Dependency-Aware and Resource-Efficient Scheduling for Heterogeneous Jobs in Clouds

Healthcare resource utilization for chronic low back pain among high-utilizers

The Internet of Things (IoT) and Mobile Edge Computing (MEC) hold great potential in e-Health. Wireless Body Area Network (WBAN) as one of the primary Internet of Things (IoT) provides real-time and continuous healthcare monitoring and has been widely deployed to improve people's quality of life. MEC can support WBAN better with reliable connection reliability and high computing performance. However, with the rapid development and increasing number of WBAN, user mobility and uncertainty make it greatly challenging to allocate resources in MEC for achieving low latency and high resource utilization. Existing bottlenecks of allocation strategy include high computational complexity, weak robustness, and low quality of service (QoS). It is vital to develop a strategy that can adaptively adjust resource allocation, detect and respond to load changing timely. To this end, we propose an adaptive resource allocation scheme based on demand forecasting. Specifically, the proposed algorithm employs forecasting model to extract user behavior characteristics, quantifies the forecast results reasonably by introducing queuing theory, which provides a basis for the matching of resources and users. Moreover, we propose the dynamic communication and computing resource allocation scheme to pre-allocate resources and release unnecessary resources. The simulation evaluations show that the proposed model achieves better resource utilization (at least 25% improvement) than existing methods, and is robust in guaranteeing user service quality.

In response to the contradiction between real-time traffic scheduling and cost control in dual active disaster recovery centers in two locations, this study proposes the AutoFlow DR automated scheduling framework, which achieves intelligent management of cross center traffic by constructing a three-layer architecture of “monitoring decision execution”. Research and design a multidimensional health assessment model that integrates link status and business priority to dynamically adjust routing weights, combined with MP2P aggregation flow optimization algorithm and P2MP distribution flow cost model, to solve the problems of high fault switching delay and low resource utilization in traditional solutions. The experiment shows that the framework controls the fault switching delay within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5 0 m s}$</tex>, improves the cross center link resource utilization rate to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 5. 2 \%}$</tex>, reduces bandwidth costs by 41.7%, and effectively balances disaster recovery reliability and operational economy. The research results provide an engineering solution for traffic automation scheduling in large-scale dual active disaster recovery scenarios, which has practical application value for improving critical business continuity.

Factors associated with high healthcare resource utilisation among COPD patients

Nowadays, we are in an era of rapid development of Internet technology and unlimited expansion of information dissemination. While the application of new media and digital multimedia has become more popular, it has also brought Earth shaking changes to our life. In order to solve the problem that the traditional static visual image has been difficult to meet people’s needs, a network media art dynamic logo design system based on convolutional neural network is proposed. Firstly, the software and hardware platform related to accelerator development is introduced, the advanced integrated design and calculation IP core are determined as the FPGA hardware accelerator, and the design objectives and requirements of the accelerator system are analyzed. The overall architecture of the accelerator system is designed. 76% of designers believe that the dynamic logo has promoted the corporate image. Then, the function and architecture of IP core are designed based on advanced synthesis, the code structure is standardized, the function is divided, and the operation acceleration is further optimized by using the instruction set of HLS. Finally, the design is integrated by Vivado HLS and Vivado IDE software. The experiment shows that the accelerator system has low power consumption and high resource utilization.

Aiming at the reduction of the effects of single event upsets (SEUs) which must be considered in Space applications of Field Programmable Gate Array (FPGA), a novel dependable dual module redundancy (DMR) system based on dynamic configurable FPGAs is presented in this paper. The system can not only detect faults but also recovery the circuits effectively. Using dynamic reconfigurable methodology, the system with low hardware redundancy and high resource utilization is achieved successfully. Through adding redundant comparison test unit, the reliability of system is improved. This new approach has been verified through Xilinx Ml403 development platform equipped with Virtex-4 FPGA chip XC4VFX12. Experimental results verified the effectiveness of the methodology proposed in this paper.

In an era of increased demands and constrained budgets, it is necessary to make the best use of all available resources. This is difficult when specialized vision care, such as low vision clinical assessment, is involved because of the heterogeneity of the patient populations seen by such clinics. PURPOSE. This research attempts to discover if these diverse patient populations can be identified and clustered into groups based upon similarity of clinical resources use. Specifically, the inquiry examines the potential for a low vision patient resource utilization classification scheme at the Low Vision Clinic (LVC) in the Centre for Sight Enhancement (CSE), University of Waterloo. METHODS. From a sample of 99 patients consulting the LVC in a 3-month period, retrospective data collection involved abstracting and coding medical records containing information detailing each patient's demographic, diagnostic, therapeutic, and resource utilization characteristics. Cluster analysis using Hartigan's block clustering algorithm was then applied to the data. A replication study was completed using a sample of 99 patients visiting the LVC 1 year later. RESULTS. Patients can be classified into five iso-resource groups, hereby termed low vision patient resource groups (LVPRGs). The clusters represent a resource consistent and clinically coherent scheme for classifying low vision patients based upon resource requirements. As a measure of repeatability, the groups reemerged in the replication study. CONCLUSIONS. If the groupings demonstrate robustness in a field test, clustering algorithms in general, and LVPRGs in specific, may offer useful tools to enhance resource utilization in the LVC setting.

