Specific Service Level Research Articles

Knowledge, Attitude, and Practice Related to Health Care Ethics among Dental Students in Davangere City - A Cross-sectional Study

Background Public awareness of the ethical treatment of emergency medical crisis has been increasing and complaints against doctors seem to be getting worse. It is a matter of immediate concern that lawsuits against doctors have risen recently. Objectives To assess the knowledge attitude and practices related to healthcare ethics among dental students in Davangere city.Study Design A descriptive cross-sectional study design was performed through the convenience sample method.Methodology The pre-validated questionnaires assessing the knowledge and practice of dental students regarding healthcare ethics were distributed to about 221 dental students in Davangere city and data were collected and analyzed using frequency distribution and Chi-square test with SPSS software version 22.0. Results A total of 221 questionnaires were distributed of which 205 participants responded recording a response rate of 93.18. About 50.2 participants were final year BDS students and 49.8 were pursuing internship. 66.3 had good knowledge about the Hippocratic oath and the Nuremberg code. 45.9 of participants said that they would refer their patients to another doctor who shares the patientrsquos belief when he she refuses treatment due to certain religious beliefs.Conclusion Though majority of the participants had a fair knowledge of healthcare ethics training regarding the practice of ethics should be the focus. Maintaining a specific level of service while in practice is the dentists responsibility. All dentists should uphold a set of professional conduct norms when performing their duties.

SLA-Driven Software Orchestration in Industry 4.0

In the Industry 4.0 vision, the orchestration of software components plays a crucial role in enabling the continuous reconfiguration, programming, and evolution of the factory floor elements. The objective of an Industry 4.0 software orchestration system is to manage the software elements controlling one or more industrial processes with the purpose of enforcing specific Service Level Agreements (SLAs) for each connected use case (e.g., cooperative robots and connected workers). The orchestration systems currently available are not equipped to guarantee programmable industrial SLA, making it difficult to meet carrier grade reliability and reduced time to repair required by Operational Technology (OT). This article investigates research directions and use cases to manage programmable SLAs with four criticality levels in order to enable the use of orchestration in industrial scenarios. The main contribution is an approach, differentiated by workload criticality level, that uses proactive and reactive orchestration actions to meet industrial functional and non-functional requirements, and to mitigate SLA violations. The approach is exemplified through Industry 4.0 use-case scenarios.

A Study on Optimal Location Selection and Semi-Finished Product Inventory Allocation in the Steel Industry

With the development of industrial production technologies and market economy, Postponement is increasingly being adopted by continuous production enterprise as a method that enables multi-product, mass customization production. In order to make use of the Postponement in manufacturing production enterprises need to achieve customized production and reduce enterprise costs. A modeling is conducted in the production process of Postponement in iron and steel enterprises in this paper. With the goal of minimizing total costs on a specific customer service level, mixed integer nonlinear programming and particle swarm algorithms are adopted for modeling and solving, to determine the optimal location and optimal semi-finished product inventory of PDP and CODP. Finally, taking an iron and steel enterprise as an example, the feasibility and effectiveness of the model and algorithm are verified. The study shows that the change of the optimal locations of PDP and CODP is affected by the change of customer service level and delay penalty coefficient, but the speed of change of the optimal location of PDP shows longer delays. In addition, the size of the capacity of semi-finished product inventory corresponding to CODP has a direct influence on whether the semi-finished product inventory corresponding to PDP participates in production, which in turn affects the optimal semi-finished product inventory on all levels. Through the analysis, it is found that the model constructed in this paper can better describe the overall situation and the influence relationship of the Postponement, and the study supplements the deficiency of the research on Postponement in the continuous manufacturing enterprises and enriches the content of the quantitative research on the Postponement.

MultiScaler: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications

Cloud computing offers a wide range of services through a pool of heterogeneous Physical Machines (PMs) hosted on cloud data centers, where each PM can host several Virtual Machines (VMs). Resource sharing among VMs comes with major benefits, but it can create technical challenges that have a detrimental effect on the performance. To ensure a specific service level requested by the cloud-based applications, there is a need for an approach to assign adequate resources to each VM. To this end, we present our novel Multi-Loop Control approach, called <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> , to allocate resources to VMs based on the Service Level Agreement (SLA) requirements and the run-time conditions. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> is mainly composed of three different levels working closely with each other to achieve an optimal resource allocation. We propose a set of tailor-made controllers to monitor VMs and take actions accordingly to regulate contention among collocated VMs, to reallocate resources if required, and to migrate VMs from one PM to another. The evaluation in a VMware cluster have shown that the <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> approach can meet applications performance goals and guarantee the SLA by assigning the exact resources that the applications require. Compared with sophisticated baselines, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiScaler</small> produces significantly better reaction to changes in workloads even under the presence of noisy neighbors.

Limiting the impact of supply chain disruptions in the face of distributional uncertainty in demand

Service‐level requirements play a crucial role in eliminating stock‐outs in a production pipeline. However, delivering a specific service level can become an unattainable goal given the various uncertainties influencing both the production pipeline and customer demand and causing the manufacturer to adapt the initial strategy in response to disruptions. Such deviations from optimality frequently result in unexpected (and potentially very high) costs and are complex to manage. On the one hand, the manufacturer can use a robust or distributionally robust approach to prepare for the worst‐case disruption, ensuring that the realized cost will be lower than the estimated cost with high probability. On the other hand, this solution may lead to overly conservative production schedules. In this article, we take a different approach and develop a bilevel stochastic optimization model with chance constraints, which allows us to make the production more predictable in the event of disruptions by driving costs and optimal schedules closer to the benchmark for each scenario considered. We introduce doubly probabilistic service‐level requirements to account for two interdependent layers of uncertainty, that is, production disruptions and distributional uncertainties in customer demand. This allows us to make high‐quality production decisions with only a limited understanding of the demand pattern. Approximating the problem for a numerical solution, we guarantee tight optimality gaps for high service levels and propose an efficient solution scheme, combining robust scenario reduction with a customized Benders decomposition procedure. In the managerial section, we use the Omega× Swatch MoonSwatches example to demonstrate that a desirable doubly probabilistic service level can be attained for disruptions with a drop in demand. In the case of disruptions followed by a peak in demand, one can tighten the optimality gaps if the service level is reduced.

Multi‐party smart contract for an AI services ecosystem: An application to smart construction

AbstractVarious smart applications, such as in the domain of smart construction, require the use of artificial intelligence (AI) based services. In order to support such environments, various AI/knowledge service provider and consumer ecosystems have started to emerge. Within such ecosystems, the goals are to improve the quality, reliability, dependability in terms of governance and trust in the data which is exchanged among the various actors, which must be supported by specific business models. This work introduces a novel multi‐party smart contract (SC) that is designed to address the above mentioned goals. Specific service level agreements that illustrate the interactions among the AI service providers and consumers are also presented. The developed multi‐party SC supports different pricing schemes, which are analyzed in detail against the goals of the study.

The stratified [formula omitted]-hub center and [formula omitted]-hub maximal covering problems

Hub networks are the foundation of many transportation and distribution systems, and real-world hub networks often transport freight or passengers of different service classes. This paper introduces the stratified multiple allocation p-hub center and p-hub maximal covering problems where the traffic corresponding to each origin–destination (O/D) pair is divided into different strata each having a specific service level requirement. The problems are formulated as mixed-integer linear programming (MILP) models and efficient Benders decomposition algorithms are developed for solving large instances. Extensive computational experiments are conducted to demonstrate the efficiency of the proposed mathematical models and the solution algorithms. MILP formulations are also proposed for the generalized versions of the problems that include fixed set-up costs for hubs and hub arcs. Results indicate that the optimal sets of hub locations and hub arcs when considering different strata can be quite dissimilar to those of the traditional p-hub center or p-hub maximal covering problem, but are similar to those of hierarchical hub location problems. Furthermore, models are provided and solved for multi-modal stratified hub location problems with fixed setup costs for hubs and hub arcs. Optimal results show a wide range of network topologies that can be generated, as compared to the classical versions.

