Forthcoming articles

 


International Journal of Web and Grid Services

 

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International Journal of Web and Grid Services (18 papers in press)

 

Regular Issues

 

  • Managing Workflows on top of a Cloud Computing Orchestrator for using heterogeneous environments on e-Science   Order a copy of this article
    by Abel Carrión, Miguel Caballer, Ignacio Blanquer, Nelson Kotowski 
    Abstract: Scientific Workflows (SWFs) are widely used to model processes in e-Science. SWFs are executed by means of Workflow Management Systems (WMSs), which orchestrate the workload on top of computing infrastructures. The advent of cloud computing infrastructures has opened the door of using on-demand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity. In this article we present an ad-hoc solution for managing workflows exploiting the capabilities of cloud orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (clusters) to minimize costs and response time. The work does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The article shows several configuration experiments from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The article computes running time and cost suggesting best practices.
    Keywords: Workflow; Workflow Management Systems; Cloud Orchestrator; Multi-platform; e-Science; Cloud Computing; Comparative genomics.
    DOI: 10.1504/IJWGS.2017.10003225
     
  • Clustering-based uncertain QoS prediction of Web services via collaborative filtering   Order a copy of this article
    by Guobing Zou, Zhimin Zhou, Sen Niu, Yanglan Gan, Bofeng Zhang 
    Abstract: Although collaborative filtering (CF) has been widely applied for QoS-aware Web service recommendation, most of these approaches mainly focus on certain QoS prediction of Web services. However, they failed to take the natural characteristic of Web services with QoS uncertainty into account in real-world service-oriented Web applications. To solve the problem, this paper proposes a novel approach for uncertain QoS prediction via collaborative filtering and service clustering. We first establish uncertain QoS model for a service user by a tree-layer tree, where each service is formalized as a QoS matrix. To mine the similar neighborhood users for an active user, we then extend Euclidean distance to calculate the similarity between two uncertain QoS models. Finally, we present two novel QoS prediction strategies of QoS prediction based on collaborative filtering and service clustering, called U-Rec and UC-Rec. Extensive experiments have been conducted on large-scale real-world dataset that has more than 1.5 million uncertain QoS transaction logs of Web services. The experimental results demonstrate the effectiveness of our proposed approach.
    Keywords: Web service; uncertain QoS prediction; collaborative filtering; service clustering.

  • Skyline Service Selection Approach based on QoS Prediction   Order a copy of this article
    by Yan Guo, Shangguang Wang, Kok-Seng Wong, Myung Ho Kim 
    Abstract: The Internet currently hosts a large number of Web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection by using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.
    Keywords: service selection; QoS prediction; autoregressive integrated moving average model; Skyline service.

  • An Overall Approach to Achieving Load-Balancing for Hadoop Distributed File System   Order a copy of this article
    by Chi-Yi Lin, Ying-Chen Lin 
    Abstract: Hadoop Distributed File System (HDFS) is a popular cloud storage system that can scale up easily to meet the increasing demand for more storage capacity. In HDFS, files are divided into fixed-size blocks, which are then replicated and randomly stored on many DataNodes to prevent data loss. It can be easily observed that the random nature of the default block placement strategy may lead to a load imbalance state among the DataNodes. Although HDFS has a built-in utility to achieve load balancing, it comes at the cost of a reduced system performance owing to moving blocks around. In this paper, we take a holistic approach to achieving load balancing by considering all situations that may influence the load-balancing state. We designed a new role named BalanceNode to help in matching heavy-loaded and light-loaded DataNodes, so those light-loaded nodes can share part of the load from heavy-loaded ones. We also designed a better block placement strategy to make the storage load as balanced as possible in the first place. The simulation results show that our approach can achieve better load-balancing state than with existing algorithms.
    Keywords: cloud computing; Hadoop Distributed File System; load balancing.

  • Real-time Adaptive QoS Prediction Using Approximate Matrix Multiplication   Order a copy of this article
    by Marin Silic, Adrian Kurdija, Sinisa Srbljic 
    Abstract: We introduce a novel QoS prediction model as a real-time support for selection of atomic service candidates based on their QoS properties while constructing composite applications. The proposed approach satisfies the following requirements: (i) fast and accurate prediction of QoS values, and (ii) adaptability with respect to environment changes. The model precomputes the similarities between users and services using approximate matrix multiplication to reduce the time complexity. When calculating a prediction for a user-service pair, the model considers similar users and services, but enhances the prediction accuracy by incorporating the number of observed records. Time complexity is further reduced by storing the lists of similar users and services which are updated in real-time. The model adapts to the changing environment: newer records are set to have greater influence on the predictions. The experiments conducted on relevant service-oriented datasets show advantages of the proposed model in accuracy and time performance.
    Keywords: web services; quality of service; QoS prediction; service recommendation; real-time adaptability; approximate matrix multiplication.

