International Journal of Web and Grid Services (8 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.
  
  
• A proactive method of the webshell detection and prevention based on deep traffic analysis  
  by Ha V. Le, Hanh P. Du, Hoa N. Nguyen, Cuong N. Nguyen, Long V. Hoang 
  Abstract: The popularity of today's web application has led to web servers frequently the objects of webshell attacks. In this paper, we propose a new deep inspection method that is composed of a deep learning algorithm and signature-based technique for webshell detection, namely DLWSD. Moreover, to avoid bottlenecks, DLWSD built-in DeepInspector inspects in real-time the large-scale traffic flows with a strategy of periodic sampling at a defined frequency and interval for only flows that do not satisfy any signature. DeepInspector can create/update rules from webshell attacking alert results to prevent in future. We also proposed a mechanism using the cross-entropy loss function to regulate the training imbalanced dataset. Our experiments allow validating the performance of DLWSD using a popular dataset CSE-CIC-IDS2018 with the metrics accuracy, F1-score, FPR of 99.99%, 99.98%, and 0.01% respectively. It is also better compared with other studies using the same dataset.
  Keywords: intrusion detection; webshell detection; webshell prevention; deep neural network; DNN; DPDK.
  DOI: 10.1504/IJWGS.2022.10048129
   
  
• NARUN: noise adaptive routing for utility networks  
  by Fabio Pagnotta, Leonardo Mostarda, Orhan Gemikonakli, Rosario Culmone, Diletta Romana Cacciagrano, Flavio Corradini 
  Abstract: Wireless Meter-Bus is an open standard for power-efficient smart metering. Data are collected from meters and transmitted to the collector for processing. In smart cities, placing meters with the best quality communication signal is often challenging for urban constraints and other communication signals. Meters can also have limited capabilities in terms of memory and CPU. Previous work has been addressing the reliability issue only in the context of direct collector-meter communication. This paper proposes a novel noise adaptive routing for utility networks (NARUN) protocol for improved performance and efficient routing in a partially connected mesh network. The collector keeps a weighted graph of the whole network where weights define the link failure index. No keep-alive or control messages are used to update the weights. Meters eavesdrop on the surrounding environment and efficiently report link failure indexes to the collector with ordinary reading messages. We validate NARUN on a real case study.
  Keywords: Wireless Meter-Bus; WM-bus; smart metering; routing.
  DOI: 10.1504/IJWGS.2022.10046188
   
  
• Distributed multi-user QoS-aware service selection  
  by Adrian Satja Kurdija, Marin Šilić, Goran Delač, Klemo Vladimir 
  Abstract: Previous research on service selection in cloud environments often assumes a centralised broker that keeps track of all candidate services and selects the appropriate candidate for each user request based on quality of service (QoS). As the number of users and services increases, the centralised service selection algorithm becomes a bottleneck. We propose a distributed, multi-broker selection algorithm that enables real-time processing of a large number of user requests. Each broker keeps track of a subset of users and a subset of service candidates, and rebalancing is performed to ensure that no broker is overloaded. The selection procedure considers the user QoS requirements, service QoS values and their processing capacities. Simulations demonstrate a clear advantage of the proposed approach over a single-broker approach by up to 96% in terms of QoS satisfaction when using 100 brokers, and especially in terms of response time satisfaction, showing positive impact of user and service balancing, and analysing various selection algorithms run by the brokers.
  Keywords: service selection; quality of service; QoS; distributed systems; multi-broker; load balancing; services computing.
  DOI: 10.1504/IJWGS.2022.10047302
   
  
• Adaptive resource management for spot workers in cloud computing environment  
  by Lung-Pin Chen, Fang-Yie Leu, Hsin-Ta Chiao, Hung-Jr Shiu 
  Abstract: Due to flexible scheduling requirements of various service applications, a cloud platform usually has some temporarily unleased machines. To make cost-effective of the platform, such a considerable number of idle workers can be collected to perform malleable tasks. However, these workers are considered unstable since they can be interrupted unexpectedly by the resource broker. This paper proposes a resource management approach that employs replication to increase resource availability. We will show that increasing the replication factor can improve the worker reliability, but on the contrary, it also worsens the overhead of computational redundancy. An algorithm that can effectively control the replication factor so as to adapt to the changing workload and maintain the system performance is also developed.
  Keywords: cloud computing; grid computing; resource allocation.
  DOI: 10.1504/IJWGS.2022.10049658
   
  
• A service selection method in mobile edge and cloud environment based on skyline and cuckoo optimisation algorithm  
  by Xiukun Yan, Ming Zhu, Jing Li, Jinling Zhao 
  Abstract: Compared with traditional cloud computing, services provided by edge computing have several advantages such as high speed and low latency, which make edge services become the key technology of 5G. However, the number of edge servers, the computing capability of an edge server and the number of services deployed on an edge server are limited. Therefore, researchers propose to combine edge computing with cloud computing. How to select appropriate cloud and edge services with low response time and cost to meet complex needs of mobile users is a NP-hard problem. To solve the problem, in this paper, we propose a mobile service selection method in an edge and cloud computing environment based on the combination of skyline and cuckoo optimisation algorithm. Firstly, the skyline method is used to pre-process candidate services and filter out services with poor quality. Secondly, by modelling the mobility of user and the service composition pattern, the cuckoo optimisation algorithm is utilised to select proper edge and cloud services to fulfil user's requirements. To verify the effectiveness and efficiency of the proposed method, experiments are carried out and results indicate that the proposed method has better performance than the referred state-of-art methods.
  Keywords: edge computing; cloud computing; service selection; skyline; cuckoo optimisation algorithm; COA.
  DOI: 10.1504/IJWGS.2022.10049659
   
  
• Measuring chatbot quality of service to predict human-machine hand-over using a character deep learning model  
  by Ebtesam Hussain Almansor, Farookh Khadeer Hussain, Omar Khadeer Hussain 
  Abstract: Recently, intelligent dialogue systems have shown promise in terms of reducing the load of human customer care agents and decreasing user wait times. In some cases, these systems still cannot understand user intent which leads to the generation of inappropriate responses. Therefore, their inability to handle inappropriate responses has limited their utility in the real world. In this work, we propose a character deep learning model for the detection of chatbot quality of services to handle inappropriate responses by intelligently transferring the dialogue to a human agent. The proposed model has two goals: detect CQoS based on the sentiment score of the utterance using a deep learning model and transferring the user to a live agent when the utterance is inappropriate. The proposed model's effectiveness is evaluated on the dialogue breakdown detection task. The results of the experiment show that our proposed model is effective in achieving the desired goals.
  Keywords: character deep learning model; CQoS; breakdown in dialogue; hand-over mechanism.
  DOI: 10.1504/IJWGS.2022.10049660
   
  

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.