2,452 research outputs found
Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding
In cloud infrastructure, accommodating multiple virtual networks on a single
physical network reduces power consumed by physical resources and minimizes
cost of operating cloud data centers. However, mapping multiple virtual network
resources to physical network components, called virtual network embedding
(VNE), is known to be NP-hard. With considering energy efficiency, the problem
becomes more complicated. In this paper, we model energy-aware virtual network
embedding, devise metrics for evaluating performance of energy aware virtual
network-embedding algorithms, and propose an energy aware virtual
network-embedding algorithm based on multi-objective particle swarm
optimization augmented with local search to speed up convergence of the
proposed algorithm and improve solutions quality. Performance of the proposed
algorithm is evaluated and compared with existing algorithms using extensive
simulations, which show that the proposed algorithm improves virtual network
embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684
Multi-Dimensional Customization Modelling Based On Metagraph For Saas Multi-Tenant Applications
Software as a Service (SaaS) is a new software delivery model in which
pre-built applications are delivered to customers as a service. SaaS providers
aim to attract a large number of tenants (users) with minimal system
modifications to meet economics of scale. To achieve this aim, SaaS
applications have to be customizable to meet requirements of each tenant.
However, due to the rapid growing of the SaaS, SaaS applications could have
thousands of tenants with a huge number of ways to customize applications.
Modularizing such customizations still is a highly complex task. Additionally,
due to the big variation of requirements for tenants, no single customization
model is appropriate for all tenants. In this paper, we propose a
multi-dimensional customization model based on metagraph. The proposed mode
addresses the modelling variability among tenants, describes customizations and
their relationships, and guarantees the correctness of SaaS customizations made
by tenants.Comment: 10 pages, 8 figure
Virtual Network Embedding Algorithms Based on Best-Fit Subgraph Detection
One of the main objectives of cloud computing providers is increasing the
revenue of their cloud datacenters by accommodating virtual network requests as
many as possible. However, arrival and departure of virtual network requests
fragment physical network's resources and reduce the possibility of accepting
more virtual network requests. To increase the number of virtual network
requests accommodated by fragmented physical networks, we propose two virtual
network embedding algorithms, which coarsen virtual networks using Heavy Edge
Matching (HEM) technique and embed coarsened virtual networks on best-fit
sub-substrate networks. The performance of the proposed algorithms are
evaluated and compared with existing algorithms using extensive simulations,
which show that the proposed algorithms increase the acceptance ratio and the
revenue.Comment: arXiv admin note: substantial text overlap with arXiv:1502.0235
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