Dynamic Network Slicing and Resource Allocation for Heterogeneous Wireless Services Jeongho Kwak
Joonyoung Moon and Hyang-Won Lee
Long Bao Le
CONNECT Trinity College Dublin, Ireland Email:
[email protected]
Department of Software Konkuk University, Korea Email: {tom721, leehw}@konkuk.ac.kr
INRS-EMT University of Quebec, Canada Email:
[email protected]
Abstract—In this paper, we study dynamic bandwidth slicing and resource allocation problems to support a mixture of IoT (Internet of Things) and video streaming services. By employing Lyapunov optimization method with time-scale separation approach, we develop algorithms for long time-scale bandwidth slicing, and short time-scale IoT device scheduling, power allocation (for IoT service) and quality decision (for video streaming service). We show through simulations that proposed dynamic bandwidth slicing and resource allocation algorithms outperform the static bandwidth slicing and resource allocation policies in terms of average total cost and average total delay.
I. I NTRODUCTION The concept of network slicing has been proposed where a physical resource is partitioned into logically independent resources. These isolated resources, each called a slice, are assigned to service providers. Each service provider can better take into account the characteristics of applications by implementing its own resource allocation policy in the slice. For these reasons, network slicing is considered by far one of the most promising approaches for supporting diverse services in 5G. In order to take into account the change in demands or channel states, slicing configuration needs to be updated. Unlike the slot-by-slot scheduling or power control, slicing is updated in long time-scale. For example, a service which is less active during a particular time period may want to use less resource so as to reduce cost. Hence, slicing may have to be changed on a hourly or even minutely basis. In this paper, we are particularly interested in heterogeneous wireless services in 5G systems. For example, IoT devices use uplink network resources to transfer sensing data to the backend servers, and they would like to save their energy to extend the lifetime of their devices [1]. On the other hand, video streaming services use downlink network resources to download video contents from content servers, and the users would like to watch the highest possible quality of video contents [2]. We consider a coexistence scenario of these two different wireless services in a single physical wireless network infrastructure employing a virtualization conc 2017 IEEE 978-1-5386-3531-5/17/$31.00
cept. To adapt time-varying network environments such as dynamic service requests and wireless network states, we propose a wireless network virtualization framework that allows dynamic bandwidth slicing to differentiate between two services, and dynamic resource allocations by taking two services’ objectives and constraints into considerations. We propose a long time-scale dynamic bandwidth slicing algorithm to provide independent network resources with two different services, and short timescale resource allocation algorithms, i.e., IoT device scheduling and power control for IoT service, and video quality decision for video streaming service. This Bandwidth Slicing and Resource Allocation (BSRA) algorithm does not require future wireless channel states, but only uses current queue backlog information and past/current channel states. Main contributions of this paper can be summarized as follows. 1) We formulate a joint bandwidth slicing and resource allocation problem in a network virtualization framework aiming to minimize the total cost for two different wireless services while ensuring finite service time for a given traffic arrival set. 2) By employing the Lyapunov optimization method, we propose a dynamic bandwidth slicing algorithm operating in long time-scale, and resource allocation algorithms operating in short time-scale. 3) Via extensive simulations, we reveal that our dynamic bandwidth slicing and resource allocation attains higher performance than static bandwidth slicing and resource allocation policies in terms of the average total cost and average total delay. II. S YSTEM M ODEL Network and service model. We consider two different network services, i.e., sensing data collection service for IoT devices (service 1, uplink) and mobile video content streaming service (service 2, downlink) as shown in Fig. 1. For the service 1, IoT devices transfer collected sensing data to the IoT backend servers connected to a base station (BS) through core networks. For the service 2, the requested video is downloaded to the end users,
0
Longer time scale for bandwidth slicing control (ܹଵ ݐ ǡ ܹଶ ሺݐ ሻ)
2T
T
Shorter time scale for user scheduling, power control (ܾଵǡ ݐǡ ଵǡ ሺݐሻ) in IoT service and video quality decision (ܣଶǡ ሺݐሻ) in video streaming service IoT service group Device 1
Device 2
Device |ओ | User 1 User 2
User |ओ |
ݎଵǡ ሺܾଵǡ ݐǡ ଵǡ ݐǡ ܹଵ ݐ ǡ ݐሻ
ܣଵǡ ሺݐሻ
Backend IoT server
is scheduled for service 1 and b1,i (t) = 0, otherwise. Due P to the TDMA constraint in service 1, we have i∈I1 b1,i (t) ≤ 1. In order to reflect power saving of IoT devices, the system decides the transmit power p1,i (t) of scheduled IoT device every time slot, and we assume p1,i (t) ≤ p1,max . We assume that the transmit power of a BS for service 2 is fixed to p2 .
