Environmental science is often fragmented: data is collected using mismatched formats and conventions, and models are misaligned and run in isolation. Cloud computing offers a lot of potential in the way of resolving such issues by supporting data from different sources and at various scales, by facilitating the integration of models to create more sophisticated software services, and by providing a sustainable source of suitable computational and storage resources. In this paper, we highlight some of our experiences in building the Environmental Virtual Observatory pilot (EVOp), a tailored cloud-based infrastructure and associated web-based tools designed to enable users from different backgrounds to access data concerning different environmental issues. We review our architecture design, the current deployment and prototypes. We also reflect on lessons learned. We believe that such experiences are of benefit to other scientific communities looking to assemble virtual observatories or similar virtual research environments.
Efforts for designing domain-driven solutions include the following two architectural proposals. @cite_9 presents a use case of similar architectural elements and hybrid infrastructure deployment, but does not use RESTful services. @cite_12 defines a generic high-level framework for assembling virtual research environments.
@cite_9 @cite_12
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Future many-core systems are envisaged to support the concurrent execution of varying mixes of different applications. Because of the vast number of binding options for such mixes on heterogeneous resources, enabling predictable application execution is far from trivial. Hybrid application mapping is an efficient way of achieving run-time predictability by combining design-time analysis of application mappings with run-time management. Existing hybrid mapping strategies focus on computation resources and either ignore communication details or make significantly simplifying assumptions like unlimited bandwidth or exclusive usage. But, actual many-core systems consist of constrained and shared computation and communication resources where the run-time decision of whether a feasible application binding on a set of preoccupied resources exists or not is an NP-complete problem. As a remedy, we present a novel hybrid application mapping approach that considers constrained shared communication and computation resources. Here, (a) a design space exploration coupled with a formal performance analysis delivers several resource reservation configurations with verified real-time guarantees for each individual application. The configurations are then transformed to (b) a novel efficient intermediate representation that is passed to the run-time management where we (c) formulate run-time resource reservation and application binding as a constraint satisfaction problem and present an adequate solving mechanism. Our experimental evaluation shows that existing approaches may produce infeasible outcomes and are thus not applicable for predictable application execution, while the proposed approach enables predictable and efficient run-time management of dynamic application mixes. Representational State Transfer (ReST) architecture provides a set of constraints that drive design decisions towards architectural properties such as interoperability, evolvability and scalability. Designing a ReSTful service API involves finding resources and their relationships, selecting uniform operations for each resource, and defining data formats for them. It is often a non-trivial exercise to refine a functional specification, expressed in terms of arbitrary actions, to a resource-oriented, descriptive state information content. We argue that this process can be described as a series of model transformations, starting from service functionality and gradually refining the phase products until a ReSTful service API is reached. This paper outlines the process phases, transformations and intermediate models based on our experiences in developing ReSTful services and service APIs at Nokia Research Center. The process captures our understanding on how to systematically transform functional specifications into ReSTful Web service interfaces. The proliferation of high-volume time-critical Web services such as online trading calls for a scalable server design that allows meeting individual response-time guarantees of real time transactions. A main challenge is to honor these guarantees despite unpredictability in incoming server load. The extremely high volume of real-time service requests mandates constant-time scheduling and schedulability analysis algorithms (as opposed to polynomial or logarithmic ones in the number of current requests). The paper makes two major contributions towards developing an architecture and theoretical foundations for scalable real-time servers operating in dynamic environments. First, we derive a tight utilization bound for schedulability of aperiodic tasks (requests) that allows implementing a constant time schedulability test on the server. We demonstrate that Liu and Layland's schedulable utilization bound of ln 2 does not apply to aperiodic tasks, and prove that an optimal arrival-time independent scheduling policy will meet all aperiodic task deadlines if utilization is maintained below 1 1+ spl radic (1 2). Second, we show that aperiodic deadline-monotonic scheduling is the optimal arrival-time-independent scheduling policy for aperiodic tasks. This result is used to optimally prioritize server requests. Evaluation of a utilization control loop that maintains server utilization below the bound shows that the approach is effective in meeting all individual deadlines in a high performance real-time server. Recovering images from undersampled linear measurements typically leads to an ill-posed linear inverse problem, that asks for proper statistical priors. Building effective priors is however challenged by the low train and test overhead dictated by real-time tasks; and the need for retrieving visually "plausible" and physically "feasible" images with minimal hallucination. To cope with these challenges, we design a cascaded network architecture that unrolls the proximal gradient iterations by permeating benefits from generative residual networks (ResNet) to modeling the proximal operator. A mixture of pixel-wise and perceptual costs is then deployed to train proximals. The overall architecture resembles back-and-forth projection onto the intersection of feasible and plausible images. Extensive computational experiments are examined for a global task of reconstructing MR images of pediatric patients, and a more local task of superresolving CelebA faces, that are insightful to design efficient architectures. Our observations indicate that for MRI reconstruction, a recurrent ResNet with a single residual block effectively learns the proximal. This simple architecture appears to significantly outperform the alternative deep ResNet architecture by 2dB SNR, and the conventional compressed-sensing MRI by 4dB SNR with 100x faster inference. For image superresolution, our preliminary results indicate that modeling the denoising proximal demands deep ResNets.