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Industrial Internet of Things applications demand trustworthiness in terms of quality of service (QoS), security, and privacy, to support the smooth transmission of data. To address these challenges, in this article, we propose a distributed and anonymous data collection (DaaC) framework based on a multilevel edge computing architecture. This framework distributes captured data among multiple level-one edge devices (LOEDs) to improve the QoS and minimize packet drop and end-to-end delay. Mobile sinks are used to collect data from LOEDs and upload to cloud servers. Before data collection, the mobile sinks are registered with a level-two edge-device to protect the underlying network. The privacy of mobile sinks is preserved through group-based signed data collection requests. Experimental results show that our proposed framework improves QoS through distributed data transmission. It also helps in protecting the underlying network through a registration scheme and preserves the privacy of mobile sinks through group-based data collection requests. [ABSTRACT FROM AUTHOR]

Nowadays, with the cloud's charismatic storage and computation power, more and more traditional services (social networking service, location-based services, etc.) are being migrated onto cloud platforms. These cloud services on different cloud platforms could be employed to form cross-cloud mobile applications of mobile cyber-physical systems (CPS). However, a cloud service may have various versions of quality of service (QoS) information revealed in different mobile CPS applications, which is often advertised as the elastic computation power. This characteristic makes it costly and time consuming to mine qualified ones from massive candidate cloud services for developing a mobile CPS application, as a service composition solution may have various evaluated values initiated by the various QoS properties. In view of this challenge, a cloud service selection method, named CSSM, is proposed in this paper. It takes the utility value as the evaluation index and aims at finding optimal or near-optimal trusted service composition solutions from a set of cloud services on users' demands. Technically, the user preference on each QoS metric is formalized as the preference interval for enhancing the fitness of a service composition solution. Furthermore, an extended top- $k$ iteration composition process is performed among cloud services to get an optimal or near-optimal trusted service composition solution. Both theoretical analysis and experimental evaluation are conducted to guarantee the feasibility and efficiency of the CSSM. [ABSTRACT FROM PUBLISHER]

Cloud computing has rendered its ever-increasing advantages in flexible service provisions, which attracts the attentions from large-scale enterprise applications to small-scale smart uses. For example, more and more multimedia services are moving towards cloud to better accommodate people's daily uses on various smart devices that support cloud, some of which are similar or equivalent in their functionality (e.g., more than 1,000 video services that share similar 'video-play' functionality are present in APP Store). In this situation, it is necessary to discriminate these functional-equivalent multimedia services, based on their Quality of Service (QoS) information. However, due to the abundant information of multimedia content, dozens of QoS criteria are often needed to evaluate a multimedia service, which places a heavy burden on users' multimedia service selection. Besides, the QoS criteria of multimedia services are usually not independent, but correlated, which cannot be accommodated very well by the traditional selection methods, e.g., traditional simple weighting methods. In view of these challenges, we put forward a multimedia service selection method based on weighted Principal Component Analysis (PCA), i.e., Weighted PCA-based Multimedia Service Selection Method (W_PCA_MSSM). The advantage of our proposal is two-fold. First, weighted PCA could reduce the number of QoS criteria for evaluation, by which the service selection process is simplified. Second, PCA could eliminate the correlations between different QoS criteria, which may bring a more accurate service selection result. Finally, the feasibility of W_PCA_MSSM is validated, by a set of experiments deployed on real-world service quality set QWS Dataset. [ABSTRACT FROM AUTHOR]

To assess the quality of services (QoS) in service selection, collaborative service QoS prediction has recently garnered increasing attention. They focus on exploring the historical QoS information generated by interactions between users and services. However, they may suffer from the data sparsity issue because interactions between users and services are usually sparse in real scenarios. They also seldom consider the network environments of users and services, which surely will affect cloud service QoS. To address the data sparsity issue and improve the QoS prediction accuracy, the following paper proposes a collaborative QoS prediction method with location-based data smoothing. The method first computes neighborhoods of users and services based on their locations which provide a basis for data smoothing. It then combines user-based and service-based collaborative filtering techniques to make QoS predictions. Experiments conducted using a real service invocation dataset validate the performance of the proposed QoS prediction method. [ABSTRACT FROM AUTHOR]

