A New Particle Swarm Optimization-Based Strategy for Cost-Effective Data Placement in Scientific Cloud Workflows

We consider the problem of optimizing the execution of data-intensive scientific workflows in the Cloud. We address the problem under the following scenario. The tasks of the workflows communicate through files; the output of a task is used by another task as an input file and if these tasks are assigned on different execution sites, a file transfer is necessary. The output files are to be stored at a site. Each execution site is to be assigned a certain percentage of the files and tasks. These percentages, called target weights, are pre-determined and reflect either user preferences or the storage capacity and computing power of the sites. The aim is to place the data files into and assign the tasks to the execution sites so as to reduce the cost associated with the file transfers, while complying with the target weights. To do this, we model the workflow as a hypergraph and with a hypergraph-partitioning-based formulation, we propose a heuristic which generates data placement and task assignment schemes simultaneously. We report simulation results on a number of real-life and synthetically generated scientific workflows. Our results show that the proposed heuristic is fast, and can find mappings and assignments which reduce file transfers, while respecting the target weights.

This paper provides a data placement optimization approach for Coarse-Grained Reconfigurable Architecture (CGRA) based computing platform in order to simultaneously optimize the performance of CGRA execution and data transformation between main memory and multi-bank memory. To achieve this goal, we have developed a performance model to evaluate the efficiency of data transformation and CGRA execution. This model is used for comparing the performances difference when using different data placement strategies. We search for the optimal data placement method by firstly choosing the method which generates the best CGRA execution efficiency from the candidates who can generate the optimal data transformation efficiency. Then we choose the best data placement strategy by comparing the performance of the selected strategy with the one generated through existing multi-bank optimization algorithm. Evaluation shows our approach is capable of optimizing the performance to 2.76x of state-of-the-art method when considering both data-transformation and CGRA execution efficiency.

Scientific workflows in collaborative cloud environments are becoming more and more popular.There is an urgent need to address the problem of large amount of data transfer across geo-distributed data centers during workflow execution.By utilizing data dependencies,we propose a two-stage data placement strategy and a task scheduling strategy for efficient workflow execution.With our strategy,the most related datasets can be placed into the same data center based on the data dependence between them at workflow build-time;then the tasks are scheduled to their most closely related data centers for execution and the newly-generated data sets are put into the data center that has the most dependency with them at workflow runtime.The experimental results show that the proposed strategy can significantly reduce the volume of data transfer among different data centers,and hence improve the performance of running scientific workflows and cut down the cost of doing science on the clouds as well.

The scale of scientific applications becomes increasingly large not only in computation, but also in data. Many of these applications also concern inter-related tasks with data dependencies, hence, they are scientific workflows. The efficient coordination of executing/running scientific workflows is of great practical importance. The core of such coordination is scheduling and resource allocation. In this paper, we present three scheduling heuristics for running large-scale, data-intensive scientific workflows in clouds. In particular, the three heuristic algorithms are designed to leverage slot queue threshold, data locality and data prefetching, respectively. We also demonstrate how these heuristics can be collectively used to tackle different issues in running "data-intensive" workflows in clouds although each of these heuristics can be used independently. The practicality of our algorithms has been realized by actually implementing and incorporating them into our workflow execution system (DEWE). Using Montage, an astronomical image mosaic engine, as an example workflow, and Amazon EC2 as the cloud environment, we evaluate the performance of our heuristics in terms primarily of completion time (make span). We also scrutinize workflow execution showing different execution phases to identify their impact on performance. Our algorithms scale well and reduce make span by up to 27%.

Cloud computing and big data: Technologies and applications

A new data-grouping-aware dynamic data placement method that take into account jobs execute frequency for Hadoop

Usually, scientists need to execute experiments that demand high performance computing environments and parallel techniques. This is the scenario found in many bioinformatics experiments modeled as scientific workflows, such as phylogenetic and phylogenomic analyses. To execute these experiments, scientists have adopted virtual machines (VMs) instantiated in clouds. Estimating the number of VMs to instantiate is a crucial task to avoid negative impacts on the execution performance and on the financial costs with under or overestimations. Previously, the necessary number of VMs to execute bioinformatics workflows have been estimated by a GRASP heuristic and have been coupled to a Cloud-based Parallel Scientific Workflow Management System. Although this work was a step forward, this approach only provided a static dimensioning. If the characteristics of the environment change (processing capacity, network speed), this static dimensioning may not be suitable. In this way, it is of interest that the dimensioning is adjusted at runtime. To achieve this, we developed a novel framework for monitoring and dynamically dimensioning resources during the execution of parallel scientific workflows in clouds, called Dynamic Dimensioning of Cloud Computing Framework (DDC-F). We have evaluated DDC-F in real executions of bioinformatics workflows. Experiments showed that DDC-F is able to efficiently calculate the number of VMs necessary to execute bioinformatics workflows of Comparative Genomics (CG), also reducing the financial costs, when compared with other works of the related literature.

