Modeling Parallel System Workloads with Temporal Locality

Abstract. In parallel systems, similar jobs tend to arrive within bursty periods. This fact leads to the existence of the locality phenomenon, a persistent similarity between nearby jobs, in real parallel computer workloads. This important phenomenon deserves to be taken into account and used as a characteristic of any workload model. Regrettably, this property has received little if any attention of researchers and synthetic workloads used for performance evaluation to date often do not have locality. With respect to this research trend, Feitelson has suggested a general repetition approach to model locality in synthetic workloads [6]. Using this approach, Li et al. recently introduced a new method for modeling temporal locality in workload attributes such as run time and memory [14]. However, with the assumption that each job in the synthetic workload requires a single processor, the parallelism has not been taken into account in their study. In this paper, we propose a new model for parallel computer workloads based on their result. In our research, we firstly improve their model to control locality of a run time process better and then model the parallelism. The key idea for modeling the parallelism is to control the cross-correlation between the run time and the number of processors. Experimental results show that not only the cross-correlation is controlled well by our model, but also the marginal distribution can be fitted nicely. Furthermore, the locality feature is also obtained in our model.

Modeliranje sistemov paralelne strežbe v GPSS-u

This article treats parallel service system modelling in the GPSS simulation language. The transactions entering such systems select between numerous different servers and we can mostly detect two rules in the selecting of the appropriate server. The first rule always gives the first few (regarding its position in the system) entities (either servers or queues) precedence over the others, while the second rule always treats all the equal entities evenly and selects among them quite randomly. Since GPSS normally operates by the first rule, we frequently come up against difficulties when modelling systems that serve by another rule. The present article offers a methodology how to solve this problem within GPSS.Članek obravnava modeliranje paralelnih strežnih sistemov v simulacijskem jeziku GPSS. Transakcije, ki vstopajo v takšne sisteme, izbirajo med večjim številom strežnih mest. Pri zasedanju teh mest pa lahko v grobem zasledimo dva različna pravila. Prvo pravilo daje prednost zasedanju prvih (po svoji poziciji v sistemu) entitet (bodisi strežnikov, bodisi čakalnih vrst), medtem ko drugo pravilo obravnava te entitete enakovredno in izbira med njimi povsem naključno. Ker GPSS v svojem delovanju privzema prvo pravilo, lahko pri modeliranju sistemov, ki strežejo po drugem pravilu, pogosto naletimo na določene težave. Pričujoči prispevek ponuja metodologijo, kako znotraj tega jezika reševati omenjeni problem

A job response time prediction method for production Grid computing environments

A major obstacle to the widespread adoption of Grid Computing in both the scientific community and industry sector is the difficulty of knowing in advance a job submission running cost that can be used to plan a correct allocation of resources. Traditional distributed computing solutions take advantage of homogeneous and open environments to propose prediction methods that use a detailed analysis of the hardware and software components. However, production Grid computing environments, which are large and use a complex and dynamic set of resources, present a different challenge. In Grid computing the source code of applications, programme libraries, and third-party software are not always available. In addition, Grid security policies may not agree to run hardware or software analysis tools to generate Grid components models. The objective of this research is the prediction of a job response time in production Grid computing environments. The solution is inspired by the concept of predicting future Grid behaviours based on previous experiences learned from heterogeneous Grid workload trace data. The research objective was selected with the aim of improving the Grid resource usability and the administration of Grid environments. The predicted data can be used to allocate resources in advance and inform forecasted finishing time and running costs before submission. The proposed Grid Computing Response Time Prediction (GRTP) method implements several internal stages where the workload traces are mined to produce a response time prediction for a given job. In addition, the GRTP method assesses the predicted result against the actual target job’s response time to inference information that is used to tune the methods setting parameters. The GRTP method was implemented and tested using a cross-validation technique to assess how the proposed solution generalises to independent data sets. The training set was taken from the Grid environment DAS (Distributed ASCI Supercomputer). The two testing sets were taken from AuverGrid and Grid5000 Grid environments Three consecutive tests assuming stable jobs, unstable jobs, and using a job type method to select the most appropriate prediction function were carried out. The tests offered a significant increase in prediction performance for data mining based methods applied in Grid computing environments. For instance, in Grid5000 the GRTP method answered 77 percent of job prediction requests with an error of less than 10 percent. While in the same environment, the most effective and accurate method using workload traces was only able to predict 32 percent of the cases within the same range of error. The GRTP method was able to handle unexpected changes in resources and services which affect the job response time trends and was able to adapt to new scenarios. The tests showed that the proposed GRTP method is capable of predicting job response time requests and it also improves the prediction quality when compared to other current solutions