Traditionally, a service is often located by its functional specification, and then evaluated by its QoS properties. In practice, a service may have multiple functional specifications for satisfying different users with different expectation descriptions around a similar functional property. For example, a song retrieval service may be enacted by the name of a song or enacted by the lyric of the song if a user does not know the name of the song. In view of this challenge, a service evaluation method promoted by multi-functional specification is proposed for cross-cloud service choreography, in this paper. Concretely speaking, a mixed integer programming model is employed to decompose global constraints into local constraints; then, the evaluation is promoted by multi-functional specification and enacted by brokers for cross-cloud service choreography. At last, experiments are presented to verify our method. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the increase in cloud service providers, and the increasing number of compute services offered, a migration of information systems to the cloud demands selecting the best mix of compute services and virtual machine (VM ) images from an abundance of possibilities. Therefore, a migration process for web applications has to automate evaluation and, in doing so, ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria like throughput and cost. When selecting compute services for multiple connected software components, web application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers, which is impossible to resolve manually. The previously proposed CloudGenius framework has proven its capability to support migrations of single-component web applications. In this paper, we expand on the additional complexity of facilitating migration support for multi-component web applications. In particular, we present an evolutionary migration process for web application clusters distributed over multiple locations, and clearly identify the most important criteria relevant to the selection problem. Moreover, we present a multi-criteria-based selection algorithm based on Analytic Hierarchy Process (AHP). Because the solution space grows exponentially, we developed a Genetic Algorithm (GA)-based approach to cope with computational complexities in a growing cloud market. Furthermore, a use case example proofs CloudGenius’ applicability. To conduct experiments, we implemented CumulusGenius, a prototype of the selection algorithm and the GA deployable on hadoop clusters. Experiments with CumulusGenius give insights on time complexities and the quality of the GA. [ABSTRACT FROM PUBLISHER]

Cloud computing promises a scalable infrastructure for processing big data applications such as medical data analysis. Cross-cloud service composition provides a concrete approach capable for large-scale big data processing. However, the complexity of potential compositions of cloud services calls for new composition and aggregation methods, especially when some private clouds refuse to disclose all details of their service transaction records due to business privacy concerns in cross-cloud scenarios. Moreover, the credibility of cross-clouds and on-line service compositions will become suspicional, if a cloud fails to deliver its services according to its “promised” quality. In view of these challenges, we propose a privacy-aware cross-cloud service composition method, named HireSome-II (History record-based Service optimization method) based on its previous basic version HireSome-I. In our method, to enhance the credibility of a composition plan, the evaluation of a service is promoted by some of its QoS history records, rather than its advertised QoS values. Besides, the k-means algorithm is introduced into our method as a data filtering tool to select representative history records. As a result, HireSome-II can protect cloud privacy, as a cloud is not required to unveil all its transaction records. Furthermore, it significantly reduces the time complexity of developing a cross-cloud service composition plan as only representative ones are recruited, which is demanded for big data processing. Simulation and analytical results demonstrate the validity of our method compared to a benchmark. [ABSTRACT FROM AUTHOR]

SUMMARY Traditionally, service discovery is often promoted by the centralized approach that typically suffers from single point of failure, poor reliability, poor scalability, to name a few. In view of this challenge, a QoS-aware service discovery method is investigated for elastic cloud computing in an unstructured peer-to-peer network in this paper. Concretely speaking, the method is deployed by two phases, that is, service registering phase and service discovery phase. More specifically, for a peer node engaged in the unstructured peer-to-peer network, it firstly registers its functional and nonfunctional information to its neighbors in a flooding way. With the multiple registered information, the QoS-aware service discovery is promoted in a probabilistic flooding way according to the network traffic. At last, extensive simulations are conducted to evaluate the feasibility of our method. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

To gain and retain competitive advantages in a competitive business arena, a business cloud-computing platform should continuously strive to offer new services and remain competitive. Unfortunately, it becomes more and more recognized by the industry that a cloud-computing platform could not cover all aspects of IT layers engaged in infrastructure, platform and application. In practice, end users' requests are nearly unlimited; while the services held by a cloud-computing platform is relatively limited, no matter in service category or in service capacity. In view of this challenge, an elastic cloud platform is investigated by recruited outside services that are absent from the cloud platform. Concretely, through dynamically hiring a qualified service on Internet to replace the absent service inside a cloud platform, an elastic cloud platform could nearly provide unlimited capabilities in an outsourcing service way, e.g., computing power, storage, application functions, etc. At last, the validity of the method is evaluated by a case study. [ABSTRACT FROM AUTHOR]

