From Vlasov fluctuations to the BGL kinetic equation

This paper shows that the spatially homogeneous Balescu-Guernsey-Lenard kinetic equation is associated, at least formally, with a stochastic process that arises naturally as the N →∞ limit of a certain N-particle Hamiltonian system. The process describes the long-time motion of a particle traveling in a Vlasov fluctuation field. The Fokker-Planck equation for the process coincides with the Balescu-Guernsey-Lenard equation whenever the solution is analytic in the velocity variables, but should also be considered as a model in its own right

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

Microservice Performance in Container- and Function-as-a-Service Architectures

Function-as-a-Service (FaaS) is a new cloud-based computing model that promises a more cost-efficient deployment of microservices with respect to other cloud paradigms, like Container-as-a-Service (CaaS). However, requests served under a FaaS approach often experience a cold start condition, that occurs when the execution of an inactive function occurs for the first time and a container environment has to be set up afresh. In such cases, performance deteriorates and response times increase. This paper proposes an analysis of the performance of the Function-as-a-Service model for two single offered microservices. Specifically, we carry out a performance evaluation of the Function-as-a-Service model, implemented through OpenWhisk, using as a baseline for comparison the Container-as-a-Service approach, implemented with Docker. Our analysis focuses on metrics related to the response time and to the usage of main server resources such as CPU and memory. For the performance comparison, we exploited two different microservices based on face recognition and image conversion, respectively, in order to evaluate the performance over popular and modern kinds of services included in artificial intelligence and multimedia applications

Placement of IoT Microservices in Fog Computing Systems: A Comparison of Heuristics

In the last few years, fog computing has been recognized as a promising approach to support modern IoT applications based on microservices. The main characteristic of this application involve the presence of geographically distributed sensors or mobile end users acting as sources of data. Relying on a cloud computing approach may not represent the most suitable solution in these scenario due to the non-negligible latency between data sources and distant cloud data centers, which may represent an issue in cases involving real-time and latency-sensitive IoT applications. Placing certain tasks, such as preprocessing or data aggregation, in a layer of fog nodes close to sensors or end users may help to decrease the response time of IoT applications as well as the traffic towards the cloud data centers. However, the fog scenario is characterized by a much more complex and heterogeneous infrastructure compared to a cloud data center, where the computing nodes and the inter-node connecting are more homogeneous. As a consequence, the the problem of efficiently placing microservices over distributed fog nodes requires novel and efficient solutions. In this paper, we address this issue by proposing and comparing different heuristics for placing the application microservices over the nodes of a fog infrastructure. We test the performance of the proposed heuristics and their ability to minimize application response times and satisfy the Service Level Agreement across a wide set of operating conditions in order to understand which approach is performs the best depending on the IoT application scenario

Use of Foundry Sands in the Production of Ceramic and Geopolymers for Sustainable Construction Materials

The aim of this research was to evaluate the possibility of reusing waste foundry sands derived from the production of cast iron as a secondary raw material for the production of building materials obtained both by high-temperature (ceramic tiles and bricks) and room-temperature (binders such as geopolymers) consolidation. This approach can reduce the current demand for quarry sand and/or aluminosilicate precursors from the construction materials industries. Samples for porcelain stoneware and bricks were produced, replacing the standard sand contained in the mixtures with waste foundry sand in percentages of 10%, 50%, and 100% by weight. For geopolymers, the sand was used as a substitution for metakaolin (30, 50, 70 wt%) as an aluminosilicate precursor rather than as an aggregate to obtain geopolymer pastes. Ceramic samples obtained using waste foundry sand were characterized by tests for linear shrinkage, water absorption, and colorimetry. Geopolymers formulations, produced with a Si/Al ratio of 1.8 and Na/Al = 1, were characterized to evaluate their chemical stability through measurements of pH and ionic conductivity, integrity in water, compressive strength, and microstructural analysis. The results show that the addition of foundry sand up to 50% did not significantly affect the chemical-physical properties of the ceramic materials. However, for geopolymers, acceptable levels of chemical stability and mechanical strength were only achieved when using samples made with 30% foundry sand as a replacement for metakaolin

Optimal Placement of Micro-services Chains in a Fog Infrastructure

Fog computing emerged as a novel approach to deliver micro-services that support innovative applications. This paradigm is consistent with the modern approach to application development, that leverages the composition of small micro-services that can be combined to create value-added applications. These applications typically require the access from distributed data sources, such as sensors located in multiple geographic locations or mobile users. In such scenarios, the traditional cloud approach is not suitable because latency constraints may not be compatible with having time-critical computations occurring on a far away data-center; furthermore, the amount of data to exchange may cause high costs imposed by the cloud pricing model. A layer of fog nodes close to application consumers can host pre-processing and data aggregation tasks that can reduce the response time of latency-sensitive elaboration as well as the traffic to the cloud data-centers. However, the problem of smartly placing micro-services over fog nodes that can fulfill Service Level Agreements is far more complex than in the more controlled scenario of cloud computing, due to the heterogeneity of fog infrastructures in terms of performance of both the computing nodes and inter-node connectivity. In this paper, we tackle such problem proposing a mathematical model for the performance of complex applications deployed on a fog infrastructure. We adapt the proposed model to be used in a genetic algorithm to achieve optimized deployment decisions about the placement of micro-services chains. Our experiments prove the viability of our proposal with respect to meeting the SLA requirements in a wide set of operating conditions

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

Performance Comparison of Technological Solutions for Spark Applications in AWS

Cloud computing is providing a pay-as-you-go in-frastructure for the deployment of complex applications, with auto-scaling support and the ability to manage and process huge amount of data. However, due to the underlying complexity of the cloud infrastructure, it is not trivial to evaluate the setup providing the best performance of such scenario. To this aim the present paper proposes a thorough performance evaluation of a real application in a Cloud platform, measuring the impact of several design choices and technological solution. The experimental results, based on a real application and on realistic data can provide a significant insight that can integrate the traditional approach of cloud performance evaluation based on synthetic benchmarks