Proposal and evaluation of a future mobile network management mechanism with attractor selection
© Motoyoshi et al.; licensee Springer. 2012
Received: 3 February 2012
Accepted: 25 July 2012
Published: 16 August 2012
Several task forces are currently working on how to design the future Internet and it is high time for research work to also move a step forward to future mobile networks on a large scale. In this article, we propose a future mobile network management method based on a combination of OpenFlow and the biologically inspired attractor selection method to achieve scalability and energy efficiency. In other words, we propose novel approaches to wireless network management by extending the attractor selection mechanism in path and cluster management for signaling cost reduction. First, in path management, we establish a control method that each mobile node selects the best suited interfaces in accordance with instantaneous live traffic volume. Then, in cluster management, we design a network management method that network devices select the best OpenFlow cluster to join in order to reduce handover signaling cost. Through autonomous decisions of each mobile node and network device, the whole wireless network can be managed in an autonomous, energy efficient, and robust manner.
We are now facing a new era when the future Internet infrastructure needs to be drastically changed from scratch in order to meet the great variety of requirements from users. In the meantime, there has been an increasing number of research activities on future Internet infrastructures applicable to the field of Information, Communication, and Energy Technology (ICET). All over the world, we can see many research task forces, such as four projects that National Science Foundation (NSF) promotes as the Future Internet Architecture (FIA) in the United States, the European Future Internet Assembly under the European Seventh Framework Program (FP7), and AKARI in Japan, which are all working on future Internet research work. From the viewpoint of user needs for our future society, the wireless communication environment is essential to provide users with mobile services and there have been several research activities on future wireless networks such as the MobilityFirst Project of the FIA program and Programmable Open Mobile Internet (POMI) 2020 of the Stanford Clean Slate Program. In addition, there are several architecture proposals for the future mobile network with separated identifier and locator. Among all these activities, one of the most promising future Internet research activities is OpenFlow technology to construct a programmable and environmentally friendly ICET infrastructure[8–10]. OpenFlow technology has the potential to meet a wide variety of requirements from users due to its programmability and it is available not only for wireless communication but also for wired networks. However, the above research has been limited within local sites such as campus networks and data center networks for the time being and OpenFlow operates in a centralized way which may lead to scalability problems.
In addition to mobility support, robustness is one of the keywords when talking about the future Internet infrastructure for coping with disasters like earthquakes or tsunamis. In addition, energy saving for protection of the environment is also an important factor. To achieve the above targets, more reliable infrastructures based on scientific theory are preferred over approaches that are simply derived from empirical experience. For instance, biological systems have evolved over a long period of time to exhibit an internal robustness against environmental changes, which helps in the survival of the species. For this reason, there have been recently several mechanisms based on biological systems applied to future ICET[11–13]. These biologically inspired mechanisms achieve a good overall system construction from several viewpoints such as performance and robustness despite changes of the operating environment. Such condition changes occur frequently especially in wireless communication networks and therefore applying mechanisms from biology is very promising. Among biologically inspired control mechanisms, attractor selection is one of the possibilities to formulate a mathematical model based on biological dynamics. Recently, there have been several research activities applying the attractor selection method to various ICET management fields in the Yuragi Project and in the Global Center of Excellence Program for Founding Ambient Information Society Infrastructure in Japan.
As a societal network infrastructure of the future, it is of great value to achieve scalability and robustness in a well-balanced manner at the same time. The future mobile network and the above-mentioned attractor selection method have so far been well investigated independently of each other. However, the combination of both has not been studied yet. In this article, we extend the above attractor selection mechanism in order to work on large-scale mobile wireless network environments based on OpenFlow technology. Our contribution in this article is as follows. First, we discuss the future mobile network on the basis of a combination of OpenFlow technology and the attractor selection method from a general perspective. Second, we propose a concrete method for a mobile node to select the best radio interfaces under varying environmental conditions with attractor selection driven by realtime user traffic volume. Third, we establish an appropriate clustering method to reduce handover signaling cost on OpenFlow-based future mobile networks with attractor selection driven by the difference of the flow directions between user traffic and signaling traffic. Finally, we evaluate our proposed multi-radio interface selection mechanism by simplified computer simulations. Simulation results show that our proposed mechanisms are feasible enough to work in the presence of fluctuations on the radio channel and in data traffic volume. Our extended attractor selection model has an ability to select environmentally optimized radio and network resources irrespective of environmental changes. As stated above, our contribution will offer a biologically inspired and robust optimization method in a self-organizing manner especially for wireless communication networks.
