14730249
Dynamic and Fault-Tolerant Clustering for Scientific Workflows
Task clustering has proven to be an effective method to reduce execution overhead and to improve the computational granularity of scientific workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a higher risk of suffering from failures than a single task job. In this paper, we conduct a theoretical analysis of the impact of transient failures on the runtime performance of scientific workflow executions. We propose a general task failure modeling framework that uses a maximum likelihood estimation-based parameter estimation process to model workflow performance. We further propose three fault-tolerant clustering strategies to improve the runtime performance of workflow executions in faulty execution environments. Experimental results show that failures can have significant impact on executions where task clustering policies are not fault-tolerant, and that our solutions yield makespan improvements in such scenarios. In addition, we propose a dynamic task clustering strategy to optimize the workflow's makespan by dynamically adjusting the clustering granularity when failures arise. A trace-based simulation of five real workflows shows that our dynamic method is able to adapt to unexpected behaviors, and yields better makespans when compared to static methods.
1594614
Workflow task clustering for best effort systems with Pegasus
Many scientific workflows are composed of fine computational granularity tasks, yet they are composed of thousands of them and are data intensive in nature, thus requiring resources such as the TeraGrid to execute efficiently. In order to improve the performance of such applications, we often employ task clustering techniques to increase the computational granularity of workflow tasks. The goal is to minimize the completion time of the workflow by reducing the impact of queue wait times. In this paper, we examine the performance impact of the clustering techniques using the Pegasus workflow management system. Experiments performed using an astronomy workflow on the NCSA TeraGrid cluster show that clustering can achieve a significant reduction in the workflow completion time (up to 97%).
6782722
Fault Tolerant Clustering in Scientific Workflows
Task clustering has been proven to be an effective method to reduce execution overhead and increase the computational granularity of workflow tasks executing on distributed resources. However, a job composed of multiple tasks may have a greater risk of suffering from failures than a job composed of a single task. Our theoretic analysis and simulation results demonstrate that failures can have a significant impact on the runtime performance of workflows that use existing clustering policies that ignore failures. We therefore propose two general failure modeling frameworks (task failure model and job failure model) to address these performance issues. We show the necessity to consider the fault tolerance in the task failure model. Based on the task failure model, we propose three methods to improve the workflow performance in dynamic environments. A simulation-based evaluation is performed and it shows that our approach can improve the workflow makespan significantly for two important applications.
630896
On-Line, Non-clairvoyant Optimization of Workflow Activity Granularity on Grids
Controlling the granularity of workflow activities executed on widely distributed computing platforms such as grids is required to reduce the impact of task queuing and data transfer time. Most existing granularity control approaches assume extensive knowledge about the applications and resources (e.g. task duration on each resource), and that both the workload and available resources do not change over time. We propose a granularity control algorithm for platforms where such clairvoyant and offline conditions are not realistic. Our method groups tasks when the fineness degree of the application, which takes into account the ratio of shared data and the queuing/round-trip time ratio, becomes higher than a threshold determined from execution traces. The algorithm also de-groups task groups when new resources arrive. The application's behavior is constantly monitored so that the characteristics useful for the optimization are progressively discovered. Experimental results, obtained with 3 workflow activities deployed on the European Grid Infrastructure, show that (i) the grouping process yields speed-ups of about 2.5 when the amount of available resources is constant and that (ii) the use of de-grouping yields speed-ups of 2 when resources progressively appear.
12713062
Job and data clustering for aggregate use of multiple production cyberinfrastructures
In this paper, we address the challenges of reducing the time-to-solution of the data intensive earthquake simulation workflow "CyberShake" by supplementing the high-performance parallel computing (HPC) resources on which it typically runs with distributed, heterogeneous resources that can be obtained opportunistically from grids and clouds. We seek to minimize time to solution by maximizing the amount of work that can be efficiently done on the distributed resources. We identify data movement as the main bottleneck in effectively utilizing the combined local and distributed resources. We address this by analyzing the I/O characteristics of the application, processor acquisition rate (from a pilot-job service), and the data movement throughput of the infrastructure. With these factors in mind, we explore a combination of strategies including partitioning of computation (over HPC and distributed resources) and job clustering.
 We validate our approach with a theoretical study and with preliminary measurements on the Ranger HPC system and distributed Open Science Grid resources. More complete performance results will be presented in the final submission of this paper.
