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In cloud environments, the high dynamics and more service failures were great obstacles to cloud applications, service substitution was a key research issue and also was a main solution to these challenges. A method of determining substitutability of cloud services was proposed using type theory, in which session types were semantically extended for modeling the behaviors of cloud service, QoS such as price, reliabil ty were introduced as QoS type, and a series of subtyping rules were constructed for SST and QoST. After that, determining consistency and context compatibility of services were put into practice. The method was proved feasibly by a case determining, and the experimental results show that it brings higher success rate of execution.

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

The reliability of data-nodes and the reliability of relevant network links were analyzed,and then the reliability model of replica service of cloud storage systems was constructed.According to the relationships among access reliability,the number of replicas and the number of user’s accesses,the reliability of data service and the trigger mechanism of replica generation were presented,and the storage node selection was aptly checked,then the replica distribution algorithm and replica deletion algorithm were proposed.Finally a series of experiments were conducted in the cloud storage system,named ERS-Cloud,and the results indicate that the approach can ensure the reliability of data service,and further decrease the number of replicas of the redundant storage.

Background Study: As flipped learning is a flexible pedagogical methodology to tailor students' needs and maximize the learning effects, this study integrated peer learning with MosoTeach-supported flipped mode in an undergraduate blended EFL course. MosoTeach is a popular online learning platform in China with a variety of learning resources. Objectives: The study aimed to test the impact of MosoTeach flipped learning (MTFL) mode on undergraduate EFL students' perceived motivation, participation, feedback, and test performance. Methods: A quasi-experimental design was carried out in Z university during the spring semester of 2020, with the experimental group (n = 34) adopting the MTFL model and a control group (n = 33) the non-flipped mode. Results and Conclusions: The independent t-test results showed that the experimental MTFL group outperformed the control group in post-test scores, and a positive correlation was found in the relationship between the experimental MTFL group's post-test scores and MosoTeach learning experience points (LEPs). Moreover, the course perception questionnaire survey indicated a higher level of students' perceived motivation, participation, and feedback in the experimental MTFL group, which corresponded to the results of the content analysis of students' responses to open-ended questions and reflective reports. But students in both groups experienced challenges such as isolation, procrastination and technological anxiety.

Researchers have worked on recovery strategies of composition transactions,most efforts focus on transaction consistency by backward recovery,compensation is commonly used for backward recovery,which have limitations since the cost of compensation tasks are rather expensive,and the compensation mechanism can not satisfy various requirements of applications.A transaction recovery algorithm was proposed including forward recovery,backward recovery and alternative recovery,which was a formal modeling method based on extended Petri nets,state token，data token and QoS token were introduced to implement relaxed ACID properties of LRT.When failure occurs,terminate dependency point (TDP) and compensation set were calculated dynamically,recovery strategies were added or deleted automatically according to the data flow dependency,control flow dependency and state dependency among tasks.Failure types are generated by log file mining,therefore,failure coordination and recovery are implemented and an executable model which support add/delete failure recovery behavior is constructed seamlessly.