1,626 research outputs found
All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey
With the Internet of Things (IoT) becoming part of our daily life and our
environment, we expect rapid growth in the number of connected devices. IoT is
expected to connect billions of devices and humans to bring promising
advantages for us. With this growth, fog computing, along with its related edge
computing paradigms, such as multi-access edge computing (MEC) and cloudlet,
are seen as promising solutions for handling the large volume of
security-critical and time-sensitive data that is being produced by the IoT. In
this paper, we first provide a tutorial on fog computing and its related
computing paradigms, including their similarities and differences. Next, we
provide a taxonomy of research topics in fog computing, and through a
comprehensive survey, we summarize and categorize the efforts on fog computing
and its related computing paradigms. Finally, we provide challenges and future
directions for research in fog computing.Comment: 48 pages, 7 tables, 11 figures, 450 references. The data (categories
and features/objectives of the papers) of this survey are now available
publicly. Accepted by Elsevier Journal of Systems Architectur
Energy-efficient Analytics for Geographically Distributed Big Data
Big data analytics on geographically distributed datasets (across data
centers or clusters) has been attracting increasing interests from both
academia and industry, but also significantly complicates the system and
algorithm designs. In this article, we systematically investigate the
geo-distributed big-data analytics framework by analyzing the fine-grained
paradigm and the key design principles. We present a dynamic global manager
selection algorithm (GMSA) to minimize energy consumption cost by fully
exploiting the system diversities in geography and variation over time. The
algorithm makes real-time decisions based on the measurable system parameters
through stochastic optimization methods, while achieving the performance
balances between energy cost and latency. Extensive trace-driven simulations
verify the effectiveness and efficiency of the proposed algorithm. We also
highlight several potential research directions that remain open and require
future elaborations in analyzing geo-distributed big data
FogStore: Toward a Distributed Data Store for Fog Computing
Stateful applications and virtualized network functions (VNFs) can benefit
from state externalization to increase their reliability, scalability, and
inter-operability. To keep and share the externalized state, distributed data
stores (DDSs) are a powerful tool allowing for the management of classical
trade-offs in consistency, availability and partitioning tolerance. With the
advent of Fog and Edge Computing, stateful applications and VNFs are pushed
from the data centers toward the network edge. This poses new challenges on
DDSs that are tailored to a deployment in Cloud data centers. In this paper, we
propose two novel design goals for DDSs that are tailored to Fog Computing: (1)
Fog-aware replica placement, and (2) context-sensitive differential
consistency. To realize those design goals on top of existing DDSs, we propose
the FogStore system. FogStore manages the needed adaptations in replica
placement and consistency management transparently, so that existing DDSs can
be plugged into the system. To show the benefits of FogStore, we perform a set
of evaluations using the Yahoo Cloud Serving Benchmark.Comment: To appear in Proceedings of 2017 IEEE Fog World Congress (FWC '17
Fog Computing in IoT Aided Smart Grid Transition- Requirements, Prospects, Status Quos and Challenges
Due to unfolded developments in both the IT sectors viz. Intelligent
Transportation and Information Technology contemporary Smart Grid (SG) systems
are leveraged with smart devices and entities. Such infrastructures when
bestowed with the Internet of Things (IoT) and sensor network make a universe
of objects active and online. The traditional cloud deployment succumbs to meet
the analytics and computational exigencies decentralized, dynamic cum
resource-time critical SG ecosystems. This paper synoptically inspects to what
extent the cloud computing utilities can satisfy the mission-critical
requirements of SG ecosystems and which subdomains and services call for fog
based computing archetypes. The objective of this work is to comprehend the
applicability of fog computing algorithms to interplay with the core centered
cloud computing support, thus enabling to come up with a new breed of real-time
and latency free SG services. The work also highlights the opportunities
brought by fog based SG deployments. Correspondingly, we also highlight the
challenges and research thrusts elucidated towards the viability of fog
computing for successful SG Transition.Comment: 13 Pages, 1 table, 1 Figur