27809970
Controlling fairness and task granularity in distributed, online, non‐clairvoyant workflow executions
Distributed computing infrastructures are commonly used for scientific computing, and science gateways provide complete middleware stacks to allow their transparent exploitation by end users. However, administrating such systems manually is time consuming and sub‐optimal because of the complexity of the execution conditions. Algorithms and frameworks aiming at automating system administration must deal with online and non‐clairvoyant conditions, where most parameters are unknown and evolve over time. We consider the problem of controlling task granularity and fairness among scientific workflows executed in these conditions. We present two self‐managing loops monitoring the fineness, coarseness, and fairness of workflow executions, comparing these metrics with thresholds extracted from knowledge acquired in previous executions and planning appropriate actions to maintain these metrics to appropriate ranges. Experiments on the European Grid Infrastructure show that our task granularity control can speed up executions up to a factor of 2 and that our fairness control reduces slowdown variability by 3–7 compared with first‐come, first‐served. We also study the interaction between granularity control and fairness control: our experiments demonstrate that controlling task granularity degrades fairness but that our fairness control algorithm can compensate this degradation. Copyright © 2014 John Wiley & Sons, Ltd.
16010276
Integration of Workflow Partitioning and Resource Provisioning
The recent increased use of workflow management systems by large scientific collaborations presents the challenge of scheduling large-scale workflows onto distributed resources. This work aims to partition large-scale scientific workflows in conjunction with resources provisioning to reduce the workflow make span and resource cost.
19457022
Failure analysis and modeling of a VAXcluster system
The authors discuss the results of a measurement-based analysis of real error data collected from a DEC VAXcluster multicomputer system. In addition to evaluating basic system dependability characteristics, such as error and failure distributions and hazard rates for both individual machines and the VAXcluster, they develop reward models to analyze the impact of failures on the system as a whole. The results show that more than 46% of all failures were due to errors in shared resources. This is despite the fact that these errors have a recovery probability greater than 0.99. The hazard rate calculations show that not only errors but also failures occur in bursts. Approximately 40% of all failures occur in bursts and involve multiple machines. This result indicates that correlated failures are significant. Analysis of rewards shows that software errors have the lowest reward (0.05 versus 0.74 for disk errors). The expected reward rate (reliability measure) of the VAXcluster drops to 0.5 in 18 hours for the 7-out-of-7 model and in 80 days for the 3-out-of-7 model. The VAXcluster system availability is evaluated to be 0.993 250 days of operation.<<ETX>>
61328582
A Large-Scale Study of Failures in High-Performance Computing Systems
Designing highly dependable systems requires a good understanding of failure characteristics. Unfortunately, little raw data on failures in large IT installations are publicly available. This paper analyzes failure data collected at two large high-performance computing sites. The first data set has been collected over the past nine years at Los Alamos National Laboratory (LANL) and has recently been made publicly available. It covers 23,000 failures recorded on more than 20 different systems at LANL, mostly large clusters of SMP and NUMA nodes. The second data set has been collected over the period of one year on one large supercomputing system comprising 20 nodes and more than 10,000 processors. We study the statistics of the data, including the root cause of failures, the mean time between failures, and the mean time to repair. We find, for example, that average failure rates differ wildly across systems, ranging from 20-1000 failures per year, and that time between failures is modeled well by a Weibull distribution with decreasing hazard rate. From one system to another, mean repair time varies from less than an hour to more than a day, and repair times are well modeled by a lognormal distribution.
10036202
Failure data analysis of a large-scale heterogeneous server environment
The growing complexity of hardware and software mandates the recognition of fault occurrence in system deployment and management. While there are several techniques to prevent and/or handle faults, there continues to be a growing need for an in-depth understanding of system errors and failures and their empirical and statistical properties. This understanding can help evaluate the effectiveness of different techniques for improving system availability, in addition to developing new solutions. In this paper, we analyze the empirical and statistical properties of system errors and failures from a network of nearly 400 heterogeneous servers running a diverse workload over a year. While improvements in system robustness continue to limit the number of actual failures to a very small fraction of the recorded errors, the failure rates are significant and highly variable. Our results also show that the system error and failure patterns are comprised of time-varying behavior containing long stationary intervals. These stationary intervals exhibit various strong correlation structures and periodic patterns, which impact performance but also can be exploited to address such performance issues.
11925228
Managing appliance launches in infrastructure clouds
Infrastructure cloud computing introduces a significant paradigm shift that has the potential to revolutionize how scientific computing is done. However, while it is actively adopted by a number of scientific communities, it is still lacking a well-developed and mature ecosystem that will allow the scientific community to better leverage the capabilities it offers. This paper introduces a specific addition to the infrastructure cloud ecosystem: the cloudinit.d program, a tool for launching, configuring, monitoring, and repairing a set of interdependent virtual machines in an infrastructure-as-a-service (IaaS) cloud or over a set of IaaS clouds. The cloudinit.d program was developed in the context of the Ocean Observatory Initiative (OOI) project to help it launch and maintain complex virtual platforms provisioned on demand on top of infrastructure clouds. Like the UNIX init.d program, cloudinit.d can launch specified groups of services and the VMs in which they run, at different run levels representing dependencies of the launched VMs. Once launched, cloudinit.d monitors the health of each running service to ensure that the overall application is operating properly. If a problem is detected in a service, cloudinit.d will restart only that service and any other service that failed that depended on it.
