Growing In this paper, we develop a neural attentive interpretable recommendation system, named NAIRS. A self-attention network, as a key component of the system, is designed to assign attention weights to interacted items of a user. This attention mechanism can distinguish the importance of the various interacted items in contributing to a user profile. Based on the user profiles obtained by the self-attention network, NAIRS offers personalized high-quality recommendation. Moreover, it develops visual cues to interpret recommendations. This demo application with the implementation of NAIRS enables users to interact with a recommendation system, and it persistently collects training data to improve the system. The demonstration and experimental results show the effectiveness of NAIRS.

Growing demand of computational power brings increasing scale and complexity of cloud datacenters. However, such increase also generates growing energy consumption and related cost incurred for cooling and maintenance. With concerns of cost and energy saving by both industry and academy, the reduction of energy consumption of cloud datacenters becomes a hotspot issue. Recently, virtual-machine-consolidationbased strategies are proposed as promising methods for reduction of cloud energy consumption. Virtual machine (VM) consolidation effectively increases the resource utilization rate. However, it remains a great challenge how to reduce energy consumption while maintaining the quality of service (QoS) at a satisfactory level. In this work, a comprehensive framework is presented for the above-mentioned problem, which aims at maximizing the number of physical machines (PMs) to be turned off within a consolidation period following the constraints of QoS, in terms of Service-Level-Agreement (SLA) violation rate. In comparison with most existing related works which consider invariant utilization rate of PMs in computing energy reduction of candidate migration plans, propose framework considers time-varying utilization rate and employs the number of PMs to be turned off within a consolidation period (NPTCP for simple) as the optimization objective. The proposed framework consists of a resource selection algorithm taking the predicted migration overhead (derived by the Pareto distribution) as inputs and another algorithm generating optimal matching plans based on preference scores of candidate VMs. For the model validation purpose, a case study is conducted on the CloudSim simulation platform and it shows that the proposed method achieves better energy reduction and less SLA violation.

Performance evaluation of cloud data-centers has drawn considerable attention from academy and industry. In this study, we present an analytical approach to the performance analysis of Infrastructure-as-a-Service cloud data-centers with unreliable task executions and resubmissions of unsuccessful tasks. Several performance metrics are considered and analyzed under variable load intensities, failure frequencies, multiplexing abilities, and service intensities. We also conduct a case study based on a real-world cloud data-center and employ a confidence interval check to validate the correctness of the proposed model.