The effect of workload dependence in systems: Experimental evaluation, analytic models, and policy development

This dissertation presents an analysis of performance effects of burstiness (formalized by the autocorrelation function) in multi-tiered systems via a 3-pronged approach, i.e., experimental measurements, analytic models, and policy development. This analysis considers (a) systems with finite buffers (e.g., systems with admission control that effectively operate as closed systems) and (b) systems with infinite buffers (i.e., systems that operate as open systems).;For multi-tiered systems with a finite buffer size, experimental measurements show that if autocorrelation exists in any of the tiers in a multi-tiered system, then autocorrelation propagates to all tiers of the system. The presence of autocorrelated flows in all tiers significantly degrades performance. Workload characterization in a real experimental environment driven by the TPC-W benchmark confirms the existence of autocorrelated flows, which originate from the autocorrelated service process of one of the tiers. A simple model is devised that captures the observed behavior. The model is in excellent agreement with experimental measurements and captures the propagation of autocorrelation in the multi-tiered system as well as the resulting performance trends.;For systems with an infinite buffer size, this study focuses on analytic models by proposing and comparing two families of approximations for the departure process of a BMAP/MAP/1 queue that admits batch correlated flows, and whose service time process may be autocorrelated. One approximation is based on the ETAQA methodology for the solution of M/G/1-type processes and the other arises from lumpability rules. Formal proofs are provided: both approximations preserve the marginal distribution of the inter-departure times and their initial correlation structures.;This dissertation also demonstrates how the knowledge of autocorrelation can be used to effectively improve system performance, D_EQAL, a new load balancing policy for clusters with dependent arrivals is proposed. D_EQAL separates jobs to servers according to their sizes as traditional load balancing policies do, but this separation is biased by the effort to reduce performance loss due to autocorrelation in the streams of jobs that are directed to each server. as a result of this, not all servers are equally utilized (i.e., the load in the system becomes unbalanced) but performance benefits of this load unbalancing are significant

A Demand Based Load Balanced Service Replication Model

Cloud computing allows service users and providers to access the applications, logical resources and files on any computer with ease. A cloud service has three distinct characteristics that differentiate it from traditional hosting. It is sold on demand, typically by the minute or the hour; it is elastic. It is a way to increase capacity or add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software. It not only promises reliable services delivered through next-generation data centers that are built on compute and storage virtualization technologies but also addresses the key issues such as scalability, reliability, fault tolerance and file load balancing. The one way to achieve this is through service replication across different machines coupled with load balancing. Though replication potentially improves fault tolerance, it leads to the problem of ensuring consistency of replicas when certain service is updated or modified. However, fewer replicas also decrease concurrency and the level of service availability. A balanced synchronization between replication mechanism and consistency not only ensures highly reliable and fault tolerant system but also improves system performance significantly. This paper presents a load balancing based service replication model that creates a replica on other servers on the basis of number of service accesses. The simulation results indicate that the proposed model reduces the number of messages exchanged for service replication by 25-55% thus improving the overall system performance significantly. Also in case of CPU load based file replication, it is observed that file access time reduces by 5.56%-7.65%

An adaptive admission control and load balancing algorithm for a QoS-aware Web system

The main objective of this thesis focuses on the design of an adaptive algorithm for admission control and content-aware load balancing for Web traffic. In order to set the context of this work, several reviews are included to introduce the reader in the background concepts of Web load balancing, admission control and the Internet traffic characteristics that may affect the good performance of a Web site. The admission control and load balancing algorithm described in this thesis manages the distribution of traffic to a Web cluster based on QoS requirements. The goal of the proposed scheduling algorithm is to avoid situations in which the system provides a lower performance than desired due to servers' congestion. This is achieved through the implementation of forecasting calculations. Obviously, the increase of the computational cost of the algorithm results in some overhead. This is the reason for designing an adaptive time slot scheduling that sets the execution times of the algorithm depending on the burstiness that is arriving to the system. Therefore, the predictive scheduling algorithm proposed includes an adaptive overhead control. Once defined the scheduling of the algorithm, we design the admission control module based on throughput predictions. The results obtained by several throughput predictors are compared and one of them is selected to be included in our algorithm. The utilisation level that the Web servers will have in the near future is also forecasted and reserved for each service depending on the Service Level Agreement (SLA). Our load balancing strategy is based on a classical policy. Hence, a comparison of several classical load balancing policies is also included in order to know which of them better fits our algorithm. A simulation model has been designed to obtain the results presented in this thesis

