Michele Ianni

Big Data Techniques For Supporting Accurate Predictions of Energy Production From Renewable Sources

Predicting the output power of renewable energy production plants distributed on a wide territory is a really valuable goal, both for marketing and energy management purposes. Vi-POC (Virtual Power Operating Center) project aims at designing and implementing a prototype which is able to achieve this goal. Due to the heterogeneity and the high volume of data, it is necessary to exploit suitable Big Data analysis techniques in order to perform a quick and secure access to data that cannot be obtained with traditional approaches for data management. In this paper, we describe Vi-POC -- a distributed system for storing huge amounts of data, gathered from energy production plants and weather prediction services. We use HBase over Hadoop framework on a cluster of commodity servers in order to provide a system that can be used as a basis for running machine learning algorithms. Indeed, we perform one-day ahead forecast of PV energy production based on Artificial Neural Networks in two learning settings, that is, structured and non-structured output prediction. Preliminary experimental results confirm the validity of the approach, also when compared with a baseline approach.

A Peer to Peer Approach to Efficient High Performance Computing

Nowadays, many applications call for collaborative solutions in order to accomplish complex projects requiring huge amounts of computing resources, e.g., physical science simulation. Many approaches have been proposed in order to design a task partitioning strategy able to assign pieces of execution to the appropriate workers. In this paper, we describe our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each others by asking the network available computational resources when they face high computing demanding tasks. Differently from many proposal available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking computation power to solve their own task or/and they can redeem them earning coins. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead).

High Performance Computing by the Crowd

Computational techniques both from a software and hardware viewpoint are nowadays growing at impressive rates leading to the development of projects whose complexity could be quite challenging, e.g., bio-medical simulations. Tackling such high demand could be quite hard in many context due to technical and economic motivation. A good trade-off can be the use of collaborative approaches. In this paper, we address this problem in a peer to peer way. More in detail, we leverage the idling computational resources of users connected to a network. We designed a framework that allows users to share their CPU and memory in a secure and efficient way. Indeed, users help each others by asking the network computational resources when they face high computing demanding tasks. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farm or cloud based executions.

Clustering Big Data.

The need to support advanced analytics on Big Data is driving data scientist’ interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+ on Map-Reduce platforms.

Distributed computing by leveraging and rewarding idling user resources from P2P networks

Abstract Currently, many emerging computer science applications call for collaborative solutions to complex projects that require huge amounts of computing resources to be completed, e.g., physical science simulation, big data analysis. Many approaches have been proposed for high performance computing designing a task partitioning strategy able to assign pieces of execution to the appropriate workers in order to parallelize task execution. In this paper, we describe the Coremuniti T M system, our peer to peer solution for solving complex works by using the idling computational resources of users connected to our network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. By doing this, users help each other by asking the network computational resources when they face high computing demanding tasks. In this respect, as users provide their computational power without providing specific human skill, our approach can be considered as a hybrid crowdsourcing. Differently from many proposals available for volunteer computing, users providing their resources are rewarded with tangible credits, i.e., they can redeem their credits by asking for computational power to solve their own task and/or by exchanging them for money. We conducted a comprehensive experimental assessment in an interesting scenario as 3D rendering, which allowed us to validate the scalability and effectiveness of our solution and its profitability for end-users. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farms or cloud based executions.

Effective High Performance Computing using Peer To Peer Networks

The advances in computational techniques both from a software and hardware viewpoint lead to the development of projects whose complexity could be quite challenging, e.g., biomedical simulations. In order to deal with the increased demand of computational power many collaborative approaches have been proposed in order apply proper partitioning strategy able to assign pieces of execution to a crowd of workers. In this paper, we address this problem in a peer to peer way. We leverage the idling computational resources of users connected to a network. More in detail, we designed a framework that allows users to share their CPU and memory in a secure and efficient way. The latter allows users help each other by asking the network computational resources when they face high computing demanding tasks. We leveraged our solution in a quite intriguing scenario as 3D rendering to validate the scalability and effectiveness of our solution and its profitability for end- users. As we do not require to power additional resources for solving tasks (we better exploit unused resources already powered instead), we hypothesize a remarkable side effect at steady state: energy consumption reduction compared with traditional server farm or cloud based executions.