International Journal of Web and Grid Services (14 papers in press)
Container Description Ontology for CaaS
by Khouloud Boukadi, Molka Rekik, Jorge Bernal Bernabe, Jaime Lloret
Abstract: Besides its classical three service models (IaaS, PaaS, and SaaS), Container as a Service (CaaS) has gained a significant acceptance since it offers without difficulty of high-performance challenges of traditional hypervisors deployable applications. As the adoption of containers is increasingly wide spreading, the use of tools to manage them across the infrastructure becomes a vital necessity. In this paper, we propose a conceptualization of a domain ontology for the container description called CDO. CDO presents, in a detailed and equal manner, the functional and non-functional capabilities of containers, dockers and container orchestration systems. In addition, we provide a framework that aims at simplifying the container management not only for the users but also for the cloud providers. In fact, this framework serves to populate CDO, help the users to deploy their application on a container orchestration system and enhance interoperability between the cloud providers by providing migration service for deploying applications among different host platforms. Finally, the CDOeffectiveness is demonstrated relying on a real case study on the deployment of a micro-service application over a containerized environment under a set of functional and non-functional requirements.
Keywords: Container as a Service; Docker; Container Orchestration System;rnOntology; Container Discovery.
An Iterative Metamorphic Testing Technique for Web Services and Case Studies
by Chang-ai Sun, An Fu, Yiqiang Liu, Zuoyi Wang, Qing Wen, Peng Wu, Tsong Yueh Chen
Abstract: For dynamic software testing, test cases are selected to execute a program and their outputs are compared with the test oracles. Unfortunately, it may be impossible or computationally too expensive to verify the correctness of the output for any given input. This is known as the oracle problem, which occurs quite frequently and affects the effectiveness and automation of testing. Metamorphic testing (MT) is an innovative approach to alleviating the oracle problem, which uses so-called metamorphic relations of the program under test, instead of the test oracles, to verify its outputs. To alleviate the oracle problem of testing Web services, Sun et al.(2011) had previously proposed an MT framework for Web services.
In this paper, we further improve the efficiency and automation of this framework by leveraging metamorphic relations to iteratively generate test cases. We present a fixed-size iterative MT algorithm and implement it in Sun et al.s(2011) MT framework. We conduct three case studies to evaluate the fault detection effectiveness and efficiency of the proposed approach. Experimental results suggest that, compared with the conventional MT, iterative MT can achieve a comparable, if not better, fault detection effectiveness for test case generation and execution, but with significantly fewer resources. Observations and limitations related to iterative MT are summarized to provide new insights into the application of iterative MT. Case studies not only demonstrate that iterative MT can further improve the efficiency of MT for Web services, but also identify the limitations of iterative MT and the factors affecting its fault detection effectiveness.
Keywords: Metamorphic Testing;Metamorphic Relations;Test Case Generation;Web Services.
Towards Big Services Composition
by Mohamed Gharbi, Haithem Mezni
Abstract: Big data has emerged a new paradigm to deal with the processing issues of the large volumes of data. In this context, designing solutions in order to scale-up with the volume, accelerate data processing and improve data extensibility and adaptability, is of paramount importance. To meet these goals, cloud computing has been combined with big data processing leading to a new model of services called Big Services. This model addresses the customers' complex requirements by reusing and aggregating existing services from various domains and delivery models, and from multiple cloud availability zones. Existing Web/cloud service composition approaches are not adequate for the big service context due to many reasons, including the large volume of data, the cross-domain and cross-cloud interoperability issues, etc. Considering the aforementioned facts, the main goal of this paper is to provide a solution to the big service composition issue. To do so, we take advantage of Relational Concept Analysis (RCA), as a clustering method, and Composite Particle Swarm Optimization (C-PSO), as an optimization technique. RCA is used to model the big service environment, whereas C-PSO helps continuously optimizing the quality of big service composition. The implementation and experimental studies on our approach have proven its feasibility and efficiency.
Keywords: Big service; Big Data; Cloud computing; Big service composition; Composite-PSO; Relational Concept Analysis.
Service Composition based on Pre-joined Service Network in Graph Database
by Jing Li, Ming Zhu, Miao Yu, Yuhong Yan, Lizhen Cui
Abstract: To perform an arbitrary service composition task in a graph database, and to support plug-in semantic matching of services, we present a novel service composition approach in a graph database named Pre-joined Service Network. Firstly, the proposed approach constructs and stores a composition network with all services and compositions in a graph database. Then, it fetches a satisfying solution by converting the user's request into queries in the graph database. To illustrate the process of searching for a solution, a simple but meaningful example is provided. Furthermore, we test the performance of the proposed approach with a challenge dataset. Experiment results show that the proposed approach can always find a valid solution and lead to higher user satisfaction when compared with the Pre-joined Semantic Indexing Graph approach.
Keywords: Graph Database; Service Composition; QoS.