Do Medicare's Facility Fees Incentivize Hospitals to Vertically Integrate with Oncologists?

Within the past decade, the U.S. health care market has undergone massive vertical integration, prompting economists to study the underlying causes and consequences of hospital-physician integration. This paper examines whether or not hospitals strategically choose to vertically integrate with clinical oncologists in order to capture facility fees, a commonly cited reason for increased consolidation in the health care market. To address this question, I match data on hospitals’ ownership of clinical oncologists with Medicare payment data disaggregated to the physician and specific service level. I leverage a 2014 policy change that drastically altered the payment structure of Medicare’s facility fees paid to hospitals for evaluation and management services—and yet, it did not alter the direct payments made to physicians. Contrary to popular belief, I find no evidence that the financial incentives of facility fees have an effect on the probability that a hospital and a clinical oncologist vertically integrate.

Machine learning-assisted cross-slice radio resource optimization: Implementation framework and algorithmic solution

Network slicing is a central feature in 5G and beyond systems to allow operators to customize their networks for different applications and customers. With network slicing, different logical networks, i.e. network slices, with specific functional and performance requirements can be created over the same physical network. A key challenge associated with the exploitation of the network slicing feature is how to efficiently allocate underlying network resources, especially radio resources, to cope with the spatio-temporal traffic variability while ensuring that network slices can be provisioned and assured within the boundaries of Service Level Agreements / Service Level Specifications (SLAs/SLSs) with customers. In this field, the use of artificial intelligence, and, specifically, Machine Learning (ML) techniques, has arisen as a promising approach to cater for the complexity of resource allocation optimization among network slices. This paper tackles the description of a feasible implementation framework for deploying ML-assisted solutions for cross-slice radio resource optimization that builds upon the work conducted by 3GPP and O-RAN Alliance. On this basis, the paper also describes and evaluates an ML-assisted solution that uses a Multi-Agent Reinforcement Learning (MARL) approach based on the Deep Q-Network (DQN) technique and fits within the presented implementation framework.

Supporting the strategic design of public bicycle sharing systems: The experience of a large Mexican city

Public bike sharing systems (BSS) comprises fleets of bicycles available for shared use at a low cost. BSS are gaining popularity as an environmentally friendly mode of transportation; however, their design entails critical decisions to transportation planners and public authorities. The objective of this study was to design a BSS for a large Mexican city taking into account the available budget, the fulfillment of a desired percentage of the predicted demand, the integration of the BSS with the existent public transportation network and the potential environmental benefit of substituting motorized transportation by cycling. A bi-objective optimization model with two objectives -minimize the total cost of the system’s implementation and maximize the reduction of CO 2 emissions- is formulated and solved to determine the optimal location of bike stations and size of the bicycle fleet required to attain a specific level of service. The analysis of the non-dominated solutions to the problem allows the identification of alternative designs that enhance the BSS’ level of service and its contribution to air quality, provided municipal authorities are willing to make tradeoffs between the two objectives and approve a larger investment.

Dynamic Resource Allocation in Metro Elastic Optical Networks Using Lyapunov Drift Optimization

Consistent growth in the volume and dynamic behavior of traffic mandates new requirements for fast and adaptive resource allocation in metro networks. We propose a dynamic resource allocation technique for adaptive minimization of spectrum usage in metro elastic optical networks. We consider optical transmission as a service specified by its bandwidth profile parameters, which are minimum, average, and maximum required transmission rates. To consider random traffic events, we use a stochastic optimization technique to develop a novel formulation for dynamic resource allocation in which service level specifications and network stability constraints are addressed. Next, we employ the elegant theory of Lyapunov optimization to solve the stochastic optimization problem and derive a fast integer linear program, which is periodically solved to create an adaptation between available resources and the dynamic network state. To quantize the performance of the proposed technique, we report its spectral efficiency as a function of the peak-to-average traffic ratio and Lyapunov penalty coefficient. Simulation results show that the dynamic resource allocation procedure can improve spectral efficiency by a factor of 3.3 for a peak-to-average traffic ratio of 1.37 and a Lyapunov penalty coefficient of 103 in comparison with fixed network planning. There is also a trade-off between transmission delay and spectrum utilization in the proposed technique, which can be adjusted by a Lyapunov penalty coefficient.

