1,720,997 research outputs found
Proposal and investigation of an ETSI NFV architecture supporting aI-based resource prediction
No full textThe high reconfiguration time of cloud resources in Network Function Virtualization architecture has led to make ineffective the reactive cloud resource allocation procedures whose application lead to over-allocate resources or to degrade Quality of Service in decreasing/increasing traffic scenario. Recently many Artificial Intelligence (AI)-based allocation procedures have been proposed to pre-allocate cloud resource according to required processing capacity predictions. In this paper we illustrate how an ETSI NFV architecture can support these predictions procedures. Furthermore they aim to exactly predict the processing capacity to be allocated and they are all based on the minimization of a symmetric loss function of the neural network. For this reason we propose a resource allocation procedure with a asymmetric loss function whose parameters are dependent on an overall cost expressed in terms of allocation and QoS degradation costs. We prove that the proposed solution allows for a cost reduction in the order of 30% in a typical NFV traffic and network scenario
Application of an Innovative Convolutional/LSTM Neural Network for Computing Resource Allocation in NFV Network Architectures
No full textAn innovative resource allocation framework for virtualized network environments and based on the application of Artificial Intelligence techniques is proposed and investigated. It integrates the needed processing capacity prediction procedure and the allocation one which determines the capacity under-allocation or over-allocation needed to minimize the resource allocation and Quality of Service degradation costs. The proposed solution is based on: i) a monitoring procedure in which the processing capacities required by virtual instances are periodically monitored; ii) an integrated allocation/prediction procedure in which the processing capacities to be allocated to the virtual instances are evaluated in time intervals successive to the monitoring periods. This second procedure uses a Convolutional/Long Short Term Memory neural network whose loss function is defined so as to minimize an overall cost dependent on both the cloud resource allocation and Quality of Service degradation costs. We evaluate the proposed solution in backbone and metropolitan traffic and network scenario. We show how in the traffic scenario of an Italian Mobile Operator in Milan zone, the proposed solution far outperforms the not integrated classical solution in which the capacity prediction and allocation procedures are separately performed. Furthermore its performance are very near to the one of an oracle that optimizes the capacity over/under dimensioning parameter
Computing and bandwidth resource allocation in multi-provider NFV environment
No full textWe propose an algorithm for the cloud and bandwidth resource allocation in Multi-Provider NFV environments. The resources are allocated so as to take into account the different costs charged by the cloud Infrastructure Providers (InP). The effectiveness of the proposed algorithm is confirmed from the comparison with the results of the optimal problem. Its application in medium and large networks has shown that it can lead to cost saving as high as 65% with respect to algorithms that allocate resources without taking into account the cost differences charged by the InPs
Proposal and investigation of a reconfiguration cost aware policy for resource allocation in multi-provider NFV infrastructures interconnected by elastic optical networks
No full textThis paper proposes and investigates a reconfiguration policy of cloud and bandwidth resources in network function virtualization environments in which the network function virtualization infrastructure-point of presences are interconnected by an elastic optical network. The proposed policy is applied in dynamic traffic scenarios, it is based on virtual network function instances migrations and reconfiguration of optical circuits. The policy aims at minimizing the sum of three cost components: the cloud resource cost, the bandwidth cost, and the reconfiguration cost. The reconfiguration costs are characterized by the revenue loss of a network operator due to the bit loss occurring when the optical circuits are reconfigured. We formulate the optimization problem and because of its complexity we propose and investigate an effective heuristic. The achieved results show how the proposed heuristic allows, in typical traffic and network scenario, for a consistent cost reduction with respect to the solutions proposed in literature in which only the sum of cloud and bandwidth resource costs are considered and reconfiguration costs are not take into account
Impact of the deployment costs on the cloud and bandwidth resource problems in multi-providers NFV environment
No full textThe introduction of Network Function Virtualization (NFV) led to a new business model in which the Telecommunication Service Provider needs to rent cloud resources to Infrastructure Provider (InP) at prices as low as possible. Lowest prices can be achieved if the cloud resources, that is long-term Virtual Machines (VM), can be rented in advance. This is in contrast with the short-term VMs that are rented on demand and have higher costs. For this reason we propose a proactive solution in which the cloud resource rent is planned in advance based on a peak traffic knowledge. We illustrate the problem of determining the cloud resources in Cloud Infrastructures managed by different InPs and so as to minimize the cloud resource, bandwidth and deployment costs. In particular to take into account the deployment costs we propose and investigate an heuristic based on the application of the Viterbi algorithm. We show how the proposed proactive approach may allow for a cost reduction in the order of 35% with respect to a reactive approach in which the resources are short-tem rented according to the current traffic demand
Proposal and investigation of a processing and bandwidth resource allocation strategy in LEO satellite networks for earth observation applications
