Adaptive scheduling solution for grid meta-brokering

The nearly optimal, interoperable utilization of various grid resources play an important role in the world of grids. Though well-designed, evaluated and widely used resource brokers have been developed, these existing solutions still cannot cope with the high uncertainty ruling current grid systems. To ease the simultaneous utilization of different middleware systems, researchers need to revise current solutions. In this paper we propose advanced scheduling techniques with a weighted fitness function for an adaptive Meta-Brokering Grid Service, which enables a higher level utilization of the existing grid brokers. We also set up a grid simulation environment to demonstrate the efficiency of the proposed meta-level scheduling solution. The presented evaluation results show that the proposed novel scheduling technique in the meta-brokering context delivers better performance

Architectural Patterns for the Semantic Grid

The Semantic Grid reference architecture, S-OGSA, includes semantic provisioning services that are able to produce semantic annotations of Grid resources, and semantically aware Gridservices that are able to exploit those annotations in various ways. In this paper we describe the dynamic aspects of S-OGSA by presenting the typical patterns of interaction among these services. A use case for a Grid meta-scheduling service is used to illustrate how the patterns are applied in practice

Meta-scheduling Issues in Interoperable HPCs, Grids and Clouds

Over the last years, interoperability among resources has been emerged as one of the most challenging research topics. However, the commonality of the complexity of the architectures (e.g., heterogeneity) and the targets that each computational paradigm including HPC, grids and clouds aims to achieve (e.g., flexibility) remain the same. This is to efficiently orchestrate resources in a distributed computing fashion by bridging the gap among local and remote participants. Initially, this is closely related with the scheduling concept which is one of the most important issues for designing a cooperative resource management system, especially in large scale settings such as in grids and clouds. Within this context, meta-scheduling offers additional functionalities in the area of interoperable resource management, this is because of its great agility to handle sudden variations and dynamic situations in user demands. Accordingly, the case of inter-infrastructures, including InterCloud, entitle that the decentralised meta-scheduling scheme overcome issues like consolidated administration management, bottleneck and local information exposition. In this work, we detail the fundamental issues for developing an effective interoperable meta-scheduler for e-infrastructures in general and InterCloud in particular. Finally, we describe a simulation and experimental configuration based on real grid workload traces to demonstrate the interoperable setting as well as provide experimental results as part of a strategic plan for integrating future meta-schedulers

Supporting simulation in industry through the application of grid computing

An increased need for collaborative research, together with continuing advances in communication technology and computer hardware, has facilitated the development of distributed systems that can provide users access to geographically dispersed computing resources that are administered in multiple computer domains. The term grid computing, or grids, is popularly used to refer to such distributed systems. Simulation is characterized by the need to run multiple sets of computationally intensive experiments. Large scale scientific simulations have traditionally been the primary benefactor of grid computing. The application of this technology to simulation in industry has, however, been negligible. This research investigates how grid technology can be effectively exploited by users to model simulations in industry. It introduces our desktop grid, WinGrid, and presents a case study conducted at a leading European investment bank. Results indicate that grid computing does indeed hold promise for simulation in industry

Performance evaluation of interoperable micro-clouds

The Internet of Things (IoT) is defined as a paradigm transforming physical objects to smart objects that are inter- connected via Internet. Today, IoT objects offer embedded intelligence that can be powerful in case of fully integration of a collective manner towards the satisfaction of user needs. This work is based on the micro-clouds that are a new proposing paradigm to highlight the collective intelligence of IoT objects. Specifically, a micro-cloud could be seen as a pool of cooperated devices and their resources that form transient smart environments. Further to this, we anticipate that the inter-cloud model can expand the micro-cloud capabilities by allowing multiple micro-clouds to communicate in order to achieve a common aim. This will further push the boundaries for studying the interaction and synergetic collaborative nature between micro-cloud systems in terms of their interoperability and performance. However as the size of the system is increased the complexity of performance is additionally increased. This emphasizes the need for decentralization where resources are changing over time without any notice. The vision of this work is that micro-clouds shall be linked together to enable a full network of usable IoT objects and at the same time maintain the required quality of service from an end-user's perspective. Specifically, the aim is to identify the specific criteria which are the most relevant to optimize performance when several micro-clouds collaborate (e.g. load-balancing, throughput, turn-around times, utilization level, etc.) as well as classify their functional requirements. So the focus is on the performance analysis and evaluation of results based on a simulated specific use case scenario