghang Li; Hongfang Yu; Gang Sun; Sabita Maharjan; Yan Zhang. 2021. "Mitigating Conflicting Transactions in Hyperledger Fabric-Permissioned Blockchain for Delay-Sensitive IoT Applications." IEEE Internet of Things Journal 8, no. 13: 10596-10607.

Journal article
Published: 08 December 2020 in IEEE Transactions on Industrial Informatics
Reads 0
Downloads 0

Network function virtualization (NFV) in 6G can use standard virtualization techniques to enable network functions via software. Resource scheduling is one of the key research areas of NFV in 6G and is mainly used to deploy service function chains (SFCs) in substrate networks. However, determining how to utilize network resources efficiently has always been a difficult problem in SFC deployment. This paper focuses on how to efficiently provision online SFC requests in NFV with 6G. We first establish a mathematical model for the problem of online SFC provisioning. Then, we propose an efficient online service function chain deployment (OSFCD) algorithm that selects the path to deploy that is close to the SFC length. Finally, we compare our proposed algorithm with three other existing algorithms by simulation experiments. The experimental results show that the OSFCD algorithm optimizes multiple performance indicators of online SFC deployment.

ACS Style

Gang Sun; Zhu Xu; Hongfang Yu; Victor Chang. Dynamic Network Function Provisioning to Enable Network in Box for Industrial Applications. IEEE Transactions on Industrial Informatics 2020, 17, 7155 -7164.

AMA Style

Gang Sun, Zhu Xu, Hongfang Yu, Victor Chang. Dynamic Network Function Provisioning to Enable Network in Box for Industrial Applications. IEEE Transactions on Industrial Informatics. 2020; 17 (10):7155-7164.

Chicago/Turabian Style

Gang Sun; Zhu Xu; Hongfang Yu; Victor Chang. 2020. "Dynamic Network Function Provisioning to Enable Network in Box for Industrial Applications." IEEE Transactions on Industrial Informatics 17, no. 10: 7155-7164.

Journal article
Published: 21 November 2020 in Information Processing & Management
Reads 0
Downloads 0

Blockchain has been one of the most attractive technologies for many modern and even future applications. Fabric, an open-source framework to implement the permissioned enterprise-grade blockchain, is getting increasing attention from innovators. The latency performance is crucial to the Fabric blockchain in assessing its effectiveness. Many empirical studies were conducted to analyze this performance based on different hardware platforms. These experimental results are not comparable as they are highly dependent on the underlying networks. Moreover, theoretical analysis on the latency of Fabric blockchain still receives much less attention. This paper provides a novel theoretical model to calculate the transaction latency under various network configurations such as block size, block interval, etc. Subsequently, we validate the proposed latency model with experiments, and the results show that the difference between analytical and experimental results is as low as 6.1%. We also identify some performance bottlenecks and give insights from the developer’s perspective.

ACS Style

Xiaoqiong Xu; Gang Sun; Long Luo; Huilong Cao; Hongfang Yu; Athanasios V. Vasilakos. Latency performance modeling and analysis for hyperledger fabric blockchain network. Information Processing & Management 2020, 58, 102436 .

AMA Style

Xiaoqiong Xu, Gang Sun, Long Luo, Huilong Cao, Hongfang Yu, Athanasios V. Vasilakos. Latency performance modeling and analysis for hyperledger fabric blockchain network. Information Processing & Management. 2020; 58 (1):102436.

Chicago/Turabian Style

Xiaoqiong Xu; Gang Sun; Long Luo; Huilong Cao; Hongfang Yu; Athanasios V. Vasilakos. 2020. "Latency performance modeling and analysis for hyperledger fabric blockchain network." Information Processing & Management 58, no. 1: 102436.

Journal article
Published: 22 October 2020 in IEEE Transactions on Network and Service Management
Reads 0
Downloads 0

To meet the increasing traffic demands characterized by large bandwidth and high burstiness, more traffic has been moving to inter-datacenter elastic optical networks (inter-DC EONs) for processing. The integration of two emerging paradigms, network function virtualization (NFV) and software-defined networking (SDN), enables Internet service providers (ISPs) to deploy service function chains (SFCs) from users flexibly while reducing operational and capital expenditures. This paper focuses on the problem of online SFC provisioning in inter-DC-EONs with the aim of maximizing ISP profits, where the challenge in jointly allocating IT and spectrum resources when deploying SFCs is balanced with the deployment costs of processing as many user requests as possible. We design two-phase time-efficient orchestration algorithms for online SFC requests and the strategy of SFC splitting is adopted to improve the utilization of spectrum resources on fiber links. Simulation results show that, compared with the existing algorithm, our proposed algorithms significantly shorten the deployment time, improve total profit of ISP by up to 40% and reduce the blocking probability by up to 35%.

ACS Style

Hongfang Yu; Zhenrong Chen; Gang Sun; Xiaojiang Du; Mohsen Guizani. Profit Maximization of Online Service Function Chain Orchestration in an Inter-Datacenter Elastic Optical Network. IEEE Transactions on Network and Service Management 2020, 18, 973 -985.

AMA Style

Hongfang Yu, Zhenrong Chen, Gang Sun, Xiaojiang Du, Mohsen Guizani. Profit Maximization of Online Service Function Chain Orchestration in an Inter-Datacenter Elastic Optical Network. IEEE Transactions on Network and Service Management. 2020; 18 (1):973-985.

Chicago/Turabian Style

Hongfang Yu; Zhenrong Chen; Gang Sun; Xiaojiang Du; Mohsen Guizani. 2020. "Profit Maximization of Online Service Function Chain Orchestration in an Inter-Datacenter Elastic Optical Network." IEEE Transactions on Network and Service Management 18, no. 1: 973-985.

Journal article
Published: 09 October 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

Since its emergence, blockchain technology has received great attention because of its advantages in terms of decentralization, transparency, traceability and the ability to be tamper-proof. These advantages help blockchain become a better option for fields such as digital currency and information storage. Specifically, consortium blockchain is preferred by researchers because it provides a certain degree of access control and a supervisory mechanism. However, in real-world applications, blockchain platforms pervasively show bottlenecks such as ultrahigh energy consumption, time inefficiency, low transaction throughput, vulnerability to targeted attacks and poor fairness of user profits, which seriously influence the performance of this technology and thus hinder its development and adoption. In this paper, we try to enhance the performance of blockchain platforms by optimizing the quality of its core module, known as the consensus algorithm. To do so, we introduce proof of assets and proof of reputation to design a voting-based decentralized consensus (VDC) algorithm for consortium blockchain. Combined with the verifiable random function (VRF), VDC realizes better fairness of user profits and time efficiency with acceptable energy consumption and without sacrificing security. The simulation results show that the proposed algorithm achieves a faster consensus process and better user fairness than existing algorithms while still maintaining a negligible energy cost and adequate security.

ACS Style

Gang Sun; Miao Dai; Jian Sun; Hongfang Yu. Voting-Based Decentralized Consensus Design for Improving the Efficiency and Security of Consortium Blockchain. IEEE Internet of Things Journal 2020, 8, 6257 -6272.

AMA Style

Gang Sun, Miao Dai, Jian Sun, Hongfang Yu. Voting-Based Decentralized Consensus Design for Improving the Efficiency and Security of Consortium Blockchain. IEEE Internet of Things Journal. 2020; 8 (8):6257-6272.

Chicago/Turabian Style

Gang Sun; Miao Dai; Jian Sun; Hongfang Yu. 2020. "Voting-Based Decentralized Consensus Design for Improving the Efficiency and Security of Consortium Blockchain." IEEE Internet of Things Journal 8, no. 8: 6257-6272.

Journal article
Published: 12 August 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this paper proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle's utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, Cwhich would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider the resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in the IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in the IoV.

ACS Style

Gang Sun; Liangjun Song; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles. IEEE Internet of Things Journal 2020, 8, 1971 -1984.

AMA Style

Gang Sun, Liangjun Song, Hongfang Yu, Xiaojiang Du, Mohsen Guizani. A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles. IEEE Internet of Things Journal. 2020; 8 (3):1971-1984.

Chicago/Turabian Style

Gang Sun; Liangjun Song; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. 2020. "A Two-Tier Collection and Processing Scheme for Fog-Based Mobile Crowdsensing in the Internet of Vehicles." IEEE Internet of Things Journal 8, no. 3: 1971-1984.

