Container technology is becoming increasingly important in cloud computing due to its efficiency and agility, but it also introduces new security risks. In particular, container escape attacks exploiting inherent vulnerabilities in container components have emerged as a primary threat to container security due to their pervasive nature and severe impact. However, existing container escape detection methods mainly rely on known attack patterns and pay insufficient attention to exploits targeting container components. In this work, we propose CEDS, a system based on container filesystem isolation boundaries to detect escape attacks caused by container component vulnerabilities in real time. We first establish an attack model by analyzing 16 container component exploits. Then, we propose a method to identify isolation boundaries by analyzing mount namespaces and container filesystem hierarchies, and subsequently detect abnormal cross-boundary file operations at the kernel level via system call monitoring. Finally, we implement a prototype of CEDS with eBPF. Experimental results demonstrate that, compared with the existing baseline methods, CEDS can effectively detect container escape attacks with minimal performance overhead.

Microservices architecture has gained popularity in modern software development due to its scalability and flexibility. However, understanding the complexity of interactions and dependencies between services presents significant challenges, which complicates the identification and analysis of errors within microservice applications. To gain insights into the architecture and interdependencies of microservices applications, prior studies have developed dependency graphs to illustrate the relationships among services. However, the methods used to construct these dependency graphs are not suitable for common microservices applications and suffer from insufficient data granularity. To address these shortcomings, we introduce LLM4MDG, an in-novative framework for constructing microservices dependency graphs using an LLM-driven multi-agent system. By leveraging optimized prompt engineering and principles of knowledge graphs, LLM4MDG can effectively identify and interpret service interactions across diverse microservice ecosystems, achieving high accuracy and adaptability across various scenarios. We also present a new open-source dataset comprising 47 microservices applications, annotated by domain experts, to validate our frame-work. Evaluation results demonstrate that LLM4MDG achieves an 88.3% accuracy in identifying data dependencies in the Train Ticket project, a benchmark application with over 80 service instances. This study provides a robust solution for constructing dependency graphs and facilitating better system understanding and management.

Speaker verification, as a biometric authentication mechanism, has been widely used due to the pervasiveness of voice control on smart devices. However, the task of “in-the-wild” speaker verification is still challenging, considering the speech samples may contain lots of identity-unrelated information, e.g., background noise, reverberation, emotion, etc. Previous works focus on optimizing the model to improve verification accuracy, without taking into account the elimination of the impact from the identity-unrelated information. To solve the above problem, we propose SEEF-ALDR, a novel Speaker Embedding Enhancement Framework via Adversarial Learning based Disentangled Representation, to reinforce the performance of existing models on speaker verification. The key idea is to retrieve as much speaker identity information as possible from the original speech, thus minimizing the impact of identity-unrelated information on the speaker verification task by using adversarial learning. Experimental results demonstrate that the proposed framework can significantly improve the performance of speaker verification by 20.3% and 23.8% on average over 13 tested baselines on dataset Voxceleb1 and 8 tested baselines on dataset Voxceleb2 respectively, without adjusting the structure or hyper-parameters of them. Furthermore, the ablation study was conducted to evaluate the contribution of each module in SEEF-ALDR. Finally, porting an existing model into the proposed framework is straightforward and cost-efficient, with very little effort from the model owners due to the modular design of the framework.

Despite the remarkable progress made in synthesizing emotional speech from text, it is still challenging to provide emotion information to existing speech segments. Previous methods mainly rely on parallel data, and few works have studied the generalization ability for one model to transfer emotion information across different languages. To cope with such problems, we propose an emotion transfer system named ET-GAN, for learning language-independent emotion transfer from one emotion to another without parallel training samples. Based on cycle-consistent generative adversarial network, our method ensures the transfer of only emotion information across speeches with simple loss designs. Besides, we introduce an approach for migrating emotion information across different languages by using transfer learning. The experiment results show that our method can efficiently generate high-quality emotional speech for any given emotion category, without aligned speech pairs.

