Hai Dong
RMIT University
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Publication
Featured researches published by Hai Dong.
IEEE Transactions on Services Computing | 2016
Zhen Ye; Sajib Mistry; Athman Bouguettaya; Hai Dong
We propose a cloud service composition framework that selects the optimal composition based on an end users long-term Quality of Service (QoS) requirements. In a typical cloud environment, existing solutions are not suitable when service providers fail to provide the long-term QoS provision advertisements. The proposed framework uses a new multivariate QoS analysis to predict the long-term QoS provisions from service providers historical QoS data and short-term advertisements represented using Time Series. The quality of the QoS prediction is improved by incorporating QoS attributes intra correlations into the multivariate analysis. To select the optimal service composition, the proposed framework uses QoS time series inter correlations and performs a novel time series group similarity approach on the predicted QoS values. Experiments are conducted on real QoS dataset and results prove the efficiency of the proposed approach.
Communications of The ACM | 2017
Athman Bouguettaya; Munindar P. Singh; Michael N. Huhns; Quan Z. Sheng; Hai Dong; Qi Yu; Azadeh Ghari Neiat; Sajib Mistry; Boualem Benatallah; Brahim Medjahed; Mourad Ouzzani; Fabio Casati; Xumin Liu; Hongbing Wang; Dimitrios Georgakopoulos; Liang Chen; Surya Nepal; Zaki Malik; Abdelkarim Erradi; Yan Wang; M. Brian Blake; Schahram Dustdar; Frank Leymann; Mike P. Papazoglou
Mapping out the challenges and strategies for the widespread adoption of service computing.
international conference on web services | 2014
Le Sun; Hai Dong; Farookh Khadeer Hussain; Omar Khadeer Hussain; Jiangang Ma; Yanchun Zhang
QoS-based service rating has made positive contributions to the area of service selection. Especially for Cloud service users, the right decision when choosing suitable Cloud services can help them improve user satisfaction and trading revenues. This work aims to address the issue of uncertainty in service requests, service descriptions, user and expert preferences, as well as evaluation criteria in a MCDM-based service selection procedure. A hybrid fuzzy framework for Cloud service selection is proposed, addressing the challenge using three approaches: a fuzzy-ontology-based approach for function matching and service filtering, a fuzzy AHP technique for informed criterion weighting, and, a fuzzy TOPSIS approach for service ranking.
international conference on web services | 2015
Sajib Mistry; Athman Bouguettaya; Hai Dong; A. K. Qin
We propose a novel composition framework for an Infrastructure-as-a-Service (IaaS) provider that selects the optimal set of long-term service requests to maximize its profit. Existing solutions consider an IaaS providers economic benefits at the time of service composition and ignore the dynamic nature of the consumer requests in a long-term period. The proposed framework deploys a new multivariate HMM and ARIMA model to predict different patterns of resource utilization and Quality of Service fluctuation tolerance levels of existing service consumers. The dynamic nature of new consumer requests with no history is modelled using a new community based heuristic approach. The predicted long-term service requests are optimized using Integer Linear Programming to find a proper configuration that maximizes the profit of an IaaS provider. Experimental results prove the feasibility of the proposed approach.
ieee international conference on fuzzy systems | 2014
Sun Le; Hai Dong; Farookh Khadeer Hussain; Omar Khadeer Hussain; Jiangang Ma; Yanchun Zhang
With the advent of Cloud computing and subsequent big data, online decision makers usually find it difficult to make informed decisions because of the great amount of irrelevant, uncertain, or inaccurate information. In this paper, we explore the application of multicriteria decision-making (MCDM) techniques in the area of Cloud computing and big data, to find an efficient way of dealing with criteria relations and fuzzy knowledge based on a great deal of information. We propose a MCDM framework, which combines the ISM-based and ANP-based techniques, to model the interactive relations between evaluation criteria, and to handle data uncertainties. We present an application of Cloud service selection to prove the efficiency of the proposed framework, in which a user-oriented sigmoid utility function is designed to evaluate the performance of each criterion.
