Ensemble deep neural network based quality of service prediction for cloud service recommendation
Introduction
Cloud services are being extensively used in many fields and are actively supported by major industrial companies. To understand these services we should be able to quantify and measure the service parameters. Quality of Service (QoS) is typically used to characterize these cloud provider’s non-functional characteristics. With the increasing demand and proliferation of cloud services in the market, QoS has become an important point for marketing and differentiating functionally similar cloud services. To identify appropriate cloud services, users have to spend a significant amount of time and effort in searching and choosing the services. It’s a tedious, and less efficient method. Further, the suggestion of services based on users’ interests, historical records, or similar data about users is found to be appealing to customers and suppliers. Thus, it is important to obtain unbiased and precise client-side QoS values for different cloud services.
For different cloud service users, client-side cloud service assessments are needed [4], [21]. Customer-side cloud service research is practically difficult, primarily because of the following reasons:
- •
Typically, users are not cloud service analysis specialists and aren’t familiar with most technical terminologies. Thus, it is complicated and unreliable for them to collect and process any data.
- •
There is a specific cost involved with each service for its invocation, so it may not be feasible for a service user to use all of them.
- •
Evaluating all cloud service applications is time-consuming and inefficient for service users, as many cloud services are available.
Thus, despite adequate tests on the client-side, it is impossible to obtain reliable QoS values for cloud services.
There are several methods that have been used to evaluate cloud services based on which these can be recommended. Recently, a considerable focus is on Collaborative filtering-based (CF) service recommendation methods [32], [41]. Based on the implementation, it is divided into two categories: Memory/Neighborhood-based and Model-based approaches. Memory-based CF approaches generally follow the following steps; Firstly, these methods find out similar users and similar services, called neighbor users and services, by computing the PCC similarity score among users and services. Then, they predict the unknown QoS values for a particular user and particular service by processing a sequence of steps with information retrieved in the last step.
The Model-based Collaborative Filtering approach needs to build a capable model based on training data and machine learning algorithms, integrating historical data from related customers to forecast the target service’s quality. Lately, the Matrix Factorization technique has seen an enormous increase in successful use cases of model-based prediction approaches.
However, predictive precision of CF-based approaches relies on neighbor selection efficiency, but in data scantiness scenario, neighbor selection quality is restricted because of less QoS related data availability. While model-based CF approaches do not depend upon neighbor selection, they directly learn latent features from service invocation; hence, under higher data sparsity conditions, model-based approaches have better prediction accuracy than memory-based models.
One of the widely used and efficient model-based approaches is Matrix Factorization (MF). As the name suggests, factorization is the core part of the this technique. A large matrix with a large dimension is converted into two smaller matrices with low dimensions called feature matrices. These matrices are multiplied and values are compared with the original matrix. Weights are updated according to the chosen algorithm (like stochastic gradient descent) until the difference in values is minimized. In our case, the user’s choices for a cloud service can depend on several factors, which can be considered as latent features in smaller matrices. MF works better than memory-based methods, even in cold start situations. But on the other side, Matrix Factorization has its features described manually, which limits its scalability. MF models are more general, and they aim to reduce some defined error function throughout the training. This method does not perform well with the increase in the size of the dataset. Hence, these models cannot learn the proper correlation between entities and treat them equally, which leads to a decline in prediction accuracy.
Lately, Deep Learning has made a great impact on fields like Image processing, Computer vision, Speech and Voice recognition. Some of the reasons why Deep Learning will be more suitable for recommendation systems than conventional methods are as follows: Nonlinear Transformation, Representation Learning, Sequence Modelling, Flexibility [48]. Deep learning can learn many complex correlations among several entities. A service user’s behavior may be non-linearly dependent on other users, which can be effectively learned using the Deep Learning model. Automatic Feature design makes it scalable and effortless and it has the support of some popular frameworks like Tensorflow, PyTorch, Keras, etc. Hence Deep Learning is a better option for service recommendation. The main contributions of this paper are as follows:
- 1.
A Neural Network based Ensemble of Deep Learning models (EDNN) to learn user and service correlation for QoS prediction in Cloud Services Recommendation.
- 2.
We have used Multi-layer Perceptron and have given a novel approach for weight prediction and balancing between User prediction and Service prediction using Deep Learning.
- 3.
We have carried out all the experiments on the WS-dream dataset; detailed analysis and results have been presented, which shows our prediction results to be accurate.
