In recent years, blockchains have been exploited in areas way beyond finance, enabling numerous innovative usage scenarios and applications. However, the extension of the existing systems and applications in order to support data persistence on a blockchain is time-consuming. Therefore, this paper proposes a model-driven based approach leveraging smart contracts with the goal to automate data persistence on blockchains. The approach is evaluated in data analytics use cases. According to our results, the proposed approach fully automates the data import and export processes without negatively affecting the predictive power of the models trained using data coming from the blockchain.

Advances in technology and economical pressure have forced many organizations to consider the migration of their legacy systems to newer platforms.

Desktop Grids are rapidly gaining popularity as a cost-effective computing platform for the execution of applications with extensive computing needs. As opposed to grids and clusters, these systems are characterized by having a non-dedicated infrastructure. These unique characteristics need to be considered in developing resource management strategies for Desktop Grids. Several frameworks for the performance evaluation of resource management strategies have been suggested for grids. However, similar projects for Desktop Grids are still lacking. This paper presents MGST, the first performance testing framework for Desktop Grids. We discuss the design of the tool and show how it can be used to analyze and improve the performance of an existing Desktop Grid scheduling policy.

Resource management systems (RMS) are an important component in heterogeneous computing (HC) systems. One of the jobs of an RMS is the mapping of arriving tasks onto the machines of the HC system. Many different mapping heuristics have been proposed in recent years. However, most of these heuristics suffer from several limitations. One of these limitations is the performance degradation that results from using outdated global information about the status of all machines in the HC system. This paper proposes several heuristics which address this limitation by only requiring partial information in making the mapping decisions. These heuristics utilize the solution to a linear programming (LP) problem which maximizes the system capacity. Simulation results show that our heuristics perform very competitively while requiring dramatically less information.