Dynamic scheduling based on particle swarm optimization for cloud-based scientific experiments

Los Experimentos de Barrido de Parámetros (PSEs) permiten a los científicos llevar a cabo simulaciones mediante la ejecución de un mismo código con diferentes entradas de datos, lo cual genera una gran cantidad de trabajos intensivos en CPU que para ser ejecutados es necesario utilizar entornos de cómputo paralelos. Un ejemplo de este tipo de entornos son las Infraestructura como un Servicio (IaaS) de Cloud, las cuales ofrecen máquinas virtuales (VM) personalizables que son asignadas a máquinas físicas disponibles para luego ejecutar los trabajos. Además, es importante planificar correctamente la asignación de las máquinas físicas del Cloud, y por lo tanto es necesario implementar estrategias eficientes de planificación para asignar adecuadamente las VMs en las máquinas físicas. Una planificación eficiente constituye un desafío, debido a que es un problema NP-Completo. En este trabajo describimos y evaluamos un planificador Cloud basado en Optimización por Enjambre de Partículas (PSO). Las métricas principales de rendimiento a estudiar son el número de usuarios que el planificador es capáz de servir exitosamente y el número total de VMs creadas en un escenario online (no por lotes). Además, en este trabajo se evalúa el número de mensajes enviados a través de la red. Los experimentos son realizados mediante el uso del simulador CloudSim y datos de trabajos de problemas científicos reales. Los resultados muestran que nuestro planificador logra el mejor rendimiento respecto de las métricas estudiadas con respecto a una asignación random y algoritmos genéticos. En este trabajo también evaluamos el rendimiento, a través de las métricas propuestas, cuando se provee al planificador información cualitativa relacionada a la longitud de los trabajos o no se provee la misma.Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which results in many CPU-intensive jobs, and hence parallel computing environments must be used. Within these, Infrastructure as a Service (IaaS) Clouds offer custom Virtual Machines (VM) that are launched in appropriate hosts available in a Cloud to handle such jobs. Then, correctly scheduling Cloud hosts is very important and thus efficient scheduling strategies to appropriately allocate VMs to physical resources must be developed. Scheduling is however challenging due to its inherent NP-completeness. We describe and evaluate a Cloud scheduler based on Particle Swarm Optimization (PSO). The main performance metrics to study are the number of Cloud users that the scheduler is able to successfully serve, and the total number of created VMs, in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performedusing CloudSim and job data from real scientific problems show that our scheduler achieves better performance than schedulers based on Random assignment and Genetic Algorithms. We also study the performance when supplying or not job information to the schedulers, namely a qualitative indication of job length.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto de Tecnologías de la Información y las Comunicaciones; Argentin

A comparative analysis of NSGA-II and NSGA-III for autoscaling parameter sweep experiments in the cloud

The Cloud Computing paradigm is focused on the provisioning of reliable and scalable virtual infrastructures that deliver execution and storage services. This paradigm is particularly suitable to solve resource-greedy scientific computing applications such as parameter sweep experiments (PSEs). Through the implementation of autoscalers, the virtual infrastructure can be scaled up and down by acquiring or terminating instances of virtual machines (VMs) at the time that application tasks are being scheduled. In this paper, we extend an existing study centered in a state-of-the-art autoscaler called multiobjective evolutionary autoscaler (MOEA). MOEA uses a multiobjective optimization algorithm to determine the set of possible virtual infrastructure settings. In this context, the performance of MOEA is greatly influenced by the underlying optimization algorithm used and its tuning. Therefore, we analyze two well-known multiobjective evolutionary algorithms (NSGA-II and NSGA-III) and how they impact on the performance of the MOEA autoscaler. Simulated experiments with three real-world PSEs show that MOEA gets significantly improved when using NSGA-III instead of NSGA-II due to the former provides a better exploitation versus exploration trade-off.Fil: Yannibelli, Virginia Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Monge, David. Universidad Nacional de Cuyo; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Rodríguez, Guillermo Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentin

Distributed Job Scheduling based on Swarm Intelligence: A Survey

Scientists and engineers need computational power to satisfy the increasing resource intensive nature of their simulations. For example, running Parameter Sweep Experiments (PSE) involve processing many independent jobs, given by multiple initial configurations (input parameter values) against the same program code. Hence, paradigms like Grid Computing and Cloud Computing are employed for gaining scalability. However, job scheduling in Grid and Cloud environments represents a difficult issue since it is basically NP-complete. Thus, many variants based on approximation techniques, specially those from Swarm Intelligence (SI), have been proposed. These techniques have the ability of searching for problem solutions in a very efficient way. This paper surveys SI-based job scheduling algorithms for bag-of-tasks applications(such as PSEs) on distributed computing environments, and uniformly compares them based on a derived comparison framework. We also discuss open problems and future research in the area.Fil: Mateos Diaz, Cristian Maximiliano. Universidad Nacional de Cuyo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo; Argentin

Schedulers based on Ant Colony Optimization for Parameter Sweep Experiments in Distributed Environments

Scientists and engineers are more and more faced to the need of computational power to satisfy the ever-increasing resource intensive nature of their experiments. An example of these experiments is Parameter Sweep Experiments (PSE). PSEs involve many independent jobs, since the experiments are executed under multiple initial configurations (input parameter values) several times. In recent years, technologies such as Grid Computing and Cloud Computing have been used for running such experiments. However, for PSEs to be executed efficiently, it is necessary to develop effective scheduling strategies to allocate jobs to machines and reduce the associated processing times. Broadly, the job scheduling problem is known to be NP-complete, and thus many variants based on approximation techniques have been developed. In this work, we conducted a survey of different scheduling algorithms based on Swarm Intelligence (SI), and more precisely Ant Colony Optimization (ACO), which is the most popular SI technique, to solve the problem of job scheduling with PSEs on different distributed computing environments.Fil: Pacini Naumovich, Elina Rocío. Consejo Nacional de Investigaciones Científicas y Técnicas. Idehesi-inst Mult Est Soc Contem (uncuyo); ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentin

ACO-based dynamic job scheduling of parametric computational mechanics studies on Cloud Computing infrastructures

Parameter Sweep Experiments (PSEs) allow scientists to perform simulations by running the same code with different input data, which typically results in many CPU-intensive jobs and thus computing environments such as Clouds must be used. Job scheduling is however challenging due to its inherent NP-completeness. Therefore, some Cloud schedulers based on Swarm Intelligence (SI) techniques, which are good at approximating combinatorial problems, have arisen. We describe a Cloud scheduler based on Ant Colony Optimization (ACO), a popular SI technique, to allocate Virtual Machines to physical resources belonging to a Cloud. Simulated experiments performed with real PSE job data and alternative classical Cloud schedulers show that our scheduler allows a fair assignment of VMs, which are requested by different users, while maximizing the number of jobs executed every time a new user connects to the Cloud. Unlike previous experiments with our algorithm, in which batch execution scenarios for jobs were used, the contribution of this paper is to experiment with our proposal in dynamic scheduling scenarios. Results suggest that our scheduler provides a better balance to the number of executed jobs per unit time versus serviced users, i.e., the number of Cloud users that the scheduler is able to successfully serve.Fil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A Three-level Scheduler to Execute Scientific Experiments on Federated Clouds

For executing current simulated scientific experiments it is necessary to have huge amounts of computing power. A solution path to this problem is the federated Cloud model, where custom virtual machines (VM) are scheduled in appropriate hosts belonging to different providers to execute such experiments, minimizing response time. In this paper, we study schedulers for federated Clouds. Scheduling is performed at three levels. First, at the broker level, datacenters are selected by their network latencies via three policies ?Lowest-Latency-Time-First, First-Latency-Time-First, and Latency-Time-In-Round?. Second, at the infrastructure level, two Cloud VM schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are implemented. At this level the scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the pre-allocated VMs. We evaluate, through simulated experiments, how the proposed three-level scheduler performs w.r.t. the response time delivered to the user as the number of Cloud machines increases, a property known as horizontal scalability.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentin

An ACO-inspired algorithm for minimizing weighted flowtime in cloud-based parameter sweep experiments

Parameter Sweep Experiments (PSEs) allow scientists and engineers to conduct experiments by running the same program code against different input data. This usually results in many jobs with high computational requirements. Thus, distributed environments, particularly Clouds, can be employed to fulfill these demands. However, job scheduling is challenging as it is an NP-complete problem. Recently, Cloud schedulers based on bio-inspired techniques-which work well in approximating problems with little input information-have been proposed. Unfortunately, existing proposals ignore job priorities, which is a very important aspect in PSEs since it allows accelerating PSE results processing and visualization in scientific Clouds. We present a new Cloud scheduler based on Ant Colony Optimization, the most popular bio-inspired technique, which also exploits well-known notions from operating systems theory. Simulated experiments performed with real PSE job data and other Cloud scheduling policies indicate that our proposal allows for a more agile job handling while reducing PSE completion time.Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Balancing throughput and response time in online scientific Clouds via Ant Colony Optimization (SP2013/2013/00006)

The Cloud Computing paradigm focuses on the provisioning of reliable and scalable infrastructures (Clouds) delivering execution and storage services. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. The goal of this work is to study private Clouds to execute scientific experiments coming from multiple users, i.e., our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate hosts available in a Cloud. Then, correctly scheduling Cloud hosts is very important and it is necessary to develop efficient scheduling strategies to appropriately allocate VMs to physical resources. The job scheduling problem is however NP-complete, and therefore many heuristics have been developed. In this work, we describe and evaluate a Cloud scheduler based on Ant Colony Optimization (ACO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. Besides, the number of intra-Cloud network messages sent are evaluated. Simulated experiments performed using CloudSim and job data from real scientific problems show that our scheduler succeeds in balancing the studied metrics compared to schedulers based on Random assignment and Genetic Algorithms.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Universidad Nacional de Cuyo. Facultad de Ingenieria; Argentin

A Bio-inspired Datacenter Selection Scheduler for Federated Clouds and its Application to Frost Prediction

Frost is an agro-meteorological event which causes both damage in crops and important economic losses, therefore frost prediction applications (FPA) are very important to help farmers to mitigate possible damages. FPA involves the execution of many CPU-intensive jobs. This work focuses on efficiently running FPAs in paid federated Clouds, where custom virtual machines (VM) are launched in appropriate resources belonging to different providers. The goal of this work is to minimize both the makespan and monetary cost. We follow a federated Cloud model where scheduling is performed at three levels. First, at the broker level, a datacenter is selected taking into account certain criteria established by the user, such as lower costs or lower latencies. Second, at the infrastructure level, a specialized scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. Our proposal mainly contributes to implementing bio-inspired strategies at two levels. Specifically, two broker-level schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), which aim to select the datacenters taking into account the network latencies, monetary cost and the availability of computational resources in datacenters, are implemented. Then, VMs are allocated in the physical machines of that datacenter by another intra-datacenter scheduler also based on ACO and PSO. Performed experiments show that our bio-inspired scheduler succeed in reducing both the makespan and the monetary cost with average gains of around 50% compared to genetic algorithms.Fil: Pacini Naumovich, Elina Rocío. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Iacono, Lucas Emanuel. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Garcia Garino, Carlos Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza; Argentina. Universidad Nacional de Cuyo. Instituto para las Tecnologías de la Informacion y las Comunicaciones; Argentin