Arrival Of Clients Research Articles

Survey and Review

This issue contains two Survey and Review papers. The first, by Qinmeng Zou and Frédéric Magoulès, is “Delayed Gradient Methods for Symmetric and Positive Definite Linear Systems.” Gradient methods are the oldest and simplest algorithms to minimize a real objective function (f(x)). The ((n+1))st approximation to the minimizer is defined as (x_n+1 = x_n -\alpha_n g_n), where \(g_n\) is the gradient (\nabla f(x_n)) and (\alpha_n>0) is a steplength that depends on the specific method being used. Old and simple as the idea may be, gradient algorithms are of much current interest in the literature; for instance, they played a major role in the influential survey devoted to optimization in machine learning published in Volume 60, Issue 2 of this journal. The paper by Zou and Magoulès focuses on the quadratic case $f(x) = (1/2)x^TAx-b^Tx$ ($A$ symmetric and positive definite), where finding the minimizer is of course equivalent to solving the linear system $Ax=b$ and the gradient $g_n$ coincides with the residual $Ax_n-b$. Well-known strategies to determine the steplength include steepest descent, where $\alpha_n$ is chosen so as to minimize $f(x_{n+1})$, and minimal gradient (or minimal residual), where one rather minimizes the length of $g_{n+1}$. In both of these strategies, the value of $\alpha_n$ depends only on $g_n$. The term “delayed” in the title of the article refers to methods where the recipe to determine $\alpha_n$ includes information from past gradients $g_{n-1}, g_{n-2}$, \dots, and/or past stepsizes $\alpha_{n-1}$, $\alpha_{n-2}$, \dots. The numerical experiments reported clearly indicate that such delayed strategies may give rise to algorithms that are competitive with conjugate gradient methods in large ill-conditioned problems. The paper presents a neat summary of the recent results in this area and of the techniques used to derive them. Mark Van der Boor, Sem C. Borst, Johan S. H. Van Leeuwaarden, and Debankur Mukherjee are the authors of the second paper, “Scalable Load Balancing in Networked Systems: A Survey of Recent Advances.” The problem under consideration is as follows. A dispatcher receives clients that arrive randomly, and her job is to direct them to one of $N\gg 1$ servers. The time required by each client to be served is also random, so that a queue (of random length) of waiting clients will be formed at each server. How should the dispatcher proceed to expedite the service? As one would expect in these days of cloud networks and data systems with massive number of individual centers, the problem is currently receiving much attention in the literature. A strategy that suggests itself is the so-called “join the shortest queue” (JSQ), where on their arrival clients are directed to the server having the shortest queue. While JSQ has been proved to possess several favorable properties, it may not be the best option, due to communication overheads: each time a client arrives, the dispatcher has to communicate with all servers to find the lengths of their queues. The paper analyzes, in the limit $N\rightarrow \infty$, many alternative strategies. Nonspecialists will have little difficulty in reading the easily accessible first few sections and may be interested in discovering how even small tweaks in the algorithms may result in substantial improvements of their performance.

Modelling Internet Traffic Streams with Ga/M/1/K Queuing Systems under Self-similarity

High-intensity concurrent arrivals of request packets in Internet traffic can cause dependence of event-to-event-times of the requests being served, which causes non-memoryless, modelled with heavy-tail distributions unlike common known traffics. The performance of Internet traffic can be examined using analytical models for the purpose of optimizing the system to reduce its operating costs. Therefore, our study examined a Ga/M/1/K Internet queue class (Gamma arrival processes, Ga; with memoryless-Poisson service process, M; a single server, 1, and K waiting room) and proposed specific derivations of its performance indicators. Real-life data of a corporate organisation Internet server was monitored at both peak and off-peak periods of its usage for Internet traffic data analysis. The minimum ‘0’ in the arrival process indicates self-similarity and was assessed using Hurst parameter, H, and their (standard deviation). ‘H’ > 0.5 arrival process in the peak period only, indicates self-similarity. Performance of Ga/M/1/K was compared with various queuing Internet traffic models used in existing literatures. Results showed that the value of the waiting room size for Ga/M/1/K has closest ties with true self-similar model at peak-periods usage of the Internet, which indicates possible concurrent arrival of clients' requests leading to more usage of the waiting room, but with light-tailed queue model at the off-peak periods. Therefore, the proposed Ga/M/1/K model can assist in evaluating the performance of high-intensity self-similar Internet traffic. 
 Keywords: Internet traffic; self-similarity; Ga/M/1/K model; gamma distribution

Online facility location with mobile facilities

We examine the Online Facility Location problem in an extended version. Fotakis showed a lower bound of Ω(log⁡nlog⁡log⁡n) for the original Online Facility Location problem, where n is the number of clients. This bound holds even on the real line and for randomized algorithms against oblivious adversaries.We propose randomized online algorithms in the following setting: We consider the Euclidean space of arbitrary dimension and allow the facilities to either move arbitrarily or to move at most a constant distance m in each time step. The costs for moving a facility from a to b is D⋅d(a,b) where D≥1 is a constant. Clients are assigned to facilities instantly and irreversibly. The cost for serving the clients is either calculated as soon as a client arrives or at the end of the computation. The algorithms for these two cost models achieve the same competitiveness on the line, which, in contrast to the original Online Facility Location problem, is independent of n. In the case of arbitrary movement, it only depends on D. In the case of a limited movement distance m, it additionally depends on m and the opening costs of the facilities. We show that our results are asymptotically tight on the real line. For the Euclidean space of higher dimensions, we give an algorithm for the model where costs for clients are evaluated immediately. The competitive ratio of our algorithm depends on the above parameters and additionally on the number of optimal facilities.

Batch service Queuing system with Phase of service and various vacation policies

This paper considers a M X/G/1 line with arbitrary breakdowns and various vacation. Service is given in phases. After the fulfilment of service,the server goes for a get-away of irregular span and subsequent to coming back from get-away, the server chooses to take an all-encompassing get-away which is optional. A idea of remain by server is presented during the hour of stretched out vacation. The server may expose to separate and it get into a fix procedure quickly with no delay. We treat Reneging in this paper when the server is under repair. Arrival of clients follows a Poisson distribution. All different parameters follow general distribution. Steady state consequences of the lining model and all the execution measures are determined. Numerical outline and graphical methodology justifies the model.

Comments on the dilemma in the March issue: Blocked cats and limited finance: an ethical obstruction?

In the dilemma discussed in the March issue of In Practice, David Williams described a scenario where it's late on a Friday evening when a long-standing client arrives at your...

THE USE OF SIMULATION TO MODEL THE DISPATCH OF INBOUND CONTAINERS IN PORT TERMINALS

ABSTRACT This paper describes a study of the dispatch planning/scheduling process for inbound containers handled with a reach stacker. Client container pickup is scheduled at least one day in advance for one of six two-hour time windows (six five-container-high stacks per time window) on a given day. A buffer area is available for the containers to be moved in when clients are being served. The aim of this study was to determine the conditions required to ensure that all the containers are dispatched within the scheduled time window and so meet the clients’ requirements. To this end, the performance indicators were identified and compared using simulations as an analytical tool. The results indicate that the shortest-processing-time (SPT) queueing discipline is preferable to the first-come-first-served (FCFS) discipline and that client arrivals can usefully be restricted to periods shorter than two hours in order to meet container-dispatch and service-quality objectives.

Analyzing of a finite buffer queue with finite number of vacation policy and correlated arrivals

This paper analyses a MAP/G/1/N queue having finite number of vacations. The server takes a finite number (say J ≥ 0) of vacations whenever the system becomes empty at service completion epoch. If no clients are found by the end of the Jth vacation, the server does not go for vacation and stays in the system (called dormant period) until one client arrives. The number of vacations being finite and the server can utilise vacation periods for any other jobs. This is obvious that J = 1 and J → 1 lead to single and multiple vacation models, respectively. This research work mainly focuses more generalised vacation policy and different use cases. The following results have been obtained: 1) the distributions of clients in the queue at various epochs; 2) the Laplace-Stieltjes transform of the actual waiting-time distribution in the queue of a client under the FCFS discipline. The numerical data and graphs are presented to establish the analytical result.