Core networks
Video streaming service group
Base station ݎଵǡ ሺܹଶ ݐ ǡ ݐሻ
ܣଶǡ ሺݐሻ
Video content server
Dynamic queue evolution for each service ܳଵǡ ݐ ͳ ൌ ܳଵǡ ݐെ ݎଵǡ ܾଵǡ ݐǡ ଵǡ ݐǡ ܹଵ ݐ ǡ ݐ ܣଵǡ ݐ ା ܳଶǡ ݐ ͳ ൌ ܳଶǡ ݐെ ݎଶǡ ܹଶ ݐ ǡ ݐ ܣଶǡ ݐ
ା
Fig. 1: Slicing and resource allocation framework. and the quality of video can be controlled in response to the network condition. Let I1 and I2 be the set of users in service 1 and service 2, respectively. The update of bandwidth slicing is carried out every T time slots whereas IoT device scheduling and power control, and video user quality decision are made every time slot (see Fig. 1). The total bandwidth BW is partitioned into two slices W1 (tk ) and W2 (tk ) such that BW = W1 (tk ) + W2 (tk ), where W1 (tk ) and W2 (tk ) denote bandwidths for service 1 and service 2 at time slot tk (= kT, k = 0, 1, 2, ...), respectively The chosen bandwidth slicing at time slot tk is not changed during the next T time slots, i.e., W1 (t) = W1 (tk ) and W2 (t) = W2 (tk ) for all t ∈ [tk , tk + T − 1]. We assume that the channel condition is flat over entire BW for simplicity1 . Moreover, we consider S subchannels where the bandwidth of each sub-channels is BW S , and W1 (tk ) and W2 (tk ) are decided by the number of sub-channels. Denote by S1 (tk ) and S2 (tk ) the number of sub-channels for service 1 and service 2, respectively, so that Wi (tk ) = BW S Si (tk ). For every time slot t ∈ [tk , tk +T −1], given a bandwidth slicing W1 (tk ) and W2 (tk ), the network system schedules an IoT device and decides the transmit power of the scheduled device for service 1 and decides the quality of requested video contents of all users for service 2. We consider a TDMA (Time Division Multiple Access)-like strategy for service 1 and FDMA (Frequency Division Multiple Access)-like strategy for service 2. Technically, in every time slot for a given bandwidth slicing, only one device for service 1 is scheduled using bandwidth W1 (tk ) whereas all the users for service 2 are equally assigned to the bandwidth W2 (tk )/|I2 |2 . Let b1,i (t) be the scheduling indicator of device i for every time slot t, so that b1,i (t) = 1 if device i 1 It can be extended to the OFDMA (Orthogonal Frequency Division Multiple Access) setting under the frequency selective fading. 2 We assume all users for service 2 have non-zero queue backlogs. Otherwise, |I2 | should be less than this value.