Summary Performance appraisal is a daily, homework, fair, and open appraisal of each hospital, which can mobilize the enthusiasm and creativity of the employees to the greatest extent so that both the company and the employees can achieve development. At present, the ascendant information revolution has become an important factor in promoting the development of the times. The use of advanced information technology to develop advanced information systems to conduct fair and open assessments of the ever‐increasing number of company employees has become the trend of the times. This article makes a historical review of the development and function of the performance appraisal system, and fully analyzes the new features and new functions of the performance appraisal system in the new period, and details the solution. This system develops and uses .NET development platform and SQL Server 2005 database. Using a distributed approach, the system's development costs have reduced, and the system's maintainability and scalability are improved. This paper studies the establishment of a performance‐based distribution model based on diagnostic relevance grouping theory (DRGs) in public hospitals. Based on the DRGs as a theoretical basis, after a long period of historical data collection, a theoretical model was established, repeated regression calculations, and focused evaluations. A performance‐distribution scheme centered on workload has established to reflect the factors such as the labor risk, technical difficulty, and labor cost of the attending doctors in relation to different patient conditions. The formulation and implementation of the new performance plan embodies the input of labor value and conforms to the spirit of the new medical reform.

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs‐oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi‐MU offloading scheme. Then we survey edge server‐ (ES‐) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

This paper studies the problem of energy-efficient scheduling for parallel tasks in high-performance computing systems, such as clusters and data centers. Our goal is to minimize the energy consumption of parallel tasks within a deadline constraint. Among the existing techniques that reduce the energy for computing systems, dynamic voltage and frequency scaling (DVFS) is generally considered as a promising technique that can strike a balance between the energy consumption and the performance for tasks. By using the DVFS technique, the main line of research is to slack the non-critical path tasks to reduce energy consumption of parallel tasks. However, the existing studies slack the tasks greedily and could not efficiently utilize the slack-room, i.e. the idle time of the processors. In this paper, we develop a novel slacking concept, partial optimal slacking (POS), which can take full advantage of the slack-room by slack-sharing. Our formal analysis shows that POS can lead to optimum energy reduction in the partial task set. Based on the POS concept, we propose a new scheduling algorithm for parallel tasks, namely enhanced an energy-efficient scheduling (EES) algorithm. Through extensive evaluation studies, the results demonstrate that the EES algorithm can further improve the energy efficiency of parallel tasks while meeting the deadline constraint.

Energy consumption has become a major concern to the widespread deployment of cloud data centers. The growing importance for parallel applications in the cloud introduces significant challenges in reducing the power consumption drawn by the hosted servers. In this paper, we propose an enhanced energy-efficient scheduling (EES) algorithm to reduce energy consumption while meeting the performance-based service level agreement (SLA). Since slacking non-critical jobs can achieve significant power saving, we exploit the slack room and allocate them in a global manner in our schedule. Using random generated and real-life application workflows, our results demonstrate that EES is able to reduce considerable energy consumption while still meeting SLA.

How to achieve energy efficiency to run a cloud data center is a major challenge in the era of rising electricity cost and environmental protection. Various techniques have been devised to help reduce energy consumption for cloud data centers that consist of a large number of identical servers, including dynamic allocation of active servers, consolidating diverse applications to run on them, and adjusting the CPU speed of an active server. Leveraging these techniques, we use an Online Coloring Bin Packing problem to model the consolidation problem and devise an effective application-aware approximation algorithm to find a near-optimal solution. We show a 1.7 asymptotic approximation ratio. We then apply a Predictive Bayesian Network model to identify daily workload patterns and adjust resource provisioning accordingly. We evaluate our approaches using traces collected from a real data center and demonstrate that (1) our prediction algorithm is effective in estimating future demands, (2) our coordinated approaches can provide significant savings of energy and operational costs close to the near-optimal offline solution, and (3) our approaches incur little reliability costs in term of wear-and-tear of server components.

In recent years large graph processing has emerged to be a popular application for companies because of the increasing large Web graph and social networks. The ever growing scale of graphs and recent emergence of cloud computing poses challenges to their efficient and cost-conscious scheduling approach for processing tasks. In this paper, we focus on the use of cloud resources for dispatching large graph processing tasks. We design a novel framework EComer that can be easily integrated into existing cloud infrastructure. The key component of this framework is a cost-conscious scheduling heuristic, called CCSH, which is an extension of Heterogeneous Earliest Finish Time (HEFT). Our algorithm CCSH first constructs a priority list of tasks and then assigns the task with the highest priority value to the cost-efficient virtual machine in a cloud setting. The comparison study, based on randomly generated large graphs and a real-life astronomy application model, demonstrates that our algorithm outperforms HEFT by exhibiting significant monetary cost savings at a reasonable increase in overall execution time.