Journal article
Published: 09 June 2020 in Future Generation Computer Systems
Reads 0
Downloads 0

Large companies operate tens of data centers (DCs) across the globe to serve their customers and store data. On the other hand, many machine learning applications need a global view of such global data to pursue high model accuracy. However, for this Geo-distributed machine learning (Geo-DML), it is infeasible to move all data together over wide-area networks (WANs) due to scarce WAN bandwidth, privacy concerns and data sovereignty laws. Therefore, most Geo-DML systems leverage geo-distributed approaches to train models, where global model synchronization is required between DCs over WAN. With the rapid increase of training data and the model sizes, it is challenging to efficiently utilize scarce and heterogeneous WAN bandwidth to synchronize models. With the advancement of optical technology, network topology becomes reconfigurable in optical WAN, which brings a new opportunity for Geo-DML training over WAN. We propose to optimize Geo-DML training with centralized joint control of the network and reconfigurable optical layers. We respectively prove the intra-job and inter-job scheduling problems are NP-hard and strongly NP-hard. For intra-job scheduling, RoWAN based on deterministic rounding algorithm, is presented to dynamically change the topology by reconfiguring the optical devices, and allocate path and rate for each flow. For inter-job scheduling, delayed SWRT is provided to schedule multiple jobs according to their priorities. The simulations in real topologies show that RoWAN reduces global model synchronization communication time of single iteration by up to 15.54%-48.2% on average in comparison with the traditional solutions. Compared to other three inter-job scheduling approaches, delayed SWRT can reduce the weighted job completion time (WJCT) by about 60%, 44.8% and 28.76%.

ACS Style

Ling Liu; Hongfang Yu; Gang Sun; Long Luo; Qixuan Jin; Shouxi Luo. Job scheduling for distributed machine learning in optical WAN. Future Generation Computer Systems 2020, 112, 549 -560.

AMA Style

Ling Liu, Hongfang Yu, Gang Sun, Long Luo, Qixuan Jin, Shouxi Luo. Job scheduling for distributed machine learning in optical WAN. Future Generation Computer Systems. 2020; 112 ():549-560.

Chicago/Turabian Style

Ling Liu; Hongfang Yu; Gang Sun; Long Luo; Qixuan Jin; Shouxi Luo. 2020. "Job scheduling for distributed machine learning in optical WAN." Future Generation Computer Systems 112, no. : 549-560.

Journal article
Published: 22 May 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

As the key infrastructure for emerging 5G and IoT applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing the delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this paper proposes a multi-source-aware adaptive data transmission solution, Parallel Push (), by leveraging the fact that data objects in cloud are generally replicated among servers by design. At the high-level, achieves efficient delivery of multi-source data by launching multiple flows in parallel; and at the low-level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.

ACS Style

Shouxi Luo; Tie Ma; Wei Shan; Pingzhi Fan; Huanlai Xing; Hongfang Yu. Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push. IEEE Internet of Things Journal 2020, 7, 10495 -10510.

AMA Style

Shouxi Luo, Tie Ma, Wei Shan, Pingzhi Fan, Huanlai Xing, Hongfang Yu. Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push. IEEE Internet of Things Journal. 2020; 7 (10):10495-10510.

Chicago/Turabian Style

Shouxi Luo; Tie Ma; Wei Shan; Pingzhi Fan; Huanlai Xing; Hongfang Yu. 2020. "Efficient Multisource Data Delivery in Edge Cloud With Rateless Parallel Push." IEEE Internet of Things Journal 7, no. 10: 10495-10510.

Journal article
Published: 12 May 2020 in Knowledge-Based Systems
Reads 0
Downloads 0

Networking has become a well-known performance bottleneck for distributed machine learning (DML). Although lots of works have focused on accelerating the communication process of DML, they ignore the impact of the physical network on the DML performance. Concurrently, optical circuit switches (OCSes) are increasingly applied in data centers and clusters, which can fundamentally improve DML performance. It is worth noting that the non-negligible OCS reconfiguration delay makes OCS scheduling algorithms have a great impact on the upper application performance. However, existing OCS scheduling solutions are not suitable for DML jobs due to the iterative nature of DML jobs and their interleaving characteristics of communication and computation stages. Therefore, in this paper, we study the online multi-job scheduling for DML in OCS networks. Firstly, we propose heaviest-load-first (HLF), a heuristic algorithm for intra-job scheduling, which is based on the fact that the completion time of flows on the heaviest load port has a significant impact on the job completion time. Furthermore, we present Shortest Weighted Remaining Time First (SWRTF) algorithm for inter-job scheduling. In SWRTF, an available DML job is scheduled when the served job moves from communication stage to the computation stage, which significantly improves the circuit utilization. Based on large-scale simulations, we demonstrate HLF can significantly reduce the iteration communication time by up to 64.97% compared to the state-of-the-art circuit scheduler Sunflow. Besides, SWRTF can save up to 42.9%, 54.2%, 27.2% of Weighted-Job-Completion-Time (WJCT) compared to Shortest-Job-First, Baraat and Weighted-First inter-job scheduling algorithms, respectively.

