The heterogeneous edge-cloud computing paradigm can provide a more optimal direction to deploy scientific workflows than traditional distributed computing or cloud computing environments. Due to the different sizes of scientific datasets and some of these datasets must keep private, it is still a difficult problem to finding an data placement strategy that can minimize data transmission as well as placement cost. To address this issue, this paper combines advantages of both edge and cloud computing to construct a data placement model, which can balance data transfer time and data placement cost using intelligent computation. The most difficult research challenge the model solved is to consider many constrain in this hybrid computing environments, which including shared datasets within individual and among multiple workflows across various geographical regions. According to the constructed model, the study propose a new data placement strategy named DE-DPSO-DPS, which using a discrete particle swarm optimization algorithm with differential evolution (DE-DPSO-DPA) to distribute these scientific datasets. The strategy also not only consider the characteristics such as the number and storage capacity of edge micro-datacenters, the bandwidth between different datacenters and the proportion of private datasets, but also analysis the performance of algorithm during the workflows execution. Comprehensive experiments are designed in simulated heterogeneous edge-cloud computing environments demonstrate that the data placement strategy can effectively reduce the data transmission time and placement cost as compared to traditional strategies for data-sharing scientific workflows.

Personalized quality of service (QoS) prediction plays an important role in helping users build high-quality service-oriented systems. To obtain accurate prediction results, many approaches have been investigated in recent years. However, these approaches do not fully address untrustworthy QoS values submitted by unreliable users, leading to inaccurate predictions. To address this issue, inspired by blockchain with distributed ledger technology, distributed consensus mechanisms, encryption algorithms, etc., we propose a personalized QoS prediction method for web services that we call blockchain-based matrix factorization (BMF). We develop a user verification approach based on homomorphic hash, and use the Byzantine agreement to remove unreliable users. Then, matrix factorization is employed to improve the accuracy of predictions and we evaluate the proposed BMF on a real-world web services dataset. Experimental results show that the proposed method significantly outperforms existing approaches, making it much more effective than traditional techniques.

User reliability is notably crucial for personalized cloud services. In cloud computing environments, large amounts of cloud services are provided for users. With the exponential increase in number of cloud services, it is difficult for users to select the appropriate services from equivalent or similar candidate services. The quality-of-service (QoS) has become an important criterion for selection, and the users can conduct personalized selection according to the observed QoS data of other users; however, it is difficult to ensure that the users are reliable. Actually, unreliable users may provide unreliable QoS data and have negative effects on the personalized cloud service selection. Therefore, how to determine reliable QoS data for personalized cloud service selection remains a significant problem. To measure the reliability for each user, we present a cloud service selection framework based on user reputation and propose a new user reputation calculation approach, which is named MeURep and includes L1-MeURep and L2-MeURep. Experiments are conducted, and the results confirm that MeURep has higher efficiency than previously proposed approaches.

No abstract

Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users' perspectives, the users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation; and therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called the OPRC, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.