Let A1,i (t) (in bits) denote the amount of sensing data generated and uploaded from IoT device i during time slot t. We assume that the values of A1,i (t) are i.i.d. over time. Let A2,i (t) (in bits) denote the amount of data requested by video user i. Note that A2,i (t) represents the video quality of user i. The network can choose the value of A2,i (t) among {0, A2,min , ..., A2,max } depending on the network condition [3]. We model the user satisfaction according to the value of A2,i (t). First, the quality of video is typically modeled as peak signal-to-noise ratio (PSNR) [2], [4] as follows: ( Ui (A2,i (t))=P SN Ri (t)=
βi log2 (A2,i (t)), if A2,i (t) > 1, 0, otherwise,
(1)
where βi denotes a predefined parameter which reflects the characteristics of the video content requested by user i [2]. The degree of user satisfaction is then defined in a normalized form as ( Ui (A2,i (t)) , if 0 ≤ Ui (A2,i (t)) ≤ Uimax , Uimax (2) si (A2,i (t))= 1, otherwise, where Uimax denotes the i-th user’s personalized quality requirement which is independent of βi . Queueing model. We consider |I1 | and |I2 | queue backlogs for each service as illustrated in Fig. 1. The queue backlog for user (or device) i, i.e., Q1,i (t) (and Q2,i (t)), evolves over time as dynamics in the figure where r1,i (b1,i (t), p1,i (t), W1 (tk ), t) (or r2,i (W2 (tk ), t)) denotes service rate of device i in service 1 (or service 2) as functions of control parameters of network slicing W1 (tk ) (or W2 (tk )), IoT device scheduling b1,i (t) and power control p1,i (t). Following the Shannon’s capacity formula [5], the data rate of device i is given by r1,i (b1,i (t), p1,i (t), W1 (tk ), t) g i,n (t)p1,i (t)b1,i (t) (3) BW log2 1+ , S σ n,i S2 (tk ) BW g (t)p2 r2,i (W2 (tk ), t) = log2 1 + , (4) |I2 | S σi = S1 (tk )
where g i,n (t) and g n,i (t) are channel power gain from device i for service 1 to BS and channel gain from the BS to device i for service 2 at time slot t, respectively. The values σ and σi are average noise powers of BS and device i for one sub-channel BW/S. Assume that data rates for service 1 and service 2 are bounded as r1,i (b1,i (t), p1,i (t), W1 (tk ), t) ≤ r1max and r2,i (W2 (tk ), t) ≤ r2max , respectively.
III. P ROBLEM S TATEMENT AND A LGORITHM D ESIGN A. Problem Formulation First, the IoT service aims at minimizing the total energy expenditure of all IoT devices while all the data generated from IoT devices should be uploaded to the IoT backend server connected to a BS through the internet within finite time. In addition, the video streaming service aims at maximizing the quality of experience (QoE) for all users while the video content requests from the users should be serviced within finite time. Moreover, we consider four types of control parameters, i.e., bandwidth slicing between service 1 and service 2, IoT device scheduling and power control of scheduled IoT device for service 1 and the requested video quality level of each user for service 2. Our longterm optimization problem is then formulated as: (P):
s.t.
min (W,b1 ,p1 ,A2 )
lim
Z→∞
! Z−1 o 1 X n E c(t) , Z t=0
(5)
( ) Z−1 X X 1 X E Q1,i (t) + Q2,i (t) < ∞,(6) lim sup Z→∞ Z t=0 i∈I i∈I 1
2
W1 (t) ≥ W1,min , W2 (t) ≥ W2,min , W1 (t) + W2 (t) = BW, p1,i (t) ≤ p1,max , ∀i ∈ I1 , 0 ≤ Ui (A2,i (t)) ≤ Uimax , ∀i ∈ I2 ,
(7) (8) (9)
P P p1,i (t) b1,i (t) − i∈I2 si (A2,i (t)), where c(t) = i∈I1 p1,max ∞ W = (W1 (t), W2 (t))∞ t=0 , b1 = (b1,i (t), ∀i ∈ I1 )t=0 , ∞ p1 = (p1,i (t), ∀i ∈ I1 )t=0 A2 = (A2,i (t), ∀i ∈ I2 )∞ t=0 and W1,min and W2,min denote the minimum bandwidth requirements for service 1 and service 2, respectively. In addition, E{·} describes the expectation of control variables over time t. Constraint (6) represents queue stability condition to guarantee finite service time for all services. B. Algorithm Design To design our algorithm, we first define Lyapunov function [6] as follows: o X 1n X 2 L(t) , Q1,i (t) + Q22,i (t) . (10) 2 i∈I1