Autonomous grid scheduling using probabilistic job runtime scheduling

Computational Grids are evolving into a global, service-oriented architecture – a universal platform for delivering future computational services to a range of applications of varying complexity and resource requirements. The thesis focuses on developing a new scheduling model for general-purpose, utility clusters based on the concept of user requested job completion deadlines. In such a system, a user would be able to request each job to finish by a certain deadline, and possibly to a certain monetary cost. Implementing deadline scheduling is dependent on the ability to predict the execution time of each queued job, and on an adaptive scheduling algorithm able to use those predictions to maximise deadline adherence. The thesis proposes novel solutions to these two problems and documents their implementation in a largely autonomous and self-managing way. The starting point of the work is an extensive analysis of a representative Grid workload revealing consistent workflow patterns, usage cycles and correlations between the execution times of jobs and its properties commonly collected by the Grid middleware for accounting purposes. An automated approach is proposed to identify these dependencies and use them to partition the highly variable workload into subsets of more consistent and predictable behaviour. A range of time-series forecasting models, applied in this context for the first time, were used to model the job execution times as a function of their historical behaviour and associated properties. Based on the resulting predictions of job runtimes a novel scheduling algorithm is able to estimate the latest job start time necessary to meet the requested deadline and sort the queue accordingly to minimise the amount of deadline overrun. The testing of the proposed approach was done using the actual job trace collected from a production Grid facility. The best performing execution time predictor (the auto-regressive moving average method) coupled to workload partitioning based on three simultaneous job properties returned the median absolute percentage error centroid of only 4.75%. This level of prediction accuracy enabled the proposed deadline scheduling method to reduce the average deadline overrun time ten-fold compared to the benchmark batch scheduler. Overall, the thesis demonstrates that deadline scheduling of computational jobs on the Grid is achievable using statistical forecasting of job execution times based on historical information. The proposed approach is easily implementable, substantially self-managing and better matched to the human workflow making it well suited for implementation in the utility Grids of the future

Workload modeling and performance evaluation in parallel systems

Scheduling plays a significant role in producing good performance for clusters and grids. Smart scheduling policies in these systems are essential to enable efficient resource allocation mechanisms. One of the key factors that have a strong effect on scheduling is the workload. This workload problem is associated with four research topics to obtain an effective scheduler, namely workload characterisation, workload modeling, performance evaluation and prediction, and scheduling design. Workload data collected from real systems are the best source for improving our knowledge about performance issues of clusters and grids. Observed features of these workloads are precious sources of clues, which can be utilized to enhance scheduling. To this end, several long-term parallel and grid workloads have been collected and this thesis used these real workloads in the study of workload characterisation, workload modeling, per formance evaluation and prediction. Our research resulted in many workload modeling tools, a performance predictor and several useful clues that are essential to develop efficient cluster and grid schedulers.UBL - phd migration 201

