QoS Ranking Prediction for Cloud Services
IEEE Transactions on Parallel and Distributed SystemsPublished 3 October 2012
Zibin Zheng, Xinmiao Wu, Yilei Zhang, Michael R. Lyu, Jianmin Wang
Citations305
SJR quartileQ1
SJR score2.10
SNIP2.28
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
This paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers, and shows that the experimental results show that the approaches outperform other competing approaches.
Abstract
Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.
Keywords
Computer Science
Item-based collaborative filtering recommendation algorithms
9,017 Citations2001Badrul Sarwar, George Karypis +2 more
This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.
Above the Clouds: A Berkeley View of Cloud Computing
5,742 Citations2009Michael Armbrust, Armando Fox +9 more
This work focuses on SaaS Providers (Cloud Users) and Cloud Providers, which have received less attention than SAAS Users, and uses the term Private Cloud to refer to internal datacenters of a business or other organization, not made available to the general public.
IEEE Internet ComputingAmazon.com recommendations: item-to-item collaborative filtering
5,369 Citations2003Greg Linden, Brent Smith +1 more
This work compares three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods, and their algorithm, which is called item-to-item collaborative filtering.
ACM Transactions on Information SystemsCumulated gain-based evaluation of IR techniques
4,634 Citations2002Kalervo Järvelin, Jaana Kekäläinen
This article proposes several novel measures that compute the cumulative gain the user obtains by examining the retrieval result up to a given ranked position, and test results indicate that the proposed measures credit IR methods for their ability to retrieve highly relevant documents and allow testing of statistical significance of effectiveness differences.
arXiv (Cornell University)Empirical Analysis of Predictive Algorithms for Collaborative Filtering
4,515 Citations2013John S. Breese, David Heckerman +1 more
User Modeling and User-Adapted InteractionHybrid Recommender Systems: Survey and Experiments
3,764 Citations2002Robin Burke
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.
IEEE Transactions on Software EngineeringQoS-aware middleware for Web services composition
2,756 Citations2004Liangzhao Zeng, Boualem Benatallah +4 more
This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service.
ACM Transactions on Information SystemsItem-based top-<i>N</i>recommendation algorithms
2,189 Citations2004Mukund Deshpande, George Karypis
This article presents one class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended, and shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.
arXiv (Cornell University)Empirical Analysis of Predictive Algorithms for Collaborative Filtering
1,507 Citations2013John S. Breese, David Heckerman +1 more
Several algorithms designed for collaborative filtering or recommender systems are described, including techniques based on correlation coefficients, vector-based similarity calculations, and statistical Bayesian methods, to compare the predictive accuracy of the various methods in a set of representative problem domains.
ACM Transactions on the WebEfficient algorithms for Web services selection with end-to-end QoS constraints
1,165 Citations2007Tao Yu, Yue Zhang +1 more
A broker-based architecture is designed to facilitate the selection of QoS-based services and efficient heuristic algorithms for service processes of different composition structures are presented.
IEEE Transactions on Parallel and Distributed SystemsPerformance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
866 Citations2011Alexandru Iosup, Simon Ostermann +4 more
The results indicate that the current clouds need an order of magnitude in performance improvement to be useful to the scientific community, and show which improvements should be considered first to address this discrepancy between offer and demand.
IEEE Transactions on Services ComputingQoS-Aware Web Service Recommendation by Collaborative Filtering
808 Citations2010Zibin Zheng, Hao Ma +2 more
WSRec: A Collaborative Filtering Based Web Service Recommender System
449 Citations2009Zibin Zheng, Hao Ma +2 more
The comprehensive experimental analysis shows that WSRec achieves better prediction accuracy than other approaches, and includes a user-contribution mechanism for Web service QoS information collection and an effective and novel hybrid collaborative filtering algorithm for Web Service QoS value prediction.
Effective missing data prediction for collaborative filtering
428 Citations2007Hao Ma, Irwin King +1 more
This paper uses the enhanced Pearson Correlation Coefficient (PCC) algorithm by adding one parameter which overcomes the potential decrease of accuracy when computing the similarity of users or items and proposes an effective missing data prediction algorithm, in which information of both users and items is taken into account.
IEEE Transactions on Parallel and Distributed SystemsPerformance Analysis of Cloud Computing Centers Using M/G/m/m+r Queuing Systems
388 Citations2011Hamzeh Khazaei, Jelena Mišić +1 more
A novel approximate analytical model is described that allows cloud operators to determine the relationship between the number of servers and input buffer size and the performance indicators such as mean number of tasks in the system, blocking probability, and probability that a task will obtain immediate service.
Journal of Artificial Intelligence ResearchLearning to Order Things
313 Citations1999W. W. Cohen, R. E. Schapire +1 more
An on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm is considered, and it is shown that the problem of finding the ordering that agrees best with a learned preference function is NP-complete.
An automatic weighting scheme for collaborative filtering
292 Citations2004Rong Jin, Joyce Chai +1 more
An optimization algorithm to automatically compute the weights for different items based on their ratings from training users will create a clustered distribution for user vectors in the item space by bringing users of similar interests closer and separating users of different interests more distant.
EigenRank
251 Citations2008Nathan N. Liu, Qiang Yang
This paper proposes a collaborative filtering approach that addresses the item ranking problem directly by modeling user preferences derived from the ratings and shows that the proposed approach outperforms traditional collaborative filtering algorithms significantly on the NDCG measure for evaluating ranked results.
IEEE Transactions on Systems Man and Cybernetics SystemsPredicting Quality of Service for Selection by Neighborhood-Based Collaborative Filtering
201 Citations2012Jian Wu, Liang Chen +4 more
A neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection and has three new features: the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; a data smoothing process to improve prediction accuracy; and a similarity fusion approach to handle the data sparsity problem.
Analyzing and Modeling Rank Data
134 Citations2014John I. Marden
On optimal service selection
120 Citations2005Piero A. Bonatti, Paola Festa
It is proved that one-time costs make the optimal selection problem computationally hard; in the absence of these costs the problem can be solved in polynomial time.
CloudRank: A QoS-Driven Component Ranking Framework for Cloud Computing
99 Citations2010Zibin Zheng, Yilei Zhang +1 more
This paper proposes a QoS-driven component ranking framework for cloud applications by taking advantage of the past component usage experiences of different component users, and shows that this approach outperforms the competing approaches.
CARES
98 Citations2009Chenxing Yang, Baogang Wei +3 more
This paper presents a recommender system for CADAL digital library, namely CARES, which makes recommendations using a ranking-oriented collaborative filtering approach based on users' access logs, avoiding the problem of the lack of user ratings.
WS-DREAM: A distributed reliability assessment Mechanism for Web Services
90 Citations2008Zibin Zheng, Michael R. Lyu
This practical experience report presents a distribute reliability assessment mechanism for Web services (WS-DREAM), allowing users to carry out Web services reliability assessment in a collaborative manner, allowing users from five locations all over the world to perform reliability assessment to Web services distributed in six countries.