54,134 research outputs found
FlexNGIA: A Flexible Internet Architecture for the Next-Generation Tactile Internet
From virtual reality and telepresence, to augmented reality, holoportation,
and remotely controlled robotics, these future network applications promise an
unprecedented development for society, economics and culture by revolutionizing
the way we live, learn, work and play. In order to deploy such futuristic
applications and to cater to their performance requirements, recent trends
stressed the need for the Tactile Internet, an Internet that, according to the
International Telecommunication Union, combines ultra low latency with
extremely high availability, reliability and security. Unfortunately, today's
Internet falls short when it comes to providing such stringent requirements due
to several fundamental limitations in the design of the current network
architecture and communication protocols. This brings the need to rethink the
network architecture and protocols, and efficiently harness recent
technological advances in terms of virtualization and network softwarization to
design the Tactile Internet of the future.
In this paper, we start by analyzing the characteristics and requirements of
future networking applications. We then highlight the limitations of the
traditional network architecture and protocols and their inability to cater to
these requirements. Afterward, we put forward a novel network architecture
adapted to the Tactile Internet called FlexNGIA, a Flexible Next-Generation
Internet Architecture. We then describe some use-cases where we discuss the
potential mechanisms and control loops that could be offered by FlexNGIA in
order to ensure the required performance and reliability guarantees for future
applications. Finally, we identify the key research challenges to further
develop FlexNGIA towards a full-fledged architecture for the future Tactile
Internet.Comment: 35 pages, 14 figure
Stratum: A Serverless Framework for Lifecycle Management of Machine Learning based Data Analytics Tasks
With the proliferation of machine learning (ML) libraries and frameworks, and
the programming languages that they use, along with operations of data loading,
transformation, preparation and mining, ML model development is becoming a
daunting task. Furthermore, with a plethora of cloud-based ML model development
platforms, heterogeneity in hardware, increased focus on exploiting edge
computing resources for low-latency prediction serving and often a lack of a
complete understanding of resources required to execute ML workflows
efficiently, ML model deployment demands expertise for managing the lifecycle
of ML workflows efficiently and with minimal cost. To address these challenges,
we propose an end-to-end data analytics, a serverless platform called Stratum.
Stratum can deploy, schedule and dynamically manage data ingestion tools, live
streaming apps, batch analytics tools, ML-as-a-service (for inference jobs),
and visualization tools across the cloud-fog-edge spectrum. This paper
describes the Stratum architecture highlighting the problems it resolves
Internet of Things (IoT) and Cloud Computing Enabled Disaster Management
Disaster management demands a near real-time information dissemina-tion so
that the emergency services can be provided to the right people at the right
time. Recent advances in information and communication technologies enable
collection of real-time information from various sources. For example, sensors
deployed in the fields collect data about the environment. Similarly, social
networks like Twitter and Facebook can help to collect data from people in the
disaster zone. On one hand, inadequate situation awareness in disasters has
been identified as one of the primary factors in human errors with grave
consequences such as loss of lives and destruction of critical infrastructure.
On the other hand, the growing ubiquity of social media and mobile devices, and
pervasive nature of the Internet-of-Things means that there are more sources of
outbound traffic, which ultimately results in the creation of a data deluge,
beginning shortly after the onset of disaster events, leading to the problem of
information tsunami. In addition, security and privacy has crucial role to
overcome the misuse of the system for either intrusions into data or overcome
the misuse of the information that was meant for a specified purpose. .... In
this chapter, we provide such a situation aware application to support disaster
management data lifecycle, i.e. from data ingestion and processing to alert
dissemination. We utilize cloud computing, Internet of Things and social
computing technologies to achieve a scalable, effi-cient, and usable
situation-aware application called Cloud4BigData.Comment: Submitted for the book titled "Integration of Cyber-Physical Systems,
Cloud, and Internet of Things
Application Management in Fog Computing Environments: A Taxonomy, Review and Future Directions
The Internet of Things (IoT) paradigm is being rapidly adopted for the
creation of smart environments in various domains. The IoT-enabled
Cyber-Physical Systems (CPSs) associated with smart city, healthcare, Industry
4.0 and Agtech handle a huge volume of data and require data processing
services from different types of applications in real-time. The Cloud-centric
execution of IoT applications barely meets such requirements as the Cloud
datacentres reside at a multi-hop distance from the IoT devices. \textit{Fog
computing}, an extension of Cloud at the edge network, can execute these
applications closer to data sources. Thus, Fog computing can improve
application service delivery time and resist network congestion. However, the
Fog nodes are highly distributed, heterogeneous and most of them are
constrained in resources and spatial sharing. Therefore, efficient management
of applications is necessary to fully exploit the capabilities of Fog nodes. In
this work, we investigate the existing application management strategies in Fog
computing and review them in terms of architecture, placement and maintenance.
Additionally, we propose a comprehensive taxonomy and highlight the research
gaps in Fog-based application management. We also discuss a perspective model
and provide future research directions for further improvement of application
management in Fog computing
Big Data Analytics for Dynamic Energy Management in Smart Grids
The smart electricity grid enables a two-way flow of power and data between
suppliers and consumers in order to facilitate the power flow optimization in
terms of economic efficiency, reliability and sustainability. This
infrastructure permits the consumers and the micro-energy producers to take a
more active role in the electricity market and the dynamic energy management
(DEM). The most important challenge in a smart grid (SG) is how to take
advantage of the users' participation in order to reduce the cost of power.
