Integrated power-saving scheduling algorithm in IEEE ... - IEEE Xplore

www.ietdl.org Published in IET Communications Received on 20th April 2011 Revised on 20th August 2012 Accepted on 11th September 2012 doi: 10.1049/iet-com.2011.0325

ISSN 1751-8628

Integrated power-saving scheduling algorithm in IEEE 802.16e networks Wen-Hwa Liao1, Kuei-Ping Shih2, Nien-Tsung Lee1 1

Department of Information Management, Tatung University, Taipei 104, Taiwan Department of Computer Science and Information Engineering, Tamkang University, New Taipei, Taiwan E-mail: [email protected]

2

Abstract: In IEEE 802.16e networks, with the popularisation of multimedia services, multicast and unicast services can coexist in one mobile subscriber station (MSS). The mobile devices are generally powered by battery, which is limited in energy. Thus, power saving is an important issue to be considered for extending the lifetime of the MSSs. The authors have proposed a multicast services-based scheduling (MSBS) algorithm that improves the energy efficiency of both unicast and multicast services, while satisfying the quality of service requirements of the MSSs in 802.16e wireless networks. MSBS schedules the packets in such a way that each packet is transmitted before its deadline and the energy consumed by the MSSs is reduced by minimising the number of state transitions by the MSSs. The simulation results show that MSBS can produce significant overall energy-saving and prolonged lifetime as compared to other scheduling schemes in 802.16e wireless networks.

1

Introduction

With the development of wireless networks and the advancement of worldwide interoperability for microwave access (WiMAX) technology, there will be a huge demand for voice over internet protocol (VoIP), data transmission and other transmission services. Currently, wireless and mobile transmission systems have certain disadvantages, for example, the cost of infrastructure is very expensive and it is inconvenient and has low data rate. In contrast, WiMAX technology is based on IEEE 802.16 standard, which presents itself as a technology that plays a key role in broadband wireless metropolitan area networks. The advantages of WiMAX over the traditional wireless and mobile transmission systems include long transmission distance (maximum distance up to 50 km), high data rate (maximum up to 70 Mbps), fast buildup and low cost [1–7]. IEEE 802.16e provides enhancements over IEEE 802.16 to provide support for mobile subscriber stations (MSSs) [8]. It also implements a specific system to support fixed and mobile broadband wireless transmission. In order to support battery-powered mobile broadband wireless access devices efficiently, IEEE 802.16e defines a sleep mode operation, in which the mobile terminals enter into sleep mode, saving significant amount of energy [9]. The IEEE 802.16e protocol classifies each service class into three power-saving classes [8, 10] depending on its quality of service (QoS) parameters. Type 1 power-saving class is used for connections with best-effort (BE) and non-real-time polling service (nrtPS) traffic. MSS enters into sleep mode at the start frame and the initial sleep window size is predetermined. Each subsequent sleep window size is twice the size of the previous one, but not IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

greater than a specified final value. Type 2 service class is recommended for connections with unsolicited grant service (UGS), real-time polling service (rtPS) and extended real-time polling service (ertPS) traffic. Type 3 service class is used for multicast connections and management operations. For multicast service, base station (BS) predicts the time when the next portion of data will appear. Then the BS allocates sleep window to the MSSs up to the time when it expects the next multicast traffic to arrive. After expiration of the sleep window, if the multicast data are available, it is transmitted to the relevant MSSs. After that the BS may decide to re-activate sleep mode of the MSSs. When the MSS enters into sleep mode, BS buffers all packets addressed to the MSS until it wakes up. In the listening interval, the MSS checks if there is any packet buffered by BS. When BS has data to be transmitted, it broadcasts a positive MOB-TRF-IND message to terminate the sleep mode of MSS [11, 12]. The IEEE 802.16e has been developed for the targets on mobile devices which are generally powered by energy-limited batteries. Thus, the power saving is an important issue to be considered for extending the lifetime of MSSs. Some papers have studied the power-saving mechanisms in IEEE 802.16e [13, 14]. However, the focus of existing mechanisms is to improve energy efficiency for unicast or multicast service separately. In reality, unicast and multicast services can coexist in one MSS with multimedia service. In [15], the author discussed the demand of integrated scheduling. All the MSSs receiving the same multicast service must be awake during the multicast data transmission periods, so if the scheduler can make use of the adjacent intervals of multicast data transmission periods to transmit unicast data for MSSs, it 255

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www.ietdl.org will achieve higher energy efficiency. In [16], the authors present a power-saving scheduling scheme to improve the energy efficiency and guarantee QoS in the IEEE 802.16e. They consider that delay and jitter types of QoS should be scheduled in the same time and integrate the sleep duration in one MSS. The packets would be scheduled successively to reduce the number of status transitions under QoS requirements of delay and jitter. In this paper, we propose a multicast services-based scheduling (MSBS) algorithm. When MSSs have subscribed for multicast services, MSBS schedules as many MSSs as possible into adjacent interval of their multicast data transmission periods. It also buffers the unicast data for the MSSs and transmits them in bursts to increase the overall sleep duration of the MSSs. MSBS reduces energy consumption of MSSs by minimising the number of state transitions in multicast transmission scheme. The rest of the paper is organised as follows: Section 2 discusses the related work in sleep mode scheduling methods. Our proposed MSBS scheme has been presented in Section 3. Performance study is discussed in Section 4, while Section 5 concludes this paper.

