5,973 research outputs found
Autonomic care platform for optimizing query performance
Background: As the amount of information in electronic health care systems increases, data operations get more complicated and time-consuming. Intensive Care platforms require a timely processing of data retrievals to guarantee the continuous display of recent data of patients. Physicians and nurses rely on this data for their decision making. Manual optimization of query executions has become difficult to handle due to the increased amount of queries across multiple sources. Hence, a more automated management is necessary to increase the performance of database queries. The autonomic computing paradigm promises an approach in which the system adapts itself and acts as self-managing entity, thereby limiting human interventions and taking actions. Despite the usage of autonomic control loops in network and software systems, this approach has not been applied so far for health information systems.
Methods: We extend the COSARA architecture, an infection surveillance and antibiotic management service platform for the Intensive Care Unit (ICU), with self-managed components to increase the performance of data retrievals. We used real-life ICU COSARA queries to analyse slow performance and measure the impact of optimizations. Each day more than 2 million COSARA queries are executed. Three control loops, which monitor the executions and take action, have been proposed: reactive, deliberative and reflective control loops. We focus on improvements of the execution time of microbiology queries directly related to the visual displays of patients' data on the bedside screens.
Results: The results show that autonomic control loops are beneficial for the optimizations in the data executions in the ICU. The application of reactive control loop results in a reduction of 8.61% of the average execution time of microbiology results. The combined application of the reactive and deliberative control loop results in an average query time reduction of 10.92% and the combination of reactive, deliberative and reflective control loops provides a reduction of 13.04%.
Conclusions: We found that by controlled reduction of queries' executions the performance for the end-user can be improved. The implementation of autonomic control loops in an existing health platform, COSARA, has a positive effect on the timely data visualization for the physician and nurse
Multisite adaptive computation offloading for mobile cloud applications
The sheer amount of mobile devices and their fast adaptability have contributed to the proliferation of modern advanced mobile applications. These applications have characteristics such as latency-critical and demand high availability. Also, these kinds of applications often require intensive computation resources and excessive energy consumption for processing, a mobile device has limited computation and energy capacity because of the physical size constraints.
The heterogeneous mobile cloud environment consists of different computing resources such as remote cloud servers in faraway data centres, cloudlets whose goal is to bring the cloud closer to the users, and nearby mobile devices that can be utilised to offload mobile tasks. Heterogeneity in mobile devices and the different sites include software, hardware, and technology variations. Resource-constrained mobile devices can leverage the shared resource environment to offload their intensive tasks to conserve battery life and improve the overall application performance. However, with such a loosely coupled and mobile device dominating network, new challenges and problems such as how to seamlessly leverage mobile devices with all the offloading sites, how to simplify deploying runtime environment for serving offloading requests from mobile devices, how to identify which parts of the mobile application to offload and how to decide whether to offload them and how to select the most optimal candidate offloading site among others.
To overcome the aforementioned challenges, this research work contributes the design and implementation of MAMoC, a loosely coupled end-to-end mobile computation offloading framework. Mobile applications can be adapted to the client library of the framework while the server components are deployed to the offloading sites for serving offloading requests. The evaluation of the offloading decision engine demonstrates the viability of the proposed solution for managing seamless and transparent offloading in distributed and dynamic mobile cloud environments. All the implemented components of this work are publicly available at the following URL: https://github.com/mamoc-repo
Fostering energy-awareness in simulations behind scientific workflow management systems
© 2014 IEEE.Scientific workflow management systems face a new challenge in the era of cloud computing. The past availability of rich information regarding the state of the used infrastructures is gone. Thus, organising virtual infrastructures so that they not only support the workflow being executed, but also optimise for several service level objectives (e.g., Maximum energy consumption limit, cost, reliability, availability) become dependent on good infrastructure modelling and prediction techniques. While simulators have been successfully used in the past to aid research on such workflow management systems, the currently available cloud related simulation toolkits suffer form several issues (e.g., Scalability, narrow scope) that hinder their applicability. To address this need, this paper introduces techniques for unifying two existing simulation toolkits by first analysing the problems with the current simulators, and then by illustrating the problems faced by workflow systems through the example of the ASKALON environment. Finally, we show how the unification of the selected simulators improve on the the discussed problems
Exploring the Impacts of Marketing Structure on Enrollment Yield in Higher Education
The ever-expanding role of marketing and its increasing influence on being a primary driver for colleges and universities to grow awareness and consideration, invite applications, and deliver enrollment yield is more critical and relied on than ever before. The marketing function’s rise to prominence in higher education is fueled by many external forces, including declining federal and state funding, rapid technological advances, changing student expectations, the precipitous reduction in numbers of potential college-aged students, and what this study refers to as the four C\u27s facing higher education. These include consumerism, commoditization, commercialism, and corporatization, each with its own set of unique challenges and opportunities from which marketing is viewed as a primary solution. In this research, a bounded case study approach is employed to look intently at the critical aspects of marketing structure, alignment, roles, strategic focus, tactical execution, and the brand management principles that have assisted a regional public four-year university in growing enrollment yield in light of ever-changing marketplace challenges, many of those unmasked during the recent pandemic. The study findings highlight how the alignment of roles and responsibilities within a structure, an operative environment that is reflective of the institution\u27s culture and allows for the illumination of the brand\u27s core personality, tone, attributes, and dimensions, can create a powerful asset to combat the external marketplace dynamics. This study offers an emergent framework as a unique model for colleges and universities to assess and analyze their marketing structure, operative environment, role and responsibility stratification, and how the consistent articulation of and purposeful management of the institution’s brand can be optimized for competitive advantage.