Scalable Prediction of Service-Level Events in Datacenter Infrastructure Using Deep Neural Networks

The complexity of cloud datacenter scenarios poses new challenges in infrastructure management processes such as the impracticality of collecting specific service level events from inside the datacenter infrastructure, and the scalability issues that can appear during event monitoring when thousands of virtual machines have to be polled at a granularity of seconds. Therefore, it would be desirable to provide mechanisms for obtaining these types of events without incurring in the previously described problems. To this end, we propose a generic and scalable method based on the application of deep neural network architectures for predicting service level events using only a reduced number of generic datacenter infrastructure statistics that can be monitored in a scalable way. We demonstrate in a controlled scenario of a real datacenter and using only three variables from a physical machine that it is possible to predict events in real-time and with decent accuracy, without needing to deploy any meter in the end-user equipment. Specifically, we demonstrate this over two service-level events: i) the so-called Noisy Neighbors effect, a harmful situation that appears in physical machines due to the interferences created by the interaction of virtual machines running on them; and ii) the jitter values of a multimedia call running in a virtual machine. We set up a testbed in a real datacenter deploying physical and virtual machines, running a large amount of different experiments for 1000 hours and collecting samples at a 10 seconds granularity in a dataset of 260,000 records. Two different scenarios, in which training and testing data sets contain significant statistical differences, are deployed to demonstrate a better generalization ability of deep models in changing scenarios when compared with traditional Machine Learning techniques. A set of different deep architectures are proposed for both use cases and approximately 4,000 deep models were trained and tested. In both use cases, the best deep models show a good performance when predicting service level events, even if the inputs do not exactly follow the statistical patterns of the data used during training.

Future integrated communication network architectures enabling heterogeneous service provision

Abstract. This paper summarizes expectations and requirements towards future converged communication systems denoted by 5th Generation (5G). Multiple research and standardization activities globally contribute to the definition and specification of an Information and Communication Technology (ICT) to provide business customers and residential users with both, existing and future upcoming services which demand for higher data rates and granted performance figures in terms of QoS parameters, such as low latency and high reliability. Representative use case families are threefold and represented as enhanced Mobile Broadband (eMBB), massive Internet of Things (mIoT), and Critical Communication, i.e. Ultra-Low Latency (ULL)/Ultra-High Reliability (UHR). To deploy and operate a dedicated network for each service or use case separately would raise the expenses and service costs to an unduly high amount. Instead provision of a commonly shared physical infrastructure offering resources for transport, processing, and storage of data to several separated logical networks (slices) individually managed and configured by potentially multiple service providers is the main concept of this new approach. Beside a multitude of other initiatives the EU-funded 5G NORMA project (5G Novel Radio Multiservice adaptive network Architecture) has developed an architecture which enables not only network programmability (configurability in software), but also network slicing and Multi Tenancy (allowing independent 3rd parties to offer an end-to-end service tailored according to their needs) in a mobile network. Major aspects dealt with here are the selectable support of mobility (on-demand) and service-aware QoE/QoS (Quality of Experience/Service) control. Specifically we will report on the outcome of the analysis of design criteria for Mobility Management schemes and the result of an exemplary application of the modular mobility function to scenarios with variable service requirements (e.g. high-terminal speed vs. on-demand mobility or portability of devices). An efficient sharing of scarce frequency resources in new radio systems demands for tight coordination of orchestration and assignment (scheduling) of resources for the different network slices as per capacity and priority (QoS) demand. Dynamicity aspects in changing algorithms and schemes to manage, configure, and optimize the resources at the radio base stations according to slice specific Service Level Agreements (SLAs) are investigated. It has been shown that architectural issues in terms of hierarchy (centralized vs. distributed) and layering, i.e. separation of control (signaling) and (user) data plane will play an essential role to increase the elasticity of network infrastructures which is in focus of applying SDN (Software Defined Networking) and NFV (Network Function Virtualization) to next generation communication systems. An outlook towards follow-on standardization and open research questions within different SDOs (Standards Defining Organizations) and recently started cooperative projects concludes the contribution.

