A5035343733- Service-Oriented Architecture and Web Services
- Cloud Computing and Resource Management
- Business Process Modeling and Analysis
- Distributed and Parallel Computing Systems
- Scientific Computing and Data Management
- IoT and Edge/Fog Computing
- Semantic Web and Ontologies
- Caching and Content Delivery
- Distributed systems and fault tolerance
- Advanced Software Engineering Methodologies
- Software System Performance and Reliability
- Software Engineering Research
- Advanced Data Storage Technologies
- Advanced Database Systems and Queries
- Blockchain Technology Applications and Security
- Usability and User Interface Design
- Multi-Agent Systems and Negotiation
- Cloud Data Security Solutions
- Privacy-Preserving Technologies in Data
- Software Engineering Techniques and Practices
- Mobile Agent-Based Network Management
- Advanced Graph Neural Networks
- Recommender Systems and Techniques
- Cryptography and Data Security
- Software Reliability and Analysis Research
Swinburne University of Technology
2016-2025
Chongqing Normal University
2025
East China Normal University
2025
Ocean University of China
2024
Beijing International Studies University
2024
Changchun Institute of Technology
2024
China National Petroleum Corporation (China)
2023-2024
Shanghai University of Traditional Chinese Medicine
2024
Tianjin University of Commerce
2023-2024
Dalian Medical University
2023
Real-time cooperative editing systems allow multiple users to view and edit the same text/graphic/image/multimedia document at time for sites connected by communication networks. Consistency maintenance is one of most significant challenges in designing implementing real-time systems. In this article, a consistency model, with properties convergence, causality preservation, intention proposed as framework Moreover, an integrated set schemes algorithms, which support are devised discussed...
Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an vendor to deploy its at hired edge servers near users the of cloud. This way, can be allocated nearby minimize network latency energy consumption. A cost-effective user allocation (EUA) requires maximum served minimum overall system cost. Finding centralized optimal solution this EUA problem is NP-hard. Thus, we propose EUAGame, game-theoretic approach that...
Edge computing (EC) has recently emerged as a novel paradigm that offers users low-latency services. Suffering from constrained resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need cooperate through peer-offloading. Deployed and managed by different stakeholders, in distrusted environment. Trust incentive are two main issues challenge cooperative between them. Another unique...
Edge computing (EC) is an emerging paradigm that extends cloud by pushing resources onto edge servers are attached to base stations or access points at the of in close proximity with end-users. Due servers' geographic distribution, EC challenged many new security threats, including notorious distributed Denial-of-Service (DDoS) attack. In environment, usually have constrained processing capacities due their limited sizes. Thus, they particularly vulnerable DDoS attacks. attacks environment...
The concept of cloud computing continues to spread widely, as it has been accepted recently. Cloud many unique advantages which can be utilized facilitate workflow execution. Instance-intensive cost-constrained workflows are with a large number instances (i.e. instance intensive) bounded by certain budget for execution cost constrained) on platform workflows). However, there are, so far, no dedicated scheduling algorithms instance-intensive workflows. This paper presents novel...
The number of Web services on the Internet has been growing rapidly. This made it increasingly difficult for users to find right from a large functionally equivalent candidate services. Inspecting every service their quality value is impractical because very resource consuming. Therefore, problem prediction attracted lot attention in past several years, with focus application Matrix Factorization (MF) technique. Recently, researchers have started employ user similarity improve MF-based...
Dynamic resource provisioning and the notion of seemingly unlimited resources are attracting scientific workflows rapidly into Cloud computing. Existing works on workflow scheduling in context Clouds either deadline or cost optimization, ignoring necessity for robustness. Robust that handles performance variations failures environment is essential Clouds. In this paper, we present a robust algorithm with allocation policies schedule tasks heterogeneous while trying to minimize total elapsed...
Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers serve nearby users. Caching can minimize users' retrieval latency. However, such cache are subject both intentional accidental corruption in the highly distributed, dynamic, volatile environment. Given a large number of limited resources, how effectively efficiently audit integrity vendors' is critical challenging problem. This article makes first attempt tackle this Data Integrity...
The service-oriented paradigm offers support for engineering service-based systems (SBSs) based on service composition where existing services are composed to create new services. selection of with the aim fulfil quality constraints becomes critical and challenging success SBSs, especially when stringent. However, none approaches quality-aware has sufficiently considered following two issues increase rate finding a solution: 1) complementarities between services; 2) competition among...
In recent years, edge computing has emerged as a prospective distributed paradigm that overcomes several limitations of cloud computing. the environment, service provider can deploy its application instances on servers at network to serve own users with low latency. Given limited budget <inline-formula><tex-math notation="LaTeX">$\mathcal {K}$</tex-math></inline-formula> for deploying applications in particular geographical area, number approaches have been proposed very recently determine...
Edge computing is a new distributed paradigm extending the cloud paradigm, offering much lower end-to-end latency, as real-time, latency-sensitive applications can now be deployed on edge servers that are closer to end-users than distant servers. In computing, user allocation (EUA) critical problem for any app vendors, who need determine which will serve users. This satisfy application-specific optimization objectives, e.g., maximizing users’ overall quality of experience, minimizing system...
Multi-access Edge Computing (MEC), as an extension of cloud computing, provides storage resources at the network edge to enable low-latency data retrieval for users. Due limited physical sizes and constrained resources, individual servers cannot store a large amount when operating independently. They often need offload other serve users collaboratively. Operated by different infrastructure providers, usually work in distrusted environment. Incentive trust are two main challenges facilitating...
Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying services on edge servers attached base stations or access points in close proximity mobile users. From the infrastructure provider's perspective, cost-effective <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> server placement ( ESP) aims place within particular geographic area maximize number of covered users, i.e., <i>user coverage</i> . However,...
The new edge computing paradigm extends cloud by allowing service vendors to deploy their instances and data on distributed servers serve users in close geographic proximity those servers. Caching profoundly reduces the retrieval latency perceived users. However, these are subject corruption due intentional and/or accidental exceptions. This is a major challenge for but has been overlooked. Thus, verifying integrity of accurately efficiently critical security problem environment. A unique...
Machine learning (ML) is powering a rapidly-increasing number of web applications. As crucial part 5G, edge computing facilitates artificial intelligence (AI) by ML model training and inference at the network on servers. Compared with centralized cloud AI, AI enables low-latency which critical to many delay-sensitive applications, e.g., AR/VR, gaming Web-of-Things Existing studies focused resource performance optimization in inference, leveraging merely as tool accelerate processes. However,...
Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of world's web traffic, making a great data source various machine learning (ML) applications, particularly federated (FL) which offers promising solution to privacy-preserving ML feeding on these data. FL allows mobile WoT train shared global model under orchestration central parameter server. In real world, due resource heterogeneity, often different versions models (e.g., VGG-16 VGG-19) or VGG ResNet)...