The steadily increasing success of Cloud Computing is causing a huge rise in its electrical power consumption, contributing to higher energy costs, as well as to the greenhouse effect and the global warming. One of the most common key strategies to reduce the power consumption of data centers is the consolidation of virtual machines, whose effectiveness strongly depends on a reliable forecasting of future computational resource needs. In fact, servers are typically configured to handle peak workload conditions even if they are often under-utilized, that results in a wastefulness of resources and inefficient energy consumption. Motivated by these issues, this paper describes a data-driven approach based on auto-regressive models to dynamically forecast virtual machine workloads, for energy-aware allocations of virtual machines on Cloud physical nodes. Virtual machine migrations across physical servers are periodically done on the basis of the estimated virtual machine demands, by minimizing the number of active servers. Experimental results show encouraging benefits in terms of energy saving, while satisfying performance constraints and service level agreement established with users.

Consolidation of virtual machines is one of the key strategies used to reduce the power consumption of Cloud data centers. For this reason it is extensively studied. Consolidation has the goal of allocating virtual machines on a few physical servers as possible, while satisfying the Service Level Agreement established with users. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of virtual machine resource needs. To this aim, data-driven predictive models can be exploited to develop intelligent consolidation policies. This paper describes a comparative analysis of consolidation strategies of virtual machines in Cloud systems, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data center, shows several insights in terms of energy saving and most efficient consolidation strategies.

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, and sensors, allows for the collections of large amounts of movement data. This amount of data can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. From a technological viewpoint, Cloud computing can play an essential role by helping city administrators to quickly acquire new capabilities and reducing initial capital costs by means of a comprehensive pay-as-you-go solution. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, in a Cloud-based framework. Experimental evaluation has been carried out on both real-world and synthetic trajectory data, up to one million of trajectories. The results show that, due to the high complexity and large volumes of data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture in terms of both execution time, speed-up and scale-up.

Consolidation of virtual machines (VM) is one of the key strategies used to reduce the power consumption of Cloud servers. For this reason it is extensively studied. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the VM resource needs. This paper describes the design and development of a system for energy-aware allocation of virtual machines, driven by predictive data mining models. In particular, migrations are driven by the forecast of the future computational needs (CPU, RAM) of each virtual machine, in order to efficiently allocate those on the available servers. Experimental results, performed on data of a real Cloud data centre, show encouraging benefits in terms of energy saving.

The increasing pervasiveness of mobile devices along with the use of technologies like GPS, Wifi networks, RFID, etc., allows for the collections of large amounts of movement data. This amount of information can be analyzed to extract descriptive and predictive models that can be profitable exploited to improve urban life. This paper presents an integrated Cloud based framework for efficiently managing and analyzing socio-environmental data in the urban context of cities. As case study, we introduce a parallel approach for discovering patterns and rules from trajectory data. Experimental evaluation shows that the trajectory pattern mining process can take advantage from a scalable execution environment offered by a Cloud architecture.