Integración del internet de las cosas en los procesos logísticos de máquinas dispensadoras

Este artículo tiene como principal objetivo definir las condiciones en las que se desarrollan algunos procesos logísticos de las organizaciones en la integración de dispositivos en objetos cotidianos conectados a internet y que, por este medio, entregan información generadora de dinámicas diferentes a las tradicionales en cada una de las actividades que conforman la labor de abastecimiento, para garantizar la disponibilidad del producto en las condiciones que el cliente lo desea y, en este caso, en las actividades enmarcadas en el denominado vending: comercialización de productos como snacks, bebidas, artículos de aseo, medicamentos básicos, entre otros, a través de dispensadores electrónicos. La metodología usada está basada en el análisis de la bibliografía especializada en el tema del vending, el Internet de las Cosas y de las “cosas” que demandan constante nivel de abastecimiento de productos dadas las características de suministro permanente a los clientes por parte de estas, como los dispensadores de snacks, refrescos y de productos perecederos tales como frutas, productos frescos y productos lácteos, así como la consulta en fuentes primarias entre las que se encuentran mediciones sobre comportamiento de agotados y frecuencia de llegada de clientes en tres máquinas ubicadas en una entidad educativa. Igualmente, se evidencia la importancia de utilizar la tecnología para la recolección de la información pertinente, ya sea con la interacción de tecnologías blandas (software) y tecnologías duras o la utilización exclusiva de software, decisión en la que juega un papel preponderante el costo del producto.

Dynamic internet pricing with service level agreements for multihomed clients

With the increase in internet coverage and the decrease in internet access price, the demand for good internet service has grown. Clients expect a guarantee in the quality of service (QoS) during internet access. In this paper, we present a model in which clients are given assurances by service providers to be able to connect to the internet and to get the bandwidth requested, and, if clients do not get the services they request, the service provider pays penalties to the clients. We consider internet clients that can dynamically connect to a number of internet service providers (ISP). When a client arrives at an ISP, he has to decide whether to accept the client, and then the price to charge from the client for the duration of its connection. Rejection of a client results in a penalty and delay in getting the requested bandwidth also incurs a penalty. We assume a Poisson arrival process with the rate of arrival sensitive to the price being charged. A client requests bandwidth for a time that is exponentially distributed, then the client is idle for a time that is also exponentially distributed; and then either the client departs or requests bandwidth again after the idle period is over. A service provider tries to maximize its income by charging appropriate prices based on its current state and deciding whether to accept more clients or not. Since penalties are imposed, such solutions also automatically balance load among service providers, and so the QoS to clients improves. We present solutions that maximize the income of service providers. The solutions are then compared using simulation. Simulation results show that our solutions significantly improve QoS of clients and increase the income of service providers as compared to a simple heuristic based solution that could otherwise be used.

Dental Surgery by Using Object Oriented Designing

This is report of using Object Oriented theory to develop system for Dental Surgery. And this whole system will include the system for book appointment, system for client arrivals, system for course of treatment, system for payment, system for stock control and system for housekeeping. And after this report, we will understand why we use the Object Oriented to design a system like this, and what kind of system could use Object Oriented design, what the benefit for using Object Oriented design.

Load balancing via random local search in closed and open systems

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach themselves to an arbitrary server, but may switch servers independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner. We first analyze the natural Random Local Search (RLS) strategy. Under this strategy, after sampling a new server randomly, clients only switch to it if their service rate is improved. In closed systems, where the client population is fixed, we derive tight estimates of the time it takes under RLS strategy to balance the load across servers. We then study open systems where clients arrive according to a random process and leave the system upon service completion. In this scenario, we analyze how client migrations within the system interact with the system dynamics induced by client arrivals and departures. We compare the load-aware RLS strategy to a load-oblivious strategy in which clients just randomly switch server without accounting for the server loads. Surprisingly, we show that both load-oblivious and load-aware strategies stabilize the system whenever this is at all possible. We use large-system asymptotics to characterize system performance, and augment this with simulations, which suggest that the average client sojourn time under the load-oblivious strategy is not considerably reduced when clients apply smarter load-aware strategies.