2

Related work

Sleep mode of the mobile users for unicast system has been proposed and discussed in several papers. In [17], the authors proposed the longest virtual burst first (LVBF) algorithm to maximise the battery lifetime of the mobile devices. LVBF allocates almost all the bandwidths to a primary MSS. The remaining bandwidth is allocated to the MSSs, which are considered to be multiple secondary MSSs. For the primary MSS, packets are transmitted in the burst mode. The secondary MSSs only meet the minimum bandwidth requirement to satisfy the delay constraint. When a primary MSS has received data larger than its predefined maximum data amount, it will start a sleep mode request process. In this way, the primary MSS may sleep as long as possible for the power-saving purpose. After the primary MSS enters into sleep state, the LVBF chooses another primary MSS from the secondary MSSs to transmit packets in virtual burst mode. As a result of the burst transmission, the number of state transitions between the sleep and active state decreases and the average sleep time of all MSSs increases. The LVBS scheduling scheme is useful under the low traffic load of the MSSs. However, when traffic loading becomes heavy, the secondary MSSs may suffer starvation. In multicast system, many hosts are likely to need the same data items, and these hosts have to wake up at the same time to receive those data items. In [18], the authors have presented the idea of ‘multicast super frame’. In this system, the BS makes a scheduling decision before the beginning of multicast super frame. It uses the greedy algorithm to determine when data should be transmitted by the BS and when each individual host should enter into the sleep mode. Our transmission scheme of multicast services uses the static model, where the association between every host and a logical broadcast channel is fixed. The integrated scheduling scheme was first addressed in [15]. It considers unicast and multicast services at the same time, and proposed a scheduling set-based integrated scheduling (SSBIS) algorithm to minimise the overall energy consumption. In SSBIS scheme, an allocation cycle is a set of contiguous time slots, which includes one unicast scheduling interval, one logical broadcast channel and its 256 & The Institution of Engineering and Technology 2013

adjacent interval. It calculates the time slots requirement for each MSS according to the minimum data rate. SSBIS partitions all the MSSs into one unicast scheduling set and several multicast scheduling sets. In the multicast scheduling sets, the MSSs belong to a broadcast group and can be scheduled in the adjacent interval of its multicast service. The principle of it generation is to select as many MSSs which can be scheduled in the adjacent interval of logical broadcast channel to satisfy the condition that energy consumption in idle mode is less than that in the state transition. The remaining MSSs are denoted as unicast scheduling set and scheduled by longest sleep duration-based (LSDB) scheduling algorithm in other time slots except the multicast service intervals and the adjacent interval of the logical broadcast channel.

3 Integrated power-saving scheduling scheme 3.1

Basic idea

The basic idea behind our scheduling algorithm is called adjacent scheduling scheme, where we try to schedule as many packets as possible for a particular MSS in adjacent timeslots. By doing this, the sleep period of the MSSs can be maximised without violating their QoS requirement and the number of mode transitions from sleep to listen periods, which consumes significant amount of energy, can be minimised. The proposed scheduling scheme can be implemented in power-saving class of type III where the length of sleep and listen periods are variable. 3.2

System model

Our system model is a centrally controlled IEEE 802.16e wireless network with one BS and multiple MSSs. In this paper, we only consider the scheduling of downlink traffic, which is transmitted from BS to MSSs. The total duration of the scheduling cycle is called a multicast super frame, which is divided into multiple scheduling frames of predetermined length. We consider the deadline of arrival of the packets as the QoS requirement for real-time service packets. We classify the unicast data into two types, depending on whether the deadline of arrival of the packets can be calculated or not. In [19], the author proposed a scheduling scheme based on the deadline of the packets. The deadline here refers to the latest time when the packet should obtain radio resources and should be sent to its destination. For UGS, ertPS and rtPS services, latency is known, so the deadline of a packet k (deadlinek) can be calculated as: deadlinek = generation time + latency. On the other hand, for nrtPS and BE services, the latency is not known, so no specific deadline can be calculated. In this case, we assume the minimum reserved traffic rate of non-real-time service data with the deadlines are the stop time in the multicast super frame. In our scheme, the contiguous packets may involve in one unicast data depending upon its minimum data rate. In our scheduling scheme, we schedule packets in the current scheduling frame, only if its deadline expires before the next scheduling frame, that is, every packet k in the unicast data needs to be scheduled in the current frame ( f ) if deadlinek < ( f + 1) × frame duration. In our transmission model, there are G logical broadcast channels, and G consecutive scheduling frames constitute a multicast super frame. Static model is used as the IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