683124
Pegasus: Mapping Scientific Workflows onto the Grid
In this paper we describe the Pegasus system that can map complex workflows onto the Grid. Pegasus takes an abstract description of a workflow and finds the appropriate data and Grid resources to execute the workflow. Pegasus is being released as part of the GriPhyN Virtual Data Toolkit and has been used in a variety of applications ranging from astronomy, biology, gravitational-wave science, and high-energy physics. A deferred planning mode of Pegasus is also introduced.
12786302
Run-time Optimisation of Grid Workflow Applications
The execution of workflow applications on the grid is a complex issue because of its dynamic and heterogeneous nature. While the grid provides good potential for achieving high performance, it also introduces a broad set of unpredictable overheads and possible failures. In this paper we present new methods for scalable and fault tolerant coordination of workflows in dynamic grid environments, including partitioning, static and dynamic optimisation, as well as virtual single execution environment, incorporated into the ASKALON distributed workflow enactment engine. We demonstrate the effectiveness of our methods on a material science workflow application executed in a real-world grid environment
14717045
A Science-Gateway Workload Archive to Study Pilot Jobs, User Activity, Bag of Tasks, Task Sub-steps, and Workflow Executions
Archives of distributed workloads acquired at the infrastructure level reputably lack information about users and application-level middleware. Science gateways provide consistent access points to the infrastructure, and therefore are an interesting information source to cope with this issue. In this paper, we describe a workload archive acquired at the science-gateway level, and we show its added value on several case studies related to user accounting, pilot jobs, fine-grained task analysis, bag of tasks, and workflows. Results show that science-gateway workload archives can detect workload wrapped in pilot jobs, improve user identification, give information on distributions of data transfer times, make bag-of-task detection accurate, and retrieve characteristics of workflow executions. Some limits are also identified.
5859403
The cost of doing science on the cloud: The Montage example
Utility grids such as the Amazon EC2 cloud and Amazon S3 offer computational and storage resources that can be used on-demand for a fee by compute and data-intensive applications. The cost of running an application on such a cloud depends on the compute, storage and communication resources it will provision and consume. Different execution plans of the same application may result in significantly different costs. Using the Amazon cloud fee structure and a real-life astronomy application, we study via simulation the cost performance tradeoffs of different execution and resource provisioning plans. We also study these trade-offs in the context of the storage and communication fees of Amazon S3 when used for long-term application data archival. Our results show that by provisioning the right amount of storage and compute resources, cost can be significantly reduced with no significant impact on application performance.
8429654
The Application of Cloud Computing to Astronomy: A Study of Cost and Performance
Cloud computing is a powerful new technology that is widely used in the business world. Recently, we have been investigating the benefits it offers to scientific computing. We have used three workflow applications to compare the performance of processing data on the Amazon EC2 cloud with the performance on the Abe high-performance cluster at the National Center for Supercomputing Applications (NCSA). We show that the Amazon EC2 cloud offers better performance and value for processor- and memory-limited applications than for I/O-bound applications. We provide an example of how the cloud is well suited to the generation of a science product: an atlas of period grams for the 210,000 light curves released by the NASA Kepler Mission. This atlas will support the identification of periodic signals, including those due to transiting exoplanets, in the Kepler data sets.
860739
Combined Fault Tolerance and Scheduling Techniques for Workflow Applications on Computational Grids
Complex scientific workflows are now Increasingly executed on computational grids. In addition to the challenges of managing and scheduling these workflows, reliability challenges arise because of the unreliable nature of large-scale grid infrastructure. Fault tolerance mechanisms like over-provisioning and checkpoint-recovery are used in current grid application management systems to address these reliability challenges. In this work, we propose new approaches that combine these fault tolerance techniques with existing workflow scheduling algorithms. We present a study on the effectiveness of the combined approaches by analyzing their impact on the reliability of workflow execution, workflow performance and resource usage under different reliability models, failure prediction accuracies and workflow application types.
16935145
Workflow-based comparison of two Distributed Computing Infrastructures
Porting applications to Distributed Computing Infrastructures (DCIs) is eased by the use of workflow abstractions. Yet, estimating the impact of the execution DCI on application performance is difficult due to the heterogeneity of the resources available, middleware and operation models. This paper describes a workflow-based experimental method to acquire objective performance comparison criterions when dealing with completely different DCIs. Experiments were conducted on the European EGI and the French Grid'5000 infrastructures to highlight raw performance variations and identify their causes. The results obtained also show that it is possible to conduct experiments on a production infrastructure with similar reproducibility as on an experimental platform.
