1,809 research outputs found
ENORM: A Framework For Edge NOde Resource Management
Current computing techniques using the cloud as a centralised server will
become untenable as billions of devices get connected to the Internet. This
raises the need for fog computing, which leverages computing at the edge of the
network on nodes, such as routers, base stations and switches, along with the
cloud. However, to realise fog computing the challenge of managing edge nodes
will need to be addressed. This paper is motivated to address the resource
management challenge. We develop the first framework to manage edge nodes,
namely the Edge NOde Resource Management (ENORM) framework. Mechanisms for
provisioning and auto-scaling edge node resources are proposed. The feasibility
of the framework is demonstrated on a PokeMon Go-like online game use-case. The
benefits of using ENORM are observed by reduced application latency between 20%
- 80% and reduced data transfer and communication frequency between the edge
node and the cloud by up to 95\%. These results highlight the potential of fog
computing for improving the quality of service and experience.Comment: 14 pages; accepted to IEEE Transactions on Services Computing on 12
September 201
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Fog Computing: A Taxonomy, Survey and Future Directions
In recent years, the number of Internet of Things (IoT) devices/sensors has
increased to a great extent. To support the computational demand of real-time
latency-sensitive applications of largely geo-distributed IoT devices/sensors,
a new computing paradigm named "Fog computing" has been introduced. Generally,
Fog computing resides closer to the IoT devices/sensors and extends the
Cloud-based computing, storage and networking facilities. In this chapter, we
comprehensively analyse the challenges in Fogs acting as an intermediate layer
between IoT devices/ sensors and Cloud datacentres and review the current
developments in this field. We present a taxonomy of Fog computing according to
the identified challenges and its key features.We also map the existing works
to the taxonomy in order to identify current research gaps in the area of Fog
computing. Moreover, based on the observations, we propose future directions
for research
Cloud-based desktop services for thin clients
Cloud computing and ubiquitous network availability have renewed people's interest in the thin client concept. By executing applications in virtual desktops on cloud servers, users can access any application from any location with any device. For this to be a successful alternative to traditional offline applications, however, researchers must overcome important challenges. The thin client protocol must display audiovisual output fluidly, and the server executing the virtual desktop should have sufficient resources and ideally be close to the user's current location to limit network delay. From a service provider viewpoint, cost reduction is also an important issue
Software-Defined Cloud Computing: Architectural Elements and Open Challenges
The variety of existing cloud services creates a challenge for service
providers to enforce reasonable Software Level Agreements (SLA) stating the
Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid
such penalties at the same time that the infrastructure operates with minimum
energy and resource wastage, constant monitoring and adaptation of the
infrastructure is needed. We refer to Software-Defined Cloud Computing, or
simply Software-Defined Clouds (SDC), as an approach for automating the process
of optimal cloud configuration by extending virtualization concept to all
resources in a data center. An SDC enables easy reconfiguration and adaptation
of physical resources in a cloud infrastructure, to better accommodate the
demand on QoS through a software that can describe and manage various aspects
comprising the cloud environment. In this paper, we present an architecture for
SDCs on data centers with emphasis on mobile cloud applications. We present an
evaluation, showcasing the potential of SDC in two use cases-QoS-aware
bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and
discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing,
Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi,
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