An Overview of Compute First Networking
by Liang Tian, Mingzhe Yang, Shangguang Wang
Abstract: Edge computing has become an important innovative business model in the 5G era, especially its low latency feature, which is considered to be not available in traditional solutions. However, the collaboration of computing power between edge computing and cloud computing in the central position has become a new technical problem. On the basis of studying the compute distribution and scheduling requirements of edge computing, this paper introduces a compute network scheme based on the deep fusion of cloud, edge and network, compute first networking. Firstly, we introduce the basic concept of compute first networking. Then, we mainly discuss the framework and key technology of compute first networking. After that, we present some applications with respect to compute first networking. Finally, we discuss the challenges and opportunities in the area of compute first networking.
Keywords: Compute first networking; mobile edge computing; cloud computing; 5G.
Virtual Vehicle based on Incremental Learning for Navigation Service
by Tao Lei, Lei Yang, Zhizhong Shi, Shangguang Wang
Abstract: In internet of vehicles (IoV),drivers always use navigation systems to plan out routes and optimally navigate real-time road congestion.However, the navigation problem cannot be solved nicely by the present approach due to the emphasis put on one point of view on this problem only. The intelligent transportation systems do not consider the drivers preferences adequately, and driverless cars do not consider real-time traffic conditions. To solve this dispersion, in this paper, we first configure an image of driver and vehicle, named virtual vehicle, to replace driver making some decisions in IoV. Then, we propose an incremental learning approach for virtual vehicles based on negative correlation learning algorithm, called divided negative correlation learning algorithm, to obtain drivers preference knowledge. In the proposed algorithm, the trained ensemble is divided into three parts, where the first part is trained on a new data set, and the second part is used to retrain the old data set. And in the end of the proposed algorithm, we combine the three parts to form a new ensemble. Finally, the experimental results show that virtual vehicles can obtain drivers preference knowledge effectively.
Keywords: internet of vehicles; navigation systems; virtual vehicle.
Automatic Incremental Recomposition Algorithm for QoS-aware Internet-of-Things Service Composition
by Hyunyoung Kil, Wonhong Nam
Abstract: In the near future, the Internet-of-Things (IoT) technology will improve dramatically our daily life as a new pervasive computing paradigm. For the IoT computing, various devices and wireless networks are the hardware infrastructure, and Service-Oriented Architecture (SOA) is a valuable software system that allows heterogeneous devices to interoperate each other. Even though IoT researchers have tackled a number of challenges for service composition, the orchestration techniques on IoT are rarely studied yet. Given a set of IoT services and a goal, the QoS-aware IoT service composition problem constructs a composite IoT service with the optimal accumulated QoS value, which satisfies the given goal specification. However, in the IoT environment, frequent changes happen inherently---for instance, temporary machine down, heavy system workload, and network failure. If the solution we have constructed is not valid anymore due to the changes, we should solve a new problem again. In this paper, we propose a novel incremental recomposition algorithm, which does not solve the new composition problem from scratch but explores only the changed space. In the experiment, our incremental recomposition algorithm can deal with the composition problem much faster than the original algorithm to solve from scratch.
Keywords: Incremental algorithm; Recomposition; Internet-of-Things (IoT); Service composition; QoS optimization.
A Generative Adversarial Network Enabled Learning Scheme for Power Grid Vulnerability Analysis
by Ying Liu, Tao Ye, Zhixiang Zeng, Yingbin Zhang, Guoshi Wang, Ning Chen, Cunli Mao, Xiaohui Yuan
Abstract: Real measurements of power grids are usually limited for research and modeling of extreme events such as the impact of typhoons due to confidentiality concerns. To overcome the dearth of valuable, trustworthy data, this paper proposes an active learning method based on the generative adversarial network. To obtain informative examples, the falsely classified examples together with examples that are correctly classified with low confidence are used to train a GAN for producing synthetic examples to reinforce the learning. The new power grid examples are selected according to the likelihood of the true data distribution. An evaluation was conducted with data acquired by the China Southern Power Grid in Hainan. Most significantly, the performance of detecting the occurrence of a power grid fault under the impact of typhoons is greatly improved. It was demonstrated that the proposed method improved the performance of predicting power grid fault in extreme events by 8.9%. Using the modulated GAN network, the synthetic data closely follows the distribution of the real data as indicated by large p-values. Our method takes minutes to complete training a model, which enables an efficient response to disasters with modern computing facilities such as edge computing.
Keywords: Power Grid; Generative Adversarial Network; Classification; Rare Events.
Voronoi Tesselation Based Load-Balanced Multi-Objective Priority-Based Heuristic Optimization for Multi-Cell Region Coverage with UAVs
by Kemal Kilic, Orhan Gemikonakli, Leonardo Mostarda
Abstract: The existing communication infrastructure can be disrupted by unexpected events or can be extended for temporary events. In such cases, UAVs can be deployed as mobile base stations to provide efficient and economic communication. This task requires the optimization of several conflicting objectives. Our work focused on the temporal region coverage with UAVs. The proposed multi-objective priority-based optimization framework utilizes an evolutionary heuristic algorithm with a scoring scheme and gives priorities to objectives to achieve such a task. Our previous single Base Station (BS) based optimization framework is extended by considering Voronoi Tesselation of the coverage region based on the existing nearby BSs. This scheme provides "cells" where the Virtual Base Stations (VBSs) are placed and helps UAVs to receive the best signal from the related center point of the cell, load balancing of the required bandwidth for the whole mission region.