The rest of this article is organized as follows. We first discuss some related work in the following section. In Section “Research background and problem statement” we explain the basic mechanism of attractor selection and discuss general issues on the adaptation of the attractor selection method into an OpenFlow-based future mobile network. In Section “Proposed extension of attractor selection model”, novel concepts to adopt the attractor selection mechanism into the future mobile network environment are proposed and discussed in detail. In addition, in Section “Evaluation of attractor selection extension”, simulation results are explained. Finally, in the last section, some conclusions are given.
The current Internet has evolved to maintain a backward compatibility by adding new functions whenever they are needed. However, a consensus was reached among research leaders from academia and industry that the new future Internet infrastructure should be redesigned from scratch. One key technology for the future Internet is network virtualization established by programmable network components based on OpenFlow technology. As an essential fraction of the future Internet, Yap et al. drew a blueprint for future wireless mobile networks that can achieve handovers between WiMAX and WiFi environments. In addition, Yap et al. deployed a testbed named as OpenRoads to offer a slicing service in wireless infrastructure on a campus network. Here, slicing service means that shared common wireless resources can be offered to users in a flexibly separated manner at a fine granularity. However, these studies are limited to localized areas like data centers and campus networks.
For utilizing multiple radio interfaces, cognitive radio technologies are promising and have been well investigated. Focusing on radio interface selection, several vertical handover methods in heterogeneous wireless networks are investigated. Zhu et al. proposed an optimization algorithm of policy-based vertical handover. Merlin et al. discussed a resource allocation method to work on multi-channel multi-interface multi-hop wireless networks. Kassar et al. surveyed vertical handover technologies and analyzed the essence of vertical handover. Most of the current studies are based on precise handover management and it is not sure if they will work on a large scale. In addition, clustering technology is often used for forming localized communication groups to gain scalability in wireless sensor networks (WSN). Yi et al. proposed an energy-efficient clustering algorithm, PEACH (Power-Efficient and Adaptive Clustering Hierarchy protocol). However, Jiang et al. compared existing clustering methods in WSNs and revealed several open issues such as cluster formation in heterogeneous networks, mobility support, and so forth.
From the viewpoint of biologically inspired networks and communications, there have been several research outcomes. The articles by Dressler and Akan and Meisel et al. provide good surveys for the large quantity of biologically inspired methods that are currently applied to networking problems. Investigated problems range from ant-based routing in mobile ad hoc networks, artificial immune systems for recovering from query losses in sensor networks, homeostatic regulation of the blood glucose level as IP resource self-management method to entire architectures operating according to biological principles[25, 26].
Our goal in this article is to extend OpenFlow technology with the robustness occurring in biological systems. To achieve this goal, we apply the attractor selection method as adaptive and robust control mechanism in OpenFlow. In its original context within biological systems, Kashiwagi et al. formulated a mathematical model of attractor selection to express an adaptive response system in the dynamics of gene expression in Escherichia coli cells. This dynamic behavior is formulated through differential equations of mRNA concentrations in a cell considering the environmental changes of nutrient conditions under the influence of noise. Special stable states exist in this dynamic system as attractors and once the system state has approached an attractor it will remain there. On the other hand, if the environment changes that this attractor becomes unstable, the inherent noise will drive the system state to a new stable attractor. This switching between convergence and search phases is controlled by an activity term, which corresponds to the cell’s growth rate. In other words, the activity locks the system to stay at the same attractor and noise works for offering the trigger to find the other attractor. Based on the above models, Murata developed and extended ambient network management to facilitate future human life closely related to environmental adaptability. Several research activities on the attractor selection have been produced within the Yuragi Project a and by other researchers.
Within the framework of the above projects Leibnitz et al. extended the Kashiwagi-model to a multi-dimensional control mechanism and showed an instance of attractor selection applied to multi-path routing in ad hoc networks. Wakamiya et al. surveyed a wide variety of biologically inspired systems and built a scalable architecture focusing on systems running in a self-organizing and autonomous manner based on attractor selection. Furthermore, Leibnitz and Murata analyzed the perturbation effects in attractor selection based on observations of the system’s responsiveness to inherent fluctuations. Kajioka et al. applied an attractor selection model to a multi-interface selection system using several wireless media such as LTE, WiMAX, and WiFi with different communication capabilities. Koizumi et al. adopted an attractor selection mechanism into virtual network topology construction. The best overlay network topology at each moment is selected according to the outputs of the attractor selection equation. Li et al. utilized attractor selection in wavelength division multiplexing mesh networks to execute intentional path reroutes especially for unpredictable future resource demands.
