Proposal and evaluation of a future mobile network management mechanism with attractor selection

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. Introduction 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 [1] 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) [2] in the United States, the European Future Internet Assembly [3] under the European Seventh Framework Program (FP7), and AKARI [4] 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 *Correspondence: [email protected] 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 Full list of author information is available at the end of the article wireless networks such as the MobilityFirst Project [5] 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 [6]. Among all these activities, one of the most promising future Internet research activities is OpenFlow technology [7] 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 © 2012 Motoyoshi et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Motoyoshi et al. EURASIP Journal onWireless Communications and Networking 2012, 2012:259 Page 2 of 13 http://jwcn.eurasipjournals.com/content/2012/1/259 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 [1113]. 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 [14] and in the Global Center of Excellence Program for Founding Ambient Information Society Infrastructure [15] 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. Related study 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 [8]. As an essential fraction of the future Internet, Yap et al. [9] drew a blueprint for future wireless mobile networks that can achieve handovers between WiMAX and WiFi environments. In addition, Yap et al. [10] 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 [16] 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. [17] proposed an optimization algorithm of policy-based vertical handover. Merlin et al. [18] discussed a resource allocation method to work on multi-channel multi-interface multi-hop wireless networks. Kassar et al. [19] 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. [20] proposed an energy-efficient clustering algorithm, PEACH (Power-Efficient and Adaptive Clustering Hierarchy protocol). However, Jiang et al. [21] compared existing clustering methods inWSNs 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 Motoyoshi et al. EURASIP Journal onWireless Communications and Networking 2012, 2012:259 Page 3 of 13 http://jwcn.eurasipjournals.com/content/2012/1/259 outcomes. The articles by Dressler and Akan [12] and Meisel et al. [13] 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 [22], artificial immune systems for recovering from query losses in sensor networks [23], homeostatic regulation of the blood glucose level as IP resource self-management method [24] 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. [27] 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 [15] 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 [14] and by other researchers. Within the framework of the above projects Leibnitz et al. [28] extended the Kashiwagi-model to a multidimensional control mechanism and showed an instance of attractor selection applied to multi-path routing in ad hoc networks. Wakamiya et al. [29] surveyed a wide variety of biologically inspired systems and built a scalable architecture focusing on systems running in a selforganizing and autonomous manner based on attractor selection. Furthermore, Leibnitz and Murata [30] analyzed the perturbation effects in attractor selection based on observations of the system’s responsiveness to inherent fluctuations. Kajioka et al. [31] 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. [32] 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. [33] 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. [34] showed that attractor selection is applicable for a real-time production scheduling. Fukuyori et al. [35] 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. [36] 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 This section first introduces a basic attractor selection model based on Adaptive Response by Attractor Selection [27]. The generalized mathematical model is expressed as follows: dm dt = f (m)× α + η (1) where function f is defined by a potential function and is a function of the state m, α ∈[ 0, 1] is an activity of the selected attractor, and η is a noise term. At time t, one attractor is selected based on the output of Equation (1). Here, the function f determines the attractors through f (m) = −dU(m)/dm, where U(m) is the potential function. The activity α indicates the suitability of the selected attractor to the current environment. The more suitable the attractor is, the larger the activity becomes and the attractor basins become deeper which prevents that noise perturbs the state away from this attractor (see Figure 1). Hence, in case of larger α, the deterministic function f is dominant over noise to find an attractor. On the other hand, smaller α makes the shape of the potential flatter and the random term η becomes dominant which permits that the state can easily leave the current basin. Changes of environmental conditions reflect on the change of activity α appropriately for controlling the system. Hence, depending on the transition of activity α, both deterministic mode and stochastic mode are switched over and it is well suited for finding a tradeoff between reducing unnecessary management cost and pursuing the highest Motoyoshi et al. EURASIP Journal onWireless Communications and Networking 2012, 2012:259 Page 4 of 13 http://jwcn.eurasipjournals.com/content/2012/1/259 Figure 1 Sketch of basic attractor selection mechanism. performance. In the end, this attractor selection procedure is an effective way to an adaptive system construction for coping with environmental changes. In addition, we will discuss our vision of a future mobile network architecture. We focus on a network based on OpenFlow due to many of its strong points such as flexible path management with reference to complicated requirements from users. However, OpenFlow also has some drawbacks. First, energy cost for path calculation increases in return for flexible path establishment. Second, traffic overhead caused by frequent handovers increases because an OpenFlow Controller (OFC) has its own local domain and is not suitable for managing a large scale network as it is. A domain of OpenFlow network consists of a single OFC and several OpenFlow Switches (OFS). The OFC maintains status information such as flow tables of all switches under the controller. Hence, the controller has processing requirements that are proportional to the number of switches in order to carry out flexible flow table management. In general, each local domain of the entire OpenFlow network is independently pre-designed by each network administrator. If a mobile nodemoves between two domains, each controller has to exchange signaling messages with each other to sustain the communication session of the mobile node. An example of the inter-domain handover sequence in an OpenFlow network is shown in Figure 2. Therefore, lightweight control is essential for the scaling of OpenFlow networkmanagement and reduction of inter-domain handover signaling. OpenFlow is a technology where centralized controllers can control user data paths of all network devices such as switches, routers, and base stations through an external standard API. In addition, data packets are handled on a flow basis, and therefore, flexible data path arrangement is possible. However, the centralized basis gives cause for concern about scalability. Hence, we introduce a method based on attractor selection to overcome the above drawbacks. Proposed extension of attractor selectionmodel In this section, we propose a future mobile network architecture taking into account both the flexibility of OpenFlow and the robustness of attractor selection. Our proposed architecture is shown in Figure 3. In this architecture, OFCs manage all OpenFlow devices and simultaneously each device uses attractor selection in an autonomous manner to achieve its own targets such as reduction of energy consumption, end-to-end delay, and so forth. This combination of a strict control by OpenFlow and a loose control by attractor selection is useful to meet a variety of user needs for the future network. Here, each OFC manages all OpenFlow devices that are within its domain. In general, an operator covers a large network area that the network consists of several OFCs and devices. In this article, we assume that the OpenFlow network is managed by a single operator. 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 [16]. 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 mobilitymanagement andmere distributedmobilitymanagement 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. We chose the well-investigated and reliable attractor selection model from [31] as a basis for our proposal. In [31], the degree of satisfaction is calculated by using Motoyoshi et al. EURASIP Journal onWireless Communications and Networking 2012, 2012:259 Page 5 of 13 http://jwcn.eurasipjournals.com/content/2012/1/259 Figure 2 An example of inter-domain handover sequence. the information of how close pre-defined QoS and live conditions are and therefore the system tends to prefer conditions that are closest to the given target QoS over conditions that are far from the target QoS, but may still be acceptable. This kind of acceptable and qualified better conditions might be avoided in the existing model. In our model, in order to aim for better wireless communication conditions, the activity equation has been extended to work in a more effective manner by using live traffic information instead of passive pre-defined QoS information as in [31]. Our extension contributes to taking care of wider ranges of wireless conditions and hence can easier cope with even unexpected events. This extension permits managing more challenging cases with large environmental changes. Our proposed model is expressed by the following equations. dmi dt = S(α) 1+max (mi) −mi − D(α)mi + ηi (2) S(α) = α ( βα + 1/√2 ) (3)