Albino Altomare

Trajectory Pattern Mining for Urban Computing in the Cloud

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.

Trajectory Pattern Mining over a Cloud-Based Framework for Urban Computing

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 information can be analyzed to extract descriptive and predictive models that can be properly exploited to improve urban life. This paper presents a workflow-based parallel approach for discovering patterns and rules from trajectory data, executed on a Cloud-based framework for urban computing. Experimental evaluation shows that, due to complexity and large data involved in the application scenario, the trajectory pattern mining process takes advantage from the scalable execution environment offered by a Cloud architecture.

Using Clouds for Smart City Applications

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.

Energy-Aware Migration of Virtual Machines Driven by Predictive Data Mining Models

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.

A Cloud of Things framework for smart home services based on Information Centric Networking

Today, the novel Cloud of Things (CoT) paradigm, where Cloud and Internet of Things (IoT) technologies are merged together, is foreseen as a promising enabler of many real-life application scenarios, like the smart home. However, several issues are still debated in the design of CoT systems, including how to effectively manage the heterogeneity of IoT devices and how to support robust and low-latency communications between the cloud and the physical world. In this paper, we present a novel CoT platform that solves such challenges in the smart home domain by leveraging two groundbreaking concepts: Information Centric Networking (ICN) and Fog Computing. The proposal, called ICN-iSapiens, is a three-layered architecture where an intermediate (Fog) layer, consisting of smart home servers (HSs), is introduced between the physical world and the remote cloud, to support real-time services and hide the heterogeneity of IoT devices. Communication at the physical layer consists of name-based ICN primitives, which facilitate the network configuration and enable simple and effective interactions between HSs and IoT devices. As proof of concept, an experimental testbed is presented and some application examples are described to showcase the advanced capabilities of ICN-iSapiens.

Mining frequent items and itemsets from distributed data streams for emergency detection and management

Sensor networks are an important technology for large-scale monitoring, that allow the collection of environmental measurement streaming data in remote areas. Such data constitute a valuable source of information to be exploited for better understanding natural phenomena. Moreover, in some cases streams of data must be analyzed in real time to provide information about trends, outlier values or regularities that must be signaled as soon as possible, to prevent emergencies or disasters (e.g., landslides, fires). For such a reason, real-time analysis of distributed data streams is a challenging task since it requires scalable solutions to handle streams of data that are generated very rapidly by multiple sources. This paper presents the design and the implementation of an architecture for the analysis of data streams in distributed environments. Experimental evaluation shows the efficiency and effectiveness of the approach.

Predictive Models for Energy-Efficient Clouds: An Analysis on Real-Life and Synthetic Data

The success of Cloud Computing and the resulting expansion of large data centers result in a huge rise of electrical power consumption by hardware facilities. Consolidation of virtual machines (VM) is one of the key strategies used to reduce the energy consumed by Cloud servers. Nevertheless, the effectiveness of a consolidation strategy strongly depends on the forecast of the needs of the VM resources. This paper describes the experimental evaluation 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 both on a real Cloud and synthetic data, show encouraging benefits in terms of energy saving.

Data analytics for energy-efficient clouds: design, implementation and evaluation

ABSTRACT The success of Cloud Computing and the resulting ever growing of large data centers is causing a huge rise in electrical power consumption by hardware facilities and cooling systems. This results in an increment of operational costs of data centres, that is becoming a crucial issue to deal with. 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. 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 the VM resource needs. Predictive data mining models can be exploited for this purpose. 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. The experimental evaluation, performed on real-world Cloud data traces, reports a comparison of performance achieved by exploiting several classification models and shows good benefits in terms of energy saving. GRAPHICAL ABSTRACT The energy-aware cloud architecture.

A Comparative Analysis of Data-Driven Consolidation Policies for Energy-Efficient Clouds

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.

A Data-driven Approach based on Auto-Regressive Models for Energy-Efficient Clouds

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.