相关论文: Novel Runtime Systems Support for Adaptive Composi…

Scientific codes are increasingly being used in compositional settings, especially problem solving environments (PSEs). Typical compositional modeling frameworks require significant buy-in, in the form of commitment to a particular style of…

This research proposes a language independent intra-process framework for object based composition of unmodified code modules. Intuitively, the two major programming models, threads and processes, can be considered as extremes along a…

The ability to harness heterogeneous, dynamically available "Grid" resources is attractive to typically resource-starved computational scientists and engineers, as in principle it can increase, by significant factors, the number of cycles…

Context: Modern Systems of Systems (SoSs) increasingly operate in dynamic environments (e.g., smart cities, autonomous vehicles) where runtime composition -- the on-the-fly discovery, integration, and coordination of constituent systems…

Adaptive Computing is an application-agnostic outer loop framework to strategically deploy simulations and experiments to guide decision making for scale-up analysis. Resources are allocated over successive batches, which makes the…

Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related…

Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large…

Influenced by the advances in data and computing, the scientific practice increasingly involves machine learning and artificial intelligence driven methods which requires specialized capabilities at the system-, science- and service-level…

Evolving smart grids require flexible and adaptive control methods. A harmonized hybrid cyber-physical framework, which considers both physical and cyber layers and ensures adaptability, is one of the critical challenges to enable…

Mission critical software is often required to comply with multiple regulations, standards or policies. Recent paradigms, such as cloud computing, also require software to operate in heterogeneous, highly distributed, and changing…

Several embedded application domains for reconfigurable systems tend to combine frequent changes with high performance demands of their workloads such as image processing, wearable computing and network processors. Time multiplexing of…

The increasing complexity of IoT applications and the continuous growth in data generated by connected devices have led to significant challenges in managing resources and meeting performance requirements in computing continuum…

The high computational demands of multiscale modeling necessitate advanced parallel and adaptive strategies. To address this challenge, we introduce an adaptive method that utilizes two microscale models based on an offline database for…

Existing generative models, such as diffusion and auto-regressive networks, are inherently static, relying on a fixed set of pretrained parameters to handle all inputs. In contrast, humans flexibly adapt their internal generative…

Grids enable the aggregation, virtualization and sharing of massive heterogeneous and geographically dispersed resources, using files, applications and storage devices, to solve computation and data intensive problems, across institutions…

Self-adaptivity allows software systems to autonomously adjust their behavior during run-time to reduce the cost complexities caused by manual maintenance. In this paper, a framework for building an external adaptation engine for…

This paper presents the Real-time Adaptive and Interpretable Detection (RAID) algorithm. The novel approach addresses the limitations of state-of-the-art anomaly detection methods for multivariate dynamic processes, which are restricted to…

Self-adaptive systems (SASs) are capable of adjusting its behavior in response to meaningful changes in the operational con-text and itself. The adaptation needs to be performed automatically through self-managed reactions and…

The continuous increase in performance requirements, for both scientific computation and industry, motivates the need of a powerful computing infrastructure. The Grid appeared as a solution for inexpensive execution of heavy applications in…

In a heterogeneous, dynamic environment, like Grid, post-mortem analysis is of no use and data needs to be collected and analysed in real time. Novel techniques are also required for dynamically tuning the application's performance and…