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During the COVID-19 pandemic, numerous people experiencing illness or senescence choose to receive home health care (HHC) services. However, a rapid increase in patients makes it a challenge to reasonably allocate nurses to provide HHC services under the condition of a paucity of nurse resources and patient time window constraints. To solve the large-scale HHC problem, a hybrid heuristic-exact optimization algorithm is proposed with three novel contributions. First, a framework of hybrid heuristic-exact optimization is designed to solve the large-scale problem where a reasonable solution is difficult to obtain under constraints. Second, a multi-objective mixed-integer linear programming modelization is formulated to get a more diverse nurse assignment. Finally, an improved branch and bound algorithm is proposed to speed up computation for the large-scale problem. Computational results on different HHC instances from 25 to 1000 patients demonstrate that the proposed algorithm can optimize the HHC problem with more than 100 patients and can provide various assignments for different numbers of nurses, which the common algorithm cannot optimize.

Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Ensemble run simulations are becoming increasingly widespread. In this work, we couple particle advection with pathline analysis to visualize and reveal the differences among the flow fields of ensemble runs. Our method first constructs a variation field using a Lagrangian-based distance metric. The variation field characterizes the variation between vector fields of the ensemble runs, by extracting and visualizing the variation of pathlines within ensemble. Parallelism in a MapReduce style is leveraged to handle data processing and computing at scale. Using our prototype system, we demonstrate how scientists can effectively explore and investigate differences within ensemble simulations.

Fuzzy c-means (FCM) algorithm is an unsupervised clustering algorithm that effectively expresses complex real world information by integrating fuzzy parameters. Due to its simplicity and operability, it is widely used in multiple fields such as image segmentation, text categorization, pattern recognition and others. The intuitionistic fuzzy c-means (IFCM) clustering has been proven to exhibit better performance than FCM due to further capturing uncertain information in the dataset. However, the IFCM algorithm has limitations such as the random initialization of cluster centers and the unrestricted influence of all samples on all cluster centers. Therefore, a novel algorithm named equidistance index IFCM (EI-IFCM) is proposed for improving shortcomings of the IFCM. Firstly, the EI-IFCM can commence its learning process from more superior initial clustering centers. The EI-IFCM algorithm organizes the initial cluster centers based on the contribution of local density information from the data samples. Secondly, the membership degree boundary is assigned for the data samples satisfying the equidistance index to avoid the unrestricted influence of all samples on all cluster centers in the clustering process. Finally, the performance of the proposed EI-IFCM is numerically validated using UCI datasets which contain data from healthcare, plant, animal, and geography. The experimental results indicate that the proposed algorithm is competitive and suitable for fields such as plant clustering, medical classification, image differentiation and others. The experimental results also indicate that the proposed algorithm is surpassing in terms of iteration and precision in the mentioned fields by comparison with other efficient clustering algorithms. [ABSTRACT FROM AUTHOR]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.compeleceng.2008.11.022 Byline: Xiaomin Zhu, Peizhong Lu Keywords: Scheduling; Heterogeneous clusters; Real-time; Security-critical; Heuristic Abstract: Nowadays, increasing attention has been directed towards the issue of security service for real-time applications with security requirements on clusters. However, the study of integrating security demands of real-time applications into scheduling is rare. In this paper, we propose a novel two-phase scheduling strategy TPSS which takes timing constraints and security needs into consideration for security-critical real-time applications on heterogeneous clusters. In the first-phase, a novel algorithm DSRF is proposed to schedule real-time tasks. When the system is in heavy burden, DSRF is able to degrade the security levels of new tasks and tasks waiting in local queues so as to enhance guarantee ratio. On the contrary, when the system is in light burden, DSRF is capable of employing slack time to improve the security quality of new tasks and adequately utilize the system resource. The minimal security level can guarantee the system security, and higher security level is able to make the system more secure. In the second-phase, a new algorithm FMSL is proposed to minimize the difference of security levels of accepted tasks and further improve the security levels of accepted tasks on the whole, which degrades the probability of the applications being attacked. We compare TPSS, DSRF, SAEDF and RF by extensive simulations. The experimental results indicate that TPSS significantly improves the flexibility of scheduling and outperforms other algorithms. Author Affiliation: School of Computer Science, Fudan University, No. 220 Handan Road, Shanghai 200433, PR China

The advancement of UAV technology makes the use of UAVs more and more widespread, and the swarm is the main mode of UAV applications owing to its robustness and adaptability. Meanwhile, task allocation plays an essential role in a swarm to obtain overall high performance and unleash the potential of each UAVs owing to the complexity of the large-scale swarm. In this paper, we pay attention to the real-time allocation problem of dynamic tasks. We design models for the task assigning problem to construct the constraints model and assigning objectives. In addition, we introduce a novel agent-based allocating mechanism based on the auction process, including the design for three kinds of agents and the cooperation mechanism among different agents. Moreover, we proposed a new algorithm to calculate the bidding values of UAVs, by which the messages passed between UAVs can be reduced. On the basis of the assigning mechanism, we put up with a novel agent-based real-time task allocation algorithm named NECTAR for dynamic tasks in the UAV swarm. Furthermore, we conduct extensive experiments to evaluate the performance of our NECTAR, and the results indicate that NECTAR is able to solve the real-time task allocation for dynamic tasks and achieve high performance of the UAV swarm. [ABSTRACT FROM AUTHOR]

