Issues and scenarios for self-managing grid middleware

Abstract

Despite significant efforts to achieve reliable grid middlewares, grid infrastructures still encounter important difficulties to implement the promise of ubiquitous, seamless and transparent computing. Identified causes are numerous, such as the complexity of middleware stacks, dependence to many distributed resources, heterogeneity of hardware and software operated or incompatibilities between software components declared as interoperable. Based on failures that occurred during a large data challenge run on a grid dedicated to neuroscience, we identify scenarios that can be handled through autonomic management associated to the grid middleware. We also outline a flexible self-adaptive framework that aims at using model-driven development to facilitate the engineering, integration and reuse of MAPE-K loops in large scale distributed systems.

Model Driven Engineering for Science Gateways

“…Application of the presented technique is being foreseen in the area of self-adaptive systems, in particular on how computational applications can benefit from autonomic computing concepts and where (g)MDE can be used to impact on running architectures to reconfigure by themselves. In [32], self-adaptive capabilities were introduced in the Grid middleware itself, regardless of executed applications, in order to make it self-reconfigurable to QoS failure scenarios.…”

Self-Healing of Operational Workflow Incidents on Distributed Computing Infrastructures

“…It has been used to address various problems related to self-healing, self-configuration, self-optimization, and self-protection of distributed systems. For instance, provisioning of virtual machines is studied by Nguyen et al [28] and an approach to tackle service overload, queue starvation, "black hole" effect and job failures is sketched by Collet et al [9].…”

AGST - AUTONOMIC GRID SIMULATION TOOL - A Simulator of Autonomic Functions based on the MAPE-K Model

“…Recent research efforts seek to apply autonomic computing techniques to grid computing, providing more autonomy and reducing the need for human intervention in the maintenance and management of these computing environments, thus creating the concept an autonomic grid. Some of the investigated subjects include the development of autonomic mechanisms for self-protection, such as detecting overloads that could potentially lead to disruption of services; self-optimization, through parameter settings and algorithms to detect performance degradations; self-healing, to overcome possible partial system failures; and self-configuration, by providing configured virtual machines on demand and their dynamic allocation to physical resources (Germain-Renaud and Rana, 2009) (Jha et al, 2009) (Collet et al, 2010) (Abraham et al, 2010).…”