Approximate dynamic programming for capacity allocation in the service industry

We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP) combined with a discrete event simulation of the service period. We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an “optimal” policy that is deduced using restrictive, simplifying assumptions.

An assessment of client satisfaction wirh services at a model primary health care centre in Ogun State, Nigeria

Assessing patient satisfaction with health care is increasingly becoming an integral component of quality monitoring in health care systems. However, studies that have been conducted in this environment often show that clients' perceptions of quality of care from public sector providers are generally low. Timeliness, which refers to obtaining needed care and minimizing unnecessary delays in getting that care, may impact considerably on patients' satisfaction. This study w as designed toexamine how the consumers of services at a model primary health centre in Ogun State, Nigeria, viewed their health-seeking experience, relating this to the level of organization.and timeliness of health services provision. This study sought to assess the satisfaction of clients with services at a model PHC centre at Pakoto, Ogun State, Nigeria. The Pakoto model primary health care centre is an outstation of the Lagos University Teaching Hospital (LUTH) Idi-Araba, Lagos State. The study was carried out on weekday mornings at the model PHC clinic during the month of June, 2006. Methods of data collection employed were a general observation of clinic activities, and the administration of a modified client flow analysis chart to clients as they exited the health facility. The peak time of arrival of clients was between 9.00 and 11.00 a.m. when the majority (58.5%) of clients arrived; the majority of clients (84%) exited the facility oetween 11:00 a.m. and 1:00 p.m. The highest proportion of clients (33.1%) spent 3-4 hours accessing services at the model PHC facility. Good staff attitude topped the list of aspects of service liked most by clients (36%), while the aspect of service liked least by respondents was lack of timeliness of services (43%). Suggestions for improvement of services included increase in the number of staff and staff training (30%) and early arrival of staff to work (13%). The study concludes that timeliness of services at the primary health care level impacts positively upon the perception of quality of services rendered to clients.

Load balancing via random local search in closed and open systems

In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch servers independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner. We first analyze the natural Random Local Search (RLS) strategy. Under this strategy, after sampling a new server randomly, clients only switch to it if their service rate is improved. In closed systems, where the client population is fixed, we derive tight estimates of the time it takes under RLS strategy to balance the load across servers. We then study open systems where clients arrive according to a random process and leave the system upon service completion. In this scenario, we analyze how client migrations within the system interact with the system dynamics induced by client arrivals and departures. We compare the load-aware RLS strategy to a load-oblivious strategy in which clients just randomly switch server without accounting for the server loads. Surprisingly, we show that both load-oblivious and load-aware strategies stabilize the system whenever this is at all possible. We further demonstrate, using large-system asymptotics, that the average client sojourn time under the load-oblivious strategy is not considerably reduced when clients apply smarter load-aware strategies.

Approximate Dynamic Programming for Capacity Allocation in the Service Industry

We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP). We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an “optimal” policy that is deduced using restrictive, simplifying assumptions.

Off-line and on-line guaranteed start-up delay for Media-on-Demand with stream merging

We address the problem of designing efficient algorithms for media-on-demand in systems that use stream merging. In the stream merging model the receiving bandwidth of clients is larger than the playback bandwidth, and clients can buffer parts of the transmission to be played back later. Our goal is to minimize the required server bandwidth for a given guaranteed start-up delay and uninterrupted playback. We construct an efficient O ( n ) optimal off-line algorithm for a time horizon that is composed of n time slots, where the length of one slot is the guaranteed start-up delay. Our algorithm works for either clients with receiving bandwidth twice as much as the playback bandwidth or for receiving bandwidth equal to the server bandwidth, independent of the clients buffer size. We describe an on-line delay guaranteed algorithm that operates without knowledge of the time horizon size, and show that it performs asymptotically close to the optimal off-line algorithm. The on-line algorithm is simpler to implement than previously proposed on-line stream merging algorithms, and empirically performs well when the intensity of client arrivals is high.

