Scalable download protocols

Scalable on-demand content delivery systems, designed to effectively handle increasing request rates, typically use service aggregation or content replication techniques. Service aggregation relies on one-to-many communication techniques, such as multicast, to efficiently deliver content from a single sender to multiple receivers. With replication, multiple geographically distributed replicas of the service or content share the load of processing client requests and enable delivery from a nearby server.Previous scalable protocols for downloading large, popular files from a single server include batching and cyclic multicast. Analytic lower bounds developed in this thesis show that neither of these protocols consistently yields performance close to optimal. New hybrid protocols are proposed that achieve within 20% of the optimal delay in homogeneous systems, as well as within 25% of the optimal maximum client delay in all heterogeneous scenarios considered.In systems utilizing both service aggregation and replication, well-designed policies determining which replica serves each request must balance the objectives of achieving high locality of service, and high efficiency of service aggregation. By comparing classes of policies, using both analysis and simulations, this thesis shows that there are significant performance advantages in using current system state information (rather than only proximities and average loads) and in deferring selection decisions when possible. Most of these performance gains can be achieved using only “local” (rather than global) request information.Finally, this thesis proposes adaptations of already proposed peer-assisted download techniques to support a streaming (rather than download) service, enabling playback to begin well before the entire media file is received. These protocols split each file into pieces, which can be downloaded from multiple sources, including other clients downloading the same file. Using simulations, a candidate protocol is presented and evaluated. The protocol includes both a piece selection technique that effectively mediates the conflict between achieving high piece diversity and the in-order requirements of media file playback, as well as a simple on-line rule for deciding when playback can safely commence

Performance of sequential batching-based methods of output data analysis in distributed steady-state stochastic simulation

Wir haben die Anpassung von Sequentiellen Analysemethoden von Stochastik Simulationen an einem Szenario von mehreren Unabhängigen Replikationen in Parallel (MRIP) untersucht. Die Hauptidee ist, die statistische Kontrole bzw. die Beschleunigung eines Simulationexperiment zu automatisieren. Die vorgeschlagenen Methoden der Literatur sind auf einzelne Prozessorszenarien orientiert. Wenig ist bekannt hinsichtlich der Anwendungen von Verfahen, die auf Methoden unter MRIP basieren. Auf den ersten Blick sind beide Ziele entgegengesetzt, denn man braucht eine grosse Menge von Beobachtungen, um eine hohe Qualität der Resultate zu erreichen. Dafür benötig man viel Zeit. Man kann jedoch durch einen ausfürlichen Entwurf zusammen mit einem robusten Werkzeug, das auf unabhängige Replikationen basiert ist, ein effizientes Mittel bezüglich Analyse der Resultate produzieren. Diese Recherche wurde mit einer sequentiellen Version des klassischen Verfahren von Nonoverlaping Batch Means (NOBM) angefangen. Obwohl NOBM sehr intuitiv und populär ist, bietet es keine gute Lösung für das Problem starker Autokorrelation zwischen den Beobachtungen an, die normalerweise bei hohen Auslastungen entstehen. Es lohnt sich nicht, grösserer Rechnerleistung zu benutzen, um diese negative Merkmale zu vermindern. Das haben wir mittles einer vollständigen Untersuchung einer Gruppe von Warteschlangsystemen bestätig. Deswegen haben wir den Entwurf von sequentiellen Versionen von ein paar Varianten von Batch Means vorgeschlagen und sie genauso untersucht. Unter den implementierten Verfahren gibt es ein sehr attraktives: Overlapping Batch Means (OBM). OBM ermöglicht eine bessere Nutzung der Daten, da jede Beobachtungen ein neues Batch anfängt, d.h., die Anzahl von Batches ist viel grösser, und das ergibt eine kleinere Varianz. In diesem Fall ist die Anwendung von MRIP empfehlenswert, da diese Kombination weniger Beobachtungen benötigt und somit eine höhere Beschleunigung. Im Laufe der Recherche haben wir eine Klasse von Methoden (Standardized Time Series - STS) untersucht, die teoretisch bessere asymptotische Resultate als NOBM produziert. Die negative Auswirkung von STS ist, dass sie mehr Beobachtungen als die Batch-Means-Verfahren benoetigt. Aber das ist kein Hindernis, wenn wir STS zusammen mit MRIP anwenden. Die experimentelle Untersuchungen bestätigte, dass die Hypothese richtig ist. Die nächste Phase war es, OBM und STS einzustellen, um beide Verfahren unter den grösstmöglichen Anzahl von Prozessoren arbeiten lassen zu können. Fallstudien zeigten uns, dass sich beide sequentiellen Verfahren für die parallele Simulation sowie MRIP einigen.We investigated the feasibility of sequential methods of analysis of stochastic simulation under an environment of Multiple Replications in Parallel (MRIP). The main idea is twofold, the automation of the statistical control and speedup of simulation experiments. The methods of analysis found suggested in the literature were conceived for a single processor environment. Very few is known concerning the application of procedures based in such methods under MRIP. At first glance, sind both goals in opposition, since one needs a large amount of observations in order to achieve good quality of the results, i.e., the simulation takes frequently long time. However, by means of a careful design, together with a robust simulation tool based on independent replications, one can produce an efficient instrument of analysis of the simulation results. This research began with a sequential version of the classical method of Nonoverlapping Batch Means (NOBM). Although intuitiv and popular, under hight traffic intensity NOBM offers no good solution to the problem of strong correlation among the observations. It is not worthwhile to apply more computing power aiming to diminish this negative effect. We have confirmed this claim by means of a detailed and exhaustive analysis of four queuing systems. Therefore, we proposed the design of sequential versions of some Batch Means variants, and we investigated their statistical properties under MRIP. Among the implemented procedures there is one very attractive : Overlapping Batch Means (OBM). OBM makes a better use of collected data, since each observation initiates a new (overlapped) batch, that is, die number of batches is much larger, and this yields smaller variance. In this case, MRIP is highly recommended, since this combination requires less observations and, therefore, speedup. During the research, we investigated also a class of methods based on Standardized Time Series -- STS, that produces theoretically better asymptotical results than NOBM. The undesired negative effect of STS is the large number of observations it requires, when compared to NOBM. But that is no obstacle when we apply STS together with MRIP. The experimental investigation confirmed this hypothesis. The next phase was to tun OBM and STS, in order to put them working with the possible largest number of processors. A case study showed us that both procedures are suitable to the environment of MRIP