Practice Summary: Intel Calculates the Right Service Level for Its Products

All too often, companies do not rigorously calculate service levels. Instead, they arbitrarily set service levels by employing a top-down mandate. They employ this arbitrary approach because they have difficulty in quantifying the economics of a specific service level; the primary difficulty these companies encounter is quantifying the cost of not satisfying demand. Intel’s Customer Fulfillment and Logistics Group has developed a data-driven approach to calculate customer service levels. The major breakthrough in this work is a simple three-step process that diverse functions across the supply chain can employ to agree on the costs associated with a given service level.

SLA-aware differentiated QoS in elastic optical networks

Abstract The quality of service (QoS) offered by optical networks can be improved by accurate provisioning of service level specifications (SLSs) included in the service level agreement (SLA). A large number of users coexisting in the network require different services. Thus, a pragmatic network needs to offer a differentiated QoS to a variety of users according to the SLA contracted for different services at varying costs. In conventional wavelength division multiplexed (WDM) optical networks, service differentiation is feasible only for a limited number of users because of its fixed-grid structure. Newly introduced flex-grid based elastic optical networks (EONs) are more adaptive to traffic requirements as compared to the WDM networks because of the flexibility in their grid structure. Thus, we propose an efficient SLA provisioning algorithm with improved QoS for these flex-grid EONs empowered by optical orthogonal frequency division multiplexing (O-OFDM). The proposed algorithm, called SLA-aware differentiated QoS (SADQ), employs differentiation at the level of routing, spectrum allocation, and connection survivability. The proposed SADQ aims to accurately provision the SLA using such multilevel differentiation with an objective to improve the spectrum utilization from the network operator’s perspective. SADQ is evaluated for three different CoSs under various traffic demand patterns and for different ratios of the number of requests belonging to the three considered CoSs. We propose two new SLA metrics for the improvement of functional QoS requirements, namely, security, confidentiality and survivability of high class of service (CoS) traffic. Since, to the best of our knowledge, the proposed SADQ is the first scheme in optical networks to employ exhaustive differentiation at the levels of routing, spectrum allocation, and survivability in a single algorithm, we first compare the performance of SADQ in EON and currently deployed WDM networks to assess the differentiation capability of EON and WDM networks under such differentiated service environment. The proposed SADQ is then compared with two existing benchmark routing and spectrum allocation (RSA) schemes that are also designed under EONs. Simulations indicate that the performance of SADQ is distinctly better in EON than in WDM network under differentiated QoS scenario. The comparative analysis of the proposed SADQ with the considered benchmark RSA strategies designed under EON shows the improved performance of SADQ in EON paradigm for offering differentiated services as per the SLA.

Measurement and verification of irrigation pumping DSM projects: Application of novel methodology designs

In South Africa the agricultural sector is a significant energy user, with irrigation pumping being the single biggest electricity-demanding farming activity. The agricultural and commercial sectors contribute 6.5% to annual South African electricity sales. Since 2004, Eskom demand side management (DSM) programmes actively engaged farmers to reduce peak period power usage. Farmers with higher power usage were also assisted to move from Landrate tariff structure to Ruraflex in order to incentivise away from peak-period power use. As part of the DSM programme, a number of large evening peak-load-shifting irrigation projects were implemented. Independent measurement and verification (M&amp;V) assessments were made to establish attained savings over the contracted project life. The M&amp;V of DSM projects normally have problems that complicate project assessments, but even taking this into account, the M&amp;V team experienced exceptional difficulties and cumbersome M&amp;V methodology challenges with certain irrigation projects. Normal baseline development methods were ineffective and novel M&amp;V methods needed to be devised and developed. This paper explains the normal M&amp;V methodology used for typical DSM projects and how it is applied. It gives guidance on baseline metering equipment, sampling and metering point selection. Further it demonstrates project specific issues and service level adjustment (SLA) anomalies that render normal M&amp;V methodologies ineffective. It shows novel and alternative baseline development and SLA methods that were incorporated on DSM projects to accurately quantify project impacts.

