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Addressing Scalability with Message Queues: Architecture and Use Cases for DIRAC Interware

Addressing Scalability with Message Queues: Architecture and Use Cases for DIRAC Interware

A Message Queue generic interface has been incorporated into the DIRAC framework to help solving the scalability challenges that must be addressed dur- ing LHC Run3, starting in 2021. It allows to use the MQ scheme for a message exchange among the DIRAC components or to communicate with third-party services. Within this contribution we describe the integration of MQ systems with DIRAC and several use cases are shown. Message Queues are foreseen to be used in the pilot logging system, and as a backbone of the DIRAC component logging system and monitoring.

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Improvements to the LHCb software performance testing infrastructure using message queues and big data technologies

Improvements to the LHCb software performance testing infrastructure using message queues and big data technologies

The workflow in the LHCbPR system is presented in figure 1. The tests are triggered by user requests and messages coming from the LHCb continuous integration system [7, 8]. In the former case, the user specifies the application, its version, the option file stored in the dedicated repository and the handler module. The message with such information is sent to the queue of the tests, which are periodically consumed by one of the jobs (implemented in python [9] and bash [10]) in the LHCb instance of the Jenkins [11] infrastructure. In the latter case, once each application for a given platform (defined by the compiler, architecture and operating system) is compiled, the message is sent to the queue of builds which is checked against the lists of tests scheduled in XML file. The implementation of the message queue is done using the RabbitMQ message broker [12]. Such a solution allows to e ffi ciently use CPU time of the build and test machines as the test jobs are scheduled to run on dedicated nodes as soon as the corresponding software is built. In particular benchmarks measuring resource consumption are run on machines where the unwanted load is minimised. As a next step of the LHCbPR workflow, output of the tests is parsed by the handlers. Those python modules produce zipped JSON [13] files with the metrics of interest and optionally ROOT [14] files. Furthermore, the zip files are uploaded to Dirac Storage Element [15] and then imported into MySQL [16] database using the back-end implemented using the Django framework [17]. The outcome is available in an AngularJS [18]-based dashboard. In the meantime, notifications about new results are sent to a Mattermost [19] channel for the interested users. The system allows to define for each monitored metric a corresponding threshold value which may trigger an alarm when exceeded. However due to high number of tests and rapidly evolving software, it appeared to be too difficult to maintain and is not commonly used.
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Fast, flexible & efficient delivery software

Fast, flexible & efficient delivery software

RabbitMQ is open source AMQP message queue software. It allows you to easily create reliable first-in-first-out message queues. MailerQ is built on top of RabbitMQ and uses RabbitMQ to store and prioritize messages. MailerQ focuses solely on the sending process.

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An Introduction to Cloud Computing

An Introduction to Cloud Computing

In addition to these four basics, cloud providers offer other services such as message queues and data mining. All of these things are lumped into the generic term[r]

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Guzmán Llambías and Raúl Ruggia Universidad de la República, Facultad de Ingeniería, Montevideo, Uruguay, {gllambi,

Guzmán Llambías and Raúl Ruggia Universidad de la República, Facultad de Ingeniería, Montevideo, Uruguay, {gllambi,

The cost/result varies according to the different choices. Experience after this work showed that using ESB middleware requires a high initial cost, both in learning and implementation for the initial configuration of the platform, which becomes marginal for each new service added to it. In turn, Web Services and message queues have short-term results given the shorter learning curve and implementation. In this type of middleware, the development cost is linear and constant for each service that you want to integrate to the platform. It’s also relevant to point out that laboratories which are not decided to use ESB as a first option may start with an implementation based on Web Services or Queues and to migrate afterwards to an ESB-based Platform, which does not constitute a complex task (using for example SOAPProxy or JMSRouter actions).
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Stratagy User Guide is included with this package for your information. Please take the time

Stratagy User Guide is included with this package for your information. Please take the time

New and Saved message queues – The user logs on, presses 1 to play messages and hears first new message (pending LIFO/FIFO option). If there are no new messages, the user is presented to the saved message queue. After listening to all new (saved) messages, the user receives an end of queue prompt. The user is then prompted to press 1 to listen to the next message. If 1 is pressed, the user is returned to the first message in the queue. The user can press 77 to toggle between the new and saved message queues.

