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The rapid growth of published cloud services in the Internet makes the service selection and recommendation a challenging task for both users and service providers. In cloud environments, software re services collaborate with other complementary services to provide complete solutions to end users. The service selection is performed based on QoS req...
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Citations
... In the first step, similarity between services is determined based on some of their internal features such as service configuration. The services' similarity detection technique proposed in [20] is adopted in our work. Then, we integrate the service similarity into low computational complexity clustering algorithm (e.g., Kmeans) to identify the clusters of sellers. ...
In this paper we propose an auction-based offloading market in the HMC (Heterogeneous Mobile Cloud) environment where mobile devices, Cloudlets, and public cloud form a shared resource pool. In this market, users can register their service offers or submit their offloading requests to the auction manager. Then, the auction manger makes buyer_seller threads as the source–destination of task offloading process. In any made buyer_seller thread a trade-off between conflicting utility functions of buyer and seller has to be guaranteed. Furthermore, auction manager should impose various policies in order to maintain market stability, increase market competition, and reduce monopolies. A stable market is a market where the chance of uncommitted sellers to be considered in the made buyer_seller thread is kept low. Also, almost equal opportunities are made for the users in a competitive and non-monopolized market. To address these issues, first of all, a new task offloading approach based on Hill Climbing Algorithm is designed to assure a trade-off between buyers’ and sellers’ utilities. Then, a new factor in name reputation is defined to make competitive market while maintain market stability. Furthermore, a Cloudlet selection algorithm is developed to find the Cloudlet as a new offloading destination in a case of reneging on the offloading commitment, leading to market stability improvement. Finally, awarding bonus to new participants is another strategy imposed by auction manager to make competitive and non-monopolized market. Extensive amounts of simulation were carried out to evaluate the performance of our proposed offloading market against (Zhou et al. in J Syst Softw 152:151–164, 2019). The results show that our designed market outperforms (Zhou et al. 2019).
... The matrix factorization method was developed and some researchers worked on it. Karim et al. [19] proposed a quality of service prediction model based on service fusion. This method uses user information and history of quality of service records; using this data, unspecified values of quality of service are calculated endto-end. ...
... R ij indicates the actual value of quality of service and is the value that user j gives to the service i. W indicates the number of predicted values of quality of service [19]. ...
Due to the proliferation of mobile, IoT, and Interlayer fog computing devices, the number of requests to the cloud network has increased. Today, cloud networks, new virtualization technologies, and virtual machines are essential for proper management and recommending the appropriate virtual machine to execute requests on the network. In addition, it can affect the quality of network service, and this is done by using the appropriate mapping between virtual machines to recommend web services in the cloud network better. This paper proposes an optimal method for assigning recommendation systems to the user. Finding users’ demands is essential, and the proposed method processes virtual machine input and the appropriate physical machine in cloud clusters in a parallel manner. As discussed in the results section, energy, response time, task execution in data centers, in different workflows, computational complexity, and spatial complexity are considered.
... [94,137]. Moreover, information systems use different techniques that today exceed the database's interrogation and involve the end-user by considering their choices [138,139]. This need has given birth to recommendation systems (RS) that are computer-based intelligent techniques to deal with finding appropriate products amongst various. ...
... On the other hand, it is due to the abundance of practical applications that help users provide personalized recommendations. [136,138,139]. Soon, as easy as it is to increase the volume and range of available Cloud services, it is equally challenging to find relevant best or optimal services among many different existing kinds. ...
... Still, they are trying to analyze this data in a subject to real business 55 needs. However, due to a large amount of digital data available, it is crucial to extract the data corresponding to the most recommended need for information; an application must express the end-users need [50,138,157]. Therefore, it is essential to classify the data elements according to their importance [158], and only the best data items are recovered. ...
... Here, as a measure to support PaaS, the components of an external platform, Cloud Foundry [10], is integrated in the architecture of Apache Brooklyn. There are various other brokering systems [1,12,19,23,26,50,56,58] that facilitate a multi-cloud environment and aid the users in choosing the best SaaS provider based on their requirements. ...
In the field of computer science, cloud computing plays an inevitable role in offering on-demand services to end users. The capability, and hence the advantage, of a cloud environment is widened when multiple clouds interact. However, this interaction is usually done in a static environment and/or with the help of insecure brokers. To the best of our knowledge, the Cloud Inter-operation Toolkit (CIT) is the only work that has broken this barrier to enable a dynamic and broker-less inter-cloud environment. In this work, we aim to improve the security measures in such an environment and to further optimize the resources and time consumed to establish the environment. A novel resource provisioning technique is also proposed for such an environment to ensure that resource accessibility by the users is not interrupted and to effectively utilize the computing resources. Furthermore, taking CIT a step forward, it is enhanced to support multiple cloud service models, thereby making it superior to all other existing inter-cloud facilitating approaches.