Modelos formales para la simulación de la epidermis humana

Tesis doctoral inédita léida en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: enero del 2014La epidermis humana es un ejemplo de sistema complejo. Está compuesto por multitud de copias de diferentes tipos de células. El comportamiento del sistema completo emerge de las conductas individuales de sus células. Se han desarrollado muchos modelos que describen la conducta individual de las células. En muchas ocasiones, el conocimiento que se tiene de la aportación de la conducta de cada célula a la conducta del tejido completo es de alto grado de abstracción y la poseen los expertos de este dominio. Estas conduc-tas individuales son bien conocidas. Son muchas las razones por las que pue-de resultar de interés la simulación de este órgano. Por todo esto, estamos ante las características que habitualmente hacen provechoso un enfoque ba-sado en modelos de cómputo bioinspirados. Los autómatas celulares serían uno de los sistemas que, por su definición, podrían considerarse ideales para abordar la simulación de la epidermis. Sin embargo, una característica fun-damental tiene que ser incorporada a cualquier simulador de la epidermis: mantener una configuración de mínima energía en cada instante. Los mode-los matemáticos más prometedores en la simulación de tejidos y que, por tanto, incorporan esta característica, son la familia de modelos que parte del modelo de Ising, sigue por el modelo de Potts y el modelo extendido a células de Potts y termina con el modelo CPM-GGH. Una de las principales limitacio-nes para la aplicación de este modelo para problemas reales relacionados con la epidermis es el rendimiento. Una vía tradicional para subsanar esta limita-ción es el acceso a recursos masivamente paralelos mediante versiones para-lelas, concurrentes y distribuidas de los algoritmos de simulación iii La presente tesis doctoral utiliza una implementación del modelo CPM-GGH para definir un modelo básico de epidermis que simula con éxito el proceso de homeostasis y regeneración de pérdida de capas celulares en rasguños. Este modelo permitirá, en líneas futuras abordar la simulación de fenómenos más complejos. También se han abordado dos posibles aproximaciones a la ejecución me-diante hardware paralelo de versiones de los algoritmos que simulan los mo-delos básicos que subyacen al CPM-GGH. Estas aproximaciones permitirán en el futuro proporcionar versiones paralelas y más eficientes que permitan abordar la simulación de fragmentos grandes de epidermis.Human skin is an example of a complex system. It is made of several copies of different cell types. The behavior of the complete system emerges from the individual behaviors of its cells. Many models have been developed that describe the behavior of individual cells. The knowledge about the contribu-tion of each cell to the behavior of the complete tissue is usually quite com-plicated and only known by the experts on the field. The behavior of individu-al cells is, however, quite well known. The simulation of skin tissue is interesting for many reasons. The system to be simulated is very appropriate for the use of bioinspired computational models such as cellular automata, which can be considered ideal for the simulation of the epidermis because they can assure at every instant the maintenance of a minimum energy situation. The most promising family of mathematical models in tissue simulation are those based on the Ising mod-el, the Potts model and the CPM-GGH model. Performance is one of the main limitations posed by this family of models. A possible way to solve it is the access to massively parallel resources by means of parallel, concurrent and distributed versions of the simulation algo-rithms. This doctoral thesis implements the CPM-GGH model and successfully repre-sents the processes of homeostasis and regeneration after the loss of cell layers in small wounds and scratches. This model will make it possible to tackle the simulation of more complex phenomena in the future. Two different ways to implement it by means of parallel hardware have been envisaged, which in the future should make it possible to address the simula-tion of bigger fragments of epidermis

Parallel Computer Workload Modeling with Markov Chains

In order to evaluate di#erent scheduling strategies for parallel computers, simulations are often executed. As the scheduling quality highly depends on the workload that is served on the parallel machine, a representative workload model is required. Common approaches such as using a probability distribution model can capture the static feature of real workloads, but they do not consider the temporal relation in the traces. In this paper, a workload model is presented which uses Markov chains for modeling job parameters. In order to consider the interdependence of individual parameters without requiring large scale Markov chains, a novel method for transforming the states in di#erent Markov chains is presented. The results show that the model yields closer results to the real workloads than other common approaches