Reserving for future clients in a multipoint application-why and how?

As applications become more sophisticated, we believe that the problem of guaranteeing their service commitment will get much harder. Currently applications requiring real-time media quality of service (QoS) are set up using a flow-reservation model. In this model, the network admits each flow (or connection) within an application separately using RSVP, asynchronous transfer mode (ATM) signaling, or some other mechanism. This model is restrictive because it cannot inherently deliver guarantees on future client additions to an ongoing application. We claim that such guarantees are going to be required to build sophisticated multipoint and multiparty applications that involve a dynamic number of clients and a real-time sharing between clients. Without such guarantees, existing clients within an application can be subjected to context disruption or unreliable communication. To reduce this form of degradation, we propose the idea of application reservation where resources for an entire application are reserved at application setup. This includes resources for the clients expected to add to the application in future. Application reservation can be implemented as an overlay to existing flow-based reservations. We present the architectural features of such a framework. As part of the service interface, applications declare space-time dynamics of future client arrivals in addition to traditional descriptors. A new class of QoS measures that quantifies the type of degradation mentioned above is also conveyed. The application manager checks to see if the application can be admitted. After the application is admitted, the manager monitors clients as they arrive, to ascertain if they conform to the declared service interface parameters. Conforming clients have resources reserved for them, so they can be simply added to the application. We review these architectural components and address the nontrivial problem of managing the nonconforming clients.

Enhanced call: a paradigm for applications with dynamic client-membership and client-level binding in ATM networks

Sophisticated applications in asynchronous transfer mode (ATM) networks require high-level quality-of-service (QoS) guarantees in conjunction with ATM QoS guarantees. We focus on applications that involve varying client-membership and client-level binding (i.e., dependence between current and future clients). Such applications include certain types of conferences, computer-supported cooperative work (CSCW), and client-server based applications. In a traditional call, these applications are set up with a known set of clients and newer clients are admitted via individual connection admission procedures. While this approach guarantees ATM QoS for every client admitted to the application, it cannot guarantee the addition of new clients to an existing application. Consequently, carried clients that depend on a new client can perceive a degraded service when the new client cannot be added because of lack of bandwidth. We refer to this degradation as client-level degradation. To guarantee an upper bound on client-level degradation, we propose a paradigm called enhanced call that statistically reserves virtual channel links (VCLs) for potential new client arrivals. The enhanced call specification encapsulates client descriptors and client-level QoS measures. We propose simple, scaleable, and practical client descriptors. Where possible, the descriptors are chosen in the same spirit as ATM descriptors. Given these descriptors, we develop an analytical framework to determine whether or not to admit an enhanced call. We also present an operational conformance definition that is enforced globally as well as locally.

How to Give Priority to Family Planning in PHC Programmes

Certain things can be done to insure more effective family planning services in the primary health care (PHC) setting. There are 4 weaknesses that can happen when family planning is integrated into a PHC service: 1) people are unaware of family planning services; 2) an inappropriate person is chosen to be the family planning nurse; 3) contraceptives and supplies are often not available or inappropriate; and 4) family planning services are often more trouble than they are worth. To correct these flaws, the following things can be done about publicity; 1) the family planning services should be publicized with a sign visible from outside the clinic; 2) the place and time of family planning services should be publicized in the preschool clinic and 3) family planning services should be publicized in the maternity unit. To correct personnel problems, a person that people like should be chosen to run the clinic. A staff member who can free the time to spend doing exclusive family planning should be chosen to go the family planning courses. Family planning workers should be trained in a family planning clinic for "hands on" experience. The family planning clinic should be supervised regularly. The family planning nurse should train another nurse to help her out. Only a few different contraceptives are needed. Items of equipment and clinic supplies are specified. As for service problems, no family planning client should wait for more than 30 minutes. Forms and questionnaires should be kept single. When the client arrives, the nurse and contraceptives should be at the clinic.

Modelo Poisson na distribuição de chegadas de clientes

The article analyses the distribution of client arrivals to a supermarket. The adherence Q-Square test is used to check if the arrivals agrees with the Poisson model.