ACS Style

Ling Liu; Hongfang Yu; Gang Sun; Huaman Zhou; Zonghang Li; Shouxi Luo. Online job scheduling for distributed machine learning in optical circuit switch networks. Knowledge-Based Systems 2020, 201-202, 106002 .

AMA Style

Ling Liu, Hongfang Yu, Gang Sun, Huaman Zhou, Zonghang Li, Shouxi Luo. Online job scheduling for distributed machine learning in optical circuit switch networks. Knowledge-Based Systems. 2020; 201-202 ():106002.

Chicago/Turabian Style

Ling Liu; Hongfang Yu; Gang Sun; Huaman Zhou; Zonghang Li; Shouxi Luo. 2020. "Online job scheduling for distributed machine learning in optical circuit switch networks." Knowledge-Based Systems 201-202, no. : 106002.

Journal article
Published: 07 May 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

To introduce the opportunities brought by plug-in hybrid electric vehicles (PHEV) to the energy Internet, we propose a local vehicle-to-vehicle (V2V) energy trading architecture based on fog computing in social hotspots and model the social welfare maximization (SWM) problem to balance the interests of both charging and discharging PHEVs. Considering transaction security and privacy protection issues, we employ consortium blockchain in our designed energy trading architecture, which is different from traditional centralized power systems, to reduce the reliance on trusted third parties. Moreover, we improve the practical Byzantine fault tolerance (PBFT) algorithm and introduce it into a consensus algorithm, called delegated proof of stake (DPOS) algorithm, to design a more efficient and promising consensus algorithm, called DPOSP, which greatly reduces resource consumption and enhances consensus efficiency. To encourage PHEVs to participate in V2V energy transactions, we design an energy iterative bidirectional auction (EIDA) mechanism to resolve the SWM problem and obtain optimal charging and discharging decisions and energy pricing. Finally, we conduct extensive simulations to verify the proposed DPOSP algorithm and provide numerical results for comparison with the performance of the genetic algorithm and the Lagrange algorithm in achieving EIDA.

ACS Style

Gang Sun; Miao Dai; Feng Zhang; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. Blockchain-Enhanced High-Confidence Energy Sharing in Internet of Electric Vehicles. IEEE Internet of Things Journal 2020, 7, 7868 -7882.

AMA Style

Gang Sun, Miao Dai, Feng Zhang, Hongfang Yu, Xiaojiang Du, Mohsen Guizani. Blockchain-Enhanced High-Confidence Energy Sharing in Internet of Electric Vehicles. IEEE Internet of Things Journal. 2020; 7 (9):7868-7882.

Chicago/Turabian Style

Gang Sun; Miao Dai; Feng Zhang; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. 2020. "Blockchain-Enhanced High-Confidence Energy Sharing in Internet of Electric Vehicles." IEEE Internet of Things Journal 7, no. 9: 7868-7882.

Journal article
Published: 21 April 2020 in IEEE Transactions on Network and Service Management
Reads 0
Downloads 0

Training a high-accuracy model requires trying hundreds of configurations of hyperparameters to search for the optimal configuration. It is common to launch a group of training jobs (named cojob) with different configurations at the same time and stop the jobs performing worst every stage (i.e., a certain number of iterations). Thus deep learning requires minimizing stage completion time (SCT) to accelerate the searching. To quickly complete the stages, each job in the cojob typically uses multiple GPUs to perform distributed training. The GPUs exchange data per iteration to synchronize their models through the network. However, data transfers of DL jobs compete for network bandwidth since the GPU cluster hosts a number of cojobs from various users, resulting in network congestion and consequently a large SCT for cojobs. Existing flow schedulers aimed at reducing flow/coflow/job completion time mismatch the requirement of hyperparameter searching. In this paper, we implement a system Grouper to minimize average SCT for cojobs. Grouper adopts a well-designed algorithm to permute stages of cojobs and schedules flows from different stages in the order of the permutation. The extensive testbed experiments and simulations show that Grouper outperforms advanced network designs Baraat, Sincrona, and per-flow fair share.

ACS Style

Pan Zhou; Hongfang Yu; Gang Sun. Grouper: Accelerating Hyperparameter Searching in Deep Learning Clusters With Network Scheduling. IEEE Transactions on Network and Service Management 2020, 17, 1879 -1895.

AMA Style

Pan Zhou, Hongfang Yu, Gang Sun. Grouper: Accelerating Hyperparameter Searching in Deep Learning Clusters With Network Scheduling. IEEE Transactions on Network and Service Management. 2020; 17 (3):1879-1895.

Chicago/Turabian Style

Pan Zhou; Hongfang Yu; Gang Sun. 2020. "Grouper: Accelerating Hyperparameter Searching in Deep Learning Clusters With Network Scheduling." IEEE Transactions on Network and Service Management 17, no. 3: 1879-1895.

Journal article
Published: 20 April 2020 in IEEE Journal on Selected Areas in Communications
Reads 0
Downloads 0

The increasing amount of data replication across datacenters introduces a need for efficient bulk data transfer protocols which provide certain guarantees, most notably timely transfer completion. We present DaRTree which leverages emerging optical reconfiguration technologies, to jointly optimize topology and multicast transfers in software-defined optical Wide- Area Networks (WANs), and thereby maximize throughput and acceptance ratio of transfer requests subject to transfer deadlines. DaRTree is based on a novel integer linear program relaxation and deterministic rounding scheme. To this end, DaRTree uses Steiner trees for forwarding and adaptive routing based on the current network load. DaRTree provides transfer completion guarantees without the need for rescheduling or preemption. Our evaluations show that DaRTree increases the network throughput and the number of accepted requests by up to 1:7×, especially for larger WANs. Moreover, DaRTree even outperforms stateof- the-art solutions when the traffic demands are only unicast transfers or when the WAN topology cannot be reconfigured. While DaRTree determines the rate and route to serve a request at the time of (online) admission control, we show that the acceptance ratio and throughput can be improved by up to 1:3× even further when DaRTree updates the rate and route of admitted transfers also at runtime.

ACS Style

Long Luo; Klaus-Tycho Foerster; Stefan Schmid; Hongfang Yu. Deadline-Aware Multicast Transfers in Software-Defined Optical Wide-Area Networks. IEEE Journal on Selected Areas in Communications 2020, 38, 1584 -1599.

AMA Style

Long Luo, Klaus-Tycho Foerster, Stefan Schmid, Hongfang Yu. Deadline-Aware Multicast Transfers in Software-Defined Optical Wide-Area Networks. IEEE Journal on Selected Areas in Communications. 2020; 38 (7):1584-1599.

Chicago/Turabian Style

Long Luo; Klaus-Tycho Foerster; Stefan Schmid; Hongfang Yu. 2020. "Deadline-Aware Multicast Transfers in Software-Defined Optical Wide-Area Networks." IEEE Journal on Selected Areas in Communications 38, no. 7: 1584-1599.

Journal article
Published: 08 April 2020 in IEEE Journal on Selected Areas in Communications
Reads 0
Downloads 0

In today’s data center networks (DCN), cloud applications commonly disseminate files from a single source to a group of receivers for service deployment, data replication, software upgrade, and etc. For these group communication tasks, recent advantages of software-defined networking (SDN) provide bandwidth-efficient ways—they enable DCN to establish and control a large number of explicit multicast trees on demand. Yet, the benefits of data center multicast are severely limited, since there does not exist a scheme that could prioritize multicast transfers respecting the performance metrics wanted by today’s cloud applications, such as pursuing small mean completion times or meeting soft-time deadlines with high probability. To this end, we propose PAM (Priority-based Adaptive Multicast), a preemptive, decentralized, and ready-deployable rate control protocol for data center multicast. At the core, switches in PAM explicitly control the sending rates of concurrent multicast transfers based on their desired priorities and the available link bandwidth. With different policies of priority generation, PAM supports a range of scheduling goals. We not only prototype PAM upon the emerged P4-based programmable switch with novel approximation designs, but also evaluate its performance with ns3-based extensive simulations. Results imply that PAM is ready-deployable; it converges very fast, has negligible impacts on coexisting TCP traffic, and always performs near-optimal priority-based multicast scheduling.

