Nancy Samaan

Towards Autonomic Network Management: an Analysis of Current and Future Research Directions

Autonomic network management is an innovative vision promising new horizons of efficient networking systems free from human control. This promise has, thus far, ushered in enormous yet dispersed research contributions in both industry and academia. The work presented in this article aims at putting these efforts into perspective deriving a more holistic view of the literature in this area. We analyze the requirements and the main contributions for the building blocks of any autonomic network management system (ANMS). We then describe a coherent classification methodology to compare existing ANMS architectures. Based on this analysis, we suggest a reference framework and highlight some open challenges and describe new research opportunities.

A Novel Economic Sharing Model in a Federation of Selfish Cloud Providers

This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices.

Hierarchical Approach for Efficient Virtual Network Embedding Based on Exact Subgraph Matching

The virtual network (VN) embedding problem is concerned with mapping the nodes and links of a VN request to a shared substrate network while maximizing some objective function such as maximizing profit or resource utilization. This mapping must satisfy specific node capacity and link bandwidth requirements. This paper presents a novel hierarchical approach for scalable VN embedding that achieves a balance between centralized schemes that have a network-wide view of available resources but represent a management bottleneck and scalable distributed ones that incur a high message overhead. The contribution of this work is two fold; we introduce a novel hierarchical substrate management framework that finds more than one candidate VN mapping in parallel, thus, increasing the chances of finding an optimal mapping. The second contribution is a novel VN mapping scheme that recasts the VN mapping problem as a subgraph matching one using modified graph-powers, and introduces a simple heuristic matching scheme to find an efficient VN mapping. In contrast to existing solutions, the proposed framework does not impose any limitations on the size or topology of the VN request, rather the search is tailored based on the VN size. Experimental results demonstrate the performance of the proposed scheme.

Network anomaly diagnosis via statistical analysis and evidential reasoning

This paper investigates the efficiency of diagnosing network anomalies using concepts of statistical analysis and evidential reasoning. A bi-cycle of auto-regression is first applied to model increments in the values of network monitoring variables to accurately detect network anomalies. To classify the rootcause of the detected anomalies, concepts of evidential reasoning of Dempster-Shafer theory are employed; the root-cause of a network failure is inferred by gathering pieces of evidence concerning different groups of candidate failures obtained from a training set of detected anomalies and their corresponding root-causes. These groups are then refined to infer the exact cause of failure when evidence accumulates using the Dempster rule of combinations. To handle cases of imbalanced training sets, two new approaches for assigning belief values to different anomaly classes are also proposed. Performance analysis and results demonstrate the accuracy of the proposed scheme in detecting anomalies using real data.

An evidence-based mobility prediction agent architecture

One of the major challenges in wireless environments is the provision of Quality of service (QoS) guarantees that different applications demand considering the highly dynamic nature of these environments. User mobility prediction represents a key factor for providing a seamless delivery of multimedia applications over wireless networks. Most of the existing approaches for mobility prediction presume that users travel in a-priori known pattern with some regularity; an assumption that may not always hold (e.g., a tourist in a foreign city). This paper presents a novel architecture of a mobility prediction agent (MPA) that accurately performs mobility prediction using knowledge of user’s preferences, goals, and spatial information without imposing any assumptions about the availability of his movements history. Using concepts of evidential reasoning of Dempster-Shafer’s theory, the MPA captures the uncertainty of the user’s navigation behavior by gathering pieces of evidence concerning different groups of candidate future locations. These groups are then refined to predict the user’s future location when evidence accumulate using Dempster rule of combination.