Autonomous migration of vertual machines for maximizing resource utilization

Virtualization of computing resources enables multiple virtual machines to run on a physical machine. When many virtual machines are deployed on a cluster of PCs, some physical machines will inevitably experience overload while others are under-utilized over time due to varying computational demands. This computational imbalance across the cluster undermines the very purpose of maximizing resource utilization through virtualization. To solve this imbalance problem, virtual machine migration has been introduced, where a virtual machine on a heavily loaded physical machine is selected and moved to a lightly loaded physical machine. The selection of the source virtual machine and the destination physical machine is based on a single fixed threshold value. Key to such threshold-based VM migration is to determine when to move which VM to what physical machine, since wrong or inadequate decisions can cause unnecessary migrations that would adversely affect the overall performance. The fixed threshold may not necessarily work for different computing infrastructures. Finding the optimal threshold is critical. In this research, a virtual machine migration framework is presented that autonomously finds and adjusts variable thresholds at runtime for different computing requirements to improve and maximize the utilization of computing resources. Central to this approach is the previous history of migrations and their effects before and after each migration in terms of standard deviation of utilization. To broaden this research, a proactive learning methodology is introduced that not only accumulates the past history of computing patterns and resulting migration decisions but more importantly searches all possibilities for the most suitable decisions. This research demonstrates through experimental results that the learning approach autonomously finds thresholds close to the optimal ones for different computing scenarios and that such varying thresholds yield an optimal number of VM migrations for maximizing resource utilization. The proposed framework is set up on a cluster of 8 and 16 PCs, each of which has multiple User-Mode Linux (UML)-based virtual machines. An extensive set of benchmark programs is deployed to closely resemble a real-world computing environment. Experimental results indicate that the proposed framework indeed autonomously finds thresholds close to the optimal ones for different computing scenarios, balances the load across the cluster through autonomous VM migration, and improves the overall performance of the dynamically changing computing environment

Workload shaping for QoS and power efficiency of storage systems

The growing popularity of hosted storage services and shared storage infrastructure in data centers is driving the recent interest in resource management and QoS in storage systems. The bursty nature of storage workloads raises significant performance and provisioning challenges, leading to increased resource requirements, management costs, and energy consumption. We present a novel dynamic workload shaping framework to handle bursty server workloads, where the arrival stream is dynamically decomposed to isolate its bursty, and then rescheduled to exploit available slack. An optimal decomposition algorithm RTT and a recombination algorithm Miser make up the scheduling framework. We evaluate this framework using several real world storage workloads traces. The results show that workload shaping: (i) reduces the server capacity requirements and power consumption dramatically while affecting QoS guarantees minimally, (ii) provides better response time distributions over non-decomposed traditional scheduling methods, and (iii) decomposition can be used to provide more accurate capacity estimates for multiplexing several clients on a shared server

Workload characterization, modeling, and prediction in grid Computing

Workloads play an important role in experimental performance studies of computer systems. This thesis presents a comprehensive characterization of real workloads on production clusters and Grids. A variety of correlation structures and rich scaling behavior are identified in workload attributes such as job arrivals and run times, including pseudo-periodicity, long range dependence, and strong temporal locality. Based on the analytic results workload models are developed to fit the real data. For job arrivals three different kinds of autocorrelations are investigated. For short to middle range dependent data, Markov modulated Poisson processes (MMPP) are good models because they can capture correlations between interarrival times while remaining analytically tractable. For long range dependent and multifractal processes, the multifractal wavelet model (MWM) is able to reconstruct the scaling behavior and it provides a coherent wavelet framework for analysis and synthesis. Pseudo-periodicity is a special kind of autocorrelation and it can be modeled by a matching pursuit approach. For workload attributes such as run time a new model is proposed that can fit not only the marginal distribution but also the second order statistics such as the autocorrelation function (ACF). The development of workload models enable the simulation studies of Grid scheduling strategies. By using the synthetic traces, the performance impacts of workload correlations in Grid scheduling is quantitatively evaluated. The results indicate that autocorrelations in workload attributes can cause performance degradation, in some situations the difference can be up to several orders of magnitude. The larger the autocorrelation, the worse the performance, it is proved both at the cluster and Grid level. This study shows the importance of realistic workload models in performance evaluation studies. Regarding performance predictions, this thesis treats the targeted resources as a ``black box'' and takes a statistical approach. It is shown that statistical learning based methods, after a well-thought and fine-tuned design, are able to deliver good accuracy and performance.UBL - phd migration 201