ACS Style

Shouxi Luo; Hongfang Yu; Ke Li; Huanlai Xing. Efficient File Dissemination in Data Center Networks With Priority-Based Adaptive Multicast. IEEE Journal on Selected Areas in Communications 2020, 38, 1161 -1175.

AMA Style

Shouxi Luo, Hongfang Yu, Ke Li, Huanlai Xing. Efficient File Dissemination in Data Center Networks With Priority-Based Adaptive Multicast. IEEE Journal on Selected Areas in Communications. 2020; 38 (6):1161-1175.

Chicago/Turabian Style

Shouxi Luo; Hongfang Yu; Ke Li; Huanlai Xing. 2020. "Efficient File Dissemination in Data Center Networks With Priority-Based Adaptive Multicast." IEEE Journal on Selected Areas in Communications 38, no. 6: 1161-1175.

Journal article
Published: 02 March 2020 in IEEE Transactions on Vehicular Technology
Reads 0
Downloads 0
ACS Style

Liangjun Song; Gang Sun; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. FBIA: A Fog-Based Identity Authentication Scheme for Privacy Preservation in Internet of Vehicles. IEEE Transactions on Vehicular Technology 2020, 69, 5403 -5415.

AMA Style

Liangjun Song, Gang Sun, Hongfang Yu, Xiaojiang Du, Mohsen Guizani. FBIA: A Fog-Based Identity Authentication Scheme for Privacy Preservation in Internet of Vehicles. IEEE Transactions on Vehicular Technology. 2020; 69 (5):5403-5415.

Chicago/Turabian Style

Liangjun Song; Gang Sun; Hongfang Yu; Xiaojiang Du; Mohsen Guizani. 2020. "FBIA: A Fog-Based Identity Authentication Scheme for Privacy Preservation in Internet of Vehicles." IEEE Transactions on Vehicular Technology 69, no. 5: 5403-5415.

Journal article
Published: 03 February 2020 in IEEE Internet of Things Journal
Reads 0
Downloads 0

The efficient deployment of virtual network functions (VNFs) for network service provisioning is key for achieving network function virtualization (NFV); however, most existing studies address only offline or one-off deployments of service function chains (SFCs) while neglecting the dynamic (i.e., online) deployment and expansion requirements. In particular, many methods of energy/resource cost reduction are achieved by merging VNFs. However, the energy waste and device wear for large-scale collections of servers (e.g., cloud networks and data centers) caused by sporadic request updating are ignored. To solve these problems, we propose an energy-aware routing and adaptive delayed shutdown (EAR-ADS) algorithm for dynamic SFC deployment, which includes the following features. 1) Energy-aware routing (EAR): By considering a practical deployment environment, a flexible solution is developed based on reusing open servers and selecting paths with the aims of balancing energy and resources and minimizing the total cost. 2) Adaptive delayed shutdown (ADS): The delayed shutdown time of the servers can be flexibly adjusted in accordance with the usage of each device in each time slot, thus eliminating the no-load wait time of the servers and frequent on/off switching. Therefore, EAR-ADS can achieve dual energy savings by both decreasing the number of open servers and reducing the idle/switching energy consumption of these servers. Simulation results show that EAR-ADS not only minimizes the cost of energy and resources but also achieves an excellent success rate and stability. Moreover, EAR-ADS is efficient compared with an improved Markov algorithm (SAMA), reducing the average deployment time by more than a factor of 40.

ACS Style

Gang Sun; Run Zhou; Jian Sun; Hongfang Yu; Athanasios V. Vasilakos. Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization. IEEE Internet of Things Journal 2020, 7, 6116 -6131.

AMA Style

Gang Sun, Run Zhou, Jian Sun, Hongfang Yu, Athanasios V. Vasilakos. Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization. IEEE Internet of Things Journal. 2020; 7 (7):6116-6131.