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
Virtual Machine Placement Literature Review
Cloud Computing Datacenters host millions of virtual machines (VMs) on real
world scenarios. In this context, Virtual Machine Placement (VMP) is one of the
most challenging problems in cloud infrastructure management, considering also
the large number of possible optimization criteria and different formulations
that could be studied. VMP literature include relevant topics such as
energy-efficiency, Service Level Agreements (SLA), cloud service markets,
Quality of Service (QoS) and carbon dioxide emissions, all of them with high
economical and ecological impact. This work presents an extensive up-to-date
review of the most relevant VMP literature in order to identify research
opportunities
Energy Efficient Virtual Machine Services Placement in Cloud-Fog Architecture
The proliferation in data volume and processing requests calls for a new
breed of on-demand computing. Fog computing is proposed to address the
limitations of cloud computing by extending processing and storage resources to
the edge of the network. Cloud and fog computing employ virtual machines (VMs)
for efficient resource utilization. In order to optimize the virtual
environment, VMs can be migrated or replicated over geo-distributed physical
machines for load balancing and energy efficiency. In this work, we investigate
the offloading of VM services from the cloud to the fog considering the British
Telecom (BT) network topology. The analysis addresses the impact of different
factors including the VM workload and the proximity of fog nodes to users
considering the data rate of state-of-the-art applications. The result show
that the optimum placement of VMs significantly decreases the total power
consumption by up to 75% compared to a single cloud placement.Comment: International Conference on Transparent Optical Networks ICTON 201
ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge
The Internet of Things (IoT) is offering unprecedented observational data
that are used for managing Smart City utilities. Edge and Fog gateway devices
are an integral part of IoT deployments to acquire real-time data and enact
controls. Recently, Edge-computing is emerging as first-class paradigm to
complement Cloud-centric analytics. But a key limitation is the lack of a
platform-as-a-service for applications spanning Edge and Cloud. Here, we
propose ECHO, an orchestration platform for dataflows across distributed
resources. ECHO's hybrid dataflow composition can operate on diverse data
models -- streams, micro-batches and files, and interface with native runtime
engines like TensorFlow and Storm to execute them. It manages the application's
lifecycle, including container-based deployment and a registry for state
management. ECHO can schedule the dataflow on different Edge, Fog and Cloud
resources, and also perform dynamic task migration between resources. We
validate the ECHO platform for executing video analytics and sensor streams for
Smart Traffic and Smart Utility applications on Raspberry Pi, NVidia TX1, ARM64
and Azure Cloud VM resources, and present our results.Comment: 17 pages, 5 figures, 2 tables, submitted to ICSOC-201
A Taxonomy and Future Directions for Sustainable Cloud Computing: 360 Degree View
The cloud computing paradigm offers on-demand services over the Internet and
supports a wide variety of applications. With the recent growth of Internet of
Things (IoT) based applications the usage of cloud services is increasing
exponentially. The next generation of cloud computing must be energy-efficient
and sustainable to fulfil the end-user requirements which are changing
dynamically. Presently, cloud providers are facing challenges to ensure the
energy efficiency and sustainability of their services. The usage of large
number of cloud datacenters increases cost as well as carbon footprints, which
further effects the sustainability of cloud services. In this paper, we propose
a comprehensive taxonomy of sustainable cloud computing. The taxonomy is used
to investigate the existing techniques for sustainability that need careful
attention and investigation as proposed by several academic and industry
groups. Further, the current research on sustainable cloud computing is
organized into several categories: application design, sustainability metrics,
capacity planning, energy management, virtualization, thermal-aware scheduling,
cooling management, renewable energy and waste heat utilization. The existing
techniques have been compared and categorized based on the common
characteristics and properties. A conceptual model for sustainable cloud
computing has been proposed along with discussion on future research
directions.Comment: 68 pages, 38 figures, ACM Computing Surveys, 201
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
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