A belief propagation-based method for task allocation in open and dynamic cloud environments

Kong, Yan; Zhang, Minjie; Ye, Dayong

doi:10.1016/j.knosys.2016.10.016

Cited by 192 publications

(63 citation statements)

References 20 publications

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“…Recently, scholars have proposed privacy personalization or adaptation as a highly dynamic, user-tailored, and context-aware approach to privacy decision support at an individual level [37]. A decentralized belief propagationbased method, PD-LBP, was proposed for multiagent task allocation in open and dynamic grid and cloud environments [38]. Chen et al proposed a coverless information hiding method using Chinese character technology with high efficiency [39].…”

Section: Related Workmentioning

confidence: 99%

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Zhang

Cui

et al. 2018

Security and Communication Networks

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Privacy issues have become a major concern in the web of resource sharing, and users often have difficulty managing their information disclosure in the context of high-quality experiences from social media and Internet of Things. Recent studies have shown that users' disclosure decisions may be influenced by heuristics from the crowds, leading to inconsistency in the disclosure volumes and reduction of the prediction accuracy. Therefore, an analysis of why this influence occurs and how to optimize the user experience is highly important. We propose a novel heuristic model that defines the data structures of items and participants in social media, utilizes a modified decision-tree classifier that can predict participants' disclosures, and puts forward a correlation analysis for detecting disclosure inconsistences. The heuristic model is applied to real-time dataset to evaluate the behavioral effects. Decision-tree classifier and correlation analysis indeed prove that some participants' behaviors in information disclosures became decreasingly correlated during item requesting. Participants can be "persuaded" to change their disclosure behaviors, and the users' answers to the mildly sensitive items tend to be more variable and less predictable. Using this approach, recommender systems in social media can thus know the users better and provide service with higher prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Zhang

Cui

et al. 2018

Security and Communication Networks

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“…In addition, some program comprehension techniques combined the strengths of both syntax and semantic clustering [7,38,[64][65][66]. The ACDC algorithm is one example of this combined approach which used name and dependency of classes to cluster all classes in a system into small clusters for comprehension [3].…”

Section: Scientific Programming 13mentioning

confidence: 99%

Clustering Classes in Packages for Program Comprehension

Sun

Liu

et al. 2017

Scientific Programming

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During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized packages, which utilizes the Latent Dirichlet Allocation (LDA) model to cluster large-sized packages. Thus, these large-sized packages are separated as small-sized clusters, which are easier for developers to comprehend. Empirical studies on four real-world software projects demonstrate the effectiveness of our approach. The results show that the effectiveness of our approach is better than Latent Semantic Indexing-(LSI-) and Probabilistic Latent Semantic Analysis-(PLSA-) based clustering approaches. In addition, we find that the topic that labels each cluster is useful for program comprehension.

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“…The "pay-as-you-go" feature and dynamics of resource provision in cloud computing [2] enable a cloud user to employ multiple cloud services from different cloud providers to seek a more economical combination of cloud services, thereby resulting in multi-source user-service quality data of cloud users. Consequently, each cloud provider only has part of the entire data about cloud users, and if a cloud service provider makes recommendation decisions based on such an incomplete view of the data, the recommendation accuracy can be considerably compromised.…”

Section: Introductionmentioning

confidence: 99%

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

Zhang

Dou

et al. 2017

IEEE J. Select. Areas Commun.

190

142

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Abstract-To maximize the economic benefits, a cloud service provider needs to recommend its services to as many users as possible based on the historical user-service quality data. However, when a cloud platform (e.g., Amazon) intends to make a service recommendation decision, considering only its own user-service quality data is insufficient because a cloud user may invoke services from multiple distributed cloud platforms (e.g., Amazon and IBM). In this situation, it is promising for Amazon to collaborate with other cloud platforms (e.g., IBM) to utilize the integrated data for the service recommendation to improve the recommendation accuracy. However, two challenges are present in the above collaboration process, where we attempt to use multi-source data for the service recommendation. First, protecting users' privacy is challenging when IBM releases its own data to Amazon. Second, the recommendation efficiency and scalability are often low when the user-service quality data of Amazon and IBM update frequently. Considering these challenges, a privacy-preserving and scalable service recommendation approach based on distributed locality-sensitive hashing (LSH), i.e., SerRec distri-LSH , is proposed in this paper to handle the service recommendation in a distributed cloud environment. Extensive experiments on the WS-DREAM dataset validate the feasibility of our approach in terms of service recommendation accuracy, scalability and privacy preservation.

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A belief propagation-based method for task allocation in open and dynamic cloud environments

Cited by 192 publications

References 20 publications

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

A Heuristic Model for Supporting Users’ Decision-Making in Privacy Disclosure for Recommendation

Clustering Classes in Packages for Program Comprehension

A Distributed Locality-Sensitive Hashing-Based Approach for Cloud Service Recommendation From Multi-Source Data

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