Cloud services request lower cost compared to traditional software of self-purchased infrastructure due to the characteristics of on-demand resource provisioning and pay-as-you-go mode. Current enterprises compact their business software as services into cloud platform to users. In the cloud services market, service providers attempt to make more profits from their services, while users hope to choose low-cost services with high-quality. The conflict of interests between users and service providers is an important challenge for the booming cloud service market. This article characterizes this application problem formally based on a utility game model of service providers and users. In the model, QoS is considered as the basis for determining the utilities of both parties from an economic point of view. By analyzing the behaviors of users and service providers, we introduce the concept of reputation cost for the first time in the model and find a QoS solution that balances the utilities of users and service providers in service transactions. In such a balance, any change in either party's strategy will result in a loss of utility. And then a QoS optimization method is designed to obtain a near-optimal QoS solution for a tradeoff between user satisfaction and provider profit. Extensive simulation experiments are conducted to substantiate the effectiveness of our method. The results are applicable to win-win service applications between service providers and users.

With the rapid development of mobile Internet applications, it is more and more difficult for mobile users to choose the right commodity in the face of a large amount of commodity information. Recommendation system is one of the effective means to solve the problem of information overload. However, the low hardware configuration and limited computing capacity are common problems in most mobile devices, which make mobile computing applications face more and more serious challenges with increasingly strong demand for computing resources. Cloud computing, as a model of providing resources on demand, can provide technical support for solving the above problems. Recommendation as a Service (RaaS), which packaged the recommendation system as a service, has attracted extensive attention from the business community and academia. In this paper, a RaaS model based on emotion analysis and double labeling is proposed. In this model, we make full use of commodity description information and user comment text to explore users' emotional preference on the commodity attributes, which is less considered in the existing recommendation models or algorithms. Firstly, we extract the keywords by analyzing the commodity description and user behaviors to establish commodity intrinsic attribute labels and user preference labels. Secondly, we mine the users' comments through emotion analysis to establish the commodity feedback labels and user concern labels. Finally, the recommendation results for users are generated by calculating the similarity of above four types of labels. Experimental results show that the proposed model and its algorithms can improve the effect of recommendation.

High radix SRT division plays an important role in contemporary microprocessors as the quotient digit selection tables effectively reduce the computation complexity of the quotient digits. The quotient digit selection table is constructed according to the rounded lower and upper bounds of the overlapping regions in the traditional method. The table construction process lacks in mathematical rigor and consequently is susceptible to error. This paper proposes an algebraic method for computing the quotient digit selection tables. We characterize the quotient digit selection functions to construct quotient digit selection tables required for SRT division and SRT square root with any valid redundancy. The functions include the maximum and minimum legal quotient digit selections. We compute the truncations of the remainder $p$ and divisor $d$ when $d \in [1,2)$ . We implement procedures to compute quotient digit selection tables by our functions. The computation of the quotient digit selection table for the case radix-4 with the quotient digit set $[-2,2]$ is presented by using the minimum quotient digit selection function. Our functions can compute quotient digit selection tables in the design phase of SRT division and square root by given radix and redundancy.

In edge computing environments, service reliability is often threatened by the sudden failure of edge nodes due to harsh deployment conditions, leading to task interruption and performance degradation. To address this challenge, EFTR-HGNet is proposed as a novel task rescheduling framework tailored for edge-fault scenarios. It leverages heterogeneous graph neural networks with a Transformer-based architecture to achieve cost-efficient task migration and adaptive decision making. Specifically, the rescheduling problem is formulated as a Markov Decision Process (MDP), and a three-dimensional fault-aware state representation that jointly encodes task attributes, resource availability, and dynamic failure status is introduced. To model the complex relationships between failed tasks and heterogeneous edge resources, a heterogeneous Transformer (HG-Trans) network is designed, which performs two-stage embedding over the constructed graph, enabling context-aware rescheduling decisions to be made by the agent. By optimizing both the policy and value functions within an Actor-Critic reinforcement learning framework, our method achieves a favorable balance between minimizing the overall Makespan and maximizing the task rescheduling success rate. Evaluated against strong baselines like HEFT, TDCA, and FixDoc, EFTR-HGNet demonstrated superior performance, achieving a Makespan reduction of at least 11.11% and a 4.20% increase in task rescheduling success. These results highlight its robustness and practical potential for fault-prone edge computing systems.

The research on personalized recommendation of Web services plays an important role in the field of Web services technology applications. Fortunately, not all users have completely different service preferences. Due to the same application scenarios and personal interests, some users have the same preferences for certain types of Web services. This paper explores the problem of user clustering in the service environment, grouping users according to their service preferences. It helps service providers to identify and characterize the preferences of similar users and provide them with customized services. We propose two combination-based clustering algorithms which make full use of the advantages of the K-means algorithm and the affinity propagation algorithm. In addition, a three-stage clustering process is elaborated to improve the accuracy of user clustering. To reduce the time complexity of the algorithms, we create a parallel execution model of the algorithms implemented by a higher-order MapReduce sequence linking technology. Extensive experiments on simulated datasets and real datasets are performed on the comparisons between the proposed algorithms and the other combination-based clustering algorithms. The experimental results substantiate that the proposed algorithms can effectively distinguish user group with different preferences.