Statistical measurement of the inventory shortage cost

ABSTRACTIn searching for the optimal inventory control policy, the objective is to minimize the expected total costs related, of which the shortage cost is an important element. Due to the difficulty in calculating the indirect cost of the loss of goodwill resulted from the shortage, practitioners and researchers often simply assume a fixed penalty cost on the inventory shortage or switch to the alternative method by assigning a specific customer service level. The development of an appropriate tool for measuring the shortage cost can help a business control the total costs and improve the productivity more effectively. This paper proposes probabilistic measurements of the shortage cost, based on mathematical relationship between the cost and the shortage amount. The derived closed-form estimates of the expected shortage cost value can then be applied to support the determination of the optimal inventory control policy.

Monitoring Plane Architecture and OAM Handler

The increase of new services such as content distribution and interdata center connectivity poses new requirements on the control and management of next generation networks. The application-based network operations (ABNO) is emerging as a paradigm integrating control and management functions for different types of services. Key functionalities lie in the ABNO operation, administration, maintenance (OAM) handler which is responsible for the verification and the actual maintenance of network services following specific service level agreements. The OAM handler collects and correlates alerts about potential problems and triggers actions to preserve services in case of failures or degradations. In this paper, a hierarchical monitoring architecture is presented. The architecture is proposed by building on the OAM handler functionalities. Monitoring information can be filtered and correlated at each hierarchical level guaranteeing high scalability of the management plane. Actions can be taken at each hierarchical layer based on the type of failure and of affected service. Measurements on a commercial systems are carried on to identify the generated alarms upon failure. Then, simulations show the high scalability achieved by the proposed architecture.

Event Pattern Discovery in Multi-Cloud Service-Based Applications

The adoption of Cloud computing in the Service Oriented Architecture (SOA) world is continuously increasing. However, as developers try to optimize their application deployment cost and performance, they may also deploy application parts redundantly on different VMs. In such heterogeneous and distributed environments, it is important to have a clear view of the system's state and its components' interrelationships. This paper aims at proposing a novel monitoring and adaptation framework for Service-based Applications (SBAs) deployed on multiple Clouds. The main functionality of this framework is the discovery of critical event patterns within monitoring event streams, leading to specific Service Level Objective (SLO) violations. Furthermore, two main meta-models are proposed for describing the SBA's components and their dependencies, and the supported adaptation actions in a specific context respectively. The proposed approach is empirically evaluated based on a real-world traffic management application.

Care on demand in nursing homes: a queueing theoretic approach

Nursing homes face ever-tightening healthcare budgets and are searching for ways to increase the efficiency of their healthcare processes without losing sight of the needs of their residents. Optimizing the allocation of care workers plays a key role in this search as care workers are responsible for the daily care of the residents and account for a significant proportion of the total labor expenses. In practice, the lack of reliable data makes it difficult for nursing home managers to make informed staffing decisions. The focus of this study lies on the 'care on demand' process in a Belgian nursing home. Based on the analysis of real-life 'call button' data, a queueing model is presented which can be used by nursing home managers to determine the number of care workers required to meet a specific service level. Based on numerical experiments an 80/10 service level is proposed for this nursing home, meaning that at least 80 percent of the clients should receive care within 10 minutes after a call button request. To the best of our knowledge, this is the first attempt to develop a quantitative model for the 'care on demand' process in a nursing home.