An Optimal Slicing Strategy for SDN based Smart Home Network

Transcription

1 An Optimal Slicing Strategy for SDN based Smart Home Network Shiwei Wang 1,2, Xiaoling Wu 2,3,4,, Hainan hen 1,2, Yanwen Wang 2, Daiping Li 1 1 Guangdong University of Technology, Guangzhou, hina 2 Guangzhou Institute of Advanced Technology, hinese Academy of Sciences 3 Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology 4 Shenzhen Institutes of Advanced Technology, hinese Academy of Sciences xl.wu@giat.ac.cn ( Dr. Xiaoling Wu is the corresponding author) Abstract Software Defined Network (SDN) has long been a research focus since born from the lab of Stanford University. Researches on traditional home networks are faced with a series of challenges due to the ever more complicated user demands. The application of SDN to the home network is an effective approach in coping with it. Now the research on the SDN based home network is in its preliminary stage. Therefore, for better user experience, it is essential to effectively manage and utilize the resources of the home network. The general slicing strategies don t show much advantage in performance within the home networks due to the increased user demands and applications. In this paper, we introduce an advanced SDN based home network prototype and analyze its compositions and application requirements. By implementing and comparing several slicing strategies in properties, we achieve an optimized slicing strategy according to the specified home network circumstance and our preference. Keywords SDN; home network; optimal; slicing strategy I. INTRODUTION The development of modern home networks is in a critical period while faced with a number of challenges. With the evolution of smart home, an increasing number of heterogeneous devices will be connected wirelessly and more complex and diverse user demands appear. However homeusers are often confused about the complexity of network configuration and resource management. Even a skilled network administrator may get stuck during the implementation of configuration. On one hand, the changes of the dynamic network status and the user demands lead to poor operability and maintainability due to the vendor-specific devices and the inconsistent interface standard [1,2]. On the other hand, due to the inherent complexity of network equipment, it becomes more difficult to achieve the goal of optimal allocation and rational use of cyber sources. This results in unsatisfactory user experience. However, this landscape may change since comes the philosophy of applying SDN to traditional home networks. SDN which clearly separates the network architecture into data plane and control plane could perfectly address the challenges as presented above. It has a logically centralized controller that integrates the functionalities of management, maintenance and monitoring together to provide a friendly interface to the home network managers. Moreover, a uniformed protocol standard (called Openflow), which resides on forwarding layer, enables the connection of the heterogeneous equipments without affecting the hardware performance [3]. To address the network virtualization requirements, a s- licing layer called Flowvisor was proposed to promise the rational and effective use of cyber sources. Flowvisor is located between the forwarding layer and the control layer. It enforces isolation from topology and traffic [6], ensures the bandwidth control and enables optimum utilization of switch PU and allocation of forwarding tables by slicing [5]. However, the general slicing mechanism among the home networks is simple and static. Moreover, the slicing process proves to be ineffective and inflexible. In most cases, researches on slicing mechanisms are not concentrated and the recent mechanisms attached to their proposed network virtualization platforms are irrelevant to the performance about which most users care. Besides, the characteristics of static, ineffective and inflexible slicing mechanisms are caused fundamentally by the simplicity of application requirements. Thus it is critical and urgent to seek efficient slicing strategies or to improve the performance of the existing ones in the ever changed user demands and home network circumstances. In this paper, we discuss and make a comparison of several slicing strategies for home networks based on SDN. An optimal slicing strategy is achieved according to the existing network status and application requirements in a specified home network topology. The rest of the paper is organized as follows. We present the related work and research background in Section II. Then we describe a specific home network prototype as well as characteristics abstraction of the devices in Section III. In Section IV we propose three types of slicing strategy and present their implementations. Through analysis on the performance of each slicing strategy in Section V, we achieve the optimal slicing strategy with regard to the user needs. Finally, we conclude the paper in Section VI. II. RELATED WORK AND RESEARH BAKGROUND Besides the description of Flowvisor, five primary resource dimensions have been proposed with regard to slicing among SDN networks: bandwidth, topology, traffic, device PU, and forwarding tables [5]. The slicing layer is designed to provide effective isolation mechanisms between slices by the above resources when deployed in production networks [8].