Keywords: Region coverage; Multi-objective optimization; UAVs; Heuristic Evolutionary Algorithms; Voronoi Tesselation.
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.
Special Issue on: Web of Things (WoT) and its Intelligent Data Processing Services
QoS-Prioritized Media Delivery with Adaptive Data Throughput in IoT-Based Home Networks
by Chih-Lin Hu, Liang-Xing Kuo, Yung-Hui Chen, Thitinan Tantidham, Pattanasak Mongkolwat
Abstract: To maintain network efficiency of a home network, it is crucial to moderately distribute limited data throughput and bandwidth resources to various home devices and services that require different network resource provisions. This paper accounts for QoS differentiation and fairness of network resource allocation which are essential to the design of an efficient IoT-based home media delivery mechanism against network traffic dynamics. Accordingly, our study in this paper proposes a QoS-prioritized media delivery mechanism with adaptive data throughput. This mechanism design includes several functions: assigning weights of relative importance to home service types, adjusting media quality of home services, reducing data workload against traffic congestion, and fairly distributing free bandwidth to prioritized home services. Furthermore, our study fulfills a simplified proof-of-concept implementation in a small-scaled IoT-based home network. Practical experiments can generate real measure data for performance examination. Results show that the proposed mechanism is able to sustain not only QoS differentiation like data throughput and delay, but also fair user satisfaction with comfortable media playing quality.
Keywords: media streaming; quality of servie (QoS); service differentiation; network resource allocation; home networks; smart home; internet of things (IoT).
Association between Alcohol Consumption and
by Jianqiang Li, Xi Xu, Yan Pei, Jason C. Hung, Weiliang Qiu
Abstract: Both Telomere length and alcohol consumption have an important impact on biological age and carcinogenesis. Researchers have conducted many efforts on this subject to study the relationship between alcohol consumption and telomere length. However, there is no agreement has been reached on this issue. In our study, a meta-analysis is performed and relevant investigation results from previous literature are integrated. 21 articles published between 2000 and 2016, which comprise 27 analyses with a total sample size 35,891, have met our eligibility criteria. A significant relationship between alcohol consumption and telomere length is found (Fishers combined p-value = 3.52E-8 and Liptaks weighted p-value = 8.24E-3). Whether the relationship between alcohol consumption and telomere length is significant also varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia). It is deduced by combined evidence that alcohol consumption is associated with telomere length. In the future, the consistent quantifications of alcohol consumption and telomere length will benefit further aggregation of the evidence from varies studies.
Keywords: Age; Alcoholism; Cancer; Meta-Analysis; Mitotic Clock; Telomere Length.
Data analysis of simulated WoT-based anti-crime scenario
by Chih-Chi Kuang, Kuei Min Wang, Lin Hui, Chuan-Yu Chang, Kuang Hui Chiu
Abstract: Police work is characterized by high risk and high cost. When a police task has no real-time information, the opaque situation could seriously compromise the success of the task. With the ap-pearance of the Internet of Things (IoT), it has become possible to create the smart things we need and to make them more effective. In addition, the Web of Things (WoT) has emerged recently. It enables us to integrate IoT more easily so that it can be applied to develop a specific system with a wireless sensor network (WSN) and platforms to perform specific tasks. This study proposes a methodology for examining the effectiveness of the WoT in support of the police work of freeing hostages held by terrorists. The proposed WoT-based police rescue squad force (RSF) concept was modeled and simu-lated using the developed Monte Carlo Simulation. The current and WoT-based RSF were the alternatives analyzed by simulation. The simulation results and t tests showed that there is a significant difference between the current and WoT-based RSF. The information gained from the simulation can support the police authoritys decision makers in upgrading the specific police information equipment with less risk, less cost and high effectiveness. The limitation is that the information from the study can only be used in the situation of hostage rescue tasks, i.e. it is inappropriate to apply it to other police work.
Keywords: WoT; IoT; RSF; Simulation; Terrorist; UAV.
A Quadratic Fusion Estimating Model Based on the Clustering Kernel for Real-Time Data in Web of Things
by Chao Li, Zhenjiang Zhang, Yingsi Zhao, Peng Zhang, Bo Shen
Abstract: Real-time data processing is a very important part of data processing in the Web of things (WoT). The devices in WoT collect data and provide real-time information. The accuracy of the collected data is critical to provide valid results. Many existing methods are devoted to modifying filter algorithms. However, little attention is devoted to the inner relationship of data and data accuracy. In the present study, a quadratic filter model based on the clustering kernel is presented. First, the common filter method is used. Second, the clustering algorithm is adopted to deliver the clustering result. The attractor of the class is gained to the clustering kernel. Finally, the quadratic filter is processed according to the clustering kernel. The simulations show that the proposed model can increase the data accuracy.
Keywords: Quadratic Estimating; Clustering Kernel; Web of Things; Fusion Estimating; Real-Time Data.