Evaluation of order picking systems using simulation

Sipari? toplama faaliyetleri, tedarik zinciri yönetiminde, hem üretim sistemleri açısından (montaj istasyonlarına alt parçaların tedarik edilmesi), hem de dağıtım i?lemleri açısından (mü?teri taleplerinin kar?ılanması) kritik rol oynamaktadır. Mü?teri sipari?lerindeki eğilimler, az sayıda ve yüksek miktarlarda sipari?lerin çok sayıda ve dü?ük miktarlarda sipari?lere dönü?tüğünü göstermektedir. Diğer yandan, talep edilen sipari? teslim süreleri ise her geçen gün kısalmaktadır. Bu deği?imler, i?letmelerin piyasada rekabet edebilmeleri için etkin ve esnek bir sipari? toplama sistemi benimsemelerini gerektirmektedir. Sipari? toplama süreci, tüm lojistik operasyonlarını ve mü?teriye sağlanan hizmet seviyesini büyük ölçüde etkilemektedir. Ayrıca, sipari? toplama süreci toplam depolama maliyetlerinin yarıdan fazlasını olu?turmaktadır. Bu nedenle, sipari? toplama faaliyetlerinin en etkin ?ekilde gerçekle?tirilmesi i?letmeler için büyük önem ta?ımaktadır. Bu çalı?manın amacı, sipari? toplama süresini kısaltarak, sipari? toplama etkinliğini arttırmaya yönelik deği?iklikler için sipari? toplama sistemini değerlendirmek ve geli?tirmektir. Sipari? toplama süresi, ürünlerin depolama alanlarından belirli bir mü?teri talebini kar?ılamak amacıyla toplanması süreci için geçen zamandır. Bu çalı?mada, ürünlerin depolama alanlarına atanma kararları ve rotalama metotları gibi kritik faktörlerin yanı sıra, daha önce gerçekle?tirilmi? çalı?malarda sıkça rastlanmayan depolama alanlarının ikmali problemi dikkate alınmı?tır. Bo?alan rafların yeniden doldurulması kararında, (S, s) envanter politikası uygulanmı?tır. Böylece, sipari? toplama sistemi dinamik olarak modellenmi?tir. Sipari? toplama performansını geli?tirmek için, bağlantı elemanları üreten bir firmanın ambarı temel alınarak olu?turulmu? hipotetik bir ambar üzerinde vii çalı?ılmı?tır. Ambara ait farklı benzetim modelleri olu?turulmu?, depolama ve rotalama politikalarının alternatif kombinasyonları geli?tirilerek bu benzetim modellerinde kullanılmı?tır. Elde edilen benzetim sonuçlarına göre, en küçük sipari? toplama süresini veren depolama ve rotalama politikası kombinasyonu belirlenmi?tir. Son olarak, benzetim sonuçları üzerinde bazı istatistiksel analiz metotları uygulanmı?tır Order picking activities play a critical role in supply chain management in terms of both production systems (supplying components to assembly operations) and distribution operations (meeting customer demands). Trends in customer orders reveal that orders are transformed from few-and-large orders to many-and-small ones. On the other hand, lead times of customer orders get consistently shorter. Because of these changes, companies need to adopt an effective and flexible order picking system in order to remain competitive in the market. Order picking affects both overall logistic operations and service level provided to customers. Additionally, order picking process constitutes more than half of the total warehousing cost. For these reasons, it is crucial for companies to design and perform an effective order picking process. The aim of this study is evaluating and improving of the order picking system so as to minimize the order retrieval time while increasing the picking efficiency. Order retrieval time can be defined as the time elapsed for the process of retrieving products from storage area to meet a specific customer demand. Besides the critical factors such as storage assignment decisions and routing methods, replenishment problem of the storage areas, which is rarely addressed in the previous studies, has been taken into consideration in this study. Replenishment of the empty storage locations has been conducted by using the (S, s) inventory policy. Thus, the order picking system was modeled as a dynamic system. A hypothetical distribution warehouse, based on the real life warehouse of a company specialized in production of fasteners, has been studied in order to improve the order picking performance. Alternative combinations of routing and storage policies have been developed. Moreover, different simulation models of the order picking process were constructed. In these models, proposed alternative storage and v routing policies were operated. According to the simulation results, the storage policy and routing policy combination which provides the shortest order retrieval time is determined. Finally, using simulation results, some statistical analysis methods have been implemented