KEYWORDS: Higher education marketing, integrated marketing communications, brand management, marketing structure and alignmen
Optimizing performance of workflow executions under authorization control
“Business processes or workflows are often used to
model enterprise or scientific applications. It has
received considerable attention to automate workflow
executions on computing resources. However, many
workflow scenarios still involve human activities and
consist of a mixture of human tasks and computing
tasks.
Human involvement introduces security and
authorization concerns, requiring restrictions on who
is allowed to perform which tasks at what time. Role-
Based Access Control (RBAC) is a popular authorization
mechanism. In RBAC, the authorization concepts such as
roles and permissions are defined, and various
authorization constraints are supported, including
separation of duty, temporal constraints, etc. Under
RBAC, users are assigned to certain roles, while the
roles are associated with prescribed permissions.
When we assess resource capacities, or evaluate the
performance of workflow executions on supporting
platforms, it is often assumed that when a task is
allocated to a resource, the resource will accept the
task and start the execution once a processor becomes available. However, when the authorization policies
are taken into account,” this assumption may not be
true and the situation becomes more complex. For
example, when a task arrives, a valid and activated
role has to be assigned to a task before the task can
start execution. The deployed authorization
constraints may delay the workflow execution due to
the roles’ availability, or other restrictions on the
role assignments, which will consequently have
negative impact on application performance.
When the authorization constraints are present to
restrict the workflow executions, it entails new
research issues that have not been studied yet in
conventional workflow management. This thesis aims to
investigate these new research issues.
First, it is important to know whether a feasible
authorization solution can be found to enable the
executions of all tasks in a workflow, i.e., check the
feasibility of the deployed authorization constraints.
This thesis studies the issue of the feasibility
checking and models the feasibility checking problem
as a constraints satisfaction problem.
Second, it is useful to know when the performance of
workflow executions will not be affected by the given
authorization constraints. This thesis proposes the
methods to determine the time durations when the given
authorization constraints do not have impact.
Third, when the authorization constraints do have
the performance impact, how can we quantitatively
analyse and determine the impact? When there are multiple choices to assign the roles to the tasks,
will different choices lead to the different
performance impact? If so, can we find an optimal way
to conduct the task-role assignments so that the
performance impact is minimized? This thesis proposes
the method to analyze the delay caused by the
authorization constraints if the workflow arrives
beyond the non-impact time duration calculated above.
Through the analysis of the delay, we realize that the
authorization method, i.e., the method to select the
roles to assign to the tasks affects the length of the
delay caused by the authorization constraints. Based
on this finding, we propose an optimal authorization
method, called the Global Authorization Aware (GAA)
method.
Fourth, a key reason why authorization constraints
may have impact on performance is because the
authorization control directs the tasks to some
particular roles. Then how to determine the level of
workload directed to each role given a set of
authorization constraints? This thesis conducts the
theoretical analysis about how the authorization
constraints direct the workload to the roles, and
proposes the methods to calculate the arriving rate of
the requests directed to each role under the role,
temporal and cardinality constraints.
Finally, the amount of resources allocated to
support each individual role may have impact on the
execution performance of the workflows. Therefore, it
is desired to develop the strategies to determine the
adequate amount of resources when the authorization
control is present in the system. This thesis presents the methods to allocate the appropriate quantity for
resources, including both human resources and
computing resources. Different features of human
resources and computing resources are taken into
account. For human resources, the objective is to
maximize the performance subject to the budgets to
hire the human resources, while for computing
resources, the strategy aims to allocate adequate
amount of computing resources to meet the QoS
requirements
Economic regulation for multi tenant infrastructures
Large scale computing infrastructures need scalable and effi cient resource allocation mechanisms to ful l the requirements of its participants and applications while the whole system is regulated to work e ciently. Computational markets provide e fficient allocation mechanisms that aggregate information from multiple sources in large, dynamic and complex systems where there is not a single source with complete information. They have been proven to be successful in matching resource demand and resource supply in the presence of sel sh multi-objective and utility-optimizing users and sel sh pro t-optimizing providers. However, global infrastructure metrics which may not directly affect participants of the computational market still need to be addressed -a.k.a. economic externalities like load balancing or energy-efficiency.
In this thesis, we point out the need to address these economic externalities, and we design and evaluate appropriate regulation mechanisms from di erent perspectives on top of existing economic models, to incorporate a wider range of objective metrics not considered otherwise. Our main contributions in this thesis are threefold; fi rst, we propose a taxation mechanism that addresses the resource congestion problem e ffectively improving the balance of load among resources when correlated economic preferences are present; second,
we propose a game theoretic model with complete information to derive an algorithm to aid resource providers to scale up and down resource supply so energy-related costs can be reduced; and third, we relax our previous assumptions about complete information on the resource provider side and design an incentive-compatible mechanism to encourage users to truthfully report their resource requirements effectively assisting providers to make energy-eff cient allocations while providing a dynamic allocation mechanism to users.Les infraestructures computacionals de gran escala necessiten mecanismes d’assignació de recursos escalables i eficients per complir amb els requisits computacionals de tots els seus participants, assegurant-se de que el sistema és regulat apropiadament per a que funcioni de manera efectiva. Els mercats computacionals són mecanismes d’assignació de recursos eficients que incorporen informació de diferents fonts considerant sistemes de gran escala, complexos i dinàmics on no existeix una única font que proveeixi informació completa de l'estat del sistema. Aquests mercats computacionals han demostrat ser exitosos per acomodar la demanda de recursos computacionals amb la seva oferta quan els seus participants son considerats estratègics des del punt de vist de teoria de jocs. Tot i això existeixen mètriques a nivell global sobre la infraestructura que no tenen per que influenciar els usuaris a priori de manera directa. Així doncs, aquestes externalitats econòmiques com poden ser el balanceig de càrrega o la eficiència energètica, conformen una línia d’investigació que cal explorar. En aquesta tesi, presentem i descrivim la problemàtica derivada d'aquestes externalitats econòmiques. Un cop establert el marc d’actuació, dissenyem i avaluem mecanismes de regulació apropiats basats en models econòmics existents per resoldre aquesta problemàtica des de diferents punts de vista per incorporar un ventall més ampli de mètriques objectiu que no havien estat considerades fins al moment. Les nostres contribucions principals tenen tres vessants: en primer lloc, proposem un mecanisme de regulació de tipus impositiu que tracta de mitigar l’aparició de recursos sobre-explotats que, efectivament, millora el balanceig de la càrrega de treball entre els recursos disponibles; en segon lloc, proposem un model teòric basat en teoria de jocs amb informació o completa que permet derivar un algorisme que facilita la tasca dels proveïdors de recursos per modi car a l'alça o a la baixa l'oferta de recursos per tal de reduir els costos relacionats amb el consum energètic; i en tercer lloc, relaxem la nostra assumpció prèvia sobre l’existència d’informació complerta per part del proveïdor de recursos i dissenyem un mecanisme basat en incentius per fomentar que els usuaris facin pública de manera verídica i explícita els seus requeriments computacionals, ajudant d'aquesta manera als proveïdors de recursos a fer assignacions eficients des del punt de vista energètic a la vegada que oferim un mecanisme l’assignació de recursos dinàmica als usuari
Nurse participation in legal executions: An ethics round-table discussion
A paper was published in 2003 discussing the ethics of nurses participating in executions by inserting the intravenous line for lethal injections and providing care until death. This paper was circulated on an international email list of senior nurses and academics to engender discussion. From that discussion, several people agreed to contribute to a paper expressing their own thoughts and feelings about the ethics of nurses participating in executions in countries where capital punishment is legal. While a range of opinions were presented, these opinions fell into two main themes. The first of these included reflections on the philosophical obligations of nurses as caregivers who support those in times of great need, including condemned prisoners at the end of life. The second theme encompassed the notion that no nurse ever should participate in the active taking of life, in line with the codes of ethics of various nursing organisations. This range of opinions suggests the complexity of this issue and the need for further public discussion
Learning workload behaviour models from monitored time-series for resource estimation towards data center optimization
In recent years there has been an extraordinary growth of the demand of Cloud Computing resources executed in Data Centers. Modern Data Centers are complex systems that need management. As distributed computing systems grow, and workloads benefit from such computing environments, the management of such systems increases in complexity. The complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen as "black boxes", where only external monitoring can be retrieved. Furthermore, given the different amount of scenarios and applications, automation is required. To deal with such complexity, Machine Learning methods become crucial to facilitate tasks that can be automatically learned from data.
