Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows , and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: (1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; (2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources ; (3) an event-driven execution model for automating execution of flows in response to arbitrary events; and (4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources . These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.

Provenance systems automate record keeping so that humans and/or machines can determine how a given result was obtained. In so doing, they enable a variety of reproducibility and reconstruction capabilities, while tracking the impact of older artifacts on newer ones. Large-scale scientific experiments are increasingly relying on workflows and other automation techniques to keep up with data-rates and perform on-line computation, notably training of machine learning models, and to provide rapid feedback to experimentalists. However, these workflows pose the challenges of: 1) adapting to errors in the experimental process both at the experiment site as well as in computation and 2) complex data provenance patterns that can result from the use machine learning and other methods that can arise from a feedback pattern in which initial experimental results drive the creation of new experimental parameters. The Braid Provenance Engine (Braid-DB) addresses this domain by integrating with workflow systems used in large-scale science and providing the additional capability to drive additional workflows or other automation in response to errors or other causes for elements of the workflow to be considered invalid. In this paper, we describe how Braid-DB responds to data marked as invalid, a common case in experimental science, and demonstrate its ability to retain artifacts unaffected by the invalid data.

Broad access to the data on which scientific results are based is essential for verification, reproducibility, and extension. Scholarly publication has long been the means to this end. But as data volumes grow, new methods beyond traditional publications are needed for communicating, discovering, and accessing scientific data. We describe data publication capabilities within the Globus research data management service, which supports publication of large datasets, with customizable policies for different institutions and researchers, the ability to publish data directly from both locally owned storage and cloud storage, extensible metadata that can be customized to describe specific attributes of different research domains, flexible publication and curation workflows that can be easily tailored to meet institutional requirements, and public and restricted collections that give complete control over who may access published data. We describe the architecture and implementation of these new capabilities and review early results from pilot projects involving nine research communities that span a range of data sizes, data types, disciplines, and publication policies.

Hadoop and its variants have been widely used for processing large scale analytics tasks in a cluster environment. However, use of a commodity cluster for analytics tasks needs to be reconsidered based on two key observations: (1) in recent years, large memory, multicore machines have become more affordable; and (2) recent studies show that most analytics tasks in practice are smaller than 100 GB. Thus, replacing a commodity cluster with a large memory, multicore machine can enable in-memory analytics at an affordable cost. However= programming on a big-memory, multicore machine is a challenge. Multi-threaded programming is notoriously difficult. Further, the memory design of most large memory servers follows non-uniform memory access (NUMA) architecture. While NUMA-aware programming often leads to high efficiency in analytics tasks, it is usually done in an ad hoc manner.

A major advantage of Service-Oriented Architectures (SOA) is composition and coordination of loosely coupled services. Because the development lifecycles of services and clients are decoupled, multiple service versions have to be maintained to continue supporting older clients. Typically versions are managed within the SOA by updating service descriptions using conventions on version numbers and namespaces. In all cases, the compatibility among services description must be evaluated, which can be hard, error-prone and costly if performed manually, particularly for complex descriptions. In this paper, we describe a method to automatically determine when two service descriptions are backward compatible. We then describe a case study to illustrate how we leveraged version compatibility information in a SOA environment and present initial performance overheads of doing so. By automatically exploring compatibility information, a) service developers can assess the impact of proposed changes; b) proper versioning requirements can be put in client implementations guaranteeing that incompatibilities will not occur during run-time; and c) messages exchanged in the SOA can be validated to ensure that only expected messages or compatible ones are exchanged.

Shared services, hosted in utility computing environments are becoming quite common in enterprises. These services could be clusters of application servers hosting various applications or even clusters of databases catering to multiple organizations within an enterprise. Automating the provisioning of these shared services is a difficult problem. We propose a model-based automation mechanism to provision such shared services. We demonstrate our model-based provisioning system in the case of a shared desktop service. The shared desktop service termed the "virtual desktop system" (VDS) hosts desktops on top of virtual machines. These desktop virtual machines are provisioned on consolidated data centers while users use thin clients to connect to their "virtual" desktops. We demonstrate how we use our model-based automation technique to simplify the provisioning of such desktops.

Utility computing environments provide on-demand IT services to customers. Such environments are dynamic in nature and continuously adapt to changes in requirements and system state. Errors are an important category of environment state changes as such environments consist of a large number of components, and hence, are subject to errors. In this paper, we design and implement a model-based simulation framework that leverages information about existing service components and their interactions, and provides concrete service behavior in presence of a variety of errors. To evaluate the framework, experiments are conducted on a virtualized blade-server based environment. Results show that the framework is effective and practical in analyzing error impacts on IT services.

Utility computing is envisioned as the future of enterprise IT environments. Achieving utility computing is a daunting task, because enterprise users have diverse and complex needs. In this paper we describe quartermaster, an integrated set of tools that addresses some of these needs. Quartermaster supports the entire lifecycle of computing tasks - including design, deployment, operation, and decommissioning of each task. Although individual components of this lifecycle have been addressed in earlier work, quartermaster integrates them in a unified framework using model-based automation. All tools within quartermaster are integrated using models based on the common information model (CIM), an industry-standard model from the distributed management task force (DMTF). The paper discusses the quartermaster implementation, and describes two case studies using quartermaster.

To manage distributed applications, we need to accurately monitor end-user service levels. There are two key challenges in monitoring end-user service levels: expressiveness and scalability. There is a big semantic gap between the metrics that administrators want to monitor and the metrics offered by commercial measurement systems. Moreover, it is hard to apply the same kind of metrics to different applications because different applications are likely to offer different types of instrumentation points. We want to apply the same metrics to very large numbers of instrumentation points, which makes scalability a key issue. In this work we present the activity monitoring language (AML) for declaratively specifying metrics, and a run-time system, called Solomon (service level objective monitor), that implements the concepts in AML. Expressiveness is a result of AML, which allows the high-level specification of user-defined metrics in an application-neutral way. Solomon's scalability is a result of reducing events and measurements as close to their physical source as is possible without the loss of accuracy.