i∈I2
Next, we define the T -slot Lyapunov drift, ∆T (L(tk )) as the expected change in the Lyapunov function over T slots as follows: ∆T (L(tk )) , E{L(tk + T ) − L(tk )|Q(tk )},
(11)
where Q(tk ) = {Q1,i (t), ∀i ∈ I1 , ∀t ∈ [tk , tk + T − 1], Q2,i (t), ∀i ∈ I2 , ∀t ∈ [tk , tk +T −1]}. In addition to the Lyapunov drift, we design Lyapunov drift-plus-penalty function where the penalty function is the sum of cost functions for service 1 and service 2 at time slot t: n tk +T o X−1 ∆T (L(tk )) + V E c(t)|Q(tk ) , (12) t=tk
where V is a controllable system parameter which captures cost-delay tradeoff of the system. Then, we derive an upper bound of the equation (11) using queueing dynamics, bounds of service request arrivals and data
rates, and taking some approximation on future queue backlogs similar to [6] in the following lemma. Lemma 1. Under any possible control parameters W1 (tk ), b1,i (t), p1,i (t) and A2,i (t) which can be taken, we have: n tk +T o X−1 ∆T (L(tk ))+V E c(t)|Q(tk ) t=tk
n tk +T o X−1 ≤ B2 T + V E c(t)|Q(tk ) t=tk
n tk +T X−1X −E Q1,i (tk )[r1,i (b1,i (t), p1,i (t), W1 (tk ), t)
(13)
t=tk i∈I1
o − A1,i (t)]|Q(tk ) n tk +T o X−1 X −E Q2,i (tk )[r2,i (W2 (tk ), t) − A2,i (t)]|Q(tk ) , t=tk
i∈I2
2 where B2 = B1 + (T − 1) |I1 |(A21,max + r1,max )+ 2 2 |I2 |(A2,max + r2,max ) /2. Proof. It can be similarly proved as work in [6] using queueing dynamics in Fig. 1 and bound conditions for arrivals, service rates and queue backlogs. The proof is presented in our technical report [7]. Then, by minimizing right hand side of (13) for all terms related to all controllable parameters, we can obtain network slicing, IoT device scheduling, power control and video quality decision making algorithms for different time scales. However, in order to decide bandwidth W1 (tk ) at time slot tk , channel states for [tk , tk + T − 1] slots are required (see the second and third lines of (13)). We take an approximation assuming the channel states during future T time slots are the same as that during previous T time slots to make a decision of bandwidth slicing. C. Algorithm Design Our bandwidth slicing and resource allocation (BSRA) algorithm which makes decisions on bandwidth slicing every T time slots, the IoT device scheduling and power control for service 1 and video quality for service 2 every time slot consists of three corresponding sub-algorithms as follows. Bandwidth Slicing (BWS) In every time tk = kT , k = 0, 1, ... 1: if k = 0, then W1 (tk ) = BW/2 and W2 (tk ) = BW/2 2: else Assume channel gain states g i,n , g n,i during [tk − T, tk − 1] are the same as that during [tk , tk + T − 1] 3: Apply ISPC for service 1 to obtain temporary short time-scale solutions (btp,1 , ptp,1 ) for 1 1 tp,2 tp,2 W1 (tk ) = W1,min and (b1 , p1 ) for
4:
W1 (tk ) = BW − W2,min Calculate
IV. P ERFORMANCE E VALUATION
Simulation setup. We evaluate the performance of the proposed BSRA algorithm via extensive simulations. D =W1,min Q1,itp,1 (t) (tk )× The network topology consists of one BS, 10 IoT device t=tk groups3 and 5 mobile devices which use video streaming tp,1 σ + g i (t),n (t)p tp,1 (t) 1,i (t) service. The total 15 (IoT and mobile) sets are randomly log2 σ distributed at locations between 10m and 500m from the BW −W1,min Ptk +T −1 P σi +g n,i (t)p2 + , BS. We use typical maximum transmit power of mobile t=tk i∈I2 Q2,i (tk ) log2 |I2 | σi tk +T −1 devices as that of IoT devices given in [8], i.e., 1W, and X Dtp,2=(BW − W2,min ) Q1,itp,2 (t) (tk )× typical value of transmit power for the macro BS given t=tk in [9], i.e., 8W. σ + g itp,2 (t),n (t)p tp,2 (t) Moreover, total bandwidth for both services, minimum 1,i (t) log2 bandwidths for service 1 and service 2 are set to be σ W2,min Ptk +T −1 P σi +g n,i (t)p2 20MHz (which is the