Map-matching is the process of aligning a sequence of observed user positions with the road network on a digital map. It is a fundamental pre-processing step for many applications, such as moving object management, traffic flow analysis, and driving directions. In practice there exists huge amount of low-sampling-rate (e.g., one point every 2--5 minutes) GPS trajectories. Unfortunately, most current map-matching approaches only deal with high-sampling-rate (typically one point every 10--30s) GPS data, and become less effective for low-sampling-rate points as the uncertainty in data increases. In this paper, we propose a novel global map-matching algorithm called ST-Matching for low-sampling-rate GPS trajectories. ST-Matching considers (1) the spatial geometric and topological structures of the road network and (2) the temporal/speed constraints of the trajectories. Based on spatio-temporal analysis, a candidate graph is constructed from which the best matching path sequence is identified. We compare ST-Matching with the incremental algorithm and Average-Frechet-Distance (AFD) based global map-matching algorithm. The experiments are performed both on synthetic and real dataset. The results show that our ST-matching algorithm significantly outperform incremental algorithm in terms of matching accuracy for low-sampling trajectories. Meanwhile, when compared with AFD-based global algorithm, ST-Matching also improves accuracy as well as running time.

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Map-matching for low-sampling-rate GPS trajectories

A method for providing load balancing and failover among a set of computing nodes running a network accessible computer service includes providing a computer service that is hosted at one or more servers comprised in a set of computing nodes and is accessible to clients via a first network. Providing a second network including a plurality of traffic processing nodes and load balancing means. The load balancing means is configured to provide load balancing among the set of computing nodes running the computer service. Providing means for redirecting network traffic comprising client requests to access the computer service from the first network to the second network. Providing means for selecting a traffic processing node of the second network for receiving the redirected network traffic comprising the client requests to access the computer service and redirecting the network traffic to the traffic processing node via the means for redirecting network traffic. For every client request for access to the computer service, determining an optimal computing node among the set of computing nodes running the computer service by the traffic processing node via the load balancing means, and then routing the client request to the optimal computing node by the traffic processing node via the second network.

System and method for network traffic management and load balancing

The recent increasing interest in ad hoc networks has motivated the study and development of interference models capable of capturing the intrinsic characteristics of this kind of network, such as the lack of a central coordination and the consequent distributed nature of some of the network functions (e.g., medium access control and routing protocols). As a consequence, a myriad of interference models for ad hoc networks can be found in the literature, that describe the effects of interference at different layers and with different levels of detail. Some of these models describe interference as a random process whose statistics depend mainly on physical layer parameters, and are better suited for the analysis of issues related to the physical layer. Other models focus on the effects of interference at higher layers, and are more appropriate when the analysis of network-related issues is of interest. This paper presents a comprehensive survey of interference models for wireless ad hoc networks, emphasizing their domains of application, illustrated with examples. The interference models are presented in this survey from the perspective of the radio capture phenomenon, resulting in a unified view of interference models, which may be helpful when selecting the appropriate model for a given purpose.

Modeling Interference in Wireless Ad Hoc Networks

Recommendation engine apparatus and associated methods provide content compiled from various sources and selected to match user preferences. In one embodiment, the recommendation apparatus comprises a headend entity; in another, it is co-located on a user's CPE. In one embodiment, the recommendation engine creates content records from content metadata for comparison to a user profile. The user profile is pre-programmed; however has the ability to dynamically shift toward a user's preferences as the user takes actions regarding content. Client applications are utilized to compile and present content; feedback mechanisms are utilized to enable “learning” from user activities to generate more precise recommendations as well as to “unlearn” stale preferences. Recommended content is displayed in the form of a playlist, or as a continuous stream on a virtual channel, or presented in an electronic program guide. A business rules “engine” useful in implementing operational or business goals is also disclosed.

Recommendation engine apparatus and methods

Review: A survey on content-centric technologies for the current Internet: CDN and P2P solutions

A platform that may be used to dynamically reallocation resources to support an Internet application is disclosed. In an embodiment, the platform may include two layers of distribution, one at the DNS layer and one at an Internet data center layer. The platform may include a dynamic resource allocation manager that causes instances of applications to be initiated in Internet data centers in response to dynamic conditions and characteristics of the application. A monitoring system module may be provided to keep the dynamic resource allocation manager informed as the health and utilization of instances of the application.

Monitoring for replica placement and request distribution

A clear trend in the evolution of network-based services is the ever-increasing amount of multimedia data involved. This trend towards big-data multimedia processing finds its natural placement together with the adoption of the cloud computing paradigm, that seems the best solution to cope with the demands of a highly fluctuating workload that characterizes this type of services. However, as cloud data centers become more and more powerful, energy consumption becomes a major challenge both for environmental concerns and for economic reasons. An effective approach to improve energy efficiency in cloud data centers is to rely on traffic engineering techniques to dynamically adapt the number of active servers to the current workload. Towards this aim, we propose a joint computing-plus-communication optimization framework exploiting virtualization technologies, called MMGreen . Our proposal specifically addresses the typical scenario of multimedia data processing with computationally intensive tasks and exchange of a big volume of data. The proposed framework not only ensures users the Quality of Service (through Service Level Agreements), but also achieves maximum energy saving and attains green cloud computing goals in a fully distributed fashion by utilizing the DVFS-based CPU frequencies. To evaluate the actual effectiveness of the proposed framework, we conduct experiments with MMGreen under real-world and synthetic workload traces. The results of the experiments show that MMGreen may significantly reduce the energy cost for computing, communication and reconfiguration with respect to the previous resource provisioning strategies, respecting the SLA constraints.

Adaptive Computing-Plus-Communication Optimization Framework for Multimedia Processing in Cloud Systems

Wireless mesh networks are a promising area for the deployment of new wireless communication and networking technologies. In this paper, we address the problem of enabling effective peer-to-peer resource sharing in this type of networks. Starting from the well-known Chord protocol for resource sharing in wired networks, we propose a specialization that accounts for peculiar features of wireless mesh networks: namely, the availability of a wireless infrastructure, and the 1-hop broadcast nature of wireless communication, which bring to the notions of location awareness and MAC layer cross-layering. Through extensive packet-level simulations, we investigate the separate effects of location awareness and MAC layer cross-layering, and of their combination, on the performance of the P2P application. The combined protocol, MeshChord, reduces message overhead of as much as 40 percent with respect to the basic Chord design, while at the same time improving the information retrieval performance. Notably, differently from the basic Chord design, our proposed MeshChord specialization displays information retrieval performance resilient to the presence of both CBR and TCP background traffic. Overall, the results of our study suggest that MeshChord can be successfully utilized for implementing file/resource sharing applications in wireless mesh networks.

Enabling Efficient Peer-to-Peer Resource Sharing in Wireless Mesh Networks

We propose a novel model, called joint computing, data transmission and migration energy costs (JCDME), for the allocation of virtual elements (VEs), with the goal of minimizing the energy consumption in a software-defined cloud data center (SDDC). More in detail, we model the energy consumption by considering the computing costs of the VEs on the physical servers, the costs for migrating VEs across the servers, and the costs for transferring data between VEs. In addition, JCDME introduces a weight parameter to avoid an excessive number of VE migrations. Specifically, we propose three different strategies to solve the JCDME problem with an automatic and adaptive computation of the weight parameter for the VEs migration costs. We then evaluate the considered strategies over a set of scenarios, ranging from a small sized SDDC up to a medium-sized SDDC composed of hundreds of VEs and hundreds of servers. Our results demonstrate that JCDME is able to save up to an additional 7% of energy with respect to previous energy-aware algorithms, without a substantial increase in the solution complexity.

Joint Minimization of the Energy Costs From Computing, Data Transmission, and Migrations in Cloud Data Centers

Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.

Claudia Canali

Papers

Monitoring for replica placement and request distribution

Adaptive Computing-Plus-Communication Optimization Framework for Multimedia Processing in Cloud Systems

Enabling Efficient Peer-to-Peer Resource Sharing in Wireless Mesh Networks

Joint Minimization of the Energy Costs From Computing, Data Transmission, and Migrations in Cloud Data Centers

GASP: Genetic Algorithms for Service Placement in Fog Computing Systems