International Journal of Web and Grid Services (6 papers in press)

Regular Issues

• A survey on the Optimization of Age of Information in Wireless Networks  
  by Wang Hongyan, Sun Qibo, Wang Shangguang 
  Abstract: This article comprehensively surveys the area of Age of Information (AoI) in the wireless networks and focuses on categorizing and reviewing the current progress on AoI from an optimization perspective. We first present the multiple definitions of AoI and its variants. Then, we give an overview of AoIoptimal sampling policies and packet management strategies from data source. We also summarize the work of minimizing AoI in the case of resource-constraint source nodes, such as energy harvesting and UAV-assited sampling. We provide a summary of many kinds of scheduling policies for efficiently managing the use of resources in different network settings, which consist of various data sources and servers. In addition, we discuss some other applications focusing on the optimization of AoI. Furthermore, we also explore the performance of those policies in practical implementation and summarize the strengths and weaknesses of different platforms. Finally, we explore some potential future directions on AoI research.
  Keywords: Time-sensitive application; AoI; queue network; optimal policy; scheduling.
  
  
• Sentiment Analysis and Counseling for COVID-19 Pandemic based on Social Media  
  by HaYoung Lee, OkRan Jeong 
  Abstract: As COVID-19 emerged and prolonged, various changes have occurred in our lives. For example, as restrictions on daily life are lengthening, the number of people complaining of depression is increasing. In this paper, we conduct a sentiment analysis by modeling public emotions and issues through social media. Text data written on Twitter is collected by dividing it into the early and late stages of COVID-19, and emotional analysis is performed to reclassify it into positive and negative tweets. Therefore, subject modeling is performed with a total of four datasets to review the results and evaluate the modeling results. Furthermore, topic modeling results are visualized using dimensional reduction, and public opinions on COVID-19 are intuitively confirmed by generating representative words consisting of each topic in the word cloud. Additionally, we implement a covid-chatbot that provides a question-and-answer service on COVID-19 and verify the performance in our experiments.
  Keywords: Social media analysis; Sentiment analysis; Topic modeling; Covid chatbot; Google BERT; Microsoft DialoGPT.
  
  
• PolarisX2: Auto-Growing Context-Aware Knowledge Graph  
  by YeonSun Ahn, SoYeop Yoo, OkRan Jeong 
  Abstract: Artificial intelligence requires advanced technologies in various fields. In particular, natural language processing consists of many tasks for computers to understand and process human languages, and knowledge graphs represent a person's common sense as a graph. Various studies exist because knowledge graphs could play a crucial role in computers understanding natural language. PolarisX is an auto-growing knowledge graph that could especially cope with neologisms. However, existing studies, including PolarisX, have a limitation in that they rarely correspond to information containing numbers representing a cardinal, ordinal, or quantity and can extract only one relationship from one sentence. We propose the auto-growing context-aware knowledge graph PolarisX2, an entity extraction model that responds to numeric information, and a relation extraction model considering type information. It also enables multiple knowledge extraction from a sentence by applying the candidate pair construction model. We verify the novelty and performance of the PolarisX2 in our experiments.
  Keywords: auto-expansion; context-aware; knowledge graph; type information; named entity recognition; multiple relation extraction.
  
  
• A feature-driven variability-enabled approach to adaptive service compositions  
  by Chang-ai Sun, Zhen Wang, Zaixing Zhang, Luo Xu, Jun Han, Yanbo Han 
  Abstract: Service compositions are widely used to construct complex applications. Due to the frequent changes of environment and requirements, service compositions need to be adaptable enough. In this work, we propose a feature-driven variability-enabled adaptive service composition approach to systematically treat the variability in the full life-cycle of service compositions. Specifically, the feature model is introduced to represent common and variable requirements and drive the variability design of service compositions. An abstract service composition model is used to define the variable business process. Rules and algorithms are then defined to transform the feature model to the abstract service composition model, from which different process instances are derived on demand to meet different requirements. We have developed a prototype tool to facilitate and automate our approach as much as possible. Finally, a case study is conducted to demonstrate the proposed approach and validate its effectiveness and efficiency.
  Keywords: Variability Management; Adaptive Service Compositions; Abstract Service Composition Model; Feature Model; Model Transformation.
  
  
• Data Locality-Aware and QoS-Aware Dynamic Cloud Workflow Scheduling in Hadoop for Heterogeneous Environment  
  by Fan Ding, Minjin Ma 
  Abstract: Cloud computing provides data-intensive application with scalability and large scale resources. Recently, many data-intensive scientific applications adopt data parallel computing framework in cloud, such as Hadoop. Most scientific applications employ workflows to portray procedures and dependencies between jobs. However, the current default scheduling policy in Hadoop does not take data locality into account. Thus, the movement of data among virtual machines (VMs) produces substantial latency in workflow scheduling. In addition, the heterogeneous and dynamics of cloud resources lead to the selection of different virtual machines that will affect the resulting scheduling performance. The current static scheduling strategy that adopted by most of the workflow scheduling in Hadoop cannot fulfill the requirement of optimal scheduling paths, as each selection of the optimal VM at the moment is not necessarily the global optimal. Moreover, static workflow scheduling also cannot satisfy the users demand for quality of service (QoS). Furthermore, there are few studies that consider data placement based on data locality in dynamic workflow scheduling. Hence, we propose a data locality-aware and QoS-aware dynamic cloud workflow scheduling algorithm (DQ-DCWS) based on dynamic programming. The algorithm balances data locality and delays by grouping nodes that hold tasks correlated with data blocks. We consider five QoS factors and normalize them as a path optimization issue to realize maximum QoS. DQ-DCWS is implemented and validated by running Montage workflow on real Hadoop clusters which are deployed on Amazon EC2.
  Keywords: Data locality; Hadoop MapReduce; Heterogeneous; Workflow scheduling.
  
  

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.