typical bandwidth for 4G LTE + |I2 | t=tk i∈I2 Q2,i (tk ) log2 σi [10]), 5MHz and 5MHz, respectively. In addition, each 5: if Dtp,1≥Dtp,2 , IoT group generates the sensing data randomly according 6: then W1 (tk ) = W1,min and W2 (tk ) = BW − to Bernoulli process with mean arrival rate of 250kbps. W1,min The number of sub-channels is set to be 150. In addition, 7: else W1 (tk ) = BW − W2,min and W2 (tk ) = time-scale T for bandwidth slicing control is set to be W2,min 100 time slots. For the service 2, a finite set of a video content size which capture the quality of the video is IoT Scheduling and Power Control for Service 1 (ISPC) set to be {0, 2.5, 3.8, 4.5, 6.8}Mbits which is based on YouTube video streaming using popular H.264 codec [11]. We use Rayleigh fading and path loss model with For given bandwidth slicing W1 (tk ) and W2 (tk ) (BW − shadowing [12] to generate channel gains between the W1 (tk )) during [tk , tk + T − 1] BS and mobile (and IoT) sets. For every time slot t ∈ [tk , tk + T − 1] We compare the proposed BSRA algorithm with other 1: For all IoT devices, calculate transmit powers as algorithms including (i) theoretical OffBSRA assuming follows that all channel states during next T time slots are known hp i,n (t)−V σ ln 2 ip1,max 1,max Q1,i (tk )W1 (tk )g priori to the system, (ii) BWS+ISPC+FVQD algorithm, p1,i (t)= , ∀i ∈ I 1 0 V g i,n (t) ln 2 (iii) BWS+RRMP+VQD algorithm and (iv) SBS+DRA p where [x]01,max becomes 0 if x < 0, p1,max , else (static bandwidth slicing and dynamic resource allocation) algorithm. The BWS+ISPC+FVQD algorithm is the if x > p1,max and x, otherwise. 2: Find a device to be scheduled for service 1 which same as BSRA except that video quality in service 2 is fixed to a constant value, the BWS+RRMP+VQD algominimizes the following metric rithm is the same as BSRA except that device scheduling n p (t) 1,i and power allocation in service 1 is performed according i∗ (t) = argmin V p1,max to round robin policy and the maximum power allocation σ + g i,n (t)p (t) o 1,i policy, respectively, and the SBS+DRA algorithm allo−Q1,i (tk )W1 (tk ) log2 σ cates static bandwidth for each service, and uses ISPC algorithm for service 1 and VQD algorithm for service 3: Choose p1,i∗ (t) corresponding to scheduled device 2 for a given static bandwidth allocation. Simulation results. We discuss the simulation results i∗ (t) as the transmit power allocation and the key observations in the following. Dynamic vs. static bandwidth slicing. Fig. 2 depicts the Video Quality Decision for Service 2 (VQD) average transmit power and/or average user satisfaction versus average queue backlog for IoT service (Fig. 2(a)), For every time slot t ∈ [tk , tk + T − 1] video streaming service and both services. Notice that 1: Decide the level of video quality of all users among the total sum costs in Fig. 2(c) are objective value [0, si (A2,min ), ...si (A2,max )] so as to maximize the in (P). Even though the SBS-DRA algorithm achieves better power-delay tradeoff than other algorithms (see following metric Fig. 2(a)), the BSRA algorithm achieves better total costβi log2 (A2,i (t)) −Q (t )A (t), ∀i ∈ I max V 2,i k 2,i 2 3 Note that each IoT group can consist of many IoT devices which Uimax A2,i (t) tk +T −1
tp,1
X
fairly share the same resources. We assume the location of devices in each IoT group is the same for simplicity.
IoT service
Video streaming service 0.7
BSRA OffBSRA BWS+ISPC+FVQD BWS+RRMP+VQD SBS+DRA
0.9 0.8
Avg. user satisfaction
Avg. transmit power [W]
1
0.7 0.6 0.5 0.4 0.3 2
4
6
8
10
12
14
Avg. queue backlog [Kbits]
(a) Avg. transmit power versus queue backlog.
BSRA OffBSRA BWS+ISPC+FVQD BWS+RRMP+VQD SBS+DRA
0.695 0.69 0.685
68.3% saving
0.68 35% saving
0.675 0.67 0
200
400
600
800
1000
Avg. queue backlog [Kbits]
(b) User satisfaction versus queue backlog.
(c) Total cost versus queue backlog.