Scientific workflows are one of the well-established pillars of modern large-scale computational science. More recently, scientists have started to leverage machine learning (ML) capabilities in their workflows, leading to a new category of scientific workflows, denoted as scientific ML workflows. ML is not only about training and inference, modern ML workflows also involve complex data processing steps before the training can start, which are not often accounted for in most performance studies. In this work, we consider scientific ML workflows, from data pre-processing to training, inference, and model evaluation. We aim to explore (i) how scientific ML workflows differ from more traditional scientific workflows and; (ii) how we can characterize ML workflows both in terms of execution time and data movements when executing on a target cloud platform. We select three representative workflows, ranging from image classification to natural language processing and image segmentation, which have been executed using the academic cloud platform, Chameleon. We build four realistic deployment scenarios for each workflow, which stress data movements during workflow executions. Then, we compare the performance observed when utilizing these different configurations and study how different settings impact overall workflows performance and efficiency when running on cloud infrastructures. Finally, we summarize our findings and discuss performance impacts when augmenting scientific workflows with ML techniques and how traditional workflow management systems can improve their support for such workflows.

I/O intensive jobs such as stage-in, stage-out or data clean-up jobs account for significant time in execution of scientific workflows. Workflow managers typically add these data management operations as supporting jobs to computational tasks with scheduling emphasis on compute jobs only. We present the integration of the Pegasus Workflow Management System with a Policy Based Data Placement Service (PDPS) to reduce overall workflow execution time. Pegasus delegates all data staging jobs to PDPS, which schedules and executes stage-in jobs based on selected data placement policies and simply executes stage-out and clean-up jobs independent of the workflow execution state. We measure the impact of using PDPS with Pegasus first with the Montage workflow, and then with a synthetic workflow. We enforce two policies and demonstrate the advantage of using PDPS for asynchronous data placement for scientific workflows. Our results show that the influence of PDPS on the overall workflow runtimes is dependent on the data characteristics of the executable workflow and the data placement policy being enforced.

In this paper, we address the scheduling of scientific workflows in hybrid clouds considering data placement and present the Hybrid Scheduling for Hybrid Clouds (HSHC) algorithm. HSHC is a two-phase scheduling algorithm with a genetic algorithm based static phase and dynamic programming based dynamic phase. We evaluate HSHC with both a real-world scientific workflow application and random workflows in terms of makespan and costs.

Executing scientific workflows in high-performance cloud computing platforms requires the use of scheduling algorithms that allow workflows execution as fast as possible, while minimizing the monetary cost of such executions. In this work we study how the use of supporting information can offer guidance to scheduling algorithms, helping them to devise more efficient execution plans in terms of the total execution time (makespan) and the total monetary cost. Using two large-scale scientific workflows, our experiments showed that simple modifications on a classical scheduling algorithm (HEFT), in conjunction with the appropriate supporting information, could reduce the monetary cost of an execution in up to 59% and reduce the makespan in up to 8.6%.

In this data era, massive volumes of data are being generated every second in variety of domains such as Geoscience, Social Web, Finance, e-Commerce, Health Care, Climate modelling, Physics, Astronomy, Government sectors etc. Hadoop has been well-recognized as de factobig data processing platform that have been extensively adopted, and is currently widely used, in many application domains processing Big Data. Even though it is considered as an efficient solution for such complex query processing, it has its own limitation when the data to be processed exhibit interest locality. The data required for any query execution follows grouping behavior wherein only a part of the Big-Data is accessed frequently. During such scenarion, the time taken to execute a queryand return results, increases exponentially as the amount of data increases leading to much waiting time for the user. Since Hadoop default data placement strategy (HDDPS) does not consider such grouping behavior, it does not perform efficiently resulting in lacunas such as decreased local map task execution, increased query execution time etc. Hence proposed an Optimal Data Placement Strategy (ODPS) based on grouping semantics. In this paper we experiment the significance oftwo most promising clustering techniques viz. Hierarchical Agglomerative Clustering (HAC) and Markov Clustering (MCL) in grouping aware data placement for data intensive applications having interest locality. Initially user access pattern is identified by dynamically analyzing history log.Then both clustering techniques (HAC & MCL) are separately applied over the access pattern to obtain independent clusters. These clusters are interpreted and validated to extract the Optimal Data Groupings (ODG). Finally proposed strategy reorganizes the default data layouts in HDFSbased on ODG to achieve maximum parallel execution per group subjective to Load Balancer and Rack Awareness. Our proposed strategy is tested in 10 node cluster placed in a multi rack with Hadoop installed in every node deployed in cloud platform. Proposed strategy reduces the query execution time, significantly improves the data locality and has proved to be more efficient for massive datasets processing in heterogeneous distributed environment. Also MCL shows a marginal improved performance over HAC for queries exhibiting interest localities.