  • PSPO: A Framework for Cost Effective Service Placement Optimization during enterprise modernization on Hybrid Clouds   Order a copy of this article
    by Ravikumar Ramadoss, N.M. Elango, A. Satheesh, Ching-Hsien Hsu 
    Abstract: An integrated cloud service model utilizing both public and private cloud services to provide a holistic deployment of the enterprise applications is the need of the hour. Adopting hybrid cloud during enterprise modernization delivers cost effective option and secured performance. Traditional existing applications getting modernized to meet the high business demands to serve various sets of end customers, with its technology enhancements adapting to big data and cloud to achieve the futuristic SLA. In this paper, we proposed a proactive cost optimization model during various service placements by considering modernization characteristics on multiple clouds. The proposed algorithm solves some of the key issues towards datacenter, environment, licenses and enterprise modernized transactions. Experimental results derived shown that the proposed work optimizes the parameters required for effective service placement.
    Keywords: Cost factors; Enterprise modernization; Hybrid clouds; Service migration.
    DOI: 10.1504/IJWGS.2018.10006675
     
  • Searchable Symmetric Encryption Based on the Inner Product for Cloud Storage   Order a copy of this article
    by Jun Yang 
    Abstract: Searchable encryption enables the data owner to store their own data after encrypting them in the cloud. Searchable encryption also allows the client to search over the data without leaking any information about it. In this paper, we first introduce a searchable symmetric encryption scheme based on the inner product: it is more efficient to compute the inner product of two vectors. In our construction, the parties can be Data Owners, Clients or the Cloud Server. The three parties communicate with each other through the inner product to achieve the goal that the client can search the data in the cloud without leaking any information on the data the owner stored in the cloud. We then perform a security analysis and performance evaluation, which show that our algorithm and construction are secure and efficient.
    Keywords: Searchable Encryption; Searchable Symmetric Encryption; Inner Product; the Cloud Server; Security.
    DOI: 10.1504/IJWGS.2018.10006676
     
  • PRE+: Dual of Proxy Re-encryption for Secure Cloud Data Sharing Service   Order a copy of this article
    by Xu An Wang 
    Abstract: In this paper, aiming at providing secure cloud data sharing services in cloud storage, we propose a scalable and controllable cloud data sharing framework for cloud users: Scanf. Toward proposing this framework, we introduce a new cryptographic primitive: PRE+, which can be seen as the dual of traditional proxy re-encryption (PRE) primitive. All the traditional PRE schemes until now require the delegator (or the delegator and the delegatee cooperatively) to generate the re-encryption keys. We observe that the encrypter also has the ability to generate re-encryption keys. Based on this observation, we construct a new PRE+ scheme, which is almost the same as the traditional PRE except the re-encryption keys generated by the encrypter. Compared with PRE, PRE+ can easily achieve the non-transferable property and message-level based fine-grained delegation. Our Scanf framework based on PRE+ can also achieve these two properties, which is very important for cloud storage sharing service.
    Keywords: Secure cloud data sharing service; Dual of proxy re-encryption; Non-transferable property; Message-level based fine-grained delegation.
    DOI: 10.1504/IJWGS.2018.10006677
     
  • Compromising Location Privacies for Vehicles Cloud Computing   Order a copy of this article
    by Chi Lin 
    Abstract: In this paper, we propose an Enhanced Vehicular Crowdsourcing Localization and Tracking (EVCLT) scheme for mounting a trajectory tracking attack in vehicular cloud computing environment. In our scheme, Crowdsourcing technique is applied to sample the location information of certain users. Then Matrix Completion technique is used to generate our predictions of the users' trajectories. To alleviate the error disturbance of the recovered location data, Kalman filter technique is implemented and the trajectories of certain users are recovered with accuracy. At last, extensive simulations are conducted to show the performance of our scheme. Simulations results reveal that the proposed approach is able to accurately track the trajectories of certain users.
    Keywords: Trajectory Tracking; Crowdsourcing; Matrix Completion; Kalman Filter.
    DOI: 10.1504/IJWGS.2018.10006678
     