Chicago/Turabian Style

Gang Sun; Run Zhou; Jian Sun; Hongfang Yu; Athanasios V. Vasilakos. 2020. "Energy-Efficient Provisioning for Service Function Chains to Support Delay-Sensitive Applications in Network Function Virtualization." IEEE Internet of Things Journal 7, no. 7: 6116-6131.

Journal article
Published: 15 January 2020 in Future Generation Computer Systems
Reads 0
Downloads 0

The bottleneck of Distributed Machine Learning (DML) has shifted from computation to communication. Lots of works have focused on speeding up communication phase from perspective of Parameter Server (PS) architecture, for example resource scheduling. Nonetheless, the performance improvement of these schemes is limited due to the agnostic of the physical topology to the communication pattern of the applications. Concurrently, some articles have also pointed out the impact of topology on DML performance. Besides, our analysis and experimental results also indicate that the general topologies cannot match well with the communication characteristics of DML based on PS architecture. However, to the best of our knowledge, no special topology is tailored for DML. Therefore, in this paper, we propose PSNet, a reconfigurable modular network topology for DML with consideration of the communication characteristics of PS architecture. The main idea of PSNet is that servers are firstly divided into two categories, namely workers configured with high-performance computing capability and parameter servers equipped with multiple Network Interface Cards (NICs). Then Electrical Circuit Switch (ECS) is exploited to connect workers and Top of Rack (ToR) switches for flexibility and reconfigurability in each module. Our theoretical analysis proves that PSNet not only provides high performance for DML tasks, but also achieves high fault tolerance and flexibility. In order to validate the performance of PSNet, we conduct large-scale simulations and small-scale testbed experiments, and the results of experiments demonstrate that PSNet performs 1.89× and 1.92× faster than FatTree for VGG-16 and ResNet50, respectively.

ACS Style

Ling Liu; Qixuan Jin; Dan Wang; Hongfang Yu; Gang Sun; Shouxi Luo. PSNet: Reconfigurable network topology design for accelerating parameter server architecture based distributed machine learning. Future Generation Computer Systems 2020, 106, 320 -332.

AMA Style

Ling Liu, Qixuan Jin, Dan Wang, Hongfang Yu, Gang Sun, Shouxi Luo. PSNet: Reconfigurable network topology design for accelerating parameter server architecture based distributed machine learning. Future Generation Computer Systems. 2020; 106 ():320-332.

Chicago/Turabian Style

Ling Liu; Qixuan Jin; Dan Wang; Hongfang Yu; Gang Sun; Shouxi Luo. 2020. "PSNet: Reconfigurable network topology design for accelerating parameter server architecture based distributed machine learning." Future Generation Computer Systems 106, no. : 320-332.

Journal article
Published: 05 November 2019 in IEEE Internet of Things Journal
Reads 0
Downloads 0

In the face of a massive number of vehicular users, a data collection paradigm based on vehicular crowdsensing requires an effective means of attracting participants. Thus, incentive mechanisms play a key role in crowdsensing procedure design, inevitably leading to problems related to user privacy leakage. In this paper, to reduce the risk of privacy leakage in the implementation of incentive mechanisms, we propose a fog computing-based crowdsensing architecture specialized for vehicular crowdsensing and corresponding privacy-preserving solutions for the processes of data reporting, reward issuing and trust management. Authentication and encryption technologies such as zeroknowledge verification, one-way hashing, partially blind signature authentication and homomorphic encryption are utilized to achieve our goals. Finally, efficiency improvements in both privacy preservation and network response are proven through analysis and simulations.

ACS Style

Gang Sun; Siyu Sun; Hongfang Yu; Mohsen Guizani. Toward Incentivizing Fog-Based Privacy-Preserving Mobile Crowdsensing in the Internet of Vehicles. IEEE Internet of Things Journal 2019, 7, 4128 -4142.

AMA Style

Gang Sun, Siyu Sun, Hongfang Yu, Mohsen Guizani. Toward Incentivizing Fog-Based Privacy-Preserving Mobile Crowdsensing in the Internet of Vehicles. IEEE Internet of Things Journal. 2019; 7 (5):4128-4142.

Chicago/Turabian Style

Gang Sun; Siyu Sun; Hongfang Yu; Mohsen Guizani. 2019. "Toward Incentivizing Fog-Based Privacy-Preserving Mobile Crowdsensing in the Internet of Vehicles." IEEE Internet of Things Journal 7, no. 5: 4128-4142.