2 A HNDR (Home Network Data Recorder) system has been proposed for events logging and home network status monitoring [1]. Facilities on the HNDR platform perform automatic data collection in a comprehensive way according to the specified intervals. By analyzing and integrating the collected data, the management program would react according to the user directives or just accommodate the network circumstances. When slicing home networks [9], four requirements were proposed to measure the properties of managing the home networks: traffic isolation, bandwidth isolation, individual control, and the ability to customize and modify. Being enabled by slicing, approaches such as sharing the physical network, outsourcing network management and customizing slices for applications could effectively address the traditional home network issues. Based on the previous work [9], Y. Yiakoumis et al. argued that it should be the homeusers rather than the Internet Service Providers (ISPs) or a third party to control their home networks [2, 10]. Namely, users express their preferences through a user-agent that can translate user demands into network semantics and the ISPs actually perform operation on the received semantics. Since configurations executed from different administrators in the identical home network might be disordered, abstractions for consistent network status updating and for functional isolation of slice have been proposed to solve it [4, 7]. Martin et al. also come up with a mechanism for traffic prioritization to address congestion issues raised by the confounding applications in home networks [11]. onsidering the impact of slicing strategies or the slicing types on the home network performance, T. Fratczak et al. deployed three slicing schemes based on traffic priority and rate limit level in the proposed Homevisor prototype [12], among which the most complicated one achieves the lowest latency. The slicing strategy only shows strength in flow and rate-limit control and couples tightly with the Homevisor platform. Yiakoumis, Y. et al. have deployed Flowvisor into seven homes [9]. Each home has a default slice other than the specific video slice. This slicing strategy is mainly proposed to test the performance of the platform rather than the strategy itself. Nevertheless, when we slice a home network utilizing a different criterion, results of the slicing strategies above may not prove an optimal performance on the basic indexes. Based on the prior work of Flowvisor and slicing home networks, we utilize four slicing types with regard to the specific circumstance of our home network model, which are topology based slicing, bandwidth based slicing, switch PU based slicing, and application based slicing respectively. On one hand, these slicing types are not fully implemented in the related works. Actually, researches on slicing mechanisms are still in preliminary stage. On the other hand, these slicing types can meet the basic resource allocation and application requirements with which we are most concerned. III. HOME NETWORK PROTOTYPE AND DEVIE HARATERIZATION A. Home network prototype We have deployed a home network prototype to explore the appropriate slicing mechanism. By manually specifying the P Printer P Lap FAX Portabl e-psp OFSwitch high speed Gate DVD STB TV Audio ontrol Slice Internet Microwave Light- TRL Washer OFSwitch Low speed FR Intelligentdoor Aircondition EMF Fig. 1. An advanced SDN based home network architecture ( refers to controller) home users requirements according to the restrained resources of the home network, we have concretely constructed an advanced modern home network scenario. The advanced home network architecture is depicted in detail in Figure 1. The supposed home network mainly comprises six sub-systems. The systems and the representative devices are: (a) Office: P, Printer, FAX, Fixed telephone. (b) Video: TV, Set-Top-Box, DVD, Audio. (c) Gaming: P, Laptop, Portable PSP. (d) omfort: Washing machine, Fridge, Fan, Air-conditioner. (e) Kitchen: Micro-wave oven, Electromagnetic Furnace. (f) Lighting and anti-theft: Light controller, urtain, Intelligent door. in the home office, home video and home gaming sub-systems are connected to the same high speed switch while devices in the remaining three systems are connected to the low speed switch. Each Openflow switch is connected to the Internet through the home gateway while the controllers may exist inside the home network (e.g. on a laptop) or outside the Internet while connected to the home over a secured channel. In addition, there will be a manager system among them for monitoring and statistics or billing purpose. Application requirements are described as follows: (a) Each sub-system performs independently. (b) The response time of each device wont exceed the corresponding tolerable delay. (c) The high speed systems (e.g. home video) wont starve the low speed systems (e.g. home kitchen). (d) The idle bandwidth of the sub-systems could be utilized by the bandwidth-insufficient sub-systems. B. characteristics extraction In our experiment, we focus mainly on the parameters characteristics of the devices for effective research of the optimal slicing mechanism. Based on the practical conditions, the devices properties about which slicing concerns could be specified in Table 1. It generally shows the bandwidth requirements, the real-time requirements, and the device usage habits over a typical smart home network. However this is the average statistics among the specific parts in the network and parameters of certain devices may differ. Since it is inadvisable

