1,720,995 research outputs found
PAFFI: Performance Analysis Framework for Fog Infrastructures in realistic scenarios
No full textThe growing popularity of applications involving the process of a huge amount of data and requiring high scalability and low latency represents the main driver for the success of the fog computing paradigm. A set of fog nodes close to the network edge and hosting functions such as data aggregation, filtering or latency sensitive applications can avoid the risk of high latency due to geographic data transfer and network links congestion that hinder the viability of the traditional cloud computing paradigm for a class of applications including support for smart cities services or autonomous driving. However, the design of fog infrastructures requires novel techniques for system modeling and performance evaluation able to capture a realistic scenario starting from the geographic location of the infrastructure elements. In this paper we propose PAFFI, a framework for the performance analysis of fog infrastructures in realistic scenarios. We describe the main features of the framework and its capability to automatically generate realistic fog topologies, with an optimized mapping between sensors, fog nodes and cloud data centers, whose performance can be evaluated by means of simulation
An analysis of Genetic Algorithms to support the management of edge computing infrastructures
No full textEdge computing is a novel paradigm aiming to push computation as close as possible to the data sources and to the end users. This paradigm is extremely important in areas such as the mobile applications and IoT. Key characteristics of edge computing are the limited computational resource of the edge nodes and the presence of non-negligible network delays that can affect the performance. Applications are typically designed as a set of inter-operating micro-services, where each service must be placed on an edge node in order to minimize network latency, while balancing the load distribution over the nodes to avoid the violation the the Service Level Agreements. An additional goal is to minimize energy consumption, meaning that the number of powered-on edge nodes must be kept as low as possible. For these reasons, the problem of micro-service placement over an edge computing infrastructure is complex and must be solved by means of heuristics that must reach suitable solutions under a wide set of operating conditions. We propose a mechanism to solve the placement problem based on genetic algorithms to solve the micro-service placement problem and we analyze the behavior of such heuristic for a wide set of problem characteristics. The proposed algorithm can automatically identify the subset of edge nodes to use and can allocate micro-services to reduce network delays and balance the load. Our experiments demonstrate that the proposed GA is a viable tool to allocate micro-services in edge infrastructures as it can find adequate solutions with a limited number of generations and provides stable performance over a wide set of problem characteristics with limited need for tuning
Microservice Performance in Container- and Function-as-a-Service Architectures
Full text linkFunction-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
Data Flows Mapping in Fog Computing Infrastructures Using Evolutionary Inspired Heuristic
No full textThe need for scalable and low-latency architectures that can process large amount of data from geographically distributed sensors and smart devices is a main driver for the popularity of the fog computing paradigm. A typical scenario to explain the fog success is a smart city where monitoring applications collect and process a huge amount of data from a plethora of sensing devices located in streets and buildings. The classical cloud paradigm may provide poor scalability as the amount of data transferred risks the congestion on the data center links, while the high latency, due to the distance of the data center from the sensors, may create problems to latency critical applications (such as the support for autonomous driving). A fog node can act as an intermediary in the sensor-to-cloud communications where pre-processing may be used to reduce the amount of data transferred to the cloud data center and to perform latency-sensitive operations. In this book chapter we address the problem of mapping sensors over the fog nodes with a twofold contribution. First, we introduce a formal model for the mapping model that aims to minimize response time considering both network latency and processing time. Second, we present an evolutionary-inspired heuristic (using Genetic Algorithms) for a fast and accurate resolution of this problem. A thorough experimental evaluation, based on a realistic scenario, provides an insight on the nature of the problem, confirms the viability of the GAs to solve the problem, and evaluates the sensitivity of such heuristic with respect to its main parameters
Collaboration Strategies for Fog Computing under Heterogeneous Network-bound Scenarios
Full text linkThe success of IoT applications increases the number of online devices and motivates the adoption of a fog computing paradigm to support large and widely distributed infrastructures. However, the heterogeneity of nodes and their connections requires the introduction of load balancing strategies to guarantee efficient operations. This aspect is particularly critical when some nodes are characterized by high communication delays. Some proposals such as the Sequential Forwarding algorithm have been presented in literature to provide load balancing in fog computing systems. However, such algorithms have not been studied for a wide range of working parameters in an heterogeneous infrastructure; furthermore, these algorithms are not designed to take advantage from highly heterogeneous network delays that are common in fog infrastructures. The contribution of this study is twofold: First, we evaluate the performance of the sequential forwarding algorithm for several load and delay conditions; second, we propose and test a delay-aware version of the algorithm that takes into account the presence of highly variable node connectivity in the infrastructure. The results of our experiments, carried out using a realistic network topology, demonstrate that a delay-blind approach to sequential forwarding may determine poor performance in the load balancing when network delay represents a major contribution to the response time. Furthermore, we show that the delay-aware variant of the algorithm may provide a benefit in this case, with a reduction in the response time up to 6%
GASP: Genetic algorithms for service placement in fog computing systems
Full text linkFog 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
Smart cities in the fog: Clearing the vision of innovative sensing applications
No full textThe new paradigm of smart cities is deeply intertwined with the development of large-scale sensing applications. An ever-growing amount of sensors are collecting data to support decision strategies for the management of the city services. Examples of such applications are traffic monitoring, autonomous driving, environmental sensing, real-time power/resource utilization metering. A traditional cloud-based approach for the deployment of such services is likely to suffer from performance and QoS problems due to the risk of congestion on the data center outbound links and due to high latency related to the geographic data exchange. An alternative paradigm to mitigate these problems is the fog computing, where a layer of intermediate fog nodes is placed between the sensors and the cloud data center to reduce the amount of data exchanges (through aggregation and filtering) and to host latency-critical services. The fog computing opens several new issues for the management and deployment of the services, especially if we consider that new applications may be dynamically deployed and also the infrastructure is subject to changes over time (e.g., adding and removing sensors and fog nodes). While this dynamic behavior can be supported by existing technologies such as containers, service orchestration frameworks, and micro-services, the fog paradigm exacerbates the problem of infrastructure and service coordination and management to the point where new solutions must be devised. The critical challenges that should be addressed by future fog infrastructures for smart cities lie in the area of service management, optimization of the infrastructure and automatic deployment of applications. In the present chapter, we discuss advantages and disadvantages of solutions for the management of smart city sensing applications, considering architectures, optimization models, algorithms for the service deployment, and the support for the applications life cycle
A fog computing service placement for smart cities based on genetic algorithms
Full text linkThe growing popularity of the Fog Computing paradigm is driven by the increasing availability of large amount of sensors and smart devices on a geographically distributed area. The scenario of a smart city is a clear example of this trend. As we face an increasing presence of sensors producing a huge volume of data, the classical cloud paradigm, with few powerful data centers that are far away from the data sources, becomes inadequate. There is the need to deploy a highly distributed layer of data processors that filter, aggregate and pre-process the incoming data according to a fog computing paradigm. However, a fog computing architecture must distribute the incoming workload over the fog nodes to minimize communication latency while avoiding overload. In the present paper we tackle this problem in a twofold way. First, we propose a formal model for the problem of mapping the data sources over the fog nodes. The proposed optimization problem considers both the communication latency and the processing time on the fog nodes (that depends on the node load). Furthermore, we propose a heuristic, based on genetic algorithms to solve the problem in a scalable way. We evaluate our proposal on a geographic testbed that represents a smart-city scenario. Our experiments demonstrate that the proposed heuristic can be used for the optimization in the considered scenario. Furthermore, we perform a sensitivity analysis on the main heuristic parameters
A distributed architecture to support infomobility services
Full text linkThe 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
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