A Framework to Analyze the Performance of Load Balancing Schemes for Ensembles of Stochastic Simulations

Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%

Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

Scalable Load Balancing Algorithms in Networked Systems

A fundamental challenge in large-scale networked systems viz., data centers and cloud networks is to distribute tasks to a pool of servers, using minimal instantaneous state information, while providing excellent delay performance. In this thesis we design and analyze load balancing algorithms that aim to achieve a highly efficient distribution of tasks, optimize server utilization, and minimize communication overhead.Comment: Ph.D. thesi

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

D11.2 Consolidated results on the performance limits of wireless communications

Deliverable D11.2 del projecte europeu NEWCOM#The report presents the Intermediate Results of N# JRAs on Performance Limits of Wireless Communications and highlights the fundamental issues that have been investigated by the WP1.1. The report illustrates the Joint Research Activities (JRAs) already identified during the first year of the project which are currently ongoing. For each activity there is a description, an illustration of the adherence and relevance with the identified fundamental open issues, a short presentation of the preliminary results, and a roadmap for the joint research work in the next year. Appendices for each JRA give technical details on the scientific activity in each JRA.Peer ReviewedPreprin

A hybrid EDA for load balancing in multicast with network coding

Load balancing is one of the most important issues in the practical deployment of multicast with network coding. However, this issue has received little research attention. This paper studies how traffic load of network coding based multicast (NCM) is disseminated in a communications network, with load balancing considered as an important factor. To this end, a hybridized estimation of distribution algorithm (EDA) is proposed, where two novel schemes are integrated into the population based incremental learning (PBIL) framework to strike a balance between exploration and exploitation, thus enhance the efficiency of the stochastic search. The first scheme is a bi-probability-vector coevolution scheme, where two probability vectors (PVs) evolve independently with periodical individual migration. This scheme can diversify the population and improve the global exploration in the search. The second scheme is a local search heuristic. It is based on the problem-specific domain knowledge and improves the NCM transmission plan at the expense of additional computational time. The heuristic can be utilized either as a local search operator to enhance the local exploitation during the evolutionary process, or as a follow-up operator to improve the best-so-far solutions found after the evolution. Experimental results show the effectiveness of the proposed algorithms against a number of existing evolutionary algorithms

Decision-making under uncertainty in short-term electricity markets

In the course of the energy transition, the share of electricity generation from renewable energy sources in Germany has increased significantly in recent years and will continue to rise. Particularly fluctuating renewables like wind and solar bring more uncertainty and volatility to the electricity system. As markets determine the unit commitment in systems with self-dispatch, many changes have been made to the design of electricity markets to meet the new challenges. Thereby, a trend towards real-time can be observed. Short-term electricity markets are becoming more important and are seen as suitable for efficient resource allocation. Therefore, it is inevitable for market participants to develop strategies for trading electricity and flexibility in these segments. The research conducted in this thesis aims to enable better decisions in short-term electricity markets. To achieve this, a multitude of quantitative methods is developed and applied: (a) forecasting methods based on econometrics and machine learning, (b) methods for stochastic modeling of time series, (c) scenario generation and reduction methods, as well as (d) stochastic programming methods. Most significantly, two- and three-stage stochastic optimization problems are formulated to derive optimal trading decisions and unit commitment in the context of short-term electricity markets. The problem formulations adequately account for the sequential structure, the characteristics and the technical requirements of the different market segments, as well as the available information regarding uncertain generation volumes and prices. The thesis contains three case studies focusing on the German electricity markets. Results confirm that, based on appropriate representations of the uncertainty of market prices and renewable generation, the optimization approaches allow to derive sound trading strategies across multiple revenue streams, with which market participants can effectively balance the inevitable trade-off between expected profit and associated risk. By considering coherent risk metrics and flexibly adaptable risk attitudes, the trading strategies allow to substantially reduce risk with only moderate expected profit losses. These results are significant, as improving trading decisions that determine the allocation of resources in the electricity system plays a key role in coping with the uncertainty from renewables and hence contributes to the ultimate success of the energy transition

Continuous reservoir model updating by ensemble Kalman filter on Grid computing architectures

A reservoir engineering Grid computing toolkit, ResGrid and its extensions, were developed and applied to designed reservoir simulation studies and continuous reservoir model updating. The toolkit provides reservoir engineers with high performance computing capacity to complete their projects without requiring them to delve into Grid resource heterogeneity, security certification, or network protocols. Continuous and real-time reservoir model updating is an important component of closed-loop model-based reservoir management. The method must rapidly and continuously update reservoir models by assimilating production data, so that the performance predictions and the associated uncertainty are up-to-date for optimization. The ensemble Kalman filter (EnKF), a Bayesian approach for model updating, uses Monte Carlo statistics for fusing observation data with forecasts from simulations to estimate a range of plausible models. The ensemble of updated models can be used for uncertainty forecasting or optimization. Grid environments aggregate geographically distributed, heterogeneous resources. Their virtual architecture can handle many large parallel simulation runs, and is thus well suited to solving model-based reservoir management problems. In the study, the ResGrid workflow for Grid-based designed reservoir simulation and an adapted workflow provide tools for building prior model ensembles, task farming and execution, extracting simulator output results, implementing the EnKF, and using a web portal for invoking those scripts. The ResGrid workflow is demonstrated for a geostatistical study of 3-D displacements in heterogeneous reservoirs. A suite of 1920 simulations assesses the effects of geostatistical methods and model parameters. Multiple runs are simultaneously executed using parallel Grid computing. Flow response analyses indicate that efficient, widely-used sequential geostatistical simulation methods may overestimate flow response variability when compared to more rigorous but computationally costly direct methods. Although the EnKF has attracted great interest in reservoir engineering, some aspects of the EnKF remain poorly understood, and are explored in the dissertation. First, guidelines are offered to select data assimilation intervals. Second, an adaptive covariance inflation method is shown to be effective to stabilize the EnKF. Third, we show that simple truncation can correct negative effects of nonlinearity and non-Gaussianity as effectively as more complex and expensive reparameterization methods