2623096
Fault Tolerance and Recovery of Scientific Workflows on Computational Grids
In this paper, we describe the design and implementation of two mechanisms for fault-tolerance and recovery for complex scientific workflows on computational grids. We present our algorithms for over-provisioning and migration, which are our primary strategies for fault-tolerance. We consider application performance models, resource reliability models, network latency and bandwidth and queue wait times for batch-queues on compute resources for determining the correct fault-tolerance strategy. Our goal is to balance reliability and performance in the presence of soft real-time constraints like deadlines and expected success probabilities, and to do it in a way that is transparent to scientists. We have evaluated our strategies by developing a Fault-Tolerance and Recovery (FTR) service and deploying it as a part of the Linked Environments for Atmospheric Discovery (LEAD) production infrastructure. Results from real usage scenarios in LEAD show that the failure rate of individual steps in workflows decreases from about 30% to 5% by using our fault-tolerance strategies.
33058449
A New Fault Tolerance Heuristic for Scientific Workflows in Highly Distributed Environments Based on Resubmission Impact
Even though highly distributed environments such as Clouds and Grids are increasingly used for e-Science high performance applications, they still cannot deliver the robustness and reliability needed for widespread acceptance as ubiquitous scientific tools. To overcome this problem, existing systems resort to fault tolerance mechanisms such as task replication and task resubmission. In this paper we propose a new heuristic called Resubmission Impact to enhance the fault tolerance support for scientific workflows in highly distributed systems. In contrast to related approaches, our method can be used effectively on systems even in the absence of historic failure trace data. Simulated experiments of three real scientific workflows in the Austrian Grid environment show that our algorithm drastically reduces the resource waste compared to conservative task replication and resubmission techniques, while having a comparable execution performance and only a slight decrease in the success probability.
9341802
Why Do Internet Services Fail, and What Can Be Done About It?
We describe the architecture, operational practices, and failure characteristics of three very large-scale Internet services. Our research on architecture and operational practices took the form of interviews with architects and operations staff at those (and several other) services. Our research on component and service failure took the form of examining the operations problem tracking databases from two of the services and a log of service failure post-mortem reports from the third. Architecturally, we find convergence on a common structure: division of nodes into service front-ends and back-ends, multiple levels of redundancy and load-balancing, and use of custom-written software for both production services and administrative tools. Operationally, we find a thin line between service developers and operators, and a need to coordinate problem detection and repair across administrative domains. With respect to failures, we find that operator errors are their primary cause, operator error is the most difficult type of failure to mask, service front-ends are responsible for more problems than service back-ends but fewer minutes of unavailability, and that online testing and more thoroughly exposing and detecting component failures could reduce system failure rates for at least one service.
14611345
Pegasus, a workflow management system for science automation
Modern science often requires the execution of large-scale, multi-stage simulation and data analysis pipelines to enable the study of complex systems. The amount of computation and data involved in these pipelines requires scalable workflow management systems that are able to reliably and efficiently coordinate and automate data movement and task execution on distributed computational resources: campus clusters, national cyberinfrastructures, and commercial and academic clouds. This paper describes the design, development and evolution of the Pegasus Workflow Management System, which maps abstract workflow descriptions onto distributed computing infrastructures. Pegasus has been used for more than twelve years by scientists in a wide variety of domains, including astronomy, seismology, bioinformatics, physics and others. This paper provides an integrated view of the Pegasus system, showing its capabilities that have been developed over time in response to application needs and to the evolution of the scientific computing platforms. The paper describes how Pegasus achieves reliable, scalable workflow execution across a wide variety of computing infrastructures. Comprehensive description of the Pegasus Workflow Management System.Detailed explanation of Pegasus workflow transformations.Data management in Pegasus.Earthquake science application example.
5724260
Failure prediction and localization in large scientific workflows
Scientific workflows provide a portable representation for scientific applications' coordinated input, output, and execution management for highly parallel executions of interdependent computations, as well as support for sharing and validating the results. As scientific workflows scale to hundreds of thousands of distinct tasks, failures due to software and hardware faults become increasingly common. Real-time execution monitoring provides a foundation for improving the transparency and resilience of the workflows in the face of stochastic and systematic faults. Building on previous work on early detection of these failure scenarios, we describe methods for guiding remediation to stochastic errors through predictions of the impact on application performance. To complement this analysis, we also describe techniques for isolating systematic sources of failures. We evaluate our methods on a representative sample of large real-world workflows.
36900919
Fault Detection, Prevention and Recovery in Current Grid Workflow Systems
The workflow paradigm is a highly successful paradigm for the creation of Grid applications. Despite the popularity of the workflow approach, the systems that support the 
execution of workflow applications in Grid environments are still not able to deliver the 
quality, robustness and reliability that their users require and demand. To understand the 
current state-of-the-art and the reasons behind the shortcomings, we sent out a detailed 
questionnaire to developers of many of the major Grid workflow systems. This paper shows the outcome of the questionnaire evaluation, reveals future directions and helps to guide research towards the identified open issues in adoption of fault tolerance techniques.
