3,315 research outputs found
A Time-driven Data Placement Strategy for a Scientific Workflow Combining Edge Computing and Cloud Computing
Compared to traditional distributed computing environments such as grids,
cloud computing provides a more cost-effective way to deploy scientific
workflows. Each task of a scientific workflow requires several large datasets
that are located in different datacenters from the cloud computing environment,
resulting in serious data transmission delays. Edge computing reduces the data
transmission delays and supports the fixed storing manner for scientific
workflow private datasets, but there is a bottleneck in its storage capacity.
It is a challenge to combine the advantages of both edge computing and cloud
computing to rationalize the data placement of scientific workflow, and
optimize the data transmission time across different datacenters. Traditional
data placement strategies maintain load balancing with a given number of
datacenters, which results in a large data transmission time. In this study, a
self-adaptive discrete particle swarm optimization algorithm with genetic
algorithm operators (GA-DPSO) was proposed to optimize the data transmission
time when placing data for a scientific workflow. This approach considered the
characteristics of data placement combining edge computing and cloud computing.
In addition, it considered the impact factors impacting transmission delay,
such as the band-width between datacenters, the number of edge datacenters, and
the storage capacity of edge datacenters. The crossover operator and mutation
operator of the genetic algorithm were adopted to avoid the premature
convergence of the traditional particle swarm optimization algorithm, which
enhanced the diversity of population evolution and effectively reduced the data
transmission time. The experimental results show that the data placement
strategy based on GA-DPSO can effectively reduce the data transmission time
during workflow execution combining edge computing and cloud computing
Towards In-Transit Analytics for Industry 4.0
Industry 4.0, or Digital Manufacturing, is a vision of inter-connected
services to facilitate innovation in the manufacturing sector. A fundamental
requirement of innovation is the ability to be able to visualise manufacturing
data, in order to discover new insight for increased competitive advantage.
This article describes the enabling technologies that facilitate In-Transit
Analytics, which is a necessary precursor for Industrial Internet of Things
(IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things
(iThings-2017), Exeter, UK, 201
On the Fly Orchestration of Unikernels: Tuning and Performance Evaluation of Virtual Infrastructure Managers
Network operators are facing significant challenges meeting the demand for
more bandwidth, agile infrastructures, innovative services, while keeping costs
low. Network Functions Virtualization (NFV) and Cloud Computing are emerging as
key trends of 5G network architectures, providing flexibility, fast
instantiation times, support of Commercial Off The Shelf hardware and
significant cost savings. NFV leverages Cloud Computing principles to move the
data-plane network functions from expensive, closed and proprietary hardware to
the so-called Virtual Network Functions (VNFs). In this paper we deal with the
management of virtual computing resources (Unikernels) for the execution of
VNFs. This functionality is performed by the Virtual Infrastructure Manager
(VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. We
discuss the instantiation process of virtual resources and propose a generic
reference model, starting from the analysis of three open source VIMs, namely
OpenStack, Nomad and OpenVIM. We improve the aforementioned VIMs introducing
the support for special-purpose Unikernels and aiming at reducing the duration
of the instantiation process. We evaluate some performance aspects of the VIMs,
considering both stock and tuned versions. The VIM extensions and performance
evaluation tools are available under a liberal open source licence
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