No full textThe processing capacity available on satellites within Low Earth Orbit constellations allows for the implementation of data processing on-board of satellites. Furthermore, by endowing satellites with Inter-Satellite Links, it is possible to obtain a network within the constellation, enabling data routing and allowing processing to take place on any satellite of the constellation, even different from the task originating one. Edge computing solutions for Earth Observation (EO) applications have been proposed in satellite environments, where the data processing is accomplished by either the satellite running the observation task (Always First) or by a datacenter connected to a ground station (Always Ground). We propose a solution in which any satellite of the constellation may process the EO task and its choice is performed so as to minimize the sum of the memory, processing and transmission costs. We propose and evaluate the effectiveness of a heuristic that jointly performs the following operations: i) the choice of the element (satellite, datacenter) performing the task processing; ii) the choice of the routing path on which the task or its processing result is routed from the observation satellite to the datacenter. The proposed solution is compared to the Always First and Always Ground benchmark solutions and both the cost advantages and the potential delivery delay reduction are evaluated
A resource allocation strategy in earth observation orbital edge computing-enabled satellite networks to minimize ground station energy consumption
No full textBy endowing satellites in Earth Observation (EO) constellations with Orbital Edge Computing (OEC) capability, i.e, with the ability of processing acquired images directly on-board, it is surely possible to use bandwidth more effectively, since only the actual useful information extracted from the image is transferred on ground. However, OEC can be fruitfully leveraged also to minimize the energy consumption on ground stations due to image processing. In fact, in EO satellites energy is allocated in advance by endowing them with appropriate solar panels and batteries, and this amount of energy is always generated, even when it is not necessary. On the contrary, energy on ground station is closely related to its demand. For this reason, we propose and investigate a strategy to allocate processing and bandwidth resources in OEC-endowed EO satellite network to minimize energy consumption on ground stations due to on-ground processing. By taking into account the energy budget on Sentine1-2, having a 26% of available energy being unused, we show how with our approach it is possible to obtain a substantial reduction in energy consumption on ground stations even by using the 1% of the total energy budget when appropriate computational capacity is available on board, while this consumption can even drop to zero by increasing the energy dedicated to OEC operations to the 15% of the energy budget
Impact of the maximum number of switching reconfigurations on the cost saving in network function virtualization environments with elastic optical interconnection
Full text linkNetwork Function Virtualization is based on the virtualization of the network functions and it is a new technology allowing for a more flexible allocation of cloud and bandwidth resources. In order to employ the flexibility of the technology and to adapt its use according to the traffic variation, reconfigurations of the cloud and bandwidth resources are needed by means of both migration of the Virtual Machines executing the network functions and reconfiguration of circuits interconnecting the Virtual Machines. The objective of the paper is to study the impact of the maximum number of switch reconfigurations on the cost saving that the Networking Function Virtualization technology allows us to achieve. The problem is studied in the case of a scenario with an elastic optical network interconnecting datacenters in which the Virtual Machines are executed. The problem can be formulated as an Integer Linear Programming one introducing a constraint on the maximum number of switch reconfigurations but due to its computational complexity we propose a low computational complexity heuristic allowing for results close to the optimization ones. The results show how the limitation on the number of possible reconfigurations has to be taken into account to evaluate the effectiveness in terms of cost saving that the Virtual Machine migrations in Network Function Virtualization environment allows us to achieve
Optimal bandwidth and computing resource allocation in low earth orbit satellite constellation for earth observation applications
No full textThe next step in Earth Observation (EO) constellations will be leveraging Inter-Satellite Links (ISLs) to form a network where information generated by the EO application can be transmitted, in such a way that, by endowing spacecrafts with processing capacity, observation data may be processed directly in orbit by any satellite of the constellation. However, since bandwidth and on-board processing capacity are valuable resources, strategies to appropriately routing the information and deciding on which node it has to be processed shall be defined. In this work, we formalize and solve an optimal bandwidth and computing resource allocation problem in Low Earth Orbit (LEO) satellite constellation for EO applications. In order to deal with the complexity of the proposed optimization problem, we also present two heuristics requiring different computational effort. In the proposed problem formalization, processing can happen on any node of the network (i.e., either on the data source satellite, on any other satellite of the constellation or on ground station). After having validated the proposed heuristics by comparing their results to the optimization problem ones, we apply them to a real orbital scenario, showing their ability to reduce both total cost and data delivery delay to ground with respect to state-of-the-art solutions
Proposal and investigation of an optical reconfiguration cost aware policy for resource allocation in network function virtualization infrastructures
No full textThe paper proposes and investigates the problem of the reconfiguration of cloud and bandwidth resources in Multi-Provider Network Function Virtualization architectures where the Cloud Infrastructures (CI) are managed by different Providers and interconnected by an elastic optical network. The resource reconfiguration is performed by taking into account the different costs charged by the Infrastructure Providers (InP) of the CIs and by exploiting the advantages of the adaptive optical modulation. The objective is to minimize the total cost given by the sum of three components: i) the cloud resource cost; ii) the bandwidth costs; iii) the reconfiguration costs characterized by the revenue loss of the Telecommunication Service Provider due to the degradation of the Quality of Service occurring during the reconfiguration of the optical circuits. We define and investigate a heuristic of polynomial complexity. The application of the heuristic to the large distance USNET network for typical traffic and network parameters allows for a saving by 40% in total cost with respect to the case in which a traditional policy is applied
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