INTRODUCTION: Presumed mucinous cystic lesions are at increased (albeit low) risk of malignant transformation and require regular imaging for ongoing surveillance. A cost-effective evaluation of imaging resource utilization (IRU) is understudied due to a paucity of actual clinical utilization data and lack of consensus among current guidelines. We assessed the value proposition of IRU in surveillance of BD-IPMN, by defining populations utilizing high volumes of imaging, and identifying the predictors of high IRU. METHODS: This is a single-center cross-sectional study, of patients with presumed BD-IPMN under active surveillance as of January 2018. Patients were identified from a prospectively maintained pancreatic cyst neoplasm (PCN) database. Presumed diagnosis of mucinous cysts with identification of worrisome features (WF) and high-risk stigmata (HRS), was made based on the accepted criteria from the literature. IRU was calculated based on both noninvasive (CT/MRI) and minimally invasive (EUS) imaging used per month during surveillance. For analysis, the “High Resource Utilization” (HRU) group is defined as the top 10% of the population consuming higher imaging resources and compared them with rest of the surveillance population: “Low Resource Utilization” (LRU) group. The clinical variables were compared using chi-square analysis and student’s t test between two groups. RESULTS: 409 patients with presumed BD-IPMNs were followed for 41.60 ± 32.24 months. By definition, the rate of surveillance imaging utilization per month in HRU group was higher, 0.74 ± 0.42 vs 0.16 ± 0.09 (P < 0.001). Similarly, the rate of non-invasive imaging and EUS utilization was also higher in HRU group. The HRU group have a higher initial cyst size (18.33 ± 14.31 mm vs. 12.90 ± 9.08 mm) (P < 0.001), higher proportion of cysts with WF (17.5% vs 13.82%) (P < 0.001), or with HRS (7.5% vs 1.36%) (P < 0.001), compared with LRU group. Cyst growth rate per year (30.00% vs 4.88% (P < 0.001)) and per two years (35.00% vs 13.82% (P < 0.001)) displayed higher proportions of fast-growing cysts (>5 mm threshold) in the HRU group compared with LRU group. CONCLUSION: Higher IRU was found in a subset of presumed BD-IPMN population undergoing active surveillance using actual prospective clinical data. Predictors of this included larger initial cyst size, rapid cyst growth rate over one and two years, and the presence of WF and HRS. Further refinement of imaging guidelines is needed for accurate long term PCN surveillance balanced by cost-effective IRU.Table 1

Decentralized saddle-point dynamics solution for optimal power flow of distribution systems with multi-microgrids

This paper proposes novel resource sharing schemes for differentiated services (DiffServ) networks, to achieve both high resource utilization and quality of service (QoS) guarantee. Service level agreements (SLAs) are negotiated at network boundaries and supported by path-oriented resource mapping within the network. The recently proposed SLA management scheme based on virtual partitioning (Bouillet et al., 2002) allows overloaded SLAs to exploit the spare capacity of underloaded SLAs for efficient resource utilization, however, at the the cost of possible SLA violation of the underloaders. In the bandwidth borrowing scheme proposed here, the dedicated bandwidth for underloaded SLAs is determined and adaptively adjusted at network boundaries according to the actual traffic load and QoS policies; the available spare capacity is then properly distributed to related links for lending to others. On the other hand, the traffic flows admitted with borrowed bandwidth are tagged and may be preempted later when the original bandwidth owner needs to claim back the resources. Through a detailed implementation design and extensive computer simulation results we show that, by bandwidth borrowing, both SLA compliance and high resource utilization can be achieved in various load conditions, with some side benefits such as call-level service differentiation, small admission overhead, and convenience for policy-based management. In addition, we propose a distributed bandwidth pushing scheme that can dynamically adjust the spare bandwidth distribution over the network. Combining bandwidth pushing with bandwidth borrowing, the resource utilization can be further improved.