Contextware Research Challenges in Ambient Networks

Network-centric context information is used to make networks more receptive to the users’ needs by personalizing the communication process and making it more sensitive to changes that may occur in the surroundings. As such, it is a natural step that context awareness should be suitably incorporated within traditional networks in order to create an ‘ambient network’. This approach is aimed at enabling the co-operation of heterogeneous networks on demand, transparently to the potential users, and without the need for preconfiguration or offline negotiation between network operators. This paper discusses the approach taken by authors to incorporate context-awareness into ambient networking concepts. It discusses how network-related context information should be utilized in ambient networks for the end user to fully experience the pervasiveness of a network and the research challenges arising from this utilization. The paper also evaluates the benefits of employing context information and contextware concepts in ambient networks.

Prediction-based policy adaptation for QoS management in wireless networks

We propose a framework for adaptive policy-based quality of service (QoS) management in wireless networks. The proposed framework is a multi-layer agent system where agents cooperate to dynamically adapt network behavior to maintain the delivered QoS level to the user. QoS prediction agents utilize user information such as the user profile, location and terminal characteristics, to predict future changes in the supplied QoS. These changes are then reported to a set of QoS adaptation agents, which dynamically adapt the network policies to meet the users requirements. The proposed adaptive approach assembles policies at run-time given sets of imposed constraints and goals to be satisfied. The new approach gives more flexibility to users and applications to dynamically change their QoS requirements while maintaining a smooth delivery of QoS.

Pricing Utility-Based Virtual Networks

This paper presents a new pricing mechanism for virtual network (VN) services to regulate the demand for their shared substrate network (SN) resources. The contributions of this article are two-fold; first, we introduce a new time-of-use pricing policy for the SN resources that reflects the effect of resource congestion introduced by VN users. The preferences of the VN users are first represented through corresponding demand-utility functions that quantify the sensitivity of the applications hosted by the VNs to resource consumption, time-of-use and prices during peak-demand periods. We then introduce a novel model of time-varying VNs, where users are allowed to up- or down-scale the requested resources to continuously maximize their utility while minimizing the cost of embedding the VNs onto the SN. The second contribution is a novel hierarchical embedding management approach tailored to efficiently map these dynamic VNs. The proposed VN embedding scheme recasts the VN embedding problem as a subgraph matching one, and introduces a simple heuristics-based matching procedure to find a good VN embedding from a number of candidate solutions obtained in parallel. In contrast to existing solutions, the proposed scheme does not impose any limitations on the size or topology of the VN requests. Instead, the search is customized according to the VN size and the associated utility. Experimental results demonstrate the performance achieved by the proposed work in terms of the increased profit, resource utilization and number of accepted requests.

Substrate network house cleaning via live virtual network migration

Network virtualization techniques aim at efficiently allocating the underlying substrate network (SN) resources to the hosted virtual networks (VNs). Unfortunately, over time, and due to the frequent initiation and termination of VNs, the available and utilized SN resources become fragmented. This in turn, gradually degrades the performance of these techniques. In this paper, we propose a novel proactive SN resource re-optimization technique that efficiently overcomes the fragmentation problem by performing appropriate re-arrangement, or house cleaning, for the available and utilized SN resources. To minimize the incurred computational overhead, the invocation of this technique is only triggered by certain events such as the departure of an expired VN. The contributions of the proposed work are two fold; first, we develop an efficient technique for the selection and re-allocation of VN portions that are contributing to the fragmentation problem. The technique takes into consideration the trade-off between the benefit from increasing the SN utilization and the cost incurred by the VN migration. The second contribution is novel VN live migration techniques that significantly reduce the service interruption time during migration. Simulation experiments demonstrate the achieved gain in the SN resource utilization as well as in the VN acceptance ratio and the net revenue.

Prediction-based advanced resource reservation in mobile environments

This paper describes a novel approach for an advanced reservation protocol, MQoS-NSLP, to provide seamless real-time services to mobile users in wireless integrated services networks. The robustness of the proposed work is the result of a two-fold contribution. The first is the utilization of knowledge about user preferences, goals, and analyzed spatial conceptual maps to predict the users future location. The second contribution is a predictive advanced resources reservation protocol suitable for mobile environments. The protocol extends the original QoS-NSLP messages with new mobility specifications