Giovanni Battista Barone

GaaS: customized grids in the clouds

Cloud Computing has been widely adopted as a new paradigm for providing resources because of the advantages it brings to both users and providers. Even if it was firstly targeted at enterprises wishing to reduce their equipment management costs, it has been rapidly recognized as both an enabler for new applications and as a mean to allow enterprises of all sizes at running high demanding applications. Recently, Cloud Providers are trying to attract new applications, such as scientific ones, that today already benefit from distributed environment like Grids. This work presents a way to remove the paradigm mismatch between Cloud and Grid Computing, enabling the use of Cloud-provided resources with well-established Grid-like interfaces, avoiding the need for users to learn new resources access and use models. The proposed approach is validated through the development of a prototype implementation and its integration in a working Grid environment.

Toward a Flexible, Environmentally Conscious, on Demand High Performance Computing Service

This work is related with planning and implementation of an on demand computing service which is able to obtain a right trade-off among managment cost reduction, environmental sustainability and user satisfaction.

An Approach to Forecast Queue Time in Adaptive Scheduling: How to Mediate System Efficiency and Users Satisfaction

The minimisation of the total cost of ownership is hard to be faced by the owners of large scale computing systems, without affecting negatively the quality of service for the users. Modern datacenters, often included in distributed environments, appear to be “elastic”, i.e., they are able to shrink or enlarge the number of local physical or virtual resources, also by recruiting them from private/public clouds. This increases the degree of dynamicity, making the infrastructure management more and more complex. Here, we report some advances in the realisation of an adaptive scheduling controller (ASC) which, by interacting with the datacenter resource manager, allows an effective and an efficient usage of resources. In particular, we focus on the mathematical formalisation of the ASC’s kernel that allows to dynamically configure, in a suitable way, the datacenter resources manager. The described formalisation is based on a probabilistic approach that, starting from both a hystorical resources usage and on the actual users request of the datacenter resources, identifies a suitable probability distribution for queue time with the aim to perform a short term forecasting. The case study is the SCoPE datacenter at the University of Naples Federico II.

SCoPE@Scuola: (In)-formative Paths on Topics Related with High Performance, Parallel and Distributed Computing

The SCoPE@Scuola initiative was born with the aim to inspire curiosity in high school students about High Performance Computing (HPC) and Parallel and Distributed Computing (PDC). The HPC/PDC world could be an interesting matter for students because is a necessary tool to solve challenging problems in science and technology and it provides context where a plenty of knowledge acquired at school can find a real application. In fact, the themes related to HPC/PDC involve a large range of knowledge and skills: from mathematical modelling of problems to algorithm design, from software implementation to design and management of complex computer systems. The initiative, begun at the end of 2014, involved several schools in the Naples (Italy) district, and has also been used for work-based learning activities and projects aimed to avoid students “dropouts”. The results collected during all the last years make us hopeful that such initiative could be useful both to increment students awareness about the utility in the real world of all the knowledge acquired at school and to help them in their future educational and/or working choices.