BackgroundEffective triage systems are crucial for prioritizing patients based on urgency and optimizing resource utilization. An ideal triage system is expected to have low resource utilization, hospitalization and mortality among patients classified at low urgency levels. Furthermore, it should exhibit an increase in the risk of hospitalization and mortality as urgency levels increase, ensuring the most critically ill patients receive priority care first. However, it is unclear which triage system performs best.ObjectiveTo compare the performance of the Manchester Triage System (MTS), the Emergency Severity Index (ESI), and the Netherlands Triage Standard (NTS) by investigating the association between urgency levels and resource utilization, hospitalization and in-hospital mortality in Emergency Department (ED) patients.MethodsObservational multicenter cohort study using data from the Netherlands Emergency department Evaluation Database, comprising seven representative EDs in six Dutch hospitals. All consecutive ED patients with a registered urgency level were included. Resource utilization, hospitalization and mortality were measured across all urgency levels. In each triage system, multivariable logistic regression was used to assess the association between urgency level and in-hospital mortality and hospitalization, adjusting for age, sex, presenting complaints and hospital type.ResultsA total of 696,518 ED visits (MTS 320,406 (46.1%), ESI 214,267 (30.8%), NTS 161,845 (23.3%) patients) were included. Resource utilization was substantially lower in the lowest urgency level of the ESI compared to the MTS and NTS. Hospitalization to a regular ward, cardiac, medium or intensive care unit in the least urgent level was 3.9% in the ESI, considerably lower than in the MTS (23.1%) and NTS (34.3%) (P < 0.05). Mortality in the lowest urgency level of the ESI was 0.8%, while in the MTS and NTS this was 6.3% and 12.4%, respectively (P < 0.05). In the ESI, the risk (Adjusted Odds Ratios) for hospitalization and mortality increased much more with increasing urgency levels compared to the MTS and NTS.ConclusionThis study suggests that the ESI may be more effective in distinguishing between patients with low and high urgency, with a reduced risk of undertriage when compared to the MTS and NTS.

Latency-critical workloads such as web search engines, social networks and finance market applications are sensitive to tail latencies for meeting Service Level Objectives (SLOs). Since unexpected tail latencies are caused by sharing hardware resources with other co-executing workloads, a service provider executes the latency-critical workload alone. Thus, the data center for the latency-critical workloads has exceedingly low hardware resource utilization. For improving hardware resource utilization, the service provider has to co-locate the latency-critical workloads and other batch processing workloads. However, because memory bandwidth cannot be provided in isolation unlike cores and cache memory, the latency-critical workloads experience poor performance isolation even though the core and the cache memory are allocated in isolation to the workloads. To solve this problem, we propose an optimized memory bandwidth management approach for ensuring Quality of Service (QoS) and high server utilization. By providing isolated shared resources including memory bandwidth to the latencycritical workload and co-executing batch processing ones, our proposed approach guarantees SLOs and improves hardware resource utilization. Firstly, we predict the size of the memory bandwidth to meet the SLO for all Queries Per Seconds (QPSs) while executing the latency-critical workload with minimal preprofiling. Then, our approach allocates the amount of the isolated memory bandwidth that guarantees the SLO to the latencycritical workload and the rest of the memory bandwidth to coexecuting batch processing workloads. As a result, our proposed approach can achieve up to 99% SLO assurance and improve server utilization up to 6.5x.

Characteristics of Sickle Cell Patients with Frequent ED Visits and Hospitalizations: Demographic, Clinical, Laboratory and Psycho-Social Aspects

Collaborative machine learning (CML) techniques, such as federated learning, have been proposed to train deep learning models across multiple mobile devices and a server. CML techniques are privacy-preserving as a local model that is trained on each device instead of the raw data from the device is shared with the server. However, CML training is inefficient due to low resource utilization. We identify idling resources on the server and devices due to sequential computation and communication as the principal cause of low resource utilization. A novel framework PiPar that leverages pipeline parallelism for CML techniques is developed to substantially improve resource utilization. A new training pipeline is designed to parallelize the computations on different hardware resources and communication on different bandwidth resources, thereby accelerating the training process in CML. A low overhead automated parameter selection method is proposed to optimize the pipeline, maximizing the utilization of available resources. The experimental results confirm the validity of the underlying approach of PiPar and highlight that when compared to federated learning: (i) the idle time of the server can be reduced by up to 64.1×, and (ii) the overall training time can be accelerated by up to 34.6× under varying network conditions for a collection of six small and large popular deep neural networks and four datasets without sacrificing accuracy. It is also experimentally demonstrated that PiPar achieves performance benefits when incorporating differential privacy methods and operating in environments with heterogeneous devices and changing bandwidths.

Assessing Mental Health and Its Association With Income and Resource Utilization in Old-Old Chinese in Hong Kong