Fig. 2: Cost (or minus cost) versus queue backlogs for IoT service, video streaming service and total services. delay tradeoff (see Fig. 2(c)). For instance, for the same total average queue backlog of 369.3Kbits, the BSRA saves up to 35% of total cost compared to the SBS+DRA algorithm. In addition, for the same total average cost of -0.01781, the BSRA reduces up to 68.3% of queue backlog4 compared to the SBS+DRA algorithm. Synergy of dynamic bandwidth slicing and resource allocation. As shown in Fig. 2, the proposed BSRA algorithm outperforms BWS+ISPC+FVQD employing fixed video quality and BWS+RRMP+VQD employing fixed scheduling and power. This implies that dynamic bandwidth alone achieves limited performance, and dynamic short time-scale resource allocation policies should be jointly incorporated in each slice. Supporting heterogeneous wireless services. The proposed BSRA algorithm is able to support two completely different services with different objectives in a single physical network and perform well for all services (see Fig. 2) whereas algorithms aiming to optimize the performance for only one service, i.e., BWS+ISPC+FVQD or BWS+RRMP+VQD exacerbate the performance in terms of the other service’s objective as shown in Fig. 2(a),(b). V. C ONCLUSIONS In this paper, we proposed a joint network slicing and resource allocation framework for two completely different types of services. By invoking Lyapunov optimization method with time scale separation, we proposed dynamic bandwidth slicing algorithm running in long time-scale and IoT device scheduling, power control and video quality decision algorithms running in the short time-scale. Finally, we verified via extensive simulations that dynamic resource slicing is important to improve the performance of each service in terms of cost and delay reduction. 4 Notice that we calculate this ratio by adding 1 to each value because the range of average total cost is between -1 and 1.
ACKNOWLEDGEMENT This work was supported by grants from The NSERC CRDPJ 461894 - 13. Also, Hyang-Won Lee was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2015R1A1A1A05001477). R EFERENCES [1] A. Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of Things: A survey on enabling technologies, protocols and applications,” IEEE Communications Survey & Tutorials, vol. 17, no. 4, pp. 2347–2376, Oct. 2015. [2] Y. Guo, Q. Yang, and K. Kwak, “Quality-oriented rate control and resource allocation in time-varying OFDMA networks,” to appear, IEEE Transactions on Vehicular Technology, Jun. 2016. [3] T. Thang, Q. Ho, J. Kang, and A. Pham, “Adaptive streaming of audiovisual content using MPEG DASH,” IEEE Trans. on Consumer Electronics, vol. 58, no. 1, pp. 78–85, Mar. 2012. [4] M. Chen, M. Ponec, and S. Sengupta, “Utility maximization in peer-to-peer systems with applications to video conferencing,” IEEE/ACM Transactions on Networking, vol. 20, no. 6, pp. 1681– 1694, Dec. 2012. [5] A. Goldsmith, Wireless communications. Cambridge Univ. Press, 2005. [6] Y. Yao, L. Huang, A. Sharma, L. Golubchik, and M. Neely, “Data centers power reduction: A two time scale approach for delay tolerant workloads,” in Proc. of IEEE INFOCOM, Dec. 2016, pp. 1–6. [7] J. Kwak, J. Moon, H. Lee, and L. Le, “Dynamic network slicing and resource allocation for heterogeneous wireless services,” Technical report. [Online]. Available: https://www.dropbox.com/ s/rif8tomrvatuv38/Kwak172techreport.pdf?dl=0 [8] J. Kwak, O. Choi, S. Chong, and P. Mohapatra, “Processornetwork speed scaling for energy-delay tradeoff in smartphone applications,” IEEE/ACM Trans. on Netw., vol. 24, no. 3, pp. 1647–1660, Jun. 2016. [9] Ofcom, “Mobile phone Base Station Database.” [Online]. Available: http://www.sitefinder.ofcom.org.uk/ [10] E. Dahlman, S. Parkvall, and J. Skold, 4G: LTE/LTE-Advanced for mobile broadband. Academic press, 2013. [11] YouTube, “Live encoder settings, bitrates, and resolutions.” [Online]. Available: https://support.google.com/youtube/answer/ 2853702?hl=en [12] B. Sklar, “Rayleigh fading channels in mobile digital communication systems. I. characterization,” IEEE Communications Magazine, vol. 35, no. 9, pp. 136–146, Sep. 1997.