3 to unify the user preferences, we don t specify the priority for each sub-system. The remaining task becomes how to meet the requirements as described through an appropriate slicing mechanism. TABLE I. Device Application THE BASI HARATERISTIS ABSTRATED FROM HOME NETWORK DEVIES Label Tolerable delay minimum bandwidth (b/s) maximum bandwidth (b/s) usage frequency Office S1 10S 512K 4M 2 Video S2 5S 1M 10M 5 Gaming S3 2S 512K 6M 2 omfort S4 20S 64K 768K 10 Kitchen S5 40S 16K 256K 6 Lighting and Security S6 30S 32K 512K 8 IV. HOME NETWORK SLIING STRATEGIES Slicing strategy, which is a criterion referred to throughout the entire slicing configuration process, is different from slicing types that are defined in accordance with resource types. A slicing strategy may comprise several rules that are proposed by users or necessarily required by the resources or made to adapt the network circumstance. In this section, we have proposed three slicing strategies for our proposed home network prototype centred on the following aspects: application, location and bandwidth. A. Application centred slicing strategy This strategy considers different types of applications, queuing applications according to the user preference or the expert experience. To conform to the actual situation, we assume that devices in sub-systems connected to the low speed Openflow Switch belong to the identical type of application, while devices in Office, Gaming and Video sub-systems are not. We have constructed four slices according to the diverse application types. The specific distribution of devices and the slice features are described in Table 2. The TP port and Priority fields distinguish application types and priority in constructing Flowspaces. B. Location centered slicing strategy Location strategy focuses on the nearby devices among household. Assume inner traffic occupies a greater proportion and mobile devices are sensitive in speed with location alteration. Though it is a dull method when we carry on the location strategy in home networks, it may prove better performance in stability when home switches and routers are wirelessly connected and the location of devices might change frequently or the devices are often in mobile status. In our prototype, the slices characteristics are roughly depicted in Table 3.. Bandwidth centred slicing strategy Bandwidth strategy conducts slicing classification based on the diverse level of bandwidth requirements. Suppose that applications with identical bandwidth requirements will function smoothly in one slice and applications with high TABLE II. TABLE III. REPRESENTATIVE APPLIATION ENTERED SLIING Slice name TP port Priority office S1 gaming S3 video S2 appliance S4 S5 S6 REPRESENTATIVE LOATION ENTERED SLIING Slice name TP port Priority officeroom S1 recreateroom S2 Gaming P intelcontrol S6 Laptop PSP kitchclean S4 S5 bandwidth and low bandwidth should separate in flow and isolate in traffic. We have conducted three slices in accordance with the bandwidth requirements in our architecture. The slice concretion of bandwidth strategy is listed in Table 4. TABLE IV. REPRESENTATIVE BANDWIDTH ENTRED SLIING Slice name TP port Priority highrate S2 S3 middlerate S1 S4 lowrate S5 S6 The total bandwidth of home network has been throttled to 20Mb/s and the bandwidth of low speed Openflow Switch to the home gateway has been throttled to 1M b/s. When conducting test, we suppose that all the traffic come from the Internet to the home network or from the opposite direction but neglect the inner traffic between devices. V. RESULT AND ANALYSIS We have deployed the home network architecture in the Mininet test-bed over the VMware Workstation and we used the latest version of Flowvisor to slice the home network. In order to test the performance of each of the slicing strategy as presented previously, we perform the same test on the three slicing strategies. We utilize a certain number of data transmitted from the home network devices to the outer host (Internet). To simulate the actual situation, we suppose that not all devices but part of them work in a time interval, meanwhile the corresponding devices are specified with certain amount of transmitted data, for example the DVD and the TV transmit the same amount of data which are larger than the Washer and the Fridge. Notice that the same device will achieve a different throughput in circumstance in which there exists different number or type of devices. We enforce data transmission in two groups of devices with the same amount of data: group A (FAX 95.4MB, Laptop 477MB, DVD 1.86GB, Washer 4.88MB) and group B (Printer 95.4MB, PSP 477MB, TV 1.86GB, Fridge 4.88MB). Firstly, we start all the devices in group A. Then after running 30 seconds, we start all the devices in group B. The two groups of devices and their different starting time are set for the purpose of simulating the dynamics of home network circumstance. Since the shortest transmission time of all devices in our experiment is 41.8 seconds (Washer), we finally chose the 30s as the starting time of group B. After around one hour of data transmission by three slicing strategies

