33 research outputs found
Quality of service based data-aware scheduling
Distributed supercomputers have been widely used for solving complex computational problems and modeling complex phenomena such as black holes, the environment, supply-chain economics, etc. In this work we analyze the use of these distributed supercomputers for time sensitive data-driven applications. We present the scheduling challenges involved in running deadline sensitive applications on shared distributed supercomputers running large parallel jobs and introduce a ``data-aware\u27\u27 scheduling paradigm that overcomes these challenges by making use of Quality of Service classes for running applications on shared resources. We evaluate the new data-aware scheduling paradigm using an event-driven hurricane simulation framework which attempts to run various simulations modeling storm surge, wave height, etc. in a timely fashion to be used by first responders and emergency officials. We further generalize the work and demonstrate with examples how data-aware computing can be used in other applications with similar requirements
Autonimic energy-aware task scheduling
International audienceThe increasing processing capability of data-centers increases considerably their energy consumption which leads to important losses for companies. Energy-aware task scheduling is a new challenge to optimize the use of the computation power provided by multiple resources. In the context of Cloud resources usage depends on users requests which are generally unpredictable. Autonomic computing paradigm provides systems with self-managing capabilities helping to react to unstable situation. This article proposes an autonomic approach to provide energy-aware scheduling tasks. The generic autonomic computing framework FrameSelf coupled with the CloudSim energy-aware simulator is presented. The proposed solution enables to detect critical schedule situations and simulate new placements for tasks on DVFS enabled hosts in order to improve the global energy efficiency
Scheduling in Grid Computing Environment
Scheduling in Grid computing has been active area of research since its
beginning. However, beginners find very difficult to understand related
concepts due to a large learning curve of Grid computing. Thus, there is a need
of concise understanding of scheduling in Grid computing area. This paper
strives to present concise understanding of scheduling and related
understanding of Grid computing system. The paper describes overall picture of
Grid computing and discusses important sub-systems that enable Grid computing
possible. Moreover, the paper also discusses concepts of resource scheduling
and application scheduling and also presents classification of scheduling
algorithms. Furthermore, the paper also presents methodology used for
evaluating scheduling algorithms including both real system and simulation
based approaches. The presented work on scheduling in Grid containing concise
understandings of scheduling system, scheduling algorithm, and scheduling
methodology would be very useful to users and researchersComment: Fourth International Conference on Advanced Computing & Communication
Technologies (ACCT), 201
Energy-aware simulation with DVFS
International audienceIn recent years, research has been conducted in the area of large systems models, especially distributed systems, to analyze and understand their behavior. Simulators are now commonly used in this area and are becoming more complex. Most of them provide frameworks for simulating application scheduling in various Grid infrastructures, others are specifically developed for modeling networks, but only a few of them simulate energy-efficient algorithms. This article describes which tools need to be implemented in a simulator in order to support energy-aware experimentation. The emphasis is on DVFS simulation, from its implementation in the simulator CloudSim to the whole methodology adopted to validate its functioning. In addition, a scientific application is used as a use case in both experiments and simulations, where the close relationship between DVFS efficiency and hardware architecture is highlighted. A second use case using Cloud applications represented by DAGs, which is also a new functionality of CloudSim, demonstrates that the DVFS efficiency also depends on the intrinsic middleware behavior
jMetal and MFHS Collaboration for Task Scheduling Optimization in Heterogeneous Distributed System
Task scheduling in distributed computing architectures has attracted considerable research interest, leading to the development of numerous algorithms aiming to approach optimal solutions. However, most of these algorithms remain confined to simulation environments and are rarely applied in real-world settings. In a previous study, we introduced the MFHS framework, which facilitates the transition of scheduling algorithms from simulation to practical deployment. Unfortunately, MFHS currently offers a limited selection of scheduling heuristics. In this work, we address this limitation by presenting the MFHS_jMetal framework, integrating the extensive task scheduling algorithms available in the well-established jMetal framework. Our implementation demonstrates the successful expansion of available scheduling algorithms while preserving the core characteristics of MFHS, bridging the gap between theoretical models and real-world deployment
Simulation énergétique de tâches distribuées avec changements dynamiques de fréquence
National audienceCes dernières années, de nombreuses recherches ont été menées dans le domaine de la simulation des systèmes distribués, afin d'analyser et de comprendre leur comportement. Certains de ces simulateurs se focalisent sur le problème d'ordonnancement de tâches, d'autres sont spécifiquement développés pour la modélisation du réseau et seulement peu d'entre eux proposent tous les outils nécessaires pour simuler la consommation énergétique d'une application, d'une machine ou d'un centre de calcul. Cet article décrit les outils qui doivent être intégrés dans un simulateur pour être en mesure de lancer des simulations destinées à améliorer le comportement énergétique des machines. L'accent est mis davantage sur le DVFS (Dynamic Voltage and Frequency Scaling) et sa mise en oeuvre dans CloudSim, le simula-teur qui a été utilisé pour les expériences décrites dans cet article, mais aussi sur la façon de simuler et la méthodologie adoptée pour assurer la qualité des mesures et des simulations
Modeling Data Center Building Blocks for Energy-efficiency and Thermal Simulations
International audienceIn this paper we present a concept and specification of Data Center Efficiency Building Blocks (DEBBs), which represent hardware components of a data center complemented by descriptions of their energy efficiency. Proposed building blocks contain hardware and thermodynamic models that can be applied to simulate a data center and to evaluate its energy efficiency. DEBBs are available in an open repository being built by the CoolEmAll project. In the paper we illustrate the concept by an example of DEBB defined for the RECS multi-server system including models of its power usage and thermodynamic properties. We also show how these models are affected by specific architecture of modeled hardware and differences between various classes of applications. Proposed models are verified by a comparison to measurements on a real infrastructure. Finally, we demonstrate how DEBBs are used in data center simulations
Empirical characterization and modeling of power consumption and energy aware scheduling in data centers
Energy-efficient management is key in modern data centers in order to reduce
operational cost and environmental contamination. Energy management
and renewable energy utilization are strategies to optimize energy consumption
in high-performance computing. In any case, understanding the power consumption
behavior of physical servers in datacenter is fundamental to implement
energy-aware policies effectively. These policies should deal with possible
performance degradation of applications to ensure quality of service.
This thesis presents an empirical evaluation of power consumption for scientific
computing applications in multicore systems. Three types of applications
are studied, in single and combined executions on Intel and AMD servers, for
evaluating the overall power consumption of each application. The main results
indicate that power consumption behavior has a strong dependency with
the type of application. Additional performance analysis shows that the best
load of the server regarding energy efficiency depends on the type of the applications,
with efficiency decreasing in heavily loaded situations. These results
allow formulating models to characterize applications according to power consumption,
efficiency, and resource sharing, which provide useful information
for resource management and scheduling policies. Several scheduling strategies
are evaluated using the proposed energy model over realistic scientific computing
workloads. Results confirm that strategies that maximize host utilization
provide the best energy efficiency.Agencia Nacional de InvestigaciĂłn e InnovaciĂłn FSE_1_2017_1_14478