... In the study [19], for computing end-to-end QoS values of vertically composed services, a prediction model was proposed, which is based on the Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Data as a Service (DaaS). The QoS values are used to predict the service selection and recommendation process. ...
By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.
... In the study [19], for computing end-to-end QoS values of vertically composed services, a prediction model was proposed, which is based on the Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Data as a Service (DaaS). The QoS values are used to predict the service selection and recommendation process. ...
By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.
... In the study [19], for computing end-to-end QoS values of vertically composed services, a prediction model was proposed, which is based on the Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Data as a Service (DaaS). The QoS values are used to predict the service selection and recommendation process. ...
p>By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy-saving problem in mobile nodes. To mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms. </p
... In the study [19], for computing end-to-end QoS values of vertically composed services, a prediction model was proposed, which is based on the Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Data as a Service (DaaS). The QoS values are used to predict the service selection and recommendation process. ...
Parkinson’s Disease (PD) is one of the most prevalent neurological disorders characterized by impairment of motor function. Early diagnosis of PD is important for initial treatment. This paper presents a newly developed method for application in remote tracking of PD progression. The method is based on deep learning and clustering approaches. Specifically, we use the Deep Belief Network (DBN) and Support Vector Regression (SVR) to predict Unified Parkinson's Disease Rating Scale (UPDRS). The DBN prediction models were developed by different epoch numbers. We use a clustering approach, namely, Self-Organizing Map (SOM), to improve the accuracy and scalability of prediction. We evaluate our method on a real-world PD dataset. In all, nine clusters were detected from the data with the best SOM map quality for clustering, and for each cluster, a DBN was developed with a specific number of epochs. The results of the DBN prediction models were integrated by the SVR technique. Further, we compare our work with other supervised learning techniques, SVR and Neuro-Fuzzy techniques. The results revealed that the hybrid of clustering and DBN with the aid of SVR for an ensemble of the DBN outputs can make relatively better predictions of Total-UPDRS and Motor-UPDRS than other learning techniques.
... In the study [19], for computing end-to-end QoS values of vertically composed services, a prediction model was proposed, which is based on the Software as a Service (SaaS), Infrastructure as a Service (IaaS) and Data as a Service (DaaS). The QoS values are used to predict the service selection and recommendation process. ...
By increasing mobile devices in technology and human life, using a runtime and mobile services has gotten more complex along with the composition of a large number of atomic services. Different services are provided by mobile cloud components to represent the non-functional properties as Quality of Service (QoS), which is applied by a set of standards. On the other hand, the growth of the energy-source heterogeneity in mobile clouds is an emerging challenge according to the energy saving problem in mobile nodes. In order to mobile cloud service composition as an NP-Hard problem, an efficient selection method should be taken by problem using optimal energy-aware methods that can extend the deployment and interoperability of mobile cloud components. Also, an energy-aware service composition mechanism is required to preserve high energy saving scenarios for mobile cloud components. In this paper, an energy-aware mechanism is applied to optimize mobile cloud service composition using a hybrid Shuffled Frog Leaping Algorithm and Genetic Algorithm (SFGA). Experimental results capture that the proposed mechanism improves the feasibility of the service composition with minimum energy consumption, response time, and cost for mobile cloud components against some current algorithms.
... So professionals are not satisfied with the way how to store Big data using Internet technologies, but they are trying to analyze this data in a way which is subject to real business needs. However, due to a large amount of digital data available, it is crucial to extract the data corresponding to a most recommended need for information, an application must actually express the need of the end user (Kaboré et al., 2015;Idrissi et al., 2016;Karim et al., 2016). It is, therefore, important to classify the data elements according to their importance (Jomsri et al., 2011), and only the best data items are recovered. ...
With the exponential growth of cloud computing services recently, several internet technologies began to require the processing of multi-criteria ranking. The collaborative filtering methods and Topk selection computations have been proven to be more effective in information retrieval. In addition, they are widely used to evaluate the QoS for cloud services recommendation. However, the biggest challenge is not only to reduce the size of skyline results, but also to have a good response quality that reflects the user requirement. To deal with these problems, we propose in this paper an approach based on Topk algorithm combined with the weighted sum method. This approach is introduced for refining the skyline result using the Topk query advantages. Then in order to evaluate the performance of our approach, we compared the proposed algorithm with Fagin’s one. The experimental results show the efficiency of our algorithm particularly in comparing the runtime results and using specific metrics of correlation.