However, effective DEM depends critically on load and renewable production
forecasting. This calls for intelligent methods and solutions for the real-time
exploitation of the large volumes of data generated by a vast amount of smart
meters. Hence, robust data analytics, high performance computing, efficient
data network management, and cloud computing techniques are critical towards
the optimized operation of SGs. This research aims to highlight the big data
issues and challenges faced by the DEM employed in SG networks. It also
provides a brief description of the most commonly used data processing methods
in the literature, and proposes a promising direction for future research in
the field.Comment: Published in ELSEVIER Big Data Researc
Resource Management and Scheduling for Big Data Applications in Cloud Computing Environments
This chapter presents software architectures of the big data processing
platforms. It will provide an in-depth knowledge on resource management
techniques involved while deploying big data processing systems on cloud
environment. It starts from the very basics and gradually introduce the core
components of resource management which we have divided in multiple layers. It
covers the state-of-art practices and researches done in SLA-based resource
management with a specific focus on the job scheduling mechanisms.Comment: 27 pages, 9 figure
Analytics for the Internet of Things: A Survey
The Internet of Things (IoT) envisions a world-wide, interconnected network
of smart physical entities. These physical entities generate a large amount of
data in operation and as the IoT gains momentum in terms of deployment, the
combined scale of those data seems destined to continue to grow. Increasingly,
applications for the IoT involve analytics. Data analytics is the process of
deriving knowledge from data, generating value like actionable insights from
them. This article reviews work in the IoT and big data analytics from the
perspective of their utility in creating efficient, effective and innovative
applications and services for a wide spectrum of domains. We review the broad
vision for the IoT as it is shaped in various communities, examine the
application of data analytics across IoT domains, provide a categorisation of
analytic approaches and propose a layered taxonomy from IoT data to analytics.
This taxonomy provides us with insights on the appropriateness of analytical
techniques, which in turn shapes a survey of enabling technology and
infrastructure for IoT analytics. Finally, we look at some tradeoffs for
analytics in the IoT that can shape future research
Internet of Things: An Overview
As technology proceeds and the number of smart devices continues to grow
substantially, need for ubiquitous context-aware platforms that support
interconnected, heterogeneous, and distributed network of devices has given
rise to what is referred today as Internet-of-Things. However, paving the path
for achieving aforementioned objectives and making the IoT paradigm more
tangible requires integration and convergence of different knowledge and
research domains, covering aspects from identification and communication to
resource discovery and service integration. Through this chapter, we aim to
highlight researches in topics including proposed architectures, security and
privacy, network communication means and protocols, and eventually conclude by
providing future directions and open challenges facing the IoT development.Comment: Keywords: Internet of Things; IoT; Web of Things; Cloud of Thing
Open storm: a complete framework for sensing and control of urban watersheds
Leveraging recent advances in technologies surrounding the Internet of
Things, "smart" water systems are poised to transform water resources
management by enabling ubiquitous real-time sensing and control. Recent
applications have demonstrated the potential to improve flood forecasting,
enhance rainwater harvesting, and prevent combined sewer overflows. However,
adoption of smart water systems has been hindered by a limited number of proven
case studies, along with a lack of guidance on how smart water systems should
be built. To this end, we review existing solutions, and introduce open
storm---an open-source, end-to-end platform for real-time monitoring and
control of watersheds. Open storm includes (i) a robust hardware stack for
distributed sensing and control in harsh environments (ii) a cloud services
platform that enables system-level supervision and coordination of water
assets, and (iii) a comprehensive, web-based "how-to" guide, available on
open-storm.org, that empowers newcomers to develop and deploy their own smart
water networks. We illustrate the capabilities of the open storm platform
through two ongoing deployments: (i) a high-resolution flash-flood monitoring
network that detects and communicates flood hazards at the level of individual
roadways and (ii) a real-time stormwater control network that actively
modulates discharges from stormwater facilities to improve water quality and
reduce stream erosion. Through these case studies, we demonstrate the
real-world potential for smart water systems to enable sustainable management
of water resources.Comment: 12 pages, 5 figure
End-to-End Service Level Agreement Specification for IoT Applications
The Internet of Things (IoT) promises to help solve a wide range of issues
that relate to our wellbeing within application domains that include smart
cities, healthcare monitoring, and environmental monitoring. IoT is bringing
new wireless sensor use cases by taking advantage of the computing power and
flexibility provided by Edge and Cloud Computing. However, the software and
hardware resources used within such applications must perform correctly and
optimally. Especially in applications where a failure of resources can be
critical. Service Level Agreements (SLA) where the performance requirements of
such applications are defined, need to be specified in a standard way that
reflects the end-to-end nature of IoT application domains, accounting for the
Quality of Service (QoS) metrics within every layer including the Edge, Network
Gateways, and Cloud. In this paper, we propose a conceptual model that captures
the key entities of an SLA and their relationships, as a prior step for
end-to-end SLA specification and composition. Service level objective (SLO)
terms are also considered to express the QoS constraints. Moreover, we propose
a new SLA grammar which considers workflow activities and the multi-layered
nature of IoT applications. Accordingly, we develop a tool for SLA
specification and composition that can be used as a template to generate SLAs
in a machine-readable format. We demonstrate the effectiveness of the proposed
specification language through a literature survey that includes an SLA
language comparison analysis, and via reflecting the user satisfaction results
of a usability study
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