www.ietdl.org Table 1 Symbol notations Symbol

Definition

M i G j PMj

number of MSSs in the cell index of MSSs, i∈(1, 2,…,M) number of broadcast groups in the cell index of broadcast groups, j∈(1, 2, …, G) total energy consumption for receiving multicast service #j number of multicast service subscribed by MSS i average energy consumed in each time slot by each MSS in the awake state total number of time slots in which MSS i stays in the awake state without receiving any multicast data number of state transitions for receiving the multicast data number of state transitions of MSS i from the sleep state to the active state for receiving the unicast data average power consumption when an MSS undergoes state transition

Ki Paw Di mj ni Ptn

transmission scheme for multicast services, which decides the broadcast channel for each multicast services. In multicast super frame, multicast service #n is assigned to the logical broadcast channel #n. The size of logical broadcast channel is fixed. We assume that an MSS can subscribe for multiple multicast services and for each multicast service, there is a broadcast group which consists of all the MSSs that subscribe to that multicast service. For example, MSSs of broadcast group 1 intend to receive data of multicast service #1 and so on. Every unicast data of MSS i in a frame j represents for one contiguous time slots transmission. The symbols that are used in this paper have been listed in Table 1. We assume that an MSS does not consume any energy when it is in the sleep state. Hence, the energy consumption of an MSS is determined by the duration of time for which the MSS is in active state and the number of state transition from the sleep mode to the active mode. MSS needs to be active while receiving unicast data and multicast data from corresponding logical broadcast channels. We can calculate the total energy consumption (Pi) of MSS i in a super frame as Pi = Ki PMj + Di Paw + (mj + ni )Ptn

(1)

Here Ki, PMj and mj are fixed because the transmission scheme of multicast is decided in advance. The goal of our power-saving scheduling scheme is to minimise the total energy consumed by all the MSSs, that is min


M i=1

Pi ;

i = (1, 2, . . . , M )

MSBS scheme

Here, we present an MSBS algorithm to schedule the unicast data of MSSs. The system architecture is shown in Fig. 1. IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

Energy is unnecessarily consumed when an MSS stays in the active state without receiving packets [17]. We call it as idle state of MSS. We need to avoid this by making the MSS to enter into the sleep state when it is not receiving any packet. In ideal case, when there is no energy consumption during state transition from sleep to active state, MSS should be entered into sleep mode whenever it is not receiving any packets. However, in practical scenario, MSS consumes significant amount of energy during transition from sleep to active mode. Hence, the most optimal solution is to determine whether the MSS should enter into sleep mode by comparing the energy consumption during the state transition to that during idle state. If the time spent in the idle state is short enough and the energy consumed during the state transition is more than that during the idle state, then it is unnecessary for the MSS to enter the sleep state. However, if the energy consumed during the idle state is more than that consumed during the state transition, then MSS should enter into the sleep state. If Eidle is the average power consumed per time slot by an MSS in idle state and t is the potential duration of the idle state, then Eidle × t . Ptn ⇒ t .

Ptn Eidle

(3)

The idle duration threshold (Tidle), which is defined as the maximum time duration for which the MSS can stay in idle mode is given by Tidle =

Ptn Eidle

(4)

Hence, if the time spent in the idle state is longer than Tidle, then the MSS should enter into sleep mode.

(2)

Total energy consumed by all MSSs during a super frame needs to be minimised without violating the QoS requirement of the MSSs. To obtain the optimal result, the scheduling algorithm should consider the characteristics of real-time unicast traffic, non-real-time unicast traffic and multicast traffic. We will present our scheduling algorithm to obtain the optimal solution in the next subsection. 3.3

Fig. 1 System architecture

3.3.1 Structure of multicast super frame: The structure of a multicast super frame is shown in Fig. 2. As already discussed, a multicast super frame consists of G scheduling frames. Each scheduling frame is further subdivided into three types of scheduling interval – multicast scheduling interval, integrated scheduling interval and unicast scheduling interval. Multicast scheduling interval is used to transmit multicast services. Integrated scheduling intervals are Tidle adjacent intervals of multicast scheduling interval, that is, (ts − Tidle, ts) ∪ (te, te + Tidle), where ts and te are the start time and stop time of the multicast scheduling interval. Other time slots except for integrated scheduling interval and multicast scheduling interval are unicast scheduling interval. 257

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www.ietdl.org interval, MSS i will receive the unicast data without entering the sleep state after (or before) receiving multicast data. We schedule our unicast data which is min(tij–ts or te– tij ) in the time duration as shown in Fig. 4.