Clouds are becoming an important platform for scientific workflow applications. However, with many nodes being deployed in clouds, managing reliability of resources becomes a critical issue, especially for the real-time scientific workflow execution where deadlines should be satisfied. Therefore, fault tolerance in clouds is extremely essential. The PB (primary backup) based scheduling is a popular technique for fault tolerance and has effectively been used in the cluster and grid computing. However, applying this technique for real-time workflows in a virtualized cloud is much more complicated and has rarely been studied. In this paper, we address this problem. We first establish a real-time workflow fault-tolerant model that extends the traditional PB model by incorporating the cloud characteristics. Based on this model, we develop approaches for task allocation and message transmission to ensure faults can be tolerated during the workflow execution. Finally, we propose a dynamic fault-tolerant scheduling algorithm, FASTER, for real-time workflows in the virtualized cloud. FASTER has three key features: 1) it employs a backward shifting method to make full use of the idle resources and incorporates task overlapping and VM migration for high resource utilization, 2) it applies the vertical/horizontal scaling-up technique to quickly provision resources for a burst of workflows, and 3) it uses the vertical scaling-down scheme to avoid unnecessary and ineffective resource changes due to fluctuated workflow requests. We evaluate our FASTER algorithm with synthetic workflows and workflows collected from the real scientific and business applications and compare it with six baseline algorithms. The experimental results demonstrate that FASTER can effectively improve the resource utilization and schedulability even in the presence of node failures in virtualized clouds. [ABSTRACT FROM AUTHOR]

Currently, most tasks submitted to earth observation satellites (EOSs) are with deadlines, to satisfy users' timing requirements. Besides, tasks are normally submitted dynamically, with uncertainties of tasks number and submission times. In this paper, we establish a multi-objective mathematic programming model for dynamic real-time scheduling of EOSs. To improve user's revenue and resource utilization, a task dynamic merging strategy is proposed. With the dynamic merging in place, plus consideration of retracting and rearranging some scheduled tasks to make room for incorporating the newly arrived tasks, we present a novel dynamic real-time scheduling algorithm—DMTRH. To demonstrate the superiority of our DMTRH, we conduct extensive experiments by simulations to compare DMTRH with two algorithms—RHTSS and STI. The experimental results indicate that DMTRH improves the scheduling quality of others and is suitable for dynamic real-time scheduling. [ABSTRACT FROM AUTHOR]

Abstract: Developing energy-efficient clusters not only can reduce power electricity cost but also can improve system reliability. Existing scheduling strategies developed for energy-efficient clusters conserve energy at the cost of performance. The performance problem becomes especially apparent when cluster computing systems are heavily loaded. To address this issue, we propose in this paper a novel scheduling strategy–adaptive energy-efficient scheduling or AEES–for aperiodic and independent real-time tasks on heterogeneous clusters with dynamic voltage scaling. The AEES scheme aims to adaptively adjust voltages according to the workload conditions of a cluster, thereby making the best trade-offs between energy conservation and schedulability. When the cluster is heavily loaded, AEES considers voltage levels of both new tasks and running tasks to meet tasks’ deadlines. Under light load, AEES aggressively reduces the voltage levels to conserve energy while maintaining higher guarantee ratios. We conducted extensive experiments to compare AEES with an existing algorithm–MEG, as well as two baseline algorithms–MELV, MEHV. Experimental results show that AEES significantly improves the scheduling quality of MELV, MEHV and MEG. [Copyright &y& Elsevier]

The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks’ scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority. [ABSTRACT FROM AUTHOR]

Cloud computing environments facilitate applications by providing virtualised resources through the network and serve the clients by the pay-as-you-go mechanism. It is based on the rapid development of the network. Normally, economic cost is the most important factor of providing cloud services. However, under some special conditions, the objectives may change. In this scenario, it is impractical to reset the scheduling mechanism only for an occasional incident. So an adaptive scheduling mechanism is needed to address the issues of scheduling under different conditions. To the best of our knowledge, no works have well solved this problem. In this paper, we convert the problem to a multi-objective scheduling problem with varying objective weights and propose a two-phase algorithm, which is called adaptive priority-based workflow scheduling ( DRAWS) algorithm. The algorithm will self regulate the priorities of tasks to adapt to different objectives. In the experimental part, simulation environments are set up and three classic workflows are used to evaluate the performance. Four comparing algorithms of resource sensitive scheduling algorithm, Best Fit-millions of instructions per second, Best Fit-random-access memory and Best Fit-Bandwidth are used to evaluate the performance of DRAWS. It is demonstrated that each phase of our algorithm would optimise the scheduling. Furthermore, we obtain the conclusion that DRAWS algorithm shows superior than the comparing algorithms. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]