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Mastering Hyper-V 2012 R2 with System Center and Windows Azure
This book will help you understand the capabilities of Microsoft Hyper-V, architect a Hyper-V solution for your datacenter, plan a deployment/migration, and then manage it all using native tools and System Center.
eBook
Introducing Microsoft Flow : automating workflows between apps and services
\"Use Microsoft Flow in your business to improve productivity through automation with this step-by-step introductory text ... You'll see the prerequisites to get started with this cloud-based service, including how to create a flow and how to use different connectors. [It] takes you through connecting with SharePoint, creating approval flows, and using mobile apps. ... The second half of the book continues with managing connections and gateways, where you'll cover the configuration, creation,, and deletion of connectors and how to connect to a data gateway. The final topic is Flow administration and techniques to manage the environment.\"--Back cover.
Book
A Survey of Data-Intensive Scientific Workflow Management
Nowadays, more and more computer-based scientific experiments need to handle massive amounts of data. Their data processing consists of multiple computational steps and dependencies within them. A
data-intensive scientific workflow
is useful for modeling such process. Since the sequential execution of data-intensive scientific workflows may take much time,
Scientific Workflow Management Systems
(
SWfMSs
) should enable the parallel execution of data-intensive scientific workflows and exploit the resources distributed in different infrastructures such as grid and cloud. This paper provides a survey of data-intensive scientific workflow management in SWfMSs and their parallelization techniques. Based on a SWfMS functional architecture, we give a comparative analysis of the existing solutions. Finally, we identify research issues for improving the execution of data-intensive scientific workflows in a multisite cloud.
Journal Article
Executing cyclic scientific workflows in the cloud
We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.
Journal Article
Combining Cloud-based Workflow Management System with SOA and CEP to Create Agility in Collaborative Environment
In current economy, technological solutions like cloud computing, service-oriented architecture (SOA) and complex event processing (CEP) are recognized as modern approaches used for increasing the business agility and achieving innovation. The complexity of collaborative business environment raises more and more the need for performant workflow management systems (WfMS) that meet current requirements. Each approach has advantages, but also faces challenges. In this paper we propose a solution for integration of cloud computing with WfMS, SOA and CEP that allows these technologies to complete each other and bank on their benefits to increase agility and reduce the challenges/problems. The paper presents a short introduction in the subject, followed by an analysis of the combination between cloud computing and WfMS and the benefits of cloud based workflow management system. The paper ends with a solution for combining cloud WfMS with SOA and CEP in order to gain business agility and real time collaboration, followed by conclusions and research directions.
Journal Article
Adaptable decentralized workflow execution with fuzzy framework in cloud computing (ADWEF.Cloud)
Centralized workflow execution engines exhibit several common weaknesses, including bottlenecks, single points of failure, poor performance, unreliability, and limited scalability. Decentralized workflow execution engines have been introduced to address these issues. Moreover, cloud computing has been embraced to accommodate the growing requests and the escalating demand for additional resources. Consequently, the provision of distributed workflow engines as a service in cloud computing can effectively meet these requirements. Despite the continuous changes occurring in cloud computing runtime, workflows must remain adaptable to environmental fluctuations and be continuously configured based on the dynamics of the runtime environment. Consequently, researching the adaptability of decentralized workflow engines in cloud computing is paramount. Dynamic and adaptable fragmentation of workflows represents one of the methods to enhance the adaptability of the workflow management system. This research delves into two aspects of runtime workflow fragmentation concerning adaptability with the runtime of cloud computing: First, the adaptability of the number of created fragments to the number of virtual machines (referred to as fragment-proportionality). Secondly, the adaptability of the number of generated fragments is based on the current conditions of the communicative media (referred to as available-bandwidth). A fuzzy algorithm has also been proposed to select appropriate fragments, considering both adaptability aspects. An analysis of test results from a reference workflow shows that our method significantly boosts throughput, response time, and message exchange volumes compared to fully decentralized configurations. Each adaptability aspect individually enhances baseline performance. The Fuzzy algorithm applies both adaptability aspects. With variable bandwidth and constant virtual machine numbers, the algorithm resulted in response time and throughput improvements of [9.16–97.43%] and [4.9–306.53%]. It also led to response time and throughput enhancements of [27.27–84.26%] and [67.61–79.74%] with constant bandwidth with variable virtual machine numbers.
Journal Article
Serverless Geospatial Data Processing Workflow System Design
Geospatial data and related technologies have become an increasingly important aspect of data analysis processes, with their prominent role in most of them. Serverless paradigm have become the most popular and frequently used technology within cloud computing. This paper reviews the serverless paradigm and examines how it could be leveraged for geospatial data processes by using open standards in the geospatial community. We propose a system design and architecture to handle complex geospatial data processing jobs with minimum human intervention and resource consumption using serverless technologies. In order to define and execute workflows in the system, we also propose new models for both workflow and task definitions models. Moreover, the proposed system has new Open Geospatial Consortium (OGC) Application Programming Interface (API) Processes specification-based web services to provide interoperability with other geospatial applications with the anticipation that it will be more commonly used in the future. We implemented the proposed system on one of the public cloud providers as a proof of concept and evaluated it with sample geospatial workflows and cloud architecture best practices.
Journal Article
Impact of concurrency on the performance of a whole exome sequencing pipeline
Background
Current high-throughput technologies—i.e. whole genome sequencing, RNA-Seq, ChIP-Seq, etc.—generate huge amounts of data and their usage gets more widespread with each passing year. Complex analysis pipelines involving several computationally-intensive steps have to be applied on an increasing number of samples. Workflow management systems allow parallelization and a more efficient usage of computational power. Nevertheless, this mostly happens by assigning the available cores to a single or few samples’ pipeline at a time. We refer to this approach as
naive parallel
strategy (NPS). Here, we discuss an alternative approach, which we refer to as
concurrent
execution strategy (CES), which equally distributes the available processors across every sample’s pipeline.
Results
Theoretically, we show that the CES results, under loose conditions, in a substantial speedup, with an ideal gain range spanning from 1 to the number of samples. Also, we observe that the CES yields even faster executions since parallelly computable tasks scale sub-linearly. Practically, we tested both strategies on a whole exome sequencing pipeline applied to three publicly available matched tumour-normal sample pairs of gastrointestinal stromal tumour. The CES achieved speedups in latency up to 2–2.4 compared to the NPS.
Conclusions
Our results hint that if resources distribution is further tailored to fit specific situations, an even greater gain in performance of multiple samples pipelines execution could be achieved. For this to be feasible, a benchmarking of the tools included in the pipeline would be necessary. It is our opinion these benchmarks should be consistently performed by the tools’ developers. Finally, these results suggest that concurrent strategies might also lead to energy and cost savings by making feasible the usage of low power machine clusters.
Journal Article