Investigating Autonomic Runtime Management Strategies for SAMR Applications

Chandra, Sumir; Parashar, Manish; Yang, Jung‐Seok; Zhang, Yeliang; Hariri, Salim

doi:10.1007/s10766-005-3589-z

Cited by 9 publications

(4 citation statements)

References 3 publications

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“…We simulated two different speed node groups (which may represent two sites). On the 16-node machine, we tested the node groups either both having eight nodes , or one having four and the other 12 nodes (4)(5)(6)(7)(8)(9)(10)(11)(12). In both cases, the first node group (eight or four nodes) was simulated as being slower by coscheduling a second application.…”

Section: Adaptation On Heterogeneous Resourcesmentioning

confidence: 99%

“…The hierarchical load balancing in [51] employs cost factors for heterogeneous nodes and network speed, while focusing on network cost. Closest to our workload estimation is the approach in [9] which autonomically adapts under time sharing, while considering dynamically varying resource availability (CPU and memory). The approach is applied to dynamic mesh refinement applications and can adapt to either application-caused imbalance or varying resource availability.…”

Section: Related Workmentioning

confidence: 99%

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Time and space adaptation for computational grids with the ATOP-Grid middleware

Sodan

Gupta

Han

et al. 2008

Future Generation Computer Systems

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Section: Adaptation On Heterogeneous Resourcesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Time and space adaptation for computational grids with the ATOP-Grid middleware

Sodan

Gupta

Han

et al. 2008

Future Generation Computer Systems

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“…Autonomic components in Accord export sensors and actuators for external monitoring, control and adaptation. -The Autonomic Runtime Environment [20] provides policies and mechanisms for both "system sensitive" and "application sensitive" runtime adaptations to manage the heterogeneity and dynamism of the applications as well as Grid environments. -The Content-based Grid Middleware Substrate [17] that supports autonomic application behaviors and interactions, and to enable simulation components, sensors/actuators, data archives and Grid resources and services to seamlessly interact as peers.…”

Section: Autonomic Grid Middleware Substratementioning

confidence: 99%

Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

Parashar

Matossian

Klíe

et al. 2006

Computational Science – ICCS 2006

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Previous work in the Instrumented Oil-Field DDDAS project has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and oil reservoir management. This work has led to the implementation of advanced multiphysics, multi-scale, and multi-block numerical models and an autonomic software stack for DDDAS applications. The stack implements a Gridbased adaptive execution engine, distributed data management services for real-time data access, exploration, and coupling, and self-managing middleware services for seamless discovery and composition of components, services, and data on the Grid. This paper investigates how these solutions can be leveraged and applied to address another DDDAS application of strategic importance -the data-driven management of Ruby Gulch Waste Repository.

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“…Autonomic components in Accord export sensors and actuators for external monitoring, control and adaptation. • The Autonomic Runtime Environment [30] provides policies and mechanisms for both "system sensitive" and "application sensitive" runtime adaptations to manage the heterogeneity and dynamism of the applications as well as Grid environments. The former are driven by the current system state and system performance predictions while the latter are based on the current state of application.…”

mentioning

confidence: 99%

Dynamic Decision and Data-Driven Strategies for the Optimal Management of Subsurface Geo-Systems

Parashar

Klíe

Kurç

et al. 2011

Journal of Algorithms & Computational Technology

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Effective geo-system management involves understanding of the interplay between surface entities (e.g., locations of injection and production wells in an oil reservoir) and appropriately effecting subsurface characteristics. This in turn requires efficient integration of complex numerical models of the environment, optimization procedures, and decision making processes. The dynamic, data-driven application systems (DDDAS) paradigm offers a promising framework to address this requirement. To achieve this goal, we have developed advanced multi-physics, multiscale, and multi-block numerical models and autonomic systems software for dynamic, data-driven applications systems. This work has enabled a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and management. These strategies have been applied to different aspects of subsurface management in strategically important application areas, including simulation-based optimization for the optimal oil well placement and the data-driven management of the Ruby Gulch Waste Repository. This paper summarizes the key outcomes and achievements of our work, as well as reports ongoing and future activities focused on uncertainty estimation and characterization. INTRODUCTIONThe dynamic, data driven application systems (DDDAS) paradigm is enabling a new generation of end-to-end multidisciplinary applications that are based on seamless aggregation of and interactions between computations, resources, and data. An important class of applications in this paradigm includes simulations of complex physical phenomena that symbiotically and opportunistically combine computations, experiments, observations, and real-time data to provide important insights into complex systems.As part of the "Instrumented Oil-Field" project [1-8], we have developed several key DDDAS technologies to enable a new generation of data-driven, interactive and dynamically adaptive strategies for subsurface characterization and reservoir management. This project aimed at completing the symbiotic feedback loop between measured data and the computational models to provide more efficient, cost-effective and environmentally safer production of oil reservoirs, which can result in enormous strategic and economic benefits. The project has led to conceptual and infrastructure solutions, which include advanced multi-physics, multi-scale and multi-block numerical models as well as a DDDAS software stack. The software stack provides a middleware for autonomic DDDAS applications and consists of a Grid-based execution engine that supports self-optimizing, dynamically adaptive applications, distributed data management services for large scale data management and processing, and self-managing middleware services for seamless discovery, access, interactions and compositions of services and data on the Grid.In this paper, we summarize these computational techniques and infrastructure components for the dynamic data-driven management and optimization of subsurface geo-systems...

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Investigating Autonomic Runtime Management Strategies for SAMR Applications

Cited by 9 publications

References 3 publications

Time and space adaptation for computational grids with the ATOP-Grid middleware

Time and space adaptation for computational grids with the ATOP-Grid middleware

Towards Dynamic Data-Driven Management of the Ruby Gulch Waste Repository

Dynamic Decision and Data-Driven Strategies for the Optimal Management of Subsurface Geo-Systems

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