International Journal of Web and Grid Services (8 papers in press)

Regular Issues

• Effectively Learn How to Learn: A Novel Few-Shot Learning with Meta-Gradient Memory  
  by Lin Hui, Yi-Cheng Chen 
  Abstract: Recently, the importance of few-shot learning has tremendously grown due to its widespread applicability. Via few-shot learning, users can train their models with few data and maintain high generalisation ability. Meta-learning and continual learning models have demonstrated elegant performance in model development. However, unstable performance and catastrophic forgetting are still two fatal issues with regard to retaining the memory of knowledge about previous tasks when facing new tasks. In this paper, a novel method, enhanced model-agnostic meta-learning (EN-MAML), is proposed for blending the flexible adaptation characteristics of meta-learning and the stable performance of continual learning to tackle the above problems. Based on the proposed learning method, users can efficiently and effectively train the model in a stable manner with few data. Experiments show that when following the N-way K-shot experimental protocol, EN-MAML has higher accuracy, more stable performance and faster convergence than other state-of-the-art models on several real datasets.
  Keywords: machine learning; deep learning; meta-learning; continual learning.
  DOI: 10.1504/IJWGS.2024.10060211
   
  
• An augmented interpretive framework based on aspect sentiment words aggregation  
  by Chao Li, Bo Shen, Yingsi Zhao, Qing-An Zeng 
  Abstract: Given the mounting anxieties surrounding the interpretability of neural models, appraising interpretability remains an unsolved puzzle owing to the ineffectual performance of existing interpretation techniques and evaluation metrics. The architecture of neural network models varies depending on the task at hand, making it challenging to devise a universal method of explanation that can produce coherent justifications for each model. This paper proposes a framework to enhance the interpretability of text sentiment classification models using aspect sentiment words (ASW) aggregation, which can be applied to web services to improve transparency, accountability, and user trust. The proposed method extracts ASW from sentences and consolidates the token importance scores to provide more credible justifications. The paper also introduces new evaluation metrics for faithfulness, which assess whether interpretations accurately reflect the model’s decision-making process. The proposed metrics are effective in evaluating the fidelity of rationales to models at the snippet-level.
  Keywords: deep learning; text sentiment classification; interpretability; aspect sentiment words aggregation.
  DOI: 10.1504/IJWGS.2024.10060552
   
  
• Efficient renewable energy-based geographical load balancing algorithms for green cloud computing  
  by Slokashree Padhi, R.B.V. Subramanyam 
  Abstract: The cloud marketplace is continuously rising as enterprises desire to streamline their processes. As adaptability increases, CSPs expand their data centres to handle any UR size. It increases the fossil fuels consumed in each data centre, increasing the overall cost. Therefore, CSPs are looking for economical ways to reduce fossil fuels. Consequently, three benchmark algorithms were developed in the literature for GLB using renewable energy sources. However, they present the UR using the processor without considering memory. This paper presents two algorithms, PM-FABEF and PM-HAREF, for GCC by incorporating both processor and memory. PM-FABEF determines the processor and memory costs for assigning URs to the data centres and assigns them to the least cost data centre. PM-HAREF determines the highest renewable energy resource slots in processor and memory for assigning the URs. The proposed algorithms are compared with three algorithms using ten datasets to show their superiority in terms of three performance metrics.
  Keywords: cloud computing; geographical load balancing; GLB; renewable energy; non-renewable energy; user request; data centre; overall cost.
  DOI: 10.1504/IJWGS.2023.10058557
   
  
• A fairness aware service recommendation method in service ecosystem  
  by Qiliang Zhu, Yaoling Fan, Shangguang Wang 
  Abstract: With the rapid development of internet technology, the number of services with the same or similar functions on the internet has increased explosively. How to provide users with more accurate service recommendation is one of the hot issues in academia and industry. However, most of the existing recommendation methods tend to recommend popular services to users, which result into serious polarisation and become a barrier for the unpopular services to startup and growth. To solve this problem, we propose a fairness aware service recommendation (FASR), which pays attention to the fair treatment of unpopular services in the process of service recommendation. FASR addresses both accuracy and fairness, and designs different recommendation algorithms for popular and unpopular services respectively. A large number of experiments and analyses show that FASR can significantly improve the fairness of recommendations with little impact on accuracy in the evolving service ecosystem.
  Keywords: fairness; service recommendation; service ecosystem; bias matrix factorisation.
  DOI: 10.1504/IJWGS.2023.10060950
   
  
• An energy-aware negotiation protocol for live migration of virtual machines  
  by Dilawaer Duolikun, Tomoya Enokido, Makoto Takizawa 
  Abstract: The energy consumption of information systems, especially scalable systems like clouds is required to be reduced to realise green computing systems. In an approach, the energy consumption of servers is decreased by the migration of virtual machines to more energy-efficient guest servers. We take a distributed approach that each server autonomously negotiates with other servers on the live migration of virtual machines. In this paper, we newly propose a migration negotiation for virtual machines (MNVM) protocol for doing the negotiation on the migration of virtual machines among servers. We propose NRB and NEA algorithms to do the energy-efficient live migration of virtual machines by taking advantage of the MNVM protocol. In the evaluation, the total energy consumption of the servers and the average execution time of processes are shown to be smaller in the NRB and NEA algorithms than non-migration algorithms.
  Keywords: green computing systems; live migration of virtual machine; energy consumption; MNVM protocol; MLE algorithm.
  DOI: 10.1504/IJWGS.2023.10059624
   
  
• Providing data security using DNA computing in the cloud computing environment  
  by Tarun Kumar, Suyel Namasudra, Prabhat Kumar 
  Abstract: With the exponential growth of data in the digital era, storing and accessing sensitive information securely has become a paramount concern. As a result, it is important to explore new and innovative ways to secure data in a cloud computing environment by utilising advanced encryption and decryption techniques. Recently, deoxyribonucleic acid (DNA) computing has emerged as a promising field that has the potential to revolutionise the way data are processed and stored. In this paper, a novel approach using DNA computing is proposed to improve data security in the cloud computing environment. Here, a 512-bit DNA-based secret key is generated by the data owners, which is utilised for data encryption before outsourcing it to the cloud server. Several encoding rules and operations are employed to make the data encryption process secure. Experimental performance analysis and security analysis of the proposed scheme show its efficiency over the existing schemes.
  Keywords: DNA-based cryptography; Morse pattern; decimal encoding rules; ASCII values; DNA-based XOR operation.
  DOI: 10.1504/IJWGS.2023.10060351
   
  
• Web service selection method based on blockchain smart contracts  
  by Yingying Ning, Jing Li, Ming Zhu 
  Abstract: In recent years, as more services become available on the internet, selecting the best quality services from a group of similar web services and ensuring their authenticity remains a challenge. This paper proposes a blockchain-based service selection method that combines blockchain and service selection to provide a decentralised and trustworthy service selection platform. A smart contract is designed and deployed on the blockchain to help solve service selection problems and prevent information tampering. The moth-flame optimisation algorithm is improved by introducing the mutation and crossover of the differential evolution algorithm for service selection on the blockchain. The experimental results demonstrate that the proposed method effectively prevents service quality tampering and reduces service prices compared to traditional service selection methods and other metaheuristic algorithms.
  Keywords: blockchain; smart contract; service selection; quality of service; QoS; moth-flame optimisation; MFO; web service.
  DOI: 10.1504/IJWGS.2023.10060636
   
  

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.