Firstly, this thesis proposes an unsupervised learning technique to learn high level representations from workload traces. Such technique provides a fast methodology to characterize workloads as sequences of abstract phases. The learned phase representation is validated on a variety of datasets and used in an auto-scaling task where we show that it can be applied in a production environment, achieving better performance than other state-of-the-art techniques.
Secondly, this thesis proposes a neural architecture, based on Sequence-to-Sequence models, that provides the expected resource usage of applications sharing hardware resources. The proposed technique provides resource managers the ability to predict resource usage over time as well as the completion time of the running applications. The technique provides lower error predicting usage when compared with other popular Machine Learning methods.
Thirdly, this thesis proposes a technique for auto-tuning Big Data workloads from the available tunable parameters. The proposed technique gathers information from the logs of an application generating a feature descriptor that captures relevant information from the application to be tuned. Using this information we demonstrate that performance models can generalize up to a 34% better when compared with other state-of-the-art solutions. Moreover, the search time to find a suitable solution can be drastically reduced, with up to a 12x speedup and almost equal quality results as modern solutions.
These results prove that modern learning algorithms, with the right feature information, provide powerful techniques to manage resource allocation for applications running in cloud environments. This thesis demonstrates that learning algorithms allow relevant optimizations in Data Center environments, where applications are externally monitored and careful resource management is paramount to efficiently use computing resources. We propose to demonstrate this thesis in three areas that orbit around resource management in server environmentsEls Centres de Dades (Data Centers) moderns són sistemes complexos que necessiten ser gestionats. A mesura que creixen els sistemes de computació distribuïda i les aplicacions es beneficien d’aquestes infraestructures, també n’augmenta la seva complexitat. La complexitat que implica gestionar recursos de còmput i d’energia en sistemes de computació al núvol fa difícil entendre el comportament de les aplicacions que s'executen de manera manual. Aquesta dificultat s’incrementa quan les aplicacions s'observen com a "caixes negres", on només es poden monitoritzar algunes mètriques de les caixes de manera externa. A més, degut a la gran varietat d’escenaris i aplicacions, és necessari automatitzar la gestió d'aquests recursos. Per afrontar-ne el repte, l'aprenentatge automàtic juga un paper cabdal que facilita aquestes tasques, que poden ser apreses automàticament en base a dades prèvies del sistema que es monitoritza. Aquesta tesi demostra que els algorismes d'aprenentatge poden aportar optimitzacions molt rellevants en la gestió de Centres de Dades, on les aplicacions són monitoritzades externament i la gestió dels recursos és de vital importància per a fer un ús eficient de la capacitat de còmput d'aquests sistemes. En primer lloc, aquesta tesi proposa emprar aprenentatge no supervisat per tal d’aprendre representacions d'alt nivell a partir de traces d'aplicacions. Aquesta tècnica ens proporciona una metodologia ràpida per a caracteritzar aplicacions vistes com a seqüències de fases abstractes. La representació apresa de fases és validada en diferents “datasets” i s'aplica a la gestió de tasques d'”auto-scaling”, on es conclou que pot ser aplicable en un medi de producció, aconseguint un millor rendiment que altres mètodes de vanguardia. En segon lloc, aquesta tesi proposa l'ús de xarxes neuronals, basades en arquitectures “Sequence-to-Sequence”, que proporcionen una estimació dels recursos usats per aplicacions que comparteixen recursos de hardware. La tècnica proposada facilita als gestors de recursos l’habilitat de predir l'ús de recursos a través del temps, així com també una estimació del temps de còmput de les aplicacions. Tanmateix, redueix l’error en l’estimació de recursos en comparació amb d’altres tècniques populars d'aprenentatge automàtic. Per acabar, aquesta tesi introdueix una tècnica per a fer “auto-tuning” dels “hyper-paràmetres” d'aplicacions de Big Data. Consisteix així en obtenir informació dels “logs” de les aplicacions, generant un vector de característiques que captura informació rellevant de les aplicacions que s'han de “tunejar”. Emprant doncs aquesta informació es valida que els ”Regresors” entrenats en la predicció del rendiment de les aplicacions són capaços de generalitzar fins a un 34% millor que d’altres “Regresors” de vanguàrdia. A més, el temps de cerca per a trobar una bona solució es pot reduir dràsticament, aconseguint un increment de millora de fins a 12 vegades més dels resultats de qualitat en contraposició a alternatives modernes. Aquests resultats posen de manifest que els algorismes moderns d'aprenentatge automàtic esdevenen tècniques molt potents per tal de gestionar l'assignació de recursos en aplicacions que s'executen al núvol.Arquitectura de computador
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