International Journal of Web and Grid Services (8 papers in press)
A Fault-tolerant Tree-based Fog Computing (FTBFC) Model for the Internet of Things (IoT)
by Ryuji Oma, Shigenari Nakamura, Dilawaer Duolikun, Tomoya Enokido, Makoto Takizawa
Abstract: In the fog computing model of the IoT, subprocesses of an application process to handle sensor data are performed on fog nodes. The IoT is composed of huge number and types of nodes and consume plenty of electric energy. We have to reduce the electric energy consumed by the IoT. The tree-based fog computing (TBFC) model is proposed to reduce the energy consumption, where fog nodes are structured in a height-balanced tree. In this paper, we propose a fault-tolerant TBFC (FTBFC) model to make the TBFC model tolerant of faults of fog nodes. Here, we newly propose a pair of fault-tolerant strategies. In one data transmission strategy, data processed by disconnected fog nodes is sent to a new parent fog node. Here, we propose an ME (Minimum Energy) algorithm to select a new parent fog node whose energy consumption is minimum. In another subprocess transmission strategy, the subprocess of the faulty fog node is sent to another fog node which takes over the faulty fog node. In the evaluation, the energy consumption and execution time of a new parent fog node can be reduced by the ME algorithm.
Keywords: energy-efficient fog computing; IoT(Internet of Things); energy-efficient IoT; tree-based fog computing (TBFC) model; fault-tolerant tree-based fog computing (FTBFC) model.
Lane detection algorithm based on Hough transform for high-speed self driving vehicles
by Hyunhee Park
Abstract: This study proposes a lane detection method based on expressway driving videos through a computer vision-based image processing system without using sensors. Both straight and curved sections can occur on a road, and thus lanes must be detected by quickly determining such sections. The proposed method detects straight and curved sections that are estimated to be lanes using the Hough transform. When lanes are detected from actual images, the scope of left and right lanes is limited to reduce computational load. In this paper, we propose a lane-detection algorithm using the color space and a stepwise algorithm for accurate lane detection. To verify the proposed algorithms, we developed a small self-driving vehicle model using a TX-2 board. The experiment results when applying the proposed Hough transform algorithm and lane- detection algorithm using the color space show that the lane detection rate of vehicles driving on curves at high speed is approximately 96%. Through the extensive simulation results, the proposed algorithm to vehicle black boxes or autonomous driving will help prevent lane departure and reduce accident rates.
Keywords: Lane detection; Hough transform; Self-driving vehicle; OpenCV; TensorFlow.
Migrating two legacy systems to SOA: A new approach for service selection based on data flow diagram
by Basel Bani-Ismail, Youcef Baghdadi
Abstract: There are many Service Identification Methods (SIMs) to simplify service identification in SOA lifecycle. These SIMs vary in terms of their features (e.g., input artifact, technique). Due to this diversity, few evaluation frameworks have been proposed to guide organizations in selecting a suitable SIM based on their available input artifacts (e.g., source code, business process). This research concerns with SIMs that consider Data Flow Diagram (DFD) as an input artifact, in order to migrate two legacy systems, modeled with DFD, to SOA. Only two SIMs are found in the literature to identify services based on DFD. However, these SIMs do not provide a way to select among the identified services to be implemented as web services. Therefore, this paper aims to bridge this gap by proposing a new approach for service selection based on DFD. This research uses two evaluation frameworks to select a suitable SIM that considers DFD as an input artifact. Then, the selected SIM is applied to identify services from DFD diagrams. Further, a new approach for service selection is proposed. It splits the services identified based on DFD into three service portfolios, in order to select the best portfolio in terms of two service quality attributes: granularity and coupling. Two case studies revealed that the first portfolio (all services identified from DFD level 1) has the best quality, as it achieves two service design principles that are high granularity and low coupling. The proposed service selection approach assists organizations in speeding up the process of migrating their legacy systems to SOA by selecting high-quality services identified from DFD level 1.
Keywords: service-oriented architecture; service identification; service identification method; service selection; service quality; evaluation framework; data flow diagram.
An Improved Public Auditing Protocol for Cloud Storage Integrity Checking
by Jindan Zhang, Baocang Wang
Abstract: Nowadays cloud storage is a more and more popular service for many data owners, they prefer to outsource their datum to the cloud servers. However the cloud servers maybe sometimes loss datum due to accidents. Thus the integrity of the outsourced datum need to be ensured by the data owners or even any other third parties publicly. Recently in the mobile cloud computing setting, Chen et al. proposed a public auditing protocol for data integrity based on adjacency-hash-table. However we find the data blocks' tags can be easily forged in their proposal, and thus the cloud servers can loss datum but still has the ability to give correct proof for data position, which breaks the security of their proposal. We show two concrete attacks to their proposal and give an improved public auditing protocol for cloud storage integrity checking and roughly analysis its security.
Keywords: Cloud storage; public auditing; outsource; attack; tag.
Toward an Aspect-oriented Cache Autoloading Framework with Annotation
by Kun Ma, Xuewei Niu, Ziqiang Yu, Ke Ji
Abstract: In recent years, researches focus on addressing the query bottleneck issue using data cache in the Internet-of-Things. However, the challenges of this method are how to implement autonomous management of data cache. In this paper, we propose an aspect-oriented cache autoloading framework (ACALFA). The architecture, annotation, expression are introduced to address cache auto loading. There are some features for improving performance, such as avoiding cache breakdown and cache penetration using load waiting and autoloading, loose coupling of business and cache logic using AOP, and batch delete of cache. The result of experiments indicated that our method is nearly 25% faster than other cache frameworks in case of high concurrency.
Keywords: Big Data; Data Cache; Aspect-Oriented Programming; Annotation; Pointcut; Grid Services.
A Service Composition Approach Based on Overall QoS and Modified Graphplan
by Ming Zhu, Guodong Fan, Jing Li, Fengying Wang
Abstract: Increasing emphasis on users preferences and the growth of services on the web make service composition a time consuming and complicated work. In this paper, an approach for web service composition that combines Overall QoS and modified Graphplan is proposed. Fuzzy logic is applied to handle uncertainty and support decision making. Specifically, values of Overall QoS of services are generated by using fuzzy analytical hierarchy process, entropy and users preferences. The Graphplan is modified by pruning services according to the values of Overall QoS of services in forward and backward expansions, while producing solutions of compositions. Furthermore, case studies and experiments are performed and show that our approach has better solutions compared with original Graphplan and some other approaches.
Keywords: web service composition; QoS; fuzzy analytical hierarchy process; entropy.
Natural Parallelization Paradigm on Accelerator Physics Example
by Nataliia Kulabukhova
Abstract: In this paper the problems of using the existing methods and algorithms for applied science with the help of high performance techniques is described. The idea is to provide the scientists with an easy to use tools for constructing the implementation of their research working on heterogeneous systems. Without a doubt the problem-solving environment for these purposes must give the exact solution in a reasonable time. The concept which is described in the article was devised specifically for such kind of tasks. The application of it in terms of high energy physics is given. To illustrate the work of the concept the analysis of the distribution of the particles in the beam under the influence of the space charge is done.
Keywords: Hybrid Systems; General Purpose computations on GPU; Parallelization; Problem-Solving Environment.
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.