In scientific workflow systems, it is critical to ensure the timely completion of scientific workflows. Therefore, temporal constraints as a type of QoS (Quality of Service) specification are usually required to be managed in scientific workflow systems. Specifically, temporal constraint management includes two basic tasks: setting temporal constraints at workflow build-time and updating temporal constraints at workflow run-time. For constraint setting, the current work mainly adopts user-specified temporal constraints without considering the system performance. Hence, it may result in frequent temporal violations which deteriorate the overall workflow execution effectiveness. As regards constraint updating, although not well investigated, so far is in fact of great importance to workflow management tasks such as workflow scheduling and exception handling. In this paper, with a systematic analysis of the above issues, we propose a probabilistic strategy for temporal constraint management which utilizes a novel probability-based temporal consistency model. Specifically for constraint setting, a negotiation process between the client and the service provider is designed to support the setting of coarse-grained temporal constraints and then automatically derive the fine-grained temporal constraints; for constraint updating, the probability time deficit/redundancy propagation process is proposed to update run-time fine-grained temporal constraints when workflow execution is either ahead of or behind the schedule. The effectiveness of our strategy is demonstrated through a case study on an example scientific workflow process in our scientific workflow system. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Scientific workflow is gaining ever-increasing attention as it integrates a wide range of heterogeneous services to enable and accelerate the scientific discovery processes. However, the exception occurred in a scientific workflow execution often decreases the robustness of a scientific workflow system. For example, if a service engaged in a scientific workflow schedule becomes unavailable during its execution, an exception might be raised and the whole workflow execution may be interrupted unexpectedly. In this situation, it is a challenge to smooth the interrupted workflow execution. In view of this challenge, an exception handling method, named RelaxingMe (constraints Relaxing Method, RelaxingMe) is proposed in this paper. This method aims at relaxing the original QoS constraint values requested by the interrupted task node, in order to find a near-to-optimal candidate service to replace the unavailable one. At last, the feasibility of our method is evaluated by an experiment. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Due to the dynamic nature of the underlying high-performance infrastructures for scientific workflows such as grid and cloud computing, failures of timely completion of important scientific activities, namely, temporal violations, often take place. Unlike conventional exception handling on functional failures, nonfunctional QoS failures such as temporal violations cannot be passively recovered. They need to be proactively prevented through dynamically monitoring and adjusting the temporal consistency states of scientific workflows at runtime. However, current research on workflow temporal verification mainly focuses on runtime monitoring, while the adjusting strategy for temporal consistency states, namely, temporal adjustment, has so far not been thoroughly investigated. For this issue, two fundamental problems of temporal adjustment, namely, where and how, are systematically analyzed and addressed in this paper. Specifically, a novel minimum probability time redundancy-based necessary and sufficient adjustment point selection strategy is proposed to address the problem of where and an innovative genetic-algorithm-based effective and efficient local rescheduling strategy is proposed to tackle the problem of how. The results of large-scale simulation experiments with generic workflows and specific real-world applications demonstrate that our temporal adjustment strategy can remarkably prevent the violations of both local and global temporal constraints in scientific workflows. [ABSTRACT FROM AUTHOR]

IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure. • Use RTM to model the historical interactions between IoT Mashups and services. • Exploit FMs to predict the interactions relationship among IoT Mashups and services. • Develop a real-world dataset from ProgrammableWeb and conduct a set of experiments. [ABSTRACT FROM AUTHOR]

Abstract: With the increasing popularity of cloud computing technologies, more and more service composition processes are enacted and executed in could environment. Compared with the various and approximately infinite application requirements from end users, the web services held by a cloud platform are usually limited. Therefore, it is often a challenging effort to develop a service composition, in such a situation that only part of the functional qualified candidate services could be found inside a cloud platform. In this situation, the absent services will be invocated in a cross-platform way outside the cloud platform. In view of this challenge, a QoS-aware composition method is investigated for supporting cross-platform service invocation in cloud environment. Furthermore, some experiments are deployed to evaluate the method presented in this paper. [Copyright &y& Elsevier]