The remaining part of this paper is organized into several sections; Section 2 has a description of the related works done in the field of recommender systems for cloud services. Section 3 presents the approach and required details of implementation for our approach. Section 4 discusses the complexity and cost of operation. Section 5 explains the experimental requirements and configurations. Section 6 consists of results and analysis, and Section 7 concludes our work.
Section snippets
Literature survey
Cloud Services Recommendation has been under research for the past few years. Overview of the present state-of-the-art methods and their approach tells us about the progress of research and allows us to extend them further for a better solution to work in real-world scenarios.
Neighborhood based neural network approach
In the past few years, Deep Neural Networks has made a more significant contribution to diverse areas of intelligence, such as computer vision, image processing, data mining, speech recognition, etc. The discovery of deep and complex hidden features is what makes Deep Learning more optimum for compounded problems. However, the use cases of Deep learning in the area of service recommendation are new. By the literature survey, it is known that, nearly all the past pioneering methods are very
Complexity analysis
In this section, the Computational Complexity of various operations performed by our proposed method is discussed. Let us fix the shape of our dataset is m x n, where m is the number of users and n is the number of services.
Data description
To assess the prediction performance of our approach, we have used a dataset of real-world web service invocations, WS-Dream, collected by Zheng et al. [54] and Zheng and Lyu [57]. This dataset has two sub-datasets, the first one contains static Response Time and Throughput values, and the other one contains a similar set of data but in time-series fashion. For our experiment, we have used the first dataset with Response Time as the quality of service parameter. As shown in Table 4, the dataset
Result and analysis
Our approach is better in learning complex correlation from conventional Collaborative Filtering and Pure Matrix Factorization based approaches because it makes use of a Deep Neural Network-based model to learn both the user features and service features separately. Further, balance the prediction using a separate model that uses Deep Neural Network to automatically assign proper weights to the output of both user model and service model to predict the final result. Table 7, Table 8 proves our
Conclusion
Quality of Service (QoS) values for cloud service recommendation using Ensemble Deep Neural Network Models (EDNN) is predicted. The results obtained are better than conventional Collaborative Filtering approaches such as memory based, model based approaches which include Matrix Factorization, other hybrid models and a CNN-based model. We tested our model with different level of data sparsity and against different number of similar neighbors. Experimental timeline starts from establishing setup
CRediT authorship contribution statement
Parth Sahu: Visualization, Investigation, Conceptualization, Methodology, Software, Writing - original draft, Writing - review & editing, Validation. S. Raghavan: Conceptualization, Writing - original draft, Writing - review & editing, Validation. K. Chandrasekaran: Supervision, Writing - review & editing.
Declaration of Competing Interest
The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus;membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed
Acknowledgements
aaaa
Parth Sahu is currently pursuing his Master of Technology in Computer Science at the National Institute of Technology Karnataka Surathkal, Mangalore, India. He completed his B.E. in Information Technology from Shri Govindram Seksariya Institute of Technology, Indore, M.P., India. His current research interests are Machine Learning, Blockchain, Cloud Computing, Internet of Things (IoT).
References (57)
- et al.
Efficient web service qos prediction using local neighborhood matrix factorization
Eng. Appl. Artif. Intell.
(2015) - et al.
An svm-based collaborative filtering approach for top-n web services recommendation
Fut. Gen. Comput. Syst.
(2018) - et al.
Tap: A personalized trust-aware qos prediction approach for web service recommendation
Knowl.-Based Syst.
(2017) - et al.
Collaborative qos prediction with context-sensitive matrix factorization
Fut. Gen. Comp. Syst.
(2018) - et al.
Exploring latent features for memory-based qos prediction in cloud computing
- C. Aggarwal, Neighborhood-based collaborative filtering, 2016, pp. 29–70....
- D. Bokde, S. Girase, D. Mukhopadhyay, Matrix factorization model in collaborative filtering algorithms: A survey. in:...
- P.A. Bonatti, P. Festa, On optimal service selection. In: Proceedings of the 14th international conference on World...
- et al.
Empirical analysis of predictive algorithms for collaborative filtering
Hybrid recommender systems: Survey and experiments
User Model. User-Adapt. Interact.
(2002)
User-qos-based web service clustering for qos prediction
A cluster feature based approach for qos prediction in web service recommendation
Regionknn: A scalable hybrid collaborative filtering algorithm for personalized web service recommendation
Web service recommendation via exploiting location and qos information
IEEE Trans. Parallel Distrib. Syst.
