1,720,987 research outputs found
Coupling QoS co-simulation with online adaptive arrival forecasting
No full textCoupled simulation, also known as co-simulation,
has been proposed to provide more information to a task scheduler by simulating at runtime the Quality of Service (QoS) arising from a scheduling action. To do so, co-simulation algorithms run the simulation assuming a static set of arrival time series, restricting the diversity of the traffic scenarios. To ensure the co-simulator can provide valuable and representative results, we present an online adaptive arrival forecasting framework that contains a change-point detection module and a probabilistic transformer model to couple co-simulators with arrival series forecasting. The framework can also update the prediction model to adapt to dynamic environments. Our experiments show that our online adaptive forecasting framework has lower forecasting errors than established prediction models, such as autoregressive processes, and lower on real-world traces the co-simulator prediction error by up to 27% on average response time and 39% on average service-level agreement (SLA) violation
iGateLink: A Gateway Library for Linking IoT, Edge, Fog, and Cloud Computing Environments
Full text linkIn recent years, the Internet of Things (IoT) has been growing in popularity, along with the increasingly important role played by IoT gateways, mediating the interactions among a plethora of heterogeneous IoT devices and cloud services. In this paper, we present iGateLink, an open-source Android library easing the development of Android applications acting as a gateway between IoT devices and Edge/Fog/Cloud Computing environments. Thanks to its pluggable design, modules providing connectivity with a number of devices acting as data sources or Fog/Cloud frameworks can be easily reused for different applications.
Using iGateLink in two case-studies replicating previous works in the healthcare and image processing domains, the library proved to be effective in adapting to different scenarios and speeding up development of gateway applications, as compared to the use of conventional methods
Transformative Effects of Iot, Blockchain and Artificial Intelligence on Cloud Computing: Evolution, Vision, Trends and Open Challenges
No full textSingh, Manmeet/0000-0002-3374-7149; Aslanpour, Mohammad Sadegh/0000-0002-1816-6901; Misra, Sanjay/0000-0002-3556-9331; Tuli, Shreshth/0000-0003-2960-1128; Gill, Sukhpal Singh/0000-0002-3913-0369; Xu, Minxian/0000-0002-0046-5153; Singh, Manmeet/0000-0002-3374-7149; Lindsay, Dominic/0000-0002-9354-4183; Garraghan, Peter/0000-0002-7103-2515; Pervaiz, Haris/0000-0002-8364-4682Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies' interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing. (C) 2019 Elsevier B.V. All rights reserved
AI and co-simulation driven resource management in fog computing environments
No full textThe title of this thesis, AI and Co-Simulation Driven Resource Management in Fog Computing Environments, covers three main aspects of the work we present here. First, we discuss on what we do, which is making resource management decisions. Second, we discuss where we take such decisions, which are Fog computing environments. Third, we discuss how we take these discussions, that is, through Artificial Intelligence (AI) and Co-Simulation based methods.
We consider the Fog computing paradigm, which consists of an infrastructure consisting of heterogeneous distributed computational devices. Fog computing is an emerging paradigm encompassing a diverse spectrum of compute nodes and is considered the future of computing. These may range from resource-abundant cloud virtual machines to resource-limited compute hardware close to the user. As cloud nodes may be at a geographically distant location and a multi-hop distance from the users, they tend to offer high communication latency to users. On the other hand, we have devices at a few hops from the user also referred to as the edge of the network, provide reduced latency but tight compute/memory constraints. In this work, we aim to improve service quality by making intelligent resource management decisions for Fog environments. This is hard with highly dynamic modern applications and volatile resource characteristics of systems.
To make intelligent decisions, we consider that a Fog environment consists of broker and worker nodes. The former make resource management decisions, such as when to provision workers, where to place incoming tasks, and how to prevent and recover from failures to ensure service resilience. The latter is where finite-running tasks are executed to return the results to the users. In our setup, we assume that users interact with the Fog environment through gateway devices, such as smartphones or tablets, and send or receive data through sensors and actuators, such as microphones or cameras. The objective is to improve metrics of interest, such as energy consumption, the average response time of tasks, the fraction of violation of task deadlines and the operational cost of the environment. We refer to all these as Quality of Service (QoS) metrics and optimizing such metrics are crucial for both end-users and service providers.
In order to make intelligent decisions, we develop novel data-driven strategies. We leverage and improve upon AI-based methods for their speed and accuracy and the ability of such approaches to identify patterns in data that are hard to encode manually. However, AI methods are typically oblivious to the system characteristics. To overcome this, we encode system knowledge in a co-simulator, a digital replica of the physical infrastructure. Such a simulator allows us to quickly generate additional out-of-distribution data for robust model training, run performance tests of decisions of interest from an AI model and perform long-term QoS estimation to eschew myopic decision-making. Such advances allow us to provide significantly higher QoS compared to prior methods for resource management in fog environments.Open Acces
SplitPlace: intelligent placement of split neural nets in mobile edge environments
Full text linkIn recent years, deep learning models have become ubiquitous in industry and
academia alike. Modern deep neural networks can solve one of the most complex
problems today, but coming with the price of massive compute and storage
requirements. This makes deploying such massive neural networks challenging in
the mobile edge computing paradigm, where edge nodes are resource-constrained,
hence limiting the input analysis power of such frameworks. Semantic and
layer-wise splitting of neural networks for distributed processing show some
hope in this direction. However, there are no intelligent algorithms that place
such modular splits to edge nodes for optimal performance. This work proposes a
novel placement policy, SplitPlace, for the placement of such neural network
split fragments on mobile edge hosts for efficient and scalable computing
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
COMPUTER IMPLEMENTED METHOD FOR OBTAINING AN OUTPUT RESPONSE TO A NATURAL LANGUAGE INPUT PROMPT
No full textComputer implemented method for obtaining an output
response to a natural language input prompt, the output
response being generated by a language model selected
from a group of language models, comprising the steps:
- receiving the input prompt;
10 - determining a complexity score of the input prompt;
- determining a time-criticality score of the input
prompt;
- for each language model, computing a quality of service
score related to the combination of the language model
15 and the input prompt, on the basis of the complexity
score of the input prompt, the time-criticality score of
the input prompt, a stored response quality function of
the language model and a stored latency function of the
language model;
20 - selecting a selected language model on the basis of
the computed quality service;
- forwarding the input prompt to the selected language
model;
- receiving the output response generated by the selected
25 language model on the basis of the input prompt
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