647687
A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids
Although Grids have been used extensively for executing applications with compute-intensive jobs, there exist several applications with a large number of lightweight jobs. The overall processing undertaking of these applications involves high overhead time and cost in terms of (i) job transmission to and from Grid resources and, (ii) job processing at the Grid resources. Therefore, there is a need for an efficient job grouping-based scheduling system to dynamically assemble the individual fine-grained jobs of an application into a group of jobs, and send these coarse-grained jobs to the Grid resources. This dynamic grouping should be done based on the processing requirements of each application, Grid resources' availability and their processing capability.In this paper, we present a scheduling strategy that performs dynamic job grouping activity at runtime and convey the detailed analysis by running simulations. In addition, job processing granularity size is introduced to facilitate the job grouping activity in determining the total amount of jobs that can be processed in a resource within a specified time.
9738979
An Adaptive And Parameterized Job Grouping Algorithm For Scheduling Grid Jobs
An unorganized deployment of grid applications with a large amount of fine-grain jobs would let the communication overhead dominate the overall processing time, resulting in a low computation-communication ratio. Grid's dynamic nature complicates the planning of the job scheduling activity for minimizing the application processing time. This paper presents a grid job scheduling algorithm, based on a parameterized job grouping strategy, which is adaptive to the runtime grid environment. Jobs are grouped based on the job processing requirements, resource policies, network conditions and user's QoS requirements. Simulations using the GridSim toolkit reveal that the algorithm reduces the overall application processing time significantly.
18851781
On-Line Task Granularity Adaptation for Dynamic Grid Applications
Deploying lightweight tasks on grid resources would let the communication overhead dominate the overall application processing time Our aim is to increase the resulting computation-communication ratio by adjusting the task granularity at the grid scheduler We propose an on-line scheduling algorithm which performs task grouping to support an unlimited number of user tasks, arriving at the scheduler at runtime The algorithm decides the task granularity based on the dynamic nature of a grid environment: task processing requirements; resource-network utilisation constraints; and users QoS requirements Simulation results reveal that our algorithm reduces the overall application processing time and communication overhead significantly while satisfying the runtime constraints set by the users and the resources.
13943656
SCHEDULING FRAMEWORK FOR BANDWIDTH-AWARE JOB GROUPING-BASED SCHEDULING IN GRID COMPUTING
In recent years, Grid computing has emerged as an evolution from the existing distributed computing systems for delivering information, resources and services to users. This new computational infrastructure offers a remarkable increase in the number of available computation capabilities that can be delivered to applications. Grid computing is increasingly being used for aggregating resources across different geographical places. Therefore, job scheduling in the Grid computing environment has posed new challenges which demand for better computing performance and well-improved utilization of resources. This paper investigates the use of bandwidth-awareness in a scheduling framework to enhance the performance of job scheduling. In this work, a framework is proposed to facilitate the scheduling of jobs using bandwidth-awareness and job grouping concept (3). The bandwidth-aware scheduling is a part of the primary principle used in the scheduling framework. The principle behind the bandwidth-aware scheduling is the scheduling priorities taking into consideration not only their computational capabilities but also the communication capabilities of the resources. The bandwidth-aware scheduling approach uses the network bottleneck bandwidth of resources to determine the priority of each resource. The job grouping approach is also used in the framework where the scheduler retrieves information of the resources' processing capability. Then, the scheduler selects the first resource and groups independent fine-grained jobs together based on the chosen resource's processing capability. These jobs are grouped in such a way to maximize the utilization of the resource. By grouping the fine-grained jobs, these jobs are represented in a coarse-grained form which will reduce the network latencies. The grouping process is repeated until all the jobs are in groups and every group is allocated with a resource. Next, these grouped jobs are sent to the corresponding resources and the results of the processing are sent back to the user after they have been computed at their respective resources. The GridSim toolkit has been used to test the framework in a simulated Grid environment. The toolkit, a Java-based discrete-event Grid simulation package, provides the modelling and simulation of heterogeneous time and space-
14175713
Grouping-Based Fine-Grained Job Scheduling in Grid Computing
Grid computing is the technology for building Internet-wide computing environment in which distributed and heterogeneous resources are integrated. However, in Grid environment, job scheduling is confronted with a great challenge. This paper focuses on lightweight jobs scheduling in Grid Computing. An Adaptive Fine-grained Job Scheduling (AFJS) algorithm is proposed. Compared with other fine-grained job scheduling algorithms based on grouping, AFJS can outperform them according to the experimental results.
17773509
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.
14691894
Task scheduling strategies for workflow-based applications in grids
Grid applications require allocating a large number of heterogeneous tasks to distributed resources. A good allocation is critical for efficient execution. However, many existing grid toolkits use matchmaking strategies that do not consider overall efficiency for the set of tasks to be run. We identify two families of resource allocation algorithms: task-based algorithms, that greedily allocate tasks to resources, and workflow-based algorithms, that search for an efficient allocation for the entire workflow. We compare the behavior of workflow-based algorithms and task-based algorithms, using simulations of workflows drawn from a real application and with varying ratios of computation cost to data transfer cost. We observe that workflow-based approaches have a potential to work better for data-intensive applications even when estimates about future tasks are inaccurate.