  • Emerging Issues and Challenges for Cloud Data at the Edge   Order a copy of this article
    by Myung Ho Kim  
    Abstract: Due to advantages such as flexibility, mobility, and cost-saving, the pushing of data storage and processing into the cloud has been a key trend in many enterprises. Recently, researchers have found that there is a growing limitation in the meeting of the computing and network bandwidths for the cloud when supported systems such as the Internet of Things (IoT) are used. Instead of establishing channels for the cloud storage and utilization, edge-centric computing places some of the processes and resources near to the users; however, its utilization has raised security concerns about the data usage, and the existing secure mechanisms in the cloud are no longer suitable for data protection at the edge. In this paper, the security issues of cloud data at the edge are studied. We present several challenges and opportunities for edge computing and demonstrate the impact of privacy leakage in IoT applications.
    Keywords: Edge-centric computing; secure data sharing; fog computing; data privacy protection; IoT applications.
    DOI: 10.1504/IJWGS.2018.10006679
     
  • Data Integrity Checking with Reliable Data Transfer for Secure Cloud Storage   Order a copy of this article
    by Yong Yu 
    Abstract: Currently, an increasing number of data owners prefer to store their data on remote servers due to a number of appealing advantages of cloud storage, say convenience and simplicity, scalability of the service, and ubiquitous network access etc. However, outsourced data
    Keywords: Cloud Storage; data integrity; data transfer; data deletion.
    DOI: 10.1504/IJWGS.2018.10006680
     
  • Performance Modeling and Analysis for IoT Services   Order a copy of this article
    by Jiwei Huang, Songyuan Li, Ying Chen, Junliang Chen 
    Abstract: With the growing popularity of Internet of Things (IoT) services on the Internet, performance has become an important issue in the design and optimization of IoT services systems. This paper proposes a theoretical approach of performance evaluation for IoT services, which is expected to provide a mathematical prediction on performance metrics at the design phase before system implementation. In specific, we firstly formulate an atomic service by a queueing system, and then an IoT system can be modeled by a queueing network. Detailed quantitative model analyses under different request arrival distributions are presented, and performance metrics are obtained. Furthermore, we briefly study two popular problems which are resource management and task scheduling in order to illuminate how the models and analytical results can be applied in the design and optimization of IoT systems. Finally, real-world data based simulation experiments are conducted to validate the effectiveness of our approach.
    Keywords: Performance Evaluation; Internet of Things (IoT); IoT Service; Quality of Service; Queueing Model.
    DOI: 10.1504/IJWGS.2018.10006681
     
  • One Size Does Not Fit All: Logic-based Clustering for On-the-fly Web Service Composition and Verification   Order a copy of this article
    by Khai Huynh, Tho Quan, Thang Bui 
    Abstract: Recently, Web Service Composition (WSC) has been widely emerging since it is obviously hopeless to develop a specific web service which can single-handedly fulfill completely a requirement previously posed by clients. Moreover, as a WSC solution often needs to satisfy various kinds of constraints, its correctness is also required to be formally verified. However, composition and verification tasks always suffer from huge computational cost when applied in practice. It prompts the consideration of clustering approaches, which group similar web services into same clusters for further processing. Unfortunately, typical clustering techniques cannot ensure that all of the web services grouped into a cluster fulfill a certainly required constraint. Likewise, those techniques do not also guarantee that all of the web services fulfilling a constraint will be put into the same cluster. In other words, the existing web service clustering approaches accomplish neither the soundness nor completeness of a composition solution. To overcome this problem, we propose a logic-based approach for clustering web services, where logic formulae are used for representing web services and their clusters. The clustering results are then further applied for WSC and verification in an on-the-fly manner. In the theoretical aspect, our approach achieves both soundness and completeness. In practice, the logic-based similarity used in our approach generates more reasonable clusters, resulting in significant performance improvement in experimentation.
    Keywords: Logic-based Web Service Clustering; Logic-based Web Service Similarity; Web Service Clustering; Web Service Composition; Web Service Formal Definition.

  • G-OPTICS: Fast Ordering Density-based Cluster Objects using Graphics Processing Units   Order a copy of this article
    by Wookey Lee, Woong-Kee Loh 
    Abstract: Clustering is the process of forming groups or clusters of similar objects in the dataset and has been used as an important tool for many data mining applications including the web-based ones. While density-based clustering algorithms are widely adopted, their clustering result is highly sensitive to parameter values. The OPTICS algorithm presents a solution to this problem; it produces an ordering of objects that is equivalent to the clustering results for a wide range of thresholds e. In this paper, we propose an algorithm named G-OPTICS to significantly improve the performance of OPTICS using a Graphics Processing Unit (GPU). The experimental results using real and synthetic datasets demonstrated that G-OPTICS outperformed the previously fastest FOPTICS algorithm by up to 118.3 times (67.7 times on the average).
    Keywords: ordering of objects; density-based clustering; graphics processing unit; parallel algorithm.