In addition, attractor selection has been investigated in a wide variety of fields beside data communication. Chujo et al. showed that attractor selection is applicable for a real-time production scheduling. Fukuyori et al. proposed a control method of a human-like robot arm with attractor selection and confirmed that the method is feasible enough to work just with simple feedbacks and without a global knowledge of the robot. Kitajima et al. achieved construction of a data broadcasting service with the filtering order decision based on an attractor selection. All these studies demonstrate that attractor selection is feasible as a simple and robust control scheme that can be applied to various types of optimization and scheduling problems.
Research background and problem statement
Proposed extension of attractor selection model
In this section, we will propose two approaches to enhance the future mobile network infrastructure by using the attractor selection method. One is the method of multi-interface selection operating on mobile node side and the other is the method of adaptive clustering method working on network side. Both of them contribute to energy saving to protect the environment.
Multi-interface selection of mobile node
In the future mobile network environment, different wireless media are assumed to be available in a mobile node on cognitive radio infrastructures. The number of access media types, application types, and volume of traffic will increase in a large scale and for this kind of explosion in diversity it is expected that conventional centralized mobility management and mere distributed mobility management cannot work well. As a matter of fact, existing simple vertical handovers are not sufficient enough to efficiently manage the wide variety of radio access technologies to meet the complicated user demands on the future mobile network. There is a need for scalability and robustness and therefore we focus on attractor selection with the capabilities of biologically mechanisms. In future mobile networks, the performance of the best interface for a mobile node might be fluctuating according to environmental changes especially due to diversity. The interface should be selected based on several aspects such as radio quality, traffic distribution, and required QoS. Handling all the conditions by the OFC is inefficient and hence a kind of abstract control mechanism should be installed which can handle any kind of objective.
In our proposed mechanism, realtime information such as live wireless communication conditions is utilized to drive the above equation and therefore it is expected that additional artificially induced noise like in is not necessarily if there is already sufficient noise on this live traffic information depending on the quality of wireless communications. This is because noise elements are already included in this model. This removal of artificial noise is expected to lead to complexity reduction in system design for network administrators to achieve maintenance cost reduction and prevent from latent human errors. In addition, in the case of worse wireless conditions, greedy selection based on instantaneous values is not necessary anymore or is even harmful from the viewpoint of system cost. Here, our system based on attractor selection has two modes such as deterministic and stochastic modes. Therefore, we can achieve more efficient system management to select system parameters accurately enough in good radio link conditions (deterministic mode) or loosely enough in noisy radio link conditions (stochastic mode). This is one of our benefits, compared with the other systems that rely on the accurate decision based on the selection of the interface with the best realtime BER.
Adaptive clustering of controller domain
In addition, in the future mobile network, several types of access edge devices are assumed to appear flexibly responding to user needs. Recently, base stations with small coverage like Femto cells are expected to increase in number owing to the growing need for high data rate transmission for richer services. In addition, cognitive radio is another promising technology to accomplish comfortable communication from the viewpoint of efficient frequency usage. Hence, the mixture of different access technologies is most likely to produce more and more opportunities for both vertical and horizontal handovers to cause large amount of signaling traffic between OFSs and OFCs.
We would like to reduce the above signaling cost caused by handovers and utilize attractor selection in adaptively sustaining appropriate clusters according to environmental dynamics from the viewpoint of signaling cost reduction. Our proposed clustering mechanism works in an autonomous manner over an ordinary OpenFlow network managed in a centralized manner due to the OpenFlow management concept. Here, universality of attractor selection is one of the important features and hence the same basic model is used for this solution. On this issue, the same equations as Equations (2–5) described in the previous section are used, where only m i is replaced by s i indicating the selection probability of a cluster group equivalent to each OFC i. The network is divided into several network clusters and each cluster is controlled by an OFC. Each domain consists of some OFSs, base stations, and mobile nodes in addition to an OFC. In our proposed model, a mobile node selects one of the clusters by using the attractor selection model. Parameters η i , β, and γ are kept the same as in previous section. While most of the equations and parameters from the previous section can directly be reused, we need to adapt α ∈[0, 1] as the activity to express suitability of the selected cluster equivalent to OFC that the device should join. This activity is expressed again as output of a sigmoid function of an environmental parameter and a gain parameter. The environmental parameter of each cluster s i is calculated based on the ratio of user data traffic volume against signaling traffic volume for the path setup on the cluster by using realtime traffic volume passing through the network device. The larger this parameter value is, the more likely the network device should stay at the current cluster. On the contrary, the smaller it is, the more aggressively the network device should change the cluster that it belongs to. Hence, fluctuating environmental information like traffic changes is embedded implicitly on this environmental parameter. This value is closely related to the activity, which is equivalent to the satisfactory degree of cluster selection. Therefore, this activity indicates the ratio of user traffic volume against signaling traffic volume for the path setup, which corresponds to high probability to alleviate frequent handovers. This dynamics provides us with the selection of the best clusters, which leads to cost reduction of frequent handover signaling overhead.
