Meng Shen

Routing on demand: toward the energy-aware traffic engineering with OSPF

Energy consumption has already become a major challenge to the current Internet. Most researches aim at lowering energy consumption under certain fixed performance constraints. Since trade-offs exist between network performance and energy saving, Internet Service Providers (ISPs) may desire to achieve different Traffic Engineering (TE) goals corresponding to changeable requirements. The major contributions of this paper are twofold: 1) we present an OSPF-based routing mechanism, Routing On Demand (ROD), that considers both performance and energy saving, and 2) we theoretically prove that a set of link weights always exists for each trade-off variant of the TE objective, under which solutions (i.e., routes) derived from ROD can be converted into shortest paths and realized through OSPF. Extensive evaluation results show that ROD can achieve various trade-offs between energy saving and performance in terms of Maximum Link Utilization, while maintaining better packet delay than that of the energy-agnostic TE.

A neuro-fuzzy approach to self-management of virtual network resources

The paper proposes an autonomous system for management of virtual network resources.Our proposals lead to a better utilisation of substrate network resources.Improved efficiency of network resources is not at the expense of QoS requirements.Knowledge base initialisation and cooperation improves the performance of system. Network virtualisation promises to lead to better manageability of the future Internet by allowing for adaptable sharing of physical network resources among different virtual networks. However, the sharing of resources is not trivial as virtual nodes and links should first be mapped onto substrate nodes and links, and thereafter the allocated resources managed throughout the lifetime of the virtual network. In this paper, we design and evaluate reinforcement learning-based neuro-fuzzy algorithms that perform dynamic, decentralised and coordinated self-management of substrate network resources. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. The proposed algorithms are evaluated through comparisons with a Q-learning-based approach as well as two static resource allocation schemes.

Online combinatorial double auction for mobile cloud computing markets

The emergence of cloud computing as an efficient means of providing computing as a form of utility can already be felt with the burgeoning of cloud service companies. Notable examples including Amazon EC2, Rackspace, Google App and Microsoft Azure have already attracted an increasing number of users over the Internet. However, due to the dynamic behaviors of some users, the traditional cloud pricing models cannot well support such popular applications as Mobile Cloud Computing (MCC). To mitigate this problem, we take our first steps towards the design of an efficient double-sided combinatorial auction model in the context of mobile cloud computing. In particular, we carefully develop the framework of online combinatorial double auctions and apply a Winner Determination Problem (WDP) model for the proposed auction mechanism. The experiment results indicate that the allocation efficiency of our proposed online auction mechanism is comparable to the social optimal solution.

A model approach to estimate Peer-to-Peer traffic matrices

Peer-to-Peer (P2P) applications have become increasingly popular in recent few years, which bring new challenges to network management and traffic engineering (TE). As basic input information, P2P traffic matrices are of significant importance for TE. Due to excessively high cost of direct measurement, a lot of studies aim at modeling and estimating general traffic matrices, but few focus on P2P traffic matrices. In this paper, we proposed a model to estimate P2P traffic matrices in networks. Important factors are considered, including the number of peers, the localization ratio of P2P traffic, and the distances among different networks. Here distance can be hop counts or geographic distance accordingly. To validate our model, we have evaluated the performance using both real P2P live steaming traces and file sharing application traces. Evaluation results show that the proposed model outperforms the other two typical models for general traffic matrices estimation, in terms of estimate errors. To the best of our knowledge, this is the first research on P2P traffic matrices estimation. P2P traffic matrices, derived from the model, can be applied to P2P traffic optimization and other TE fields.

A Model Approach to the Estimation of Peer-to-Peer Traffic Matrices

Peer-to-Peer (P2P) applications have witnessed an increasing popularity in recent years, which brings new challenges to network management and traffic engineering (TE). As basic input information, P2P traffic matrices are of significant importance for TE. Because of the excessively high cost of direct measurement, many studies aim to model and estimate general traffic matrices, but few focus on P2P traffic matrices. In this paper, we propose a model to estimate P2P traffic matrices in operational networks. Important factors are considered, including the number of peers, the localization ratio of P2P traffic, and the network distance. Here, the distance can be measured with AS hop counts or geographic distance. To validate our model, we evaluate its performance using traffic traces collected from both the real P2P video-on-demand (VoD) and file-sharing applications. Evaluation results show that the proposed model outperforms the other two typical models for the estimation of the general traffic matrices in several metrics, including spatial and temporal estimation errors, stability in the cases of oscillating and dynamic flows, and estimation bias. To the best of our knowledge, this is the first research on P2P traffic matrices estimation. P2P traffic matrices, derived from the model, can be applied to P2P traffic optimization and other TE fields.