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Bayesian inference for double Pareto lognormal queues

Bayesian inference for double Pareto lognormal queues

Secondly, we have combined this approach with techniques from the queue- ing literature in order to estimate predictive equilibrium distributions for the dP lN/M/1 and M/dP lN/1. To do this, we have adapted the Transform Ap- proximation method, in order to estimate the Laplace transform of the dPlN distribution and the waiting time distribution in the M/dP lN/1 system. Finally, we have illustrated this methodology with real data sets, estimating first waiting times and congestion in internet and computing the probability of ruin in the insurance context, making use of the duality between queues and risk theory. Comparisons with the M/M/1, P areto/M/1 and M/P areto/1 have been also presented. Large differences among these queueing systems when the service process is heavy-tailed were found.
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ATLAS Global Shares implementation in PanDA

ATLAS Global Shares implementation in PanDA

Global-Share-unaware pilot schedulers serving separate queues on a same site, usually submit pilots proportionally to the amount of assigned jobs at each queue. They do not respect the ratio needed to honour Global Shares and generate competition between the queues. In order to control the ratio between single and multi-core slots, we are rolling out the concept of unified queues. Here all the jobs with different slot types are queued together and PanDA needs to control the order of execution. There are two different modes of control:

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On the distribution of queueing times for queues with two servers

On the distribution of queueing times for queues with two servers

On the other hand it is also clear that if one attempts b then irrespective of whether one is merely seeking the ergodic limiting distribution for the queue or a generating function whic[r]

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REST and SOAP Services with Apache CXF

REST and SOAP Services with Apache CXF

Configuration Admin: work queues, keystores, http conduits.[r]

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VERSION Online Help Administrators Guide

VERSION Online Help Administrators Guide

Queue based access rights: A queue allows a user to see all documents that meet a certain criteria. This criterion is specified by an Administrator (in the case of system queues) or the user (in the case of private queues). The Queue looks at all the documents and lists files that match what was specified in the search field. While a Queue may be able to find a document, this does not mean that the user has access rights to the file. By removing a security group’s ability to access a queue, only the ability to see that Queue is removed; the user is still able to access the file by creating a Private Queue or by using the GOTO function to access the
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Longest Path in Networks of Queues in the Steady-State

Longest Path in Networks of Queues in the Steady-State

In this paper, we develop a method to compute the distribution function of longest path in networks of queues. Our method is an extension of the Kulkarni and Adlakha [10] method and it is adaptable to solve many practical problems. For example in PERT networks, if we assume there are some service stations in the nodes of the network and one should wait to receive the service in these stations, our method can be used for computing the distribution function of project completion time. We also may refer to another important application in the area of production systems. Each dynamic job shop system can be represented as a network of queues, in which a service station indicates a machine or a production department. It is clear each part of the product spends some time equal to the waiting time in the system, in a machine or a service station. Furthermore, clearly the product is completed when all required operations are finished in all the service stations. Therefore, the longest path distribution of such network of queues is equal to the flow time distribution, and consequently the expected longest path is equal to the mean flow time, or in other words the time between arrival the demand until the completion of the product. Obviously, this duration is an important factor in production systems and can easily be computed by our method. Azaron and Fatemi Ghomi [2] developed a similar procedure for the optimal control of service rates of the service stations in a class of Jackson networks, in which the expected shortest path of the network and also the total operating costs of the service stations of the network per period are minimized.
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Legal declarations. Copyright. Trademarks. Warranty. Limitation of liability. License agreement

Legal declarations. Copyright. Trademarks. Warranty. Limitation of liability. License agreement

Introduction to MobileTEC Host .............................................................. 1-1 What’s included in this document............................................................. 1-1 MobileTEC Client enhancements .............................................................. 1-1 About Microsoft Message Queuing (MSMQ)? .............................................. 1-1 About event logs ................................................................................... 1-2 Installing and Configuring MobileTEC Host.............................................. 2-1 System requirements............................................................................. 2-1 Installing the software ........................................................................... 2-1 Configuring MobileTEC Host .................................................................. 2-17 Verifying security for MobileHOST private queues .................................... 2-18 Verifying message queuing triggers are set to local system account ........... 2-20 Hiding inventory item costs from technicians........................................... 2-20 Customizing the Call Summary Report for technicians .............................. 2-21 Setting up and processing in Service Management.................................. 3-1 What gets broadcasted from the host to the MobileTEC devices?.................. 3-1 Setting up technicians to use MobileTEC ................................................... 3-2 Setting up TimeTrack for unbilled transactions .......................................... 3-3 Creating transactions for inventory and non-inventory items
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A Comparison of Task Pool Variants in OpenMP and a Proposal for a Solution to the Busy Waiting Problem

A Comparison of Task Pool Variants in OpenMP and a Proposal for a Solution to the Busy Waiting Problem

tasks, and therefore good load balancing is crucial. The use of private queues turns out to be a drawback in this case, because all tasks remain in the private queues and the idle threads have no chance to fetch them. The performance of dq9 is good, though, because this variant makes the distribution of tasks among the queues dependent on the number of tasks in the pool. If there are only a few tasks in the pool (shared queues are empty and at least one thread is idle), a new task will be inserted into a shared queue (and not, like e. g. for dq9-1 into a private queue). If there are enough tasks in the pool, however, dq9 will insert a new task into a private queue to avoid synchronization operations. Using this technique, dq9 achieves much better performance than the other task pool variants with distributed queues.
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NOQ OUR CURE FOR QUEUES AT HOSPITALS

NOQ OUR CURE FOR QUEUES AT HOSPITALS

NoQ or “No Queue” replaces physical lines and waiting rooms with virtual mobile phone queues and incorporates dynamic appointment scheduling, predictive self learning logic, [r]

