ترغب بنشر مسار تعليمي؟ اضغط هنا

Experiences with advanced CORBA services

57   0   0.0 ( 0 )
 نشر من قبل Grega Milcinski
 تاريخ النشر 2001
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

The Common Object Request Broker Architecture (CORBA) is successfully used in many control systems (CS) for data transfer and device modeling. Communication rates below 1 millisecond, high reliability, scalability, language independence and other features make it very attractive. For common types of applications like error logging, alarm messaging or slow monitoring, one can benefit from standard CORBA services that are implemented by third parties and save tremendous amount of developing time. We have started using few CORBA services on our previous CORBA-based control system for the light source ANKA [1] and use now several CORBA services for the ALMA Common Software (ACS) [2], the core of the control system of the Atacama Large Millimeter Array. Our experiences with the interface repository (IFR), the implementation repository, the naming service, the property service, telecom log service and the notify service from different vendors are presented. Performance and scalability benchmarks have been performed.



قيم البحث

اقرأ أيضاً

AI models and services are used in a growing number of highstakes areas, resulting in a need for increased transparency. Consistent with this, several proposals for higher quality and more consistent documentation of AI data, models, and systems have emerged. Little is known, however, about the needs of those who would produce or consume these new forms of documentation. Through semi-structured developer interviews, and two document creation exercises, we have assembled a clearer picture of these needs and the various challenges faced in creating accurate and useful AI documentation. Based on the observations from this work, supplemented by feedback received during multiple design explorations and stakeholder conversations, we make recommendations for easing the collection and flexible presentation of AI facts to promote transparency.
Cloud computing delivers value to users by facilitating their access to computing capacity in periods when their need arises. An approach is to provide both on-demand and spot services on shared servers. The former allows users to access servers on d emand at a fixed price and users occupy different periods of servers. The latter allows users to bid for the remaining unoccupied periods via dynamic pricing; however, without appropriate design, such periods may be arbitrarily small since on-demand users arrive randomly. This is also the current service model adopted by Amazon Elastic Cloud Compute. In this paper, we provide the first integral framework for sharing the time of servers between on-demand and spot services while optimally pricing spot instances. It guarantees that on-demand users can get served quickly while spot users can stably utilize servers for a properly long period once accepted, which is a key feature to make both on-demand and spot services accessible. Simulation results show that, by complementing the on-demand market with a spot market, a cloud provider can improve revenue by up to 464.7%. The framework is designed under assumptions which are met in real environments. It is a new tool that cloud operators can use to quantify the advantage of a hybrid spot and on-demand service, eventually making the case for operating such service model in their own infrastructures.
Star sampling (SS) is a random sampling procedure on a graph wherein each sample consists of a randomly selected vertex (the star center) and its one-hop neighbors (the star endpoints). We consider the use of star sampling to find any member of an ar bitrary target set of vertices in a graph, where the figure of merit (cost) is either the expected number of samples (unit cost) or the expected number of star centers plus star endpoints (linear cost) until a vertex in the target set is encountered, either as a star center or as a star point. We analyze this performance measure on three related star sampling paradigms: SS with replacement (SSR), SS without center replacement (SSC), and SS without star replacement (SSS). We derive exact and approximate expressions for the expected unit and linear costs of SSR, SSC, and SSS on Erdos-Renyi (ER) graphs. Our results show there is i) little difference in unit cost, but ii) significant difference in linear cost, across the three paradigms. Although our results are derived for ER graphs, experiments on real-world graphs suggest our performance expressions are reasonably accurate for non-ER graphs.
229 - Ian Fisk 2010
In this presentation the experiences of the LHC experiments using grid computing were presented with a focus on experience with distributed analysis. After many years of development, preparation, exercises, and validation the LHC (Large Hadron Collid er) experiments are in operations. The computing infrastructure has been heavily utilized in the first 6 months of data collection. The general experience of exploiting the grid infrastructure for organized processing and preparation is described, as well as the successes employing the infrastructure for distributed analysis. At the end the expected evolution and future plans are outlined.
Measuring airways in chest computed tomography (CT) images is important for characterizing diseases such as cystic fibrosis, yet very time-consuming to perform manually. Machine learning algorithms offer an alternative, but need large sets of annotat ed data to perform well. We investigate whether crowdsourcing can be used to gather airway annotations which can serve directly for measuring the airways, or as training data for the algorithms. We generate image slices at known locations of airways and request untrained crowd workers to outline the airway lumen and airway wall. Our results show that the workers are able to interpret the images, but that the instructions are too complex, leading to many unusable annotations. After excluding unusable annotations, quantitative results show medium to high correlations with expert measurements of the airways. Based on this positive experience, we describe a number of further research directions and provide insight into the challenges of crowdsourcing in medical images from the perspective of first-time users.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا