Power Management in Grid Computing with Xen

International audienceWhile chip vendor still stick to Moore's law, and the performance per dollar keeps going up, the performance per watt has been stagnant for the last few years. Moreover energy prices continue to rise world-wide. This poses a major challenge to organisations running grids, indeed such architectures require cooling systems. Indeed the one-year cost of a cooling system and of the power consumption may outfit the grid initial investement. We observe , however, that a grid does not constantly run at peak performance. In this paper, we propose a workload concentration strategy to reduce grid power consumption. Using the Xen virtual machine migration technology, our power management policy can dispatch transparently and dynamically any applications of the grid. Our policy concentrates the workload to shutdown nodes that are unused with a neglectable impact on performance. We show through evaluations that this policy decreases the overall power consumption of the grid significantl

Secure Integration of Desktop Grids and Compute Clusters Based on Virtualization and Meta-Scheduling

Reducing the cost for business or scientific computations, is a commonly expressed goal in today’s companies. Using the available computers of local employees or the outsourcing of such computations are two obvious solutions to save money for additional hardware. Both possibilities exhibit security related disadvantages, since the deployed software and data can be copied or tampered if appropriate countermeasures are not taken. In this paper, an approach is presented to let a local desktop machines and remote cluster resources be securely combined into a singel Grid environment. Solutions to several problems in the areas of secure virtual networks, meta-scheduling and accessing cluster schedulers from desktop Grids are proposed

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications