GASP: Genetic algorithms for service placement in fog computing systems

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

A distributed architecture to support infomobility services

The growing popularity of mobile and location aware devices allows the deployment of infomobility systems that provide access to information and services for the support of user mobility. Current systems for infomobility services assume that most information is already available on the mobile device and the device connectivity is used for receiving critical messages from a central server. However, we argue that the next generation of infomobility services will be characterized by collaboration and interaction among the users, provided through real-time bidirectional communication between the client devices and the infomobility system.In this paper we propose an innovative architecture to support next generation infomobility services providing interaction and collaboration among the mobile users that can travel by several different transportation means, ranging from cars to trains to foot. We discuss the design issues of the architecture, with particular emphasis on scalability, availability and user data privacy, which are critical in a collaborative infomobility scenario. Copyright 2006 ACM

Randomized Load Balancing under Loosely Correlated State Information in Fog Computing

Fog computing infrastructures must support increasingly complex applications where a large number of sensors send data to intermediate fog nodes for processing. As the load in such applications (as in the case of a smart cities scenario) is subject to significant fluctuations both over time and space, load balancing is a fundamental task. In this paper we study a fully distributed algorithm for load balancing based on random probing of the neighbors' status. A qualifying point of our study is considering the impact of delay during the probe phase and analyzing the impact of stale load information. We propose a theoretical model for the loss of correlation between actual load on a node and stale information arriving to the neighbors. Furthermore, we analyze through simulation the performance of the proposed algorithm considering a wide set of parameters and comparing it with an approach from the literature based on random walks. Our analysis points out under which conditions the proposed algorithm can outperform the alternatives

Left ventricular heart failure and pulmonary hypertension

In patients with left ventricular heart failure (HF), the development of pulmonary hypertension (PH) and right ventricular (RV) dysfunction are frequent and have important impact on disease progression, morbidity, and mortality, and therefore warrant clinical attention. Pulmonary hypertension related to left heart disease (LHD) by far represents the most common form of PH, accounting for 65–80% of cases. The proper distinction between pulmonary arterial hypertension and PH-LHD may be challenging, yet it has direct therapeutic consequences. Despite recent advances in the pathophysiological understanding and clinical assessment, and adjustments in the haemodynamic definitions and classification of PH-LHD, the haemodynamic interrelations in combined post- and pre-capillary PH are complex, definitions and prognostic significance of haemodynamic variables characterizing the degree of pre-capillary PH in LHD remain suboptimal, and there are currently no evidence-based recommendations for the management of PH-LHD. Here, we highlight the prevalence and significance of PH and RV dysfunction in patients with both HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), and provide insights into the complex pathophysiology of cardiopulmonary interaction in LHD, which may lead to the evolution from a ‘left ventricular phenotype’ to a ‘right ventricular phenotype’ across the natural history of HF. Furthermore, we propose to better define the individual phenotype of PH by integrating the clinical context, non-invasive assessment, and invasive haemodynamic variables in a structured diagnostic work-up. Finally, we challenge current definitions and diagnostic short falls, and discuss gaps in evidence, therapeutic options and the necessity for future developments in this context

Intrapancreatic accessory spleen false positive to 68Ga-Dotatoc: case report and literature review

Background: Intrapancreatic accessory spleen (IPAS) is an uncommon finding of pancreatic mass. Differential diagnosis with pancreatic tumor, especially with non-functional neuroendocrine tumor (NF-NET), may be very hard and sometimes it entails unnecessary surgery. A combination of CT scan, MRI, and nuclear medicine can confirm the diagnosis of IPAS. 68-Ga-Dotatoc PET/CT is the gold standard in NET diagnosis and it can allow to distinguish between IPAS and NET. Case presentation: A 69-year-old man was admitted to our hospital for an incidental nodule in the tail of the pancreas with focal uptake of 68-Ga-dotatate at PET/CT. NET was suspected and open distal splenopancreatectomy was performed. Pathologic examination revealed an IPAS. Conclusion: This is the second IPAS case in which a positive 68Ga-Dotatoc uptake led to a false diagnosis of pancreatic NET. Here is a proposal of a literature review

A Location-allocation model for fog computing infrastructures

The trend of an ever-increasing number of geographically distributed sensors producing data for a plethora of applications, from environmental monitoring to smart cities and autonomous driving, is shifting the computing paradigm from cloud to fog. The increase in the volume of produced data makes the processing and the aggregation of information at a single remote data center unfeasible or too expensive, while latency-critical applications cannot cope with the high network delays of a remote data center. Fog computing is a preferred solution as latency-sensitive tasks can be moved closer to the sensors. Furthermore, the same fog nodes can perform data aggregation and filtering to reduce the volume of data that is forwarded to the cloud data centers, reducing the risk of network overload. In this paper, we focus on the problem of designing a fog infrastructure considering both the location of how many fog nodes are required, which nodes should be considered (from a list of potential candidates), and how to allocate data flows from sensors to fog nodes and from there to cloud data centers. To this aim, we propose and evaluate a formal model based on a multi-objective optimization problem. We thoroughly test our proposal for a wide range of parameters and exploiting a reference scenario setup taken from a realistic smart city application. We compare the performance of our proposal with other approaches to the problem available in literature, taking into account two objective functions. Our experiments demonstrate that the proposed model is viable for the design of fog infrastructure and can outperform the alternative models, with results that in several cases are close to an ideal solution

Phase transition in the collisionless regime for wave-particle interaction

Gibbs statistical mechanics is derived for the Hamiltonian system coupling self-consistently a wave to N particles. This identifies Landau damping with a regime where a second order phase transition occurs. For nonequilibrium initial data with warm particles, a critical initial wave intensity is found: above it, thermodynamics predicts a finite wave amplitude in the limit of infinite N; below it, the equilibrium amplitude vanishes. Simulations support these predictions providing new insight on the long-time nonlinear fate of the wave due to Landau damping in plasmas.Comment: 12 pages (RevTeX), 2 figures (PostScript

A Decision Support System for Multi-Trip Vehicle Routing Problems

Emerging trends, driven by industry 4.0 and Big Data, are pushing to combine optimization techniques with Decision Support Systems (DSS). The use of DSS can reduce the risk of uncertainty of the decision-maker regarding the economic feasibility of a project and the technical design. Designing a DSS can be very hard, due to the inherent complexity of these types of systems. Therefore, monolithic software architectures are not a viable solution. This paper describes the DSS developed for an Italian company based on a micro-services architecture. In particular, the services handle geo-referenced information to solve a multi-trip vehicle routing problem with time windows. To face the problem, we follow a two-step approach. First, we generate a set of routes solving a vehicle routing problem with time windows using a metaheuristic algorithm. Second, we calculate the interval in which each route can start and end, and then combine the routes together, with an integer linear programming model, to minimize the number of used vehicles. Computational tests are conducted on real and random instances and prove the efficiency of the approach