Evaluation of attractor selection extension
In this section, we introduce simulation results of multi-radio interface selection by attractor selection. We simulate wireless communication based on a code division multiple access channel on a transmission line under frequency selective fading and analyze basic tendencies of our proposed mechanism.
Simulation model of multi-interface selection
As a simulation model, on the transmitter side, binary data are modulated by Phase Shift Keying, spread by M sequence (maximal length sequence), and transmitted after multiplying the carrier frequency. This M sequence is categorized as Pseudo Noise (PN) code due to its code generation approach. On the receiver side, the signal is decoded after despreading and demodulation. As for the transmission line, a multi-path fading transmission line is assumed. A mobile node has two radio interfaces with different frequency band usage and each interface is influenced by two path Rayleigh fading. In addition, additive white Gaussian noise (AWGN) is added after the fading effect to account for shadow fading.
Basic simulation parameters in evaluations of multi-interface selection method
Number of radio interfaces
Spreading code length
Data transmission rate
Data decision method
Maximum likelihood decision
Transmission line model
AWGN 2-path Rayleigh fading
Minimum threshold for E b /N0
Minimum threshold for Bandwidth
Simulation and numerical analysis
Simulation results are shown in this section for the multi-interface selection problem. Furthermore, we show some numerical evaluation for the clustering case to demonstrate that our proposed clustering method can achieve a lower handover cost, which which shows that it is more energy efficient.
Simulation results of multi-interface selection
As a first step, we introduced simulation results with typical parameters in this article. However, simulations with well adjusted and wide variety of parameters have a potential to show more clarifying results in a later study.
Numerical analysis of adaptive cluster selection
Basic parameters in adaptive clustering cost analysis
Average packet length
500 bytes (= 4000 bits)
Average message service rate
0.00025 (= 1/4000)
Capacity of channel
Average session arrival rate
Number of message hops for one handover
where S a , S h , H a , and H h are the message sizes and the number of hops for session arrivals and handovers, respectively. Here, n a and n h are the number of messages for session arrivals and handovers.
In this article, we discussed the future mobile network environment and the possibility of combining OpenFlow with the biologically inspired attractor selection method. In particular, we proposed two novel concepts of mobile network management based on the application of attractor selection.
First, we considered a concept to select the most appropriate interfaces for mobile nodes depending on instantaneous traffic volume. Instead of using preselected static information as a driving parameter of the attractor selection model as in, our proposed method uses realtime dynamic information and therefore, the controlling function can be more sensitively operated. In the end, the network resources are expected to be utilized in an effective way at the right place and at the right time. Second, a concept to build the most appropriate management domains was proposed. This is targeted to effectively reduce handover signaling cost. In the future, a wide variety of wireless access technologies will bring users more convenience, but bring operators additional management cost at the same time. Hence, we believe that our suggested concepts are feasible and could become one of the essential schemes for management cost reduction.
Our future work is to evaluate each method based on our concepts by greater variety of simulations to show advantages of our proposal and to formulate specific attractor selection models especially on issues for the future mobile network. In addition, a detailed quantitative analysis for energy saving is also our future work. Finally, we intend to confirm that they can work on a real system through a testbed implementation.
a “Yuragi” is the Japanese expression for noise or fluctuations which is a key aspect in the attractor selection mechanism.
1Cloud Systems Research Laboratories, NEC Corporation, 1753 Shimonumabe, Nakahara-ku, Kawasaki, Kanagawa, 211–8666, Japan. 2Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565–0871, Japan. 3Center for Information and Neural Networks, National Institute of Information and Communications Technology, 1-3 Yamadaoka, Suita, Osaka 565–0871, Japan.
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