international conference on web services | 2015
Chii Chang; Seng Wai Loke; Hai Dong; Flora Dilys Salim; Satish Narayana Srirama; Mohan Liyanage; Sea Ling
Internet of Things (IoT) represents a cyber-physical world where physical things are interconnected on the Web. This paper presents an architecture designed for Energy-efficient Inter-organizational wireless sensor data collection Framework (EnIF). Environmental monitoring and urban sensing are two major application scenarios in IoT. Different from the traditional sensor environments, environmental sensing in IoT may require battery-powered nodes to perform the sensing tasks. Such a requirement raises a critical challenge to ensure that sensor data gathering can be collected in a timely and energy-efficient manner. Although numerous energy-efficient approaches for IoT scenarios have been proposed, previous works assumed the entire network was managed by a single organization in which the network establishment and communication have been pre-configured. This assumption is inconsistent with the fact that IoT is established in a federated network with heterogeneous devices controlled by different organizations. The aim of the framework is to enable a dynamic inter-organizational collaborative topology towards saving energy from data transmissions using a service-oriented architecture.
international conference on service oriented computing | 2014
Azadeh Ghari Neiat; Athman Bouguettaya; Timos K. Sellis; Hai Dong
We propose a new failure-proof composition model for Sensor-Cloud services based on dynamic features such as spatio-temporal aspects. To evaluate Sensor-Cloud services, a novel spatio-temporal quality model is introduced. We present a new failure-proof composition algorithm based on D* Lite to handle QoS changes of Sensor-Cloud services at run-time. Analytical and simulation results are presented to show the performance of the proposed approach.
IEEE Transactions on Services Computing | 2018
Sajib Mistry; Athman Bouguettaya; Hai Dong; A. K. Qin
We propose a novel dynamic metaheuristic optimization approach to compose an optimal set of IaaS service requests to align with an IaaS provider’s long-term economic expectation. This approach is designed for the context that the IaaS provisioning subjects to resource and QoS constraints. In addition, the IaaS service requests have the features of dynamic resource and QoS requirements and variable arrival times. A new economic model is proposed to evaluate the similarity between the provider’s long-term economic expectation and a composition of service requests. The evaluation incorporates the factors of dynamic pricing and operation cost modeling of the service requests. An innovative hybrid genetic algorithm is proposed that incorporates the economic inter-dependency among the requests as a heuristic operator and performs repair operations in local solutions to meet the resource and QoS constraints. The proposed approach generates dynamic global solutions by updating the heuristic operator at regular intervals with the runtime behavior data of an existing service composition. Experimental results preliminarily prove the feasibility of the proposed approach.
international conference on service oriented computing | 2016
Tingting Liang; Liang Chen; Jian Wu; Hai Dong; Athman Bouguettaya
In the scenario of service recommendation, there are multiple object types (e.g. services, mashups, categories, contents and providers) and rich relationships among these objects, which naturally constitute a heterogeneous information network (HIN). In this paper, we propose to recommend services for mashup creation by exploiting different types of relationships in service related HIN. Specifically, we first introduce meta-path based measure for similarity estimation between mashups along different types of paths in HIN. We then design a recommendation model based on collaborative filtering and meta-path based similarities, and employ Bayesian ranking based optimization algorithm for model learning. Comprehensive experiments based on real data demonstrate the effectiveness of the HIN based service recommendation approach.
international conference on service oriented computing | 2016
Sajib Mistry; Athman Bouguettaya; Hai Dong; Abdelkarim Erradi
We propose a new qualitative economic model based optimization approach to compose an optimal set of infrastructure service requests over a long-term period. The economic model is represented as a temporal CP-Net to capture the provider’s dynamic business strategies in qualitative service provisions. The multidimensional qualitative preferences are indexed in a k-d tree to compute the preference ranking of a set of incoming requests. We propose a heuristic based sequential optimization process to select the most preferred composition without the knowledge of historical request patterns. Experimental results prove the feasibility of the proposed approach.