Eliminierung negativer Effekte autokorrelierter Prozesse an Zusammenführungen

Im Kern der vorliegenden Arbeit wird eine neue Vorfahrtstrategie zur Steuerung von Materialflüssen an Zusammenführungen vorgestellt. Das Hauptanwendungsgebiet stellen innerbetriebliche Transportsysteme dar, wobei die Erkenntnisse auf beliebige Transport- bzw. Bediensysteme übertragbar sind. Die Arbeit grenzt sich mit der Annahme autokorrelierter Ankunftsprozesse von bisheriger Forschung und Entwicklung ab. Bis dato werden stets unkorrelierte Ströme angenommen bzw. findet keine spezielle Beachtung autokorrelierter Ströme bei der Vorfahrtsteuerung statt. Untersuchungen zeigen aber, dass zum einen mit hoher Konfidenz mit autokorrelierten Materialflüssen zu rechnen ist und in diesem Fall zum anderen von einem erheblichen Einfluss auf die Systemleistung ausgegangen werden muss. Zusammengefasst konnten im Rahmen der vorliegenden Arbeit 68 Realdatensätze verschiedener Unternehmen untersucht werden, mit dem Ergebnis, dass ca. 95% der Materialflüsse Autokorrelation aufweisen. Ferner wird hergeleitet, dass Autokorrelation intrinsisch in Materialflusssystemen entsteht. Die Folgen autokorrelierter Prozesse bestehen dabei in längeren Durchlaufzeiten, einem volatileren Systemverhalten und höheren Wahrscheinlichkeiten von Systemblockaden. Um die genannten Effekte an Zusammenführungen zu eliminieren, stellt die Arbeit eine neue Vorfahrtstrategie HAFI – Highest Autocorrelated First vor. Diese priorisiert die Ankunftsprozesse anhand deren Autokorrelation. Konkret wird die Vorfahrt zunächst so lange nach dem Prinzip First Come First Served gewährt, bis richtungsweise eine spezifische Warteschlangenlänge überschritten wird. Der jeweilige Wert ergibt sich aus der Höhe der Autokorrelation der Ankunftsprozesse. Vorfahrt bekommt der Strom mit der höchsten Überschreitung seines Grenzwertes. Die Arbeit stellt ferner eine Heuristik DyDeT zur automatischen Bestimmung und dynamischen Anpassung der Grenzwerte vor. Mit einer Simulationsstudie wird gezeigt, dass HAFI mit Anwendung von DyDeT die Vorzüge der etablierten Vorfahrtstrategien First Come First Served und Longest Queue First vereint. Dabei wird auch deutlich, dass die zwei letztgenannten Strategien den besonderen Herausforderungen autokorrelierter Ankunftsprozesse nicht gerecht werden. Bei einer Anwendung von HAFI zur Vorfahrtsteuerung können Durchlaufzeiten und Warteschlangenlängen auf dem Niveau von First Come First Served erreicht werden, wobei dieses ca. 10% unter dem von Longest Queue First liegt. Gleichzeitig ermöglicht HAFI, im Gegensatz zu First Come First Served, eine ähnlich gute Lastbalancierung wie Longest Queue First. Die Ergebnisse stellen sich robust gegenüber Änderungen der Auslastung sowie der Höhe der Autokorrelation dar. Gleichzeitig sind die Erkenntnisse unabhängig der Analyse einer isolierten Zusammenführung und der Anordnung mehrerer Zusammenführungen in einem Netzwerk.:1 Einleitung 1 1.1 Motivation 1 1.2 Zielsetzung, wissenschaftlicher Beitrag 4 1.3 Konzeption 5 2 Grundlagen 7 2.1 Automatisierung, Steuern, Regeln 7 2.2 System, Modell 10 2.3 Stochastik, Statistik 14 2.3.1 Wahrscheinlichkeitsverteilungen 14 2.3.2 Zufallszahlengeneratoren 21 2.3.3 Autokorrelation als Ähnlichkeits- bzw. Abhängigkeitsmaß 24 2.4 Simulation 29 2.5 Warteschlangentheorie und -modelle 32 2.6 Materialflusssystem 35 2.7 Materialflusssteuerung 37 2.7.1 Steuerungssysteme 37 2.7.2 Steuerungsstrategien 40 2.8 Materialflusssystem charakterisierende Kennzahlen 46 3 Stand der Forschung und Technik 51 3.1 Erzeugung autokorrelierter Zufallszahlen 51 3.1.1 Autoregressive Prozesse nach der Box-Jenkins-Methode 52 3.1.2 Distorsions-Methoden 54 3.1.3 Copulae 56 3.1.4 Markovian Arrival Processes 58 3.1.5 Autoregressive Prozesse mit beliebiger Randverteilung 61 3.1.6 Weitere Verfahren 64 3.1.7 Bewertung der Verfahren und Werkzeuge zur Generierung 65 3.2 Wirken von Autokorrelation in Bediensystemen 68 3.3 Fallstudien über Autokorrelation in logistischen Systemen 75 3.4 Ursachen von Autokorrelation in logistischen Systemen 89 3.5 Steuerung von Ankunftsprozessen an Zusammenführungen 96 3.6 Steuerung autokorrelierter Ankunftsprozesse 100 4 Steuerung autokorrelierter Ankunftsprozesse an Zusammenführungen 105 4.1 Modellannahmen, Methodenauswahl, Vorbetrachtungen 106 4.2 First Come First Served und Longest Queue First 114 4.3 Highest Autocorrelated First 117 4.3.1 Grundprinzip 117 4.3.2 Bestimmung der Grenzwerte 127 4.3.3 Dynamische Bestimmung der Grenzwerte mittels „DyDeT“ 133 4.4 Highest Autocorrelated First in Netzwerken 150 4.5 Abschließende Bewertung und Diskussion 161 5 Zusammenfassung