One More Weight is Enough: Toward the Optimal Traffic Engineering with OSPF

Traffic Engineering (TE) leverages information of network traffic to generate a routing scheme optimizing the traffic distribution so as to advance network performance. However, optimizing the link weights for OSPF to the offered traffic is an known NP-hard problem. In this paper, we model the optimal TE as the utility maximization of multi-commodity flows and theoretically prove that any given set of optimal routes corresponding to a particular objective function can be converted to shortest paths with respect to a set of positive link weights, which can be explicitly formulated using the optimal distribution of traffic and objective function. This can be directly configured on OSPF-based protocols. On these bases, we employ the Network Entropy Maximization (NEM) framework and develop a new OSPF-based routing protocol, SPEF, to realize a flexible way to split traffic over shortest paths in a distributed fashion. Actually, comparing to OSPF, SPEF only needs one more weight for each link and provably achieves optimal TE. Numerical experiments have been done to compare SPEF with the current version of OSPF, showing the effectiveness of SPEF in terms of link utilization and network load distribution.

Achieving bandwidth guarantees in multi-tenant cloud networks using a dual-hose model

In public cloud networks, applications of different tenants compete for the shared network bandwidth and thus might suffer from unpredictable performance. It is desirable for cloud providers to offer tenants with bandwidth guarantees. However, it is challenging to precisely abstract tenant bandwidth requirements for their intra- and inter-tenant communications and to achieve work conservation simultaneously. In this paper, we first propose a dual-hose model, a novel tenant requirement abstraction that decouples bandwidth guarantees for a tenants inter-tenant communications from those for its intra-tenant communications. We then develop a new VM placement algorithm to optimize operational goals of cloud providers, while providing tenants with minimum bandwidth guarantees captured by the dual-hose model. Finally, we design a dynamic bandwidth allocation strategy to achieve work conservation. Through extensive simulation results, we show that our solution provides bandwidth guarantees for tenant requests while improving the overall request throughput by 5.3%.

Classification of Encrypted Traffic With Second-Order Markov Chains and Application Attribute Bigrams

With a profusion of network applications, traffic classification plays a crucial role in network management and policy-based security control. The widely used encryption transmission protocols, such as the secure socket layer/transport layer security (SSL/TLS) protocols, lead to the failure of traditional payload-based classification methods. Existing methods for encrypted traffic classification cannot achieve high discrimination accuracy for applications with similar fingerprints. In this paper, we propose an attribute-aware encrypted traffic classification method based on the second-order Markov Chains. We start by exploring approaches that can further improve the performance of existing methods in terms of discrimination accuracy, and make promising observations that the application attribute bigram, which consists of the certificate packet length and the first application data size in SSL/TLS sessions, contributes to application discrimination. To increase the diversity of application fingerprints, we develop a new method by incorporating the attribute bigrams into the second-order homogeneous Markov chains. Extensive evaluation results show that the proposed method can improve the classification accuracy by 29% on the average compared with the state-of-the-art Markov-based method.

Certificate-aware encrypted traffic classification using Second-Order Markov Chain

With the prosperity of network applications, traffic classification serves as a crucial role in network management and malicious attack detection. The widely used encryption transmission protocols, such as the Secure Socket Layer/Transport Layer Security (SSL/TLS) protocols, leads to the failure of traditional payload-based classification methods. Existing methods for encrypted traffic classification suffer from low accuracy. In this paper, we propose a certificate-aware encrypted traffic classification method based on the Second-Order Markov Chain. We start by exploring reasons why existing methods not perform well, and make a novel observation that certificate packet length in SSL/TLS sessions contributes to application discrimination. To increase the diversity of application fingerprints, we develop a new model by incorporating the certificate packet length clustering into the Second-Order homogeneous Markov chains. Extensive evaluation results show that the proposed method lead to a 30% improvement on average compared with the state-of-the-art method, in terms of classification accuracy.

Achieving Optimal Traffic Engineering Using a Generalized Routing Framework

The open shortest path first (OSPF) protocol has been widely applied to intra-domain routing in todays Internet. Since a router running OSPF distributes traffic uniformly over equal-cost multi-path (ECMP), the OSPF-based optimal traffic engineering (TE) problem (i.e., deriving optimal link weights for a given traffic demand) is computationally intractable for large-scale networks. Therefore, many studies resort to multi-protocol label switching (MPLS) based approaches to solve the optimal TE problem. In this paper we present a generalized routing framework to realize the optimal TE, which can be potentially implemented via OSPFor MPLS-based approaches. We start with viewing the conventional optimal TE problem in a fresh way, i.e., optimally allocating the residual capacity to every link. Then we make a generalization of network utility maximization (NUM) to close this problem, where the network operator is associated with a utility function of the residual capacity to be maximized. We demonstrate that under this framework, the optimal routes resulting from the optimal TE are also the shortest paths in terms of a set of non-negative link weights that are explicitly determined by the optimal residual capacity and the objective function. The network entropy maximization theory is employed to enable routers to exponentially, instead of uniformly, split traffic over ECMP. The shortest-path penalizing exponential flow-splitting (SPEF) is designed as a link-state protocol with hop-by-hop forwarding to implement our theoretical findings. An alternative MPLS-based implementation is also discussed here. Numerical simulation results have demonstrated the effectiveness of the proposed framework as well as SPEF.