3013807
Scheduling of scientific workflows in the ASKALON grid environment
Scheduling is a key concern for the execution of performance-driven Grid applications. In this paper we comparatively examine different existing approaches for scheduling of scientific workflow applications in a Grid environment. We evaluate three algorithms namely genetic, HEFT, and simple "myopic" and compare incremental workflow partitioning against the full-graph scheduling strategy. We demonstrate experiments using real-world scientific applications covering both balanced (symmetric) and unbalanced (asymmetric) workflows. Our results demonstrate that full-graph scheduling with the HEFT algorithm performs best compared to the other strategies examined in this paper.
2150258
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
Grid schedulers require individual activity performance predictions to map workflow activities on different Grid sites. The effectiveness of the scheduling systems is hampered by inaccurate predictions due to the inability of existing predictors to effectively model the dynamic and heterogeneous nature of Grid resources, or the wide range of problem sizes and runtime arguments. To address this deficiency, we propose a hybrid Bayesian-neural network approach to dynamically model and predict the execution time of activities in real workflow applications. Bayesian network is used for a high-level representation of activities performance probability distribution against different factors affecting the performance. The important attributes are dynamically selected by the Bayesian network and fed into a radial basis function neural network to make further predictions. Our approach is generic to any type of scientific applications, and flexible to import expert knowledge to further improve accuracies. Experimental results for activities from three realworld workflow applications are presented to show effectivenessof our approach.
5322069
Efficient response time predictions by exploiting application and resource state similarities
In large-scale grids with many possible resources (clusters of computing elements) to run applications, it is useful that the resources can provide predictions of job response times so users or resource brokers can make better scheduling decisions. Two metrics need to be estimated for response time predictions: one is how long a job executes on the resource (application run time), the other is how long the job waits in the queue before starting (queue wait time). In this paper we propose an instance based learning technique to predict these two metrics by mining historical workloads. The novelty of our approach is to introduce policy attributes in representing and comparing resource states, which is defined as the pool of running and queued jobs on the resource at the time to make a prediction. The policy attributes reflect the local resource scheduling policies and they can be automatically discovered using a genetic search algorithm. The main advantages of this approach compared with scheduler simulation are two-folds: Firstly, it has a better performance to meet the real time requirement of Grid resource brokering; secondly, it is more general because the scheduling policies are learned from past observations. Our experimental results on the NIKHEF LCG production cluster show that acceptable prediction accuracy can be obtained, where the relative prediction errors for response times are between 0.35 and 0.70.
17277309
Toward fine-grained online task characteristics estimation in scientific workflows
Task characteristics estimations such as runtime, disk space, and memory consumption, are commonly used by scheduling algorithms and resource provisioning techniques to provide successful and efficient workflow executions. These methods assume that accurate estimations are available, but in production systems it is hard to compute such estimates with good accuracy. In this work, we first profile three real scientific workflows collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize workflow task needs based on these profiles. Our method estimates task runtime, disk space, and memory consumption based on the size of tasks input data. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets by using a clustering technique. Task behavior estimates are done based on the ratio parameter/input data size if they are correlated, or based on the mean value. However, task dependencies in scientific workflows lead to a chain of estimation errors. To correct such errors, we propose an online estimation process based on the MAPE-K loop where task executions are constantly monitored and estimates are updated accordingly. Experiment results show that our online estimation process yields much more accurate predictions than an offline approach, where all task needs are estimated at once.
9686280
Workflow overhead analysis and optimizations
The execution of scientific workflows often suffers from a variety of overheads in distributed environments. It is essential to identify the different overheads and to evaluate how optimization methods help reduce overheads and improve runtime performance. In this paper, we present an overhead analysis for a set of workflow runs on cloud and grid platforms. We present the overhead distributions and conclude that they satisfy an exponential or uniform distribution. We compare three methods to calculate the cumulative sum of the overheads based on how they overlap. In addition, we indicate how experimental parameters impact the overhead and thereby the overall workflow performance. We then show how popular optimization methods improve runtime performance by reducing some or all types of overheads.
15322730
ASKALON: A Development and Grid Computing Environment for Scientific Workflows
Most existing Grid application development environments provide the application developer with a nontransparent Grid. Commonly, application developers are explicitly involved in tedious tasks such as selecting software components deployed on specific sites, mapping applications onto the Grid, or selecting appropriate computers for their applications. Moreover, many programming interfaces are either implementation-technology-specific (e.g., based on Web services [24]) or force the application developer to program at a low-level middleware abstraction (e.g., start task, transfer data [22, 153]). While a variety of graphical workflow composition tools are currently being proposed, none of them is based on standard modeling techniques such as Unified Modeling Language (UML).