Next-generation e-Science features large-scale, compute-intensive workflows of many computing modules that are typically executed in a distributed manner. With the recent emergence of cloud computing and the rapid deployment of cloud infrastructures, an increasing number of scientific workflows have been shifted or are in active transition to cloud environments. As cloud computing makes computing a utility, scientists across different application domains are facing the same challenge of reducing financial cost in addition to meeting the traditional goal of performance optimization. We develop a prototype generic workflow system by leveraging existing technologies for a quick evaluation of scientific workflow optimization strategies. We construct analytical models to quantify the network performance of scientific workflows using cloud-based computing resources, and formulate a task scheduling problem to minimize the workflow end-to-end delay under a user-specified financial constraint. We rigorously prove that the proposed problem is not only NP-complete but also non-approximable. We design a heuristic solution to this problem, and illustrate its performance superiority over existing methods through extensive simulations and real-life workflow experiments based on proof-of-concept implementation and deployment in a local cloud testbed.

Workflow technology has been adopted in scientific domains to orchestrate and automate scientific processes in order to facilitate experimentation. Such scientific workflows often involve large data sets and intensive computation that necessitate the use of the Grid, which offers supercomputing power through shared distributed resources. To execute a scientific workflow in the Grid, tasks within the workflow that represent steps in the scientific process are assigned to Grid resources for execution. To ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources. Although many Grid workflow scheduling techniques exist, they are mainly designed for the execution of a single workflow. This is not the case with parameter sweep workflows which focus on parametric study and parameter optimisation. A parameter sweep workflow is executed numerous times with different input parameters in order to determine the effect of each parameter combination on the experiment. While executing multiple instances of a parameter sweep workflow in parallel can reduce the time required for the overall execution, this execution introduces new challenges to Grid workflow scheduling. Not only is a scheduling algorithm that is able to manage multiple workflow instances required, but this algorithm also needs the ability to schedule tasks across multiple workflow instances judiciously, as tasks may require the same set of Grid resources. Without appropriate resource allocation, resource competition problem could arise. In the thesis, we propose a new Grid workflow scheduling technique for parameter sweep workflow called the Besom scheduling algorithm. The scheduling decision of our algorithm is based on the resource dependencies of tasks in the workflow, as well as conventional Grid resource-performance metrics. In addition, the proposed technique is extended to handle loop structures in scientific workflows without using existing loop-unrolling techniques. We evaluate the Besom algorithm under a variety of conditions. A comparison between the simulation results of the Besom algorithm and of the three existing Grid workflow scheduling algorithms shows that the Besom algorithm is able to perform better than the existing algorithms for workflows that have complex structures and that involve overlapping resource dependencies of tasks. The Besom scheduling algorithm advances the ability to schedule parallel execution of parameter sweep workflows in the Grid. The outcomes of this thesis justify continuing research in this area to increase our understanding of scheduling multiple Grid workflow instances and to provide support to those involved in parametric study and scientific workflow management.

Most data in the real world have spatial and temporal attributes, and some other essential data attributes also have temporal and spatial variability. In the research field of cloud and edge computing, these features always have great impact on data placement and task scheduling. However, these critical spatiotemporal features have been largely ignored by existing studies. To this end, this work firstly synthesizes data popularity, geographical location distribution and other spatiotemporal features to abstract the definition of data temperature, and a temperature calculation model is proposed to reflect the spatiotemporal correlation and variation trend. Then, we put forward a multi-cloud dynamic storage strategy considering data temperature to improve service quality and reflect the value of data temperature. Experiments are performed to evaluate the proposed strategy, which can effectively reduce the total cost and improve data availability.KeywordsMulti-cloudData temperatureData placementSpatiotemporal featuresMulti-objective optimization