und Ausblick 167 Primärliteratur 172 Normen und Standards 194 Abbildungsverzeichnis 197 Tabellenverzeichnis 199 Pseudocodeverzeichnis 201 Abkürzungsverzeichnis 203 Symbolverzeichnis 205 Erklärung an Eides statt 209The work at hand presents a novel strategy to control arrival processes at merges. The main fields of application are intralogistics transport systems. Nevertheless, the findings can be adapted to any queuing system. In contrast to further research and development the thesis assumes autocorrelated arrival processes. Up until now, arrivals are usually assumed to be uncorrelated and there are no special treatments for autocorrelated arrivals in the context of merge controlling. However, surveys show with high reliability the existence of autocorrelated arrivals, resulting in some major impacts on the systems\' performance. In detail, 68 real-world datasets of different companies have been tested in the scope of this work, and in 95% of the cases arrival processes significantly show autocorrelations. Furthermore, the research shows that autocorrelation comes from the system itself. As a direct consequence it was observed that there were longer cycle times, more volatile system behavior, and a higher likelihood of deadlocks. In order to eliminate these effects at merges, this thesis introduces a new priority rule called HAFI-Highest Autocorrelated First. It assesses the arrivals\' priority in accordance to their autocorrelation. More concretely, priority initially is given in accordance to the First Come First Served scheme as long as specific direction-wise queue lengths are not exceeded. The particular thresholds are determined by the arrival processes\' autocorrelation, wherein the process with the highest volume gets priority. Furthermore, the thesis introduces a heuristic to automatically and dynamically determine the specific thresholds of HAFI-so called DyDeT. With a simulation study it can be shown that HAFI in connection with DyDeT, combines the advantages of the well-established priority rules First Come First Served and Longest Queue First. It also becomes obvious that the latter ones are not able to deal with the challenges of autocorrelated arrival processes. By applying HAFI cycling times and mean queue lengths on the level of First Come First Served can be achieved. These are about 10% lower than for Longest Queue First. Concomitantly and in contrast to First Come First Served, HAFI also shows well balanced queues like Longest Queue First. The results are robust against different levels of throughput and autocorrelation, respectively. Furthermore, the findings are independent from analyzing a single instance of a merge or several merges in a network.:1 Einleitung 1 1.1 Motivation 1 1.2 Zielsetzung, wissenschaftlicher Beitrag 4 1.3 Konzeption 5 2 Grundlagen 7 2.1 Automatisierung, Steuern, Regeln 7 2.2 System, Modell 10 2.3 Stochastik, Statistik 14 2.3.1 Wahrscheinlichkeitsverteilungen 14 2.3.2 Zufallszahlengeneratoren 21 2.3.3 Autokorrelation als Ähnlichkeits- bzw. Abhängigkeitsmaß 24 2.4 Simulation 29 2.5 Warteschlangentheorie und -modelle 32 2.6 Materialflusssystem 35 2.7 Materialflusssteuerung 37 2.7.1 Steuerungssysteme 37 2.7.2 Steuerungsstrategien 40 2.8 Materialflusssystem charakterisierende Kennzahlen 46 3 Stand der Forschung und Technik 51 3.1 Erzeugung autokorrelierter Zufallszahlen 51 3.1.1 Autoregressive Prozesse nach der Box-Jenkins-Methode 52 3.1.2 Distorsions-Methoden 54 3.1.3 Copulae 56 3.1.4 Markovian Arrival Processes 58 3.1.5 Autoregressive Prozesse mit beliebiger Randverteilung 61 3.1.6 Weitere Verfahren 64 3.1.7 Bewertung der Verfahren und Werkzeuge zur Generierung 65 3.2 Wirken von Autokorrelation in Bediensystemen 68 3.3 Fallstudien über Autokorrelation in logistischen Systemen 75 3.4 Ursachen von Autokorrelation in logistischen Systemen 89 3.5 Steuerung von Ankunftsprozessen an Zusammenführungen 96 3.6 Steuerung autokorrelierter Ankunftsprozesse 100 4 Steuerung autokorrelierter Ankunftsprozesse an Zusammenführungen 105 4.1 Modellannahmen, Methodenauswahl, Vorbetrachtungen 106 4.2 First Come First Served und Longest Queue First 114 4.3 Highest Autocorrelated First 117 4.3.1 Grundprinzip 117 4.3.2 Bestimmung der Grenzwerte 127 4.3.3 Dynamische Bestimmung der Grenzwerte mittels „DyDeT“ 133 4.4 Highest Autocorrelated First in Netzwerken 150 4.5 Abschließende Bewertung und Diskussion 161 5 Zusammenfassung und Ausblick 167 Primärliteratur 172 Normen und Standards 194 Abbildungsverzeichnis 197 Tabellenverzeichnis 199 Pseudocodeverzeichnis 201 Abkürzungsverzeichnis 203 Symbolverzeichnis 205 Erklärung an Eides statt 20