A user dependent web service qos collaborative prediction approach using neighborhood regularized matrix factorization
A hierarchical matrix factorization approach for location-based web service qos prediction
Missing qos-values predictions using neural networks for cloud computing environments
Web service qos prediction based on adaptive dynamic programming using fuzzy neural networks for cloud services
IEEE Access
Incorporation of efficient second-order solvers into latent factor models for accurate prediction of missing qos data
IEEE Trans. Cybern.
Generating highly accurate predictions for missing qos data via aggregating nonnegative latent factor models
IEEE Trans. Neural Networks Learn. Syst.
An adaptive service selection approach to service composition
Classification of short time series in early parkinson s disease with deep learning of fuzzy recurrence plots
IEEE/CAA J. Autom. Sin.
Cited by (11)
Stable and efficient resource management using deep neural network on cloud computing
2023, NeurocomputingCitation Excerpt :The remainder of this paper is organized as follows: related works on resource autoscaling are described in Section 2; the ProHPA scheme proposed in this study is explained in Section 3, and the overall architecture of ProHPA is presented in Section 4; ProHPA is implemented in Section 5, and the performance of ProHPA is evaluated in Section 6; lastly, the research findings on ProHPA are summarized and future research directions are proposed in Section 7. Various studies have been conducted to efficiently manage resources in cloud computing [11–19]. In this study, several universal methods for managing container resources using horizontal, vertical and hybrid autoscaling techniques are described.
AutoTrust: A privacy-enhanced trust-based intrusion detection approach for internet of smart things
2022, Future Generation Computer SystemsCitation Excerpt :The application layer is divided into different sectors, i.e., buildings (commercial or industrial), energy (supply/demand, oil/gas), consumer and homes (infrastructure, convenience, and entertainment), health and life science (care and research), industrial (resource automation, fluid/process, distribution), transportation (trans systems, vehicular, and non-vehicular), retail (stores, hospitality, and specialty), security (surveillance, equipment, tracking, public infrastructure, and emergency services), and information technology (public or enterprise). All the sectors of application layer are location-based, and provide services with respect to the location A requirement of real-time response at the IoT side exhibits the necessity for QoS assurance at the cloud side [13]. IoT requires efficient power consumption of applications [14], and the cloud needs a mechanism that maintains the energy resources efficiently [15].
Deep learning based web service recommendation methods: A survey
2023, Journal of Intelligent and Fuzzy SystemsA Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction
2023, IEEE Transactions on Services ComputingTentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises
2023, Journal of Supercomputing
Parth Sahu is currently pursuing his Master of Technology in Computer Science at the National Institute of Technology Karnataka Surathkal, Mangalore, India. He completed his B.E. in Information Technology from Shri Govindram Seksariya Institute of Technology, Indore, M.P., India. His current research interests are Machine Learning, Blockchain, Cloud Computing, Internet of Things (IoT).
S. Raghavan is currently doing his Doctoral Research at National Institute of Technology Karnataka Surathkal, Mangalore, India. He completed his B. Tech from SASTRA University, Thanjavur, India and M. Tech from National Institute of Technology Karnataka Surathkal, India. His area of interest include Cloud Computing, Distributed Computing, Membrane Computing and Machine Learning.
K. Chandrasekaran is currently Professor in the Department of Computer Science & Engineering, National Institute of Technology Karnataka, India, having 31 years of experience. He has more than 300 research papers published by various reputed and peer-reviewed International journals, and conferences. He has received best paper awards and best teacher awards. He serves as a member of various reputed professional societies including IEEE (Senior Member), ACM (Senior Member), CSI (Life Member), ISTE (Life Member) and Association of British Scholars (ABS). He is also a member in IEEE Computer Society’s Cloud Computing STC. He is in the Editorial Team of IEEE Transactions on Cloud Computing, one of the recent and reputed journals of IEEE publication. He has coordinated many sponsored projects, and, some consultancy projects. He has organized numerous events such as International conferences, International Symposium, workshops and several academic short term programs at NITK. He was a visiting fellow at LMU Leeds, UK in 1995, Visiting Professor at AIT, Bangkok in 2007, and Visitor at UF, USA in 2008 and a Visitor at Univ. of Melbourne, CLOUDS LAB in 2012. He had also worked as Visiting (Professor) at DoMS, IIT Madras during Feb-Dec. 2010. His areas of interest - research include: Computer Networks and Distributed Computing, Federated Cloud Computing, Big Data Management, Internet of Things, Cyber Security, Enterprise Computing & Information Systems Management, and Knowledge Management.