  • Implementation and Evaluation of Hybrid Broadcasting System for Webcasts   Order a copy of this article
    by Takeshi Ozaki, Yusuke Gotoh 
    Abstract: Due to the recent popularization of grid environments, streaming delivery using broadcast and telecommunication method systems is attracting great attention. In streaming delivery under hybrid broadcasting environments, clients concurrently receive such streaming data as music or movies from both broadcasting and telecommunication systems. Although these systems compensate for their respective the demerits, the waiting time is lengthened if the system does not deliver the data efficiently. To reduce this waiting time, many researches have proposed scheduling methods. However, since most scheduling methods use a computer simulation, they do not clearly show their the effectiveness. In this paper, we design and implement a hybrid broadcasting system to solve the problems of conventional methods. In our proposed system, we evaluate how efficiently they reduce waiting times using scheduling methods.
    Keywords: Hybrid broadcasting; Continuous media data; Scheduling; Waiting time.

Special Issue on: Security for Cloud Computing

  • Searchable Symmetric Encryption Based on the Inner Product for Cloud Storage
    by Jun Yang, Shujuan Li, Xiaodan Yan, Baihui Zhang, Baojiang Cui 
    Abstract: Searchable encryption enables the data owner to store their own data after encrypting them in the cloud. Searchable encryption also allows the client to search over the data without leaking any information about it. In this paper, we rst introduce a searchable symmetric encryption scheme based on the inner product: it is more ecient to compute the inner product of two vectors. In our construction, the parties can be Data Owners, Clients or the Cloud Server. The three parties communicate with each other through the inner product to achieve the goal that the client can search the data in the cloud without leaking any information on the data the owner stored in the cloud. We then perform a security analysis and performance evaluation, which show that our algorithm and construction are secure and ecient.
    Keywords: Searchable Encryption; Searchable Symmetric Encryption; Inner Product; the Cloud Server; Security.

  • Lattice-based Searchable Public-key Encryption Scheme for Secure Cloud Storage   Order a copy of this article
    by Run Xie, Chunxiang Xu, Changlian He, Xiaojun Zhang 
    Abstract: With the popularity of cloud storage and the improvement of awareness of data privacy, the user's sensitive data is usually encrypted before uploading them to the cloud. Searchable encryption is a critical technique on promoting secure and efficient cloud storage. In particular, public key encryption with keyword search (PEKS) provides an elegant approach to achieve data retrieval in encrypted storage. However, all existing searchable public-key encryption schemes only provide the security based on classical cryptography hardness assumption. With the enhancement of cloud-computing power and the development of quantum computers, these schemes will be insecure. In this paper, we propose a new searchable public-key encryption scheme with a designated tester(dPEKS). Our scheme has notable advantages: Firstly, our scheme is the first searchable public-key encryption scheme based on lattice hardness assumptions. Currently, the lattice-based cryptography is considered to be secure even if quantum computers are ever developed. Therefore, our scheme is the promising candidate for traditional schemes. Secondly, our scheme achieves the trapdoor indistinguishability. The trapdoor indistinguishability implies the security against outside off-line keyword guessing attacks(KGAs). Until now, only few schemes can resist outside off-line KGA. In Boneh et als original framework, the inside keyword guessing attacks(KGAs) is considered inevitable. In this sense, our scheme provides the strongest security level. Lastly, our scheme can achieve the trapdoor anonymity for server.
    Keywords: dPEKS ; searchable encryption; trapdoor indistinguishability; lattice; keyword-guessing attack; cloud storage.

  • Key-Aggregate Searchable Encryption under Multi-owner Setting for Group Data Sharing in the Cloud   Order a copy of this article
    by Tong Li, Zheli Liu, Chunfu Jia, Zhangjie Fu, Jin Li 
    Abstract: In recent years, the encryption with keyword search has been widely used in cloud data sharing system to protect privacy and confidentiality when the ciphertext is retrieving. However, selectively sharing encrypted data and related searching abilities among different users via the existing searchable encryption technology certainly will generate a large number of searching trapdoors making the system inflexible and impractical. In this paper, we propose the concept of ``multi-owner key-aggregate searchable encryption'' scheme and its implementation, in which a user can only submit a trapdoor for querying the documents shared by multiple owners who only need to distribute an aggregate key for sharing massive data. Thus, the scheme supports effective data sharing for both multiple owners and users by reducing unnecessary trapdoors which is hard for generating by mobile devices during the querying step. Finally we conduct security analysis and performance evaluation which can show that our system is practical and secure.
    Keywords: cloud storage; searchable encryption; data sharing; key-aggregate.