Unbalanced manual flow line operating characteristics.

This thesis studies the operating behaviour of the manual unpaced lines, which are the most important of the flow lines' systems. The lines examined are unbalanced and six types of imbalance are considered, namely, the imbalances of mean service times, coefficients of variation (Covars), buffers'capacities, means and Covars, means and buffers, and Covars and buffers. It is argued that the deep understanding of the behavioural characteristics of such lines, contributes towards the achievement of practical solutions to many of their problems. The lines are simulated under both steady and non-steady states conditions, with positively skewed weibull work times distributions, different values of line length (N), buffer capacity (B), degree of imbalance (DI), and pattern of imbalance, utilizing full factorial designs.The data are subjected to the analysis of variance, multiple regression, multiple comparisons with control, pairwise comparisons, canonical correlation, and utilitv analysis A simple utility approach is also explored briefly.Some of the important conclusions for all the unbalanced lines' investigations are:(1) At least one unbalanced pattern generates superior idle time (I) and/or mean buffer level (ABL), over those of a balanced line. The superiority in I decreases as DI rises, whereas the advantage in ABL reduces as DI is decreased.(2) The DI of the best unbalanced pattern can substantially or moderately be increased and still yields approximately equal I to that of a balanced configuration.(3) If a line is unbalanced in the wrong direction, significantly inferior performance to that of a balanced design will result.(4) The unbalanced patterns' I tends to decrease, when N and DI reduce and B increases, while ABL falls directly with B.(5) The I's transient size increases as N and B become higher and DI increases, while the ABL's transient size rises whenever B reduces