International Journal of Web and Grid Services (11 papers in press)
An Auction Framework for DaaS in Cloud Computing and Its Evaluation
by Anjan Bandyopadhyay, Fatos Xhafa, Sajal Mukhopadhyay, Vikash Kumar Singh, Aniruddh Sharma
Abstract: Data as a Service (DaaS) is the next emerging technology in cloud computing research. Small clouds operating as a group may exploit the DaaS efficiently to perform the substantial amount of work. In this paper, an auction framework is studied and evaluated when the small clouds are strategic in nature. We present the system model and formal definition of the problem and its
experimental evaluation. Several auction DaaS-based mechanisms are proposed and their correctness and computational complexity is analyzed. To the best of our knowledge, this is the first and realistic attempt to study the DaaS in a strategic setting. We have evaluated the proposed approach under various simulation scenarios to judge on its usefulness and efficiency.
Keywords: Data as a Service; Auction; Mechanism Design; Micro Cloud.
Controlling Astroturfing on the Internet: A Survey on Detection Techniques and Research Challenges
by Syed Mahbub, Eric Pardede, A. S. M. Kayes, Wenny Rahayu
Abstract: Astroturfing is one of the most impactful threats on todays internet. It is the process of masking and portraying a doctored message to the general population in a way as though it originated from the grass-root level. The concept of astroturfing detection is started to gain popularity among researchers in social media, e-commerce and politics. With the recent growth of crowdsourcing systems, astroturfing is also creating a profound impact on peoples opinions. Political blogs, news portals and review websites are being flooded with astroturfs. Some groups are using astroturfing to promote their interest and some are using it to demote the interest of competitors. Researchers have adopted many approaches to detect astroturfing on the web. These approaches include content analysis techniques, individual and group identification techniques, analysing linguistic features, authorship attribution techniques, machine learning and so on. We present a taxonomy of these approaches based on the key issues in online astroturfing detection techniques and discuss the relevant approaches in each category. The paper also summarises the discussed literature and highlights research challenges and directions for future work that have not aligned with the currently available research.
Keywords: Astroturfing detection; Astroturf; Social media astroturfing; Crowdturfing; Collusive spamming; Internet threats.
Efficient Resource Allocation for Consumers Power Requests in Cloud-Fog based System
by Rasool Bukhsh, Nadeem Javaid, Sakeena Javaid, Manzoor Ilahi, Itrat Fatima
Abstract: A user's requirement and designing goal for a computing machine are to process and respond the requests in real time with cost efficiency. System modeling with efficient resource allocation develops time and cost efficient platform. The cloud has enhanced resources with resource sharing techniques for efficient processing. However, it suffers from long Response Time (RT), which can degrade the real time applications for smart grid. This paper introduces a fog computing layer between cloud and client to process their energy requests in the fog instead of the cloud in order to optimize the computational cost, processing time and RT. A hybrid service broker policy and modified honey bee colony optimization algorithm are proposed for efficient fog selection and balancing load requests on virtual machines in the fog. The six geographical regions are considered. In each region, two clusters of residential buildings have access to two fogs for processing of their requests. The Micro Grids (MGs) are introduced in the proposed system model between the fogs and the clusters for uninterrupted and
cost-efficient power supply. The recurring cost of MGs and computing cost of the
fogs make the system operation cost, which is added in the consumers' electricity
bill. The simulation validate the efficient system operation cost, processing and
response time for power consumers.
Keywords: Cloud computing; Fog computing; Response time; Processing time,
Smart grid; Micro grid; Load balancing.
Application of parallel algorithm optimization method to relational queries by reducing interprocessor data exchange time
by Yulia Shichkina, Mohammed Al-Mardi, Nikita Storublevtcev, Alexander Degtyarev
Abstract: The article presents the results of studies on the adaptation of methods for optimizing parallel algorithms by time and volume of computational resources to queries in relational databases. Research focuses on a method that allows to improve the execution schedule of a parallel query by execution time due to the redistribution of operations between processes. It leads to a reduction in amount of messages transferred between processors, and to the time spent on transfer of data. It does not affect the amount of computational resources. The method is based on adjacency lists, that correspond to the information graph of the algorithm. It can be applied in conjunction with other scheduling methods, for example, focused on computation node count optimization, to achieve optimal result within multiple parameters.
Keywords: parallel algorithm; algorithm optimization; information graph; operation execution time; algorithm execution schedule; process; processor; interprocessor data transfer; relational database.
A smart tableware based meal information collection system using machine learning
by Liyang Zhang, Kohei Kaiya, Hiroyuki Suzuki, Akio Koyama
Abstract: In recent years, due to lifestyle-related diseases, people have paid more and more attention to the management of healthy meals. Some meal management systems are entering people's lives gradually. Existing studies have found that the proper meal habits, such as a correct meal sequence, can help prevent disease to a certain extent. In this paper, we introduce the smart tableware consisting of an acceleration sensor and a pressure sensor to obtain meal information such as meal sequence and meal content automatically. Moreover, feature extraction is performed on the meal information captured by sensors, and the machine learning algorithms are used to analyze and process the information. Finally, the meal content and meal sequence are fed back to the user to help people prevent diseases affected by lifestyle habits such as obesity and diabetes. In the experiment, we compare a variety of different machine learning algorithms and analyze the experimental results.
Keywords: meal information; smart tableware; Internet of Things; machine learning algorithms; support vector machine; multilayer perceptron; lifestyle-related diseases; meal management systems; meal sequence; meal content; meal time.
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.
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.