Abstract Financial Monte Carlo simulations are computationally intensive applications that must meet tight deadlines in terms of job completion times. The completion time might have a huge impact on the financial profits made from decisions derived from the simulation results. Naturally, there is a huge interest in being able to simulate as fast as possible. While single simulations can be done on one machine, decisions often depend on portfolios of simulations. Distributing the workload among resources is crucial to achieve low latency. In this article we present a combination of a middleware with a high‐performance implementation of an Asian options evaluation code on the Cell Broadband Engine (CBE). We handle workload distribution with our PHASTGrid middleware and provide users with a web service interface to the whole infrastructure. The CBE is particularly suitable for Monte Carlo simulations. We implemented a well‐known algorithm on both the CBE and the Intel x86 multicore architectures. Both codes are integrated in our middleware, allowing a direct comparison of the performance and scalability. In addition to the Monte Carlo simulation, we also use different applications and compare our middleware with Globus. Copyright © 2009 John Wiley & Sons, Ltd.

Software license management allows independent software vendors (ISVs) to control the access of their products. It is a fundamental part of the ISVs' business strategy. A wide range of products has been developed in order to address license management. There are, however, only few ongoing works with regard to license management in grid and cloud computing environments. This paper presents our work on GenLM, a license management solution suitable for these environments. It has been built in order to provide a secure and robust solution for ISVs that want to extend their software usage to these systems. We provide ISVs a toolchain to implement arbitrary software licensing models. At the same time we ensure that licenses are mobile, i.e. they can be used on any resource the user has access to.

As the adoption of grid technology moves from science to industry, new requirements arise. In todays grid middlewares, the notion of paying for a job is a secondary requirement. In addition, the concept of selling computational power on a market is not established. On the other hand, the lack of billing capabilities hinders the commercial adoption. In this paper, we present our resource allocation protocol that suits the needs of commercial solution providers. We have developed an auction-based resource broker which uses a distributed agent infrastructure to communicate the user's requirements to resource providers and monetary prices back. The protocol has been formally verified and guarantees certain properties - for example, we can guarantee that the right stakeholder is billed for a job.

Grid resource allocation is a complex task usually solved by systems relying on a centralized information system. In order to create a lightweight scheduling system, we investigated the potential of auctions for resource allocation. Each resource provider runs an agent bidding on the execution of software with respect to local restrictions. This way, the information system becomes obsolete. In addition, each provider can implement different bidding strategies in order to reflect his preferences.