1375038
Taverna: a tool for the composition and enactment of bioinformatics workflows
MOTIVATION
In silico experiments in bioinformatics involve the co-ordinated use of computational tools and information repositories. A growing number of these resources are being made available with programmatic access in the form of Web services. Bioinformatics scientists will need to orchestrate these Web services in workflows as part of their analyses.


RESULTS
The Taverna project has developed a tool for the composition and enactment of bioinformatics workflows for the life sciences community. The tool includes a workbench application which provides a graphical user interface for the composition of workflows. These workflows are written in a new language called the simple conceptual unified flow language (Scufl), where by each step within a workflow represents one atomic task. Two examples are used to illustrate the ease by which in silico experiments can be represented as Scufl workflows using the workbench application.
2621380
Grid Harvest Service: a system for long-term, application-level task scheduling
With the emergence of Grid computing environment, performance measurement, analysis and prediction of non-dedicated distributed systems have become increasingly important. In this study, we put forward a novel performance model for non-dedicated network computing. Based on this model, a performance prediction and task scheduling system called Grid Harvest Service (GHS), has been designed and implemented. GHS consists of a performance measurement component, a prediction component and a scheduling component. Different scheduling algorithms are proposed for different situations. Experimental results show that the GHS system provides satisfactory solution for performance prediction and scheduling of large applications and that GHS has a real potential.
81315
The performance of bags-of-tasks in large-scale distributed systems
Ever more scientists are employing large-scale distributed systems such as grids for their computational work, instead of tightly coupled high-performance computing systems. However, while these distributed systems are more cost-effective, their heterogeneity in terms of hardware, software, and systems administration, and the lack of accurate resource information leads to inefficient scheduling. In addition, and in contrast to the workloads of tightly coupled high-performance computing systems, a large part of the workloads submitted to these distributed systems consists of large sets (bags) of sequential tasks. Therefore, a realistic performance analysis of scheduling bags-of-tasks in large-scale distributed systems is important. Towards this end, we introduce in this paper a realistic workload model for bags-of-tasks, and we explore through trace-based simulations the design space of scheduling bags-of-tasks. Finally, we identify three new scheduling policies that use only inaccurate information when scheduling, and we compare them against known classes of proposed scheduling policies.
119977630
A Review of Results on Sums of Random Variables
Sums of random variables arise naturally in wireless communications and related areas. Here, we provide a review of the known results on sums of exponential, gamma, lognormal, Rayleigh and Weibull random variables. A discussion is provided of two applications. We expect that this review could serve as a useful reference and help to advance further research in this area.
15899930
Balanced Task Clustering in Scientific Workflows
Scientific workflows can be composed of many fine computational granularity tasks. The runtime of these tasks may be shorter than the duration of system overheads, for example, when using multiple resources of a cloud infrastructure. Task clustering is a runtime optimization technique that merges multiple short tasks into a single job such that the scheduling overhead is reduced and the overall runtime performance is improved. However, existing task clustering strategies only provide a coarse-grained approach that relies on an over-simplified workflow model. In our work, we examine the reasons that cause Runtime Imbalance and Dependency Imbalance in task clustering. Next, we propose quantitative metrics to evaluate the severity of the two imbalance problems respectively. Furthermore, we propose a series of task balancing methods to address these imbalance problems. Finally, we analyze their relationship with the performance of these task balancing methods. A trace-based simulation shows our methods can significantly improve the runtime performance of two widely used workflows compared to the actual implementation of task clustering.
13651409
Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand
This paper describes the design of a grid-enabled version of Montage, an astronomical image mosaic service, suitable for large scale processing of the sky. All the re-projection jobs can be added to a pool of tasks and performed by as many processors as are available, exploiting the parallelization inherent in the Montage architecture. We show how we can describe the Montage application in terms of an abstract workflow so that a planning tool such as Pegasus can derive an executable workflow that can be run in the Grid environment. The execution of the workflow is performed by the workflow manager DAGMan and the associated Condor-G. The grid processing will support tiling of images to a manageable size when the input images can no longer be held in memory. Montage will ultimately run operationally on the Teragrid. We describe science applications of Montage, including its application to science product generation by Spitzer Legacy Program teams and large-scale, all-sky image processing projects.
