8,870 research outputs found
An Energy-driven Network Function Virtualization for Multi-domain Software Defined Networks
Network Functions Virtualization (NFV) in Software Defined Networks (SDN)
emerged as a new technology for creating virtual instances for smooth execution
of multiple applications. Their amalgamation provides flexible and programmable
platforms to utilize the network resources for providing Quality of Service
(QoS) to various applications. In SDN-enabled NFV setups, the underlying
network services can be viewed as a series of virtual network functions (VNFs)
and their optimal deployment on physical/virtual nodes is considered a
challenging task to perform. However, SDNs have evolved from single-domain to
multi-domain setups in the recent era. Thus, the complexity of the underlying
VNF deployment problem in multi-domain setups has increased manifold. Moreover,
the energy utilization aspect is relatively unexplored with respect to an
optimal mapping of VNFs across multiple SDN domains. Hence, in this work, the
VNF deployment problem in multi-domain SDN setup has been addressed with a
primary emphasis on reducing the overall energy consumption for deploying the
maximum number of VNFs with guaranteed QoS. The problem in hand is initially
formulated as a "Multi-objective Optimization Problem" based on Integer Linear
Programming (ILP) to obtain an optimal solution. However, the formulated ILP
becomes complex to solve with an increasing number of decision variables and
constraints with an increase in the size of the network. Thus, we leverage the
benefits of the popular evolutionary optimization algorithms to solve the
problem under consideration. In order to deduce the most appropriate
evolutionary optimization algorithm to solve the considered problem, it is
subjected to different variants of evolutionary algorithms on the widely used
MOEA framework (an open source java framework based on multi-objective
evolutionary algorithms).Comment: Accepted for publication in IEEE INFOCOM 2019 Workshop on Intelligent
Cloud Computing and Networking (ICCN 2019
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Content-aware resource allocation model for IPTV delivery networks
Nowadays, with the evolution of digital video broadcasting, as well as, the advent of high speed broadband networks, a new era of TV services has emerged known as IPTV. IPTV is a system that employs the high speed broadband networks to deliver TV services to the subscribers. From the service provider viewpoint, the challenge in IPTV systems is how to build delivery networks that exploits the resources efficiently and reduces the service cost, as well. However, designing such delivery networks affected by many factors including choosing the suitable network architecture, load balancing, resources waste, and cost reduction. Furthermore, IPTV contents characteristics, particularly; size, popularity, and interactivity play an important role in balancing the load and avoiding the resources waste for delivery networks. In this paper, we investigate the problem of resource allocation for IPTV delivery networks over the recent architecture, peer-service area architecture. The Genetic Algorithm as an optimization tool has been used to find the optimal provisioning parameters including storage, bandwidth, and CPU consumption. The experiments have been conducted on two data sets with different popularity distributions. The experiments have been conducted on two popularity distributions. The experimental results showed the impact of content status on the resource allocation process
InterCloud: Utility-Oriented Federation of Cloud Computing Environments for Scaling of Application Services
Cloud computing providers have setup several data centers at different
geographical locations over the Internet in order to optimally serve needs of
their customers around the world. However, existing systems do not support
mechanisms and policies for dynamically coordinating load distribution among
different Cloud-based data centers in order to determine optimal location for
hosting application services to achieve reasonable QoS levels. Further, the
Cloud computing providers are unable to predict geographic distribution of
users consuming their services, hence the load coordination must happen
automatically, and distribution of services must change in response to changes
in the load. To counter this problem, we advocate creation of federated Cloud
computing environment (InterCloud) that facilitates just-in-time,
opportunistic, and scalable provisioning of application services, consistently
achieving QoS targets under variable workload, resource and network conditions.
The overall goal is to create a computing environment that supports dynamic
expansion or contraction of capabilities (VMs, services, storage, and database)
for handling sudden variations in service demands.
This paper presents vision, challenges, and architectural elements of
InterCloud for utility-oriented federation of Cloud computing environments. The
proposed InterCloud environment supports scaling of applications across
multiple vendor clouds. We have validated our approach by conducting a set of
rigorous performance evaluation study using the CloudSim toolkit. The results
demonstrate that federated Cloud computing model has immense potential as it
offers significant performance gains as regards to response time and cost
saving under dynamic workload scenarios.Comment: 20 pages, 4 figures, 3 tables, conference pape
Distributed Environment for Efficient Virtual Machine Image Management in Federated Cloud Architectures
The use of Virtual Machines (VM) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. VM as software artifacts are created using provider-specific templates, called VM images (VMI), and are stored in proprietary or public repositories for further use. However, some technology specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation and ultimately vendor lock-in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling: (i) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements; (ii) multi-objective VMI repository optimization; and (iii) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms, can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%
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