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Stochastic Modelling of Aircraft Queues: A Review

Stochastic Modelling of Aircraft Queues: A Review

Throughout this section we are concerned with the processes by which aircraft join queues waiting to use the runway(s) at airports. In the case of departing aircraft, these queues are located on the ground, usually at the threshold of the departure runway(s). Arriving aircraft, on the other hand, must wait in airborne “holding stacks” which are usually located near the terminal airspace, although in some cases they may also be “held” at other stages of their journeys by air traffic controllers (to control the flow of traffic into a congested air sector, for example). In many cases, a plane which lands at an airport will take off again (not necessarily from the same runway) within a couple of hours. This implies that the demand processes for arrivals and departures are not independent of each other, but in fact it is quite common in existing mathematical models for arrivals and departures to be treated as independent queues with time-varying demand rates which are configured according to the schedule of operations. The assumption of independence is undoubtedly an oversimplification, but it may not be particularly harmful if one considers a large airport with separate runways being used for arrivals and departures (this system is referred to as “segregated operations” and is used at London Heathrow, for example).
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Recent Advances in Accumulating Priority Queues

Recent Advances in Accumulating Priority Queues

The last direction in which the thesis extends upon the existing work pertains to the matter of optimality of APQ systems. Optimization problems are formulated for the queues under waiting time limits, such as the KPIs in Table 1.1. As stated earlier, KPIs are widely used to regulate the health care systems in Canada, not only for “visible” queues but also for wait lists. The time limits and the corresponding compliance probabilities in KPIs were determined by medical professionals according to the clinical need of di ff erent patient classes from the history, prior to any consideration of the tra ffi c characteristics of the patient classes (i.e., frequency of demand, treatment time distributions). A simple patten can be observed from Table 1.1, such that for the patients from Level 2 and onwards, the time limit for the next level doubles the one above and the compliance probability is simply reduced by 5%. The systems which meet the KPIs are considered to be in compliance. Even in such systems, the small percentage of patients who miss their time limits tend to be ignored, with no consequence specified for them. Certainly, it is irrational and unacceptable to neglect these patients; on the contrary, they should become a greater concern to the system managers.
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Intuitionistic Fuzzy Queues with Priority Discipline

Intuitionistic Fuzzy Queues with Priority Discipline

[11] Kao, C., Li, C., & Chen, S. (1999). Parametric programming to the analysis of fuzzy queues, Fuzzy Sets and Systems, 107, 93–100. [12] Li, R.J., & Lee, E.S. (1989). Analysis of fuzzy queues, Computers and Mathematics with Applications, 17 :7, 1143–1147. [13] Negi, D.S., & Lee, E.S. (1992). Analysisand simulation of fuzzy queues: Fuzzy Sets and Systems, 46, 321–330. [14] Taha, H.A. (2003 Operations Research, Introduction. (7 th ed.). New Jersey: Prentice-Hall.

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ANALYSIS OF PRIORITY QUEUES WITH PENTAGON FUZZY NUMBER

ANALYSIS OF PRIORITY QUEUES WITH PENTAGON FUZZY NUMBER

Fuzziness is a sort of recent incoherence. Fuzzy set theory is asserted to depict vagueness. This study explores the queuing model of priority classes adopting pentagon fuzzy number with the inclusions of fuzzy set operations. A mathematical programming method is designed to establish the membership function of the system performance, in which the arrival rate and service rate of the system performance of two priority classes are utilized as fuzzy numbers. Based on α-cut approach and Zadeh’s extension principle, the fuzzy queues are scaled down to a family of ordinary queues. The potency of the performance measures of the characteristics of the queuing model is ensured with an illustration and its graph.
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Estimating Change Point in Single Server Queues

Estimating Change Point in Single Server Queues

Statistical inference plays a major role in any use of a queueing models in decision making. Many authors have studied the parameter estimation problem in queueing models. Basawa and Prabhu[4,5] have discussed moment and likelihood estimation of the model parameters for single server queues using various sampling plans. Bhat and Rao [6] have provided an exhaustive survey of results on inference for queueing systems. Basawa and Bhat [2] studied sequential inference for the parameters of a GI/G/1 queue. An empirical Bayes approach was used for estimating by Thiruvaiyaru and Basawa [15]. Basawa et al. [3] presented a maximum likelihood method for estimating the parameters of the arrival and service time distribution using only the information on the waiting times of customers in GI/G/1 queue with “first come first served” queue disciline. Clarke [10] obtained the maximum likelihood estimates for the arrival and service parameters of an M/M/1 queue. A review of the literature on the subject reveals that so far only single server queues have been considered from an inferential viewpoint. crane and lemoine [14] have applied simulation techniques to the problem of estimating the steady state mean waiting time in a single server queue. Acharya [1] have discussed the rate of convergence of the distribution of the maximum likelihood estimators of the arrival and the service rates in a GI/G/1 model.
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