Abdallah Jarray

Decomposition approaches for virtual network embedding with one-shot node and link mapping

Network virtualization is a promising new resource management approach that allows customized virtual networks (VNs) to be multiplexed on a shared physical infrastructure. In this paper, our focus is on the embedding of VN resources onto this infrastructure. Since this problem is known to be NP-hard, embedding proposals in literature are heuristic-based approaches that restrict the problem space in different dimensions. Limitations of these proposals are: (1) as embedding of VN links and nodes is performed in two separate stages, it may ensue in a high blocking of VN requests and a less efficient usage of substrate resources; and (2) as pricing of embedding resources is based on linear functions, it triggers no competition among VN users in order to maximize infrastructure provider profits. These drawbacks motivate us to propose a mathematical model that makes use of large-scale optimization tools and proposes a Column Generation (CG) formulation of the problem, coupled with branch-and-bound technique or rounding-off heuristic. We also propose a periodical planning of embedding process where profitable VN requests are selected through an auction mechanism. In our experiments with different substrate network topologies and many different VN request patterns, we show a clear advantage of auction-based CG models over present benchmarks .

Resource allocation in macrocell-femtocell network using genetic algorithm

In this paper, we consider the problem of resource allocation in two-tier networks taking into consideration nondense femtocell deployments. The following limitations can be remarked from the prior work in the field of resource allocation: (1) resources are underutilized due to the equal power distribution in macrocell, (2) access to public users in femtocells is restricted to avoid depriving own subscribers transmissions, and (3) degradation of signal-to-noise ratio due to noise effects has not been evaluated. To overcome these limitations, we propose a joint power and bandwidth allocation among two tiers together with base station selection using genetic algorithm. Our solution is able to: (1) maximize the overall system throughput, (2) find an appropriate serving base station for each user, and (3) bandwidth and power assigned to each user. Simulations were conducted and a comparison with a Weighted Water Filling algorithm is carried out.

Energy-Efficient Resource-Allocation Model for OFDMA Macrocell/Femtocell Networks

Femtocells (FCs) are introduced to enhance the indoor coverage and system capacity of traditional cellular networks. However, the network performance could be significantly deteriorated due to the increase in cochannel interference in dense deployment. In the literature, three spectrum usage schemes that deal with the cross-tier interference have been proposed, i.e., orthogonal assignment, underlay, and controlled underlay using mainly closed-access FCs. This paper targets the optimization of resource allocation, together with the selection of base stations (BSs) in two-tier networks assuming hybrid access FCs, to reduce the cross-tier interference. Moreover, this model aims to achieve effective spatial reuse between the macrocell (MC) and FCs while guaranteeing quality-of-service (QoS) transmissions by means of joint power control in both tiers. Simulations are conducted to show a comparison with the other three approaches.

Cost-Efficient Mapping for Fault-Tolerant Virtual Networks

Network virtualization provides more flexibility in network provisioning as it offers physical infrastructure providers (PIP) the possibility of smoothly rolling out many separate networks on top of an existing infrastructure. A major challenge is the embedding problem of mapping virtual networks (VNs) onto PIP infrastructure. In the literature, a good deal of research has focused on providing heuristic approaches to this NP-hard problem, usually with the assumption that the PIP infrastructure is operational at all times. In virtualization environment, a single physical node/link failure can result in one or more logical link failures as it effects all VNs with a mapping that spans over. Setting up a dedicated backup for each VN embedding that is not shared with others is an inefficient use of resources. To address these concerns, this paper proposes two classes of periodic VN protection against link and node failures: (a) in the physical layer, by using a path or segment p-cycle technique and a column generation optimization model, and (b) in the VN layer, by augmenting the topology with redundant resources and subsequently applying a column generation mapping model. Our simulations show a clear advantage of our approaches over benchmarks in terms of PIP profit, backup cost/rate and resource use.

Periodical auctioning for QoS aware virtual network embedding

In this paper, our focus is on the embedding problem which consists on the mapping of VN resources onto physical infrastructure network. More specifically, we consider the problem of optimizing the Physical Infrastructure Providers (PIP) profit while minimizing the dissatisfaction of VN customers. We propose to dynamically partition the PIP resources over VN requests belonging to different Quality of Service (QoS) classes using periodical auction mechanism. We formulate the dynamic embedding problem as an Integer Linear Program (ILP) that allows us to: (i) maximize the PIP profit, and (ii) calculate the optimal embedding scheme of VN requests without disruption of those previously accepted in order to uphold QoS guarantees.