Fig. 2 Structure of a multicast super frame

3.3.2 Scheduling in integrated scheduling interval: Unicast data packets can be scheduled in unicast scheduling interval as well as in integrated scheduling interval. The advantage of scheduling unicast data packets in integrated scheduling interval is that MSS do not require a state transition and thus saving energy required for the state transition. If the unicast data packet is scheduled in the integrated scheduling interval between ts − Tidle and ts, MSSs need not enter the sleep mode after receiving the unicast packet and can stay in idle mode until the multicast scheduling interval. On the other hand, for packets scheduled in integrated scheduling interval between te and te + Tidle, MSS can stay idle after the multicast scheduling interval until the scheduled timeslot of the unicast packets. In both the cases, MSS saves the energy that would have been required for state transition from sleep to active state. Our MSBS scheduling scheme tries to maximise the number of MSSs that are scheduled during the integrated scheduling interval in order to minimise the energy consumption. While scheduling data in integrated scheduling interval, many scheduling algorithms have started scheduling from start time of integrated scheduling interval. However, this would produce unnecessary idle time for MSSs, since the integrated scheduling interval may not be fully occupied as shown in Fig. 3a. In our proposal, the scheduling starts from the timeslot that is closest to the multicast scheduling interval. Therefore timeslots that are not scheduled would be included in unicast scheduling interval to prevent unnecessary idle time, as shown in Fig. 3b. This allows us to minimise the idle time of the MSSs during integrated scheduling interval. We assume that tij represents the start time of unicast data in a frame. When tij is scheduled in integrated scheduling

3.3.3 Scheduling in unicast scheduling interval: During the unicast scheduling interval, scheduling a unicast data would consume an additional amount of energy required for state transition from sleep state to active state. When the size of a unicast data is very small, the energy consumed for state transition would overwhelm the cost of data transmission. To avoid this situation, the MSBS combines the unicast data that are destined for the same MSS and waits until the last useful frame, before the delay guarantee of the MSS is violated. This way MSBS reduces the number of times an MSS is scheduled in the unicast scheduling interval, and therefore also the number of mode transition of the MSS. In Fig. 5, MSS 2 has two unicast packets U1j and U1j + 1 in frame j and j + 1, respectively. We check whether scheduling of unicast data U1j in the frame j + 1 violated its deadline. If not, then U1j and U1j+1 can be scheduled in contiguous time slots so that the number of mode transitions of MSS 2 can be reduced. 3.3.4 MSBS scheduling algorithm: Fig. 6 shows the steps of MSBS scheduling algorithm in a super frame. MSBS algorithm calculates the deadline of each unicast data packets and notates the size of unicast data of MSS i as tli. From the beginning of the super frame, we know all the broadcast groups which MSS i belongs to. If MSS i∈BGj, then during scheduling frame j, we sort all the data packets of MSS i in the ascending order of their deadlines, and then allocate time slots from usable integrated scheduling interval to those unicast data packets. When its unicast data is scheduled in integrated scheduling interval, MSS i will receive the unicast data without entering the sleep state after (or before) receiving multicast data. When MSS i∈BGj in the current frame, we check whether the unicast data of MSS i scheduled in frame j + 1 violates its deadline. If unicast data of MSS i does not violate its deadline, we will store these unicast data in the buffer in an

Fig. 4 Data scheduling in the integrated scheduling interval

Fig. 3 Concept of data scheduling a Traditional method b Our proposed method 258 & The Institution of Engineering and Technology 2013

Fig. 5 Data scheduling in unicast scheduling interval IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

www.ietdl.org ascending order and will update the unicast data in every frame. Let us describe the advantage of MSBS scheduling scheme by an example, as shown in Fig. 7. Assume that broadcast group 1 = {MSS 1, MSS 2} and broadcast group 2 = {MSS 2, MSS 3, MSS 4}. Initially, the service BS contains four unicast data packets of different sizes. For those MSSs, which belong to the broadcast group of the current scheduling frame, MSBS tries to schedule as many MSSs as possible in the integrated scheduling interval. In frame j since the broadcast scheduling group contains MSS 1 and MSS 2, we schedule their data packets in integrated scheduling interval. Rest of the MSSs (3 and 4) will be considered for unicast scheduling interval. We see that the unicast data packet for MSS 4 has to be scheduled in current timeslot in order to meet its deadline. Hence, we schedule the data packet of MSS4 in the frame j. On the other hand, the data packet for MSS 3 can be scheduled in