The cloud computing paradigm encompasses several key differentiating elements and technologies, tackling a number of inefficiencies, limitations and problems that have been identified in the distributed and virtualized computing domain. Nonetheless, and as it is the case for all emerging technologies, their adoption led to the presentation of new challenges and new complexities. In this paper we present key application areas and capabilities of future scenarios, which are not tackled by current advancements and highlight specific requirements and goals for advancements in the cloud computing domain. We discuss these requirements and goals across different focus areas of cloud computing, ranging from cloud service and application integration, development environments and abstractions, to interoperability and relevant to it aspects such as legislation. The future application areas and their requirements are also mapped to the aforementioned areas in order to highlight their dependencies and potential for moving cloud technologies forward and contributing towards their wider adoption
Abstract:The key factors for a successful smart-city project are its initial cost and its scalability. The initial cost depends on several inter-related aspects that cannot be designed and optimized separately. After the pilot deployment, scaling-up takes place only if the cost remains affordable: an initial financial support may induce dependencies from technologies that become unsustainable in the long period. In addition, the initial adoption of an emerging technology that fails to affirm may jeopardize investment return. This paper investigates a smart-village use case, the success of which strongly depends on the initial cost and scalability, exploring a low-cost way for Internet of Things (IoT). We propose a simple conceptual framework for cost evaluation, and we verify its effectiveness with an exhaustive use case: a prototype sensor designed and tested with its surrounding eco-system. Using experimental results, we can estimate both performance and cost for a pilot system made of fifty sensors deployed in an urban area. We show that such cost grows linearly with system size, taking advantage of widely adopted technologies. The code and the design of the prototype are available, so that all steps are reproducible.
In this work, we address the problem of locally estimating the size of a Peerto-Peer (P2P) network using local information. We present a novel approach for estimating the size of a peer-to-peer (P2P) network, fitting the sum of new neighbors discovered at each iteration of a breadth-first search (BFS) with a logarithmic function, and then using Lambert's W function to solve a root of a ln(n) + b − n = 0, where n is the network size. With rather little computation, we reach an estimation error of at most 10 percent, only allowing the BFS to iterate to the third level.
We introduce a new paradigm, based on an extension of the Open Cloud Computing Interface (OCCI), for the on demand monitoring of the cloud resources provided to a user. We have extended the OCCI with two new sub-types of core entities: one to collect the measurements, and the other to process them. The user can request instances of such entities to implement a monitoring infrastructure.\ud The paradigm does not target a specific cloud model, and is therefore applicable to any kind of resource provided as a service. The specifications include only the minimum needed to describe a monitoring infrastructure, thus making this standard extension simple and easily adoptable. Despite its simplicity the model is able to describe complex solutions, including private/public clouds, and covers both infrastructure and application monitoring.\ud To highlight the impact of our proposal in practice, we have designed an engine that deploys a monitoring infrastructure using its OCCI-compliant descriptions. The design is implemented in a prototype that is available as open source
We explore the specification and the automated deployment of a monitoring infrastructure in a container-based distributed system. This result shows that highly customizable monitoring infrastructures can be effectively provided as a service, and that a key step in this process is the definition of an expandable abstract model for them.\ud \ud So we start defining a simple model of the monitoring infrastructure that provides an interface between the user and the cloud management system. The interface follows the guidelines of Open Cloud Computing Interface (OCCI), the cloud interface standard proposed by the Open Grid Forum. The definition is simple and generic and it is a first step towards the definition of a standard interface for Monitoring Services. It allows the definition of complex, hierarchical monitoring infrastructure by composing multiple instances of two basic components, one for measurement and another for data distribution,.\ud \ud We illustrate how the monitoring functionalities that are defined through the interface are implemented as microservices embedded in containers. The internals of each microservice reflects the distinction between core functionalities which are bound to the standard, and custom plugin modules.\ud \ud We describe the engine that automatically deploys a system of microservices that implements the monitoring infrastructure. Special attention is paid to preserve the distinction between core and custom functionalities, and the on demand nature of a cloud service.\ud \ud A proof of concept demo is available through the Docker hub and consists of two multi-threaded Java appli- cations that implement the two basic components
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.