4 TABLE V. MAIN INDEXES OF NODES IN GROUP A IN THREE SLIING STRATEGIES Nodes Total time (s) throughput 0 30s (b/s) throughput 30 40s (b/s) Start time (s) App Loc Bw App Loc Bw App Loc Bw Fax M 13.03M 10.38M 6.82M 9.54M 5.98M Laptop M 3.49M 5.52M 5.87M 4.30M 5.24M DVD M 3.00M 3.46M 5.45M 4.19M 5.56M Washer M 979K 980K 839K 944K 944K suma M 20.5M 20.3M 18.98M 18.98M 17.7M TABLE VI. MAIN INDEXES OF NODES IN GROUP B IN THREE SLIING STRATEGIES Nodes Total time (s) throughput 0 30s (b/s) throughput 30 40s (b/s) Start time (s) App Loc Bw App Loc Bw App Loc Bw Printer M 1.99M 1.47M 2.62M 2.73M 2.10M PSP K 1.12M 1.54M 1.57M 1.89M 2.31M TV M 978K 1.50M 2.83M 1.68M 2.20M Fridge K 559K 734K 1.05M 944K 1.05M sumb M 4.65M 5.24M 8.07M 7.24M 7.66M sumab respectively, we complete the test. The results are shown in Table 5 and Table 6. In these tables, column Total time represents the transmission time of a device from its start time to its end time. The interval of 0 30s (I1) or 30 40s (I2) in the next two column of throughput is based on the start time of each device. Note that the application, location and bandwidth strategy scenarios are denoted as App, Loc, Bw respectively. In terms of total time consumed by the eight devices when transmitting the corresponding amount of data, the application centred slicing strategy proves to be the fastest while the location centred strategy shows the worst performance as shown in the row sumab. When compared the three scenarios, traffic heavy devices that transmit larger amount of data achieve a difference of ten seconds magnitude in data transmission (e.g. row DVD, PSP ) while differences of the sums of the total time of eight devices rise to hundred magnitudes (row sumab ). For the total time of a single device, the Loc scenario has longer total time compared to the other two scenarios, which is caused by the fact that the Loc strategy failed to handle the data transmission of a changeable application. The Bw scenario may have an advantage in regular bandwidth changes among the devices. The App scenario seems exactly suitable for the current data transmission and application mode. In terms of average throughput, in all of the three circumstances, devices in group B are obviously starved by the devices in group A by bandwidths (e.g. the later PSP or TV only achieves a throughput of merely 978K, 1.12M, 1.54M; 1.50M, 978K, 1.50M) in a short time. When we compare the total throughput that changes from I1 to I2 within each group, it has decreased by 1.72M, 1.52M, and 2.6M in group A (A s total throughput decreases because devices in group B start data transmission), and increased by 2.72M, 2.59M, and 2.42M in the meantime in group B (B s total throughput increases which is caused by the slightly decrease of A s total throughput and that the Washer has completed data transmission). Note that the Laptop and DVD have stolen slightly in throughput from Fax because they may have higher priority than the devices in group B. Assume that the network status will be more stable with fewer changes of the total throughput. From the perspective of all the devices, the most dull strategy (Loc scene) is actually the most stable strategy in throughput changes while with poorer performance on time consumption, while the App scene achieves an intermediate performance. From the view of a single device, the early-started devices in group A behave more sensitive in instantaneous throughput change especially in App scenario (about 6M bandwidth of the Fax device has been stolen ). As we can see, devices in group B that transmit smaller amount of data only have a small portion of changes in bandwidth (e.g. Washer 0.3K or PSP 0.8K). This can be explained as that unexpected data transmission task can be processed flexibly in App scenario in the premise of impairment to stability. We can achieve an optimized strategy from the results, as most of people only concern about the time index when using these devices at home. From the perspective, the application centred one shows the best performance. VI. ONLUSION Due to the complicated application requirements and the variability of home network circumstance, the application of SDN to the home networks encounters a series of challenges among which is the research of slicing strategies. In this paper, we have presented an advanced home network prototype, analyzed its compositions and abstracted the devices properties. We have proposed three types of slicing strategies based on the concrete home network prototype. In addition, we have applied these slicing strategies to the home network on the test-bed. By comparing the results, the optimal strategy according to the user demands and the specified home network circumstance is achieved. Slicing strategies may comprise other types and we have only implemented the current three types. AKNOWLEDGMENT This work is supported by the 2014 Guangzhou Pearl River New-star Plan of Science and Technology Project (No. 2014J ), the Open Fund of Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis (No. GDUPTKLAB201304) and the 2014 Shenzhen ity Knowledge Innovation Program Project (No. JYJ ). REFERENES [1] K. L. alvert, W. K. Edwards, N. Feamster, R. E.Grinter, Y. Deng, and X. 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