17155663
CyberShake: A Physics-Based Seismic Hazard Model for Southern California
CyberShake, as part of the Southern California Earthquake Center’s (SCEC) Community Modeling Environment, is developing a methodology that explicitly incorporates deterministic source and wave propagation effects within seismic hazard calculations through the use of physics-based 3D ground motion simulations. To calculate a waveform-based seismic hazard estimate for a site of interest, we begin with Uniform California Earthquake Rupture Forecast, Version 2.0 (UCERF2.0) and identify all ruptures within 200 km of the site of interest. We convert the UCERF2.0 rupture definition into multiple rupture variations with differing hypocenter locations and slip distributions, resulting in about 415,000 rupture variations per site. Strain Green Tensors are calculated for the site of interest using the SCEC Community Velocity Model, Version 4 (CVM4), and then, using reciprocity, we calculate synthetic seismograms for each rupture variation. Peak intensity measures are then extracted from these synthetics and combined with the original rupture probabilities to produce probabilistic seismic hazard curves for the site. Being explicitly site-based, CyberShake directly samples the ground motion variability at that site over many earthquake cycles (i.e., rupture scenarios) and alleviates the need for the ergodic assumption that is implicitly included in traditional empirically based calculations. Thus far, we have simulated ruptures at over 200 sites in the Los Angeles region for ground shaking periods of 2 s and longer, providing the basis for the first generation CyberShake hazard maps. Our results indicate that the combination of rupture directivity and basin response effects can lead to an increase in the hazard level for some sites, relative to that given by a conventional Ground Motion Prediction Equation (GMPE). Additionally, and perhaps more importantly, we find that the physics-based hazard results are much more sensitive to the assumed magnitude-area relations and magnitude uncertainty estimates used in the definition of the ruptures than is found in the traditional GMPE approach. This reinforces the need for continued development of a better understanding of earthquake source characterization and the constitutive relations that govern the earthquake rupture process.
5611210
WorkflowSim: A toolkit for simulating scientific workflows in distributed environments
Simulation is one of the most popular evaluation methods in scientific workflow studies. However, existing workflow simulators fail to provide a framework that takes into consideration heterogeneous system overheads and failures. They also lack the support for widely used workflow optimization techniques such as task clustering. In this paper, we introduce WorkflowSim, which extends the existing CloudSim simulator by providing a higher layer of workflow management. We also indicate that to ignore system overheads and failures in simulating scientific workflows could cause significant inaccuracies in the predicted workflow runtime. To further validate its value in promoting other research work, we introduce two promising research areas for which WorkflowSim provides a unique and effective evaluation platform.
2174753
A broker-based framework for multi-cloud workflows
Computational science workflows have been successfully run on traditional HPC systems like clusters and Grids for many years. Today, users are interested to execute their workflow applications in the Cloud to exploit the economic and technical benefits of this new emerging technology. The deployment and management of workflows over the current existing heterogeneous and not yet interoperable Cloud providers, however, is still a challenging task for the workflow developers. In this paper, we present a broker-based framework for running workflows in a multi-Cloud environment. The framework allows an automatic selection of the target Clouds, a uniform access to the Clouds, and workflow data management with respect to user Service Level Agreement (SLA) requirements. Following a simulation approach, we evaluated the framework with a real scientific workflow application in different deployment scenarios. The results show that our framework offers benefits to users by executing workflows with the expected performance and service quality at lowest cost.
8571261
Characterizing and profiling scientific workflows
Researchers working on the planning, scheduling, and execution of scientific workflows need access to a wide variety of scientific workflows to evaluate the performance of their implementations. This paper provides a characterization of workflows from six diverse scientific applications, including astronomy, bioinformatics, earthquake science, and gravitational-wave physics. The characterization is based on novel workflow profiling tools that provide detailed information about the various computational tasks that are present in the workflow. This information includes I/O, memory and computational characteristics. Although the workflows are diverse, there is evidence that each workflow has a job type that consumes the most amount of runtime. The study also uncovered inefficiency in a workflow component implementation, where the component was re-reading the same data multiple times.
865090
Analysis and modeling of time-correlated failures in large-scale distributed systems
The analysis and modeling of the failures bound to occur in today's large-scale production systems is invaluable in providing the understanding needed to make these systems fault-tolerant yet efficient. Many previous studies have modeled failures without taking into account the time-varying behavior of failures, under the assumption that failures are identically, but independently distributed. However, the presence of time correlations between failures (such as peak periods with increased failure rate) refutes this assumption and can have a significant impact on the effectiveness of fault-tolerance mechanisms. For example, the performance of a proactive fault-tolerance mechanism is more effective if the failures are periodic or predictable; similarly, the performance of checkpointing, redundancy, and scheduling solutions depends on the frequency of failures. In this study we analyze and model the time-varying behavior of failures in large-scale distributed systems. Our study is based on nineteen failure traces obtained from (mostly) production large-scale distributed systems, including grids, P2P systems, DNS servers, web servers, and desktop grids. We first investigate the time correlation of failures, and find that many of the studied traces exhibit strong daily patterns and high autocorrelation. Then, we derive a model that focuses on the peak failure periods occurring in real large-scale distributed systems. Our model characterizes the duration of peaks, the peak inter-arrival time, the inter-arrival time of failures during the peaks, and the duration of failures during peaks; we determine for each the best-fitting probability distribution from a set of several candidate distributions, and present the parameters of the (best) fit. Last, we validate our model against the nineteen real failure traces, and find that the failures it characterizes are responsible on average for over 50% and up to 95% of the downtime of these systems.