Column generation approach for one-shot virtual network embedding

Network Virtualization has been proposed as new promising resource management approach that allows multiple customized Virtual Networks (VNs) to be setup on a shared physical infrastructure. In this paper, our focus is on the embedding problem which consists on the mapping of VN resources onto physical infrastructure network. Since this problem is known to be NP-hard, in literature, number of works have been proposed heuristic based-embedding approaches. The mapping of VN links and nodes are performed on two separate stages, which may ensue in a high blocking of VN requests and an under-utilization of substrate resources. To handle these drawbacks, we propose a mathematical model which makes use of large scale optimization tools and propose a Column Generation formulation coupled with a Branch and Bound technique in order to solve it efficiently.

Base station selection and resource allocation in macro–femtocell networks under noisy scenario

In this paper, we address the problem of optimizing resources for downlink transmission in a macro–femtocell network under non-dense femtocell deployment. In the literature, some approaches perform bandwidth or power optimization depending on the air interface technology and others optimize both types of resources, but only in femtocell network. However, the following limitations can be noticed: (1) Equal distribution of transmitted power among all subcarriers, even if they are not used, leads to resource underutilization, (2) femtocell data rates are reduced in order to minimize the interference from femto base stations to macro users, and (3) the impact of noise has not been evaluated. Moreover, there is lack of optimal selection of users that can be served by femtocells. To overcome these limitations, we propose a model that finds a tradeoff between bandwidth and power to reduce the bandwidth usage per user and to minimize the impact of noise. By means of Linear Programming, our solution maximizes user satisfaction and provides optimal: serving base station, power and bandwidth for each mobile user taking into account its location and demand. Furthermore, we present a performance analysis under changes of signal to noise ratio. Simulations were conducted and a comparison with a modified version of Weighted Water Filling algorithm is presented.

Two-phase ontology-based resource allocation approach for IaaS cloud service

This paper proposes a composition-based resource allocation approach to cloud infrastructure-as-a-service (IaaS). A number of previous proposals have addressed resource allocation for IaaS services with bandwidth guarantee as the main obstacle to accessing datacenter-based cloud resources. Less focus has been put on utilization of hosting resources, i.e. computing and storage. Shortcomings with these proposals (i) may ensue in a high blocking of IaaS requests and (ii) a less efficient use of datacenter resources, which may impact a providers revenue. In this research paper, a Two-Phase IaaS resource allocation approach (2P-IaaS) is proposed to address these shortcomings: (i) hosting resources mapping phase, and (ii) connectivity composition phase. This approach adopts a semantic ontology model with its associated reasoning capabilities to represent, discover and assign diverse cloud resources to IaaS requests. Furthermore, the proposed approach utilizes semantic similarity and closeness centrality to define an efficient cloud IaaS topology that connects the assigned resources. In this research, experiments on various sets of IaaS requests show significant advantages when compared with selected benchmarks.

Resilient virtual network embedding

Our focus, in this paper is on the embedding problem which consists on the mapping of Virtual Network (VN) resources onto physical infrastructure network. In relevant literature, number of works have been proposed to solve this challenging problem. However, few proposals have been dedicated to provide backup mapping mechanism for failing physical links. In virtualization environment a failing link will affect all VNs that span over this resource. To address these concerns, we propose in this work a resilient VN embedding (RVNE) approach that: (a) uses an adaptation of a Column Generation-based technique developed in our previous work, to calculate in a first stage a cost-efficient VN mapping while minimizing the affects of a single link failure in VN layer, and (b) proposes in a second stage a p-Cycle based VN protection approach that minimizes the backup resources while providing a full protection scheme. Experiments on large mix of VN requests show a clear advantage of resilient VN embedding model over benchmarks in terms of VN revenue, VN protection cost and resources utilization.

MILP-Based Approach for Efficient Cloud IaaS Resource Allocation

Current data center designs suffer from poor resource utilization. Several research works have addressed the data center resource allocation problem, and recent proposals have focused on network resource utilization as a bottleneck problem in data centers. However, little attention has been paid to manage the convergence of networking and other infrastructure resources. In this paper, we present a solution for improving data centers resource utilization. We introduce a unified cloud resource representation model and build a general resources repository using that model. We define a combined controller to manipulate infrastructure resources collected in the repository. A joint optimization model that performs the resource allocation as the main controller operation is also presented. This model represents the integration of semantic similarity and closeness centrality concepts and is formulated on a two-phase Mixed Integer Linear Programming (MILP-2P-IaaS): (i) mapping of hosting resources, and (ii) connectivity composition. Simulation results show that the (MILP-2P-IaaS) resource allocation approach improves data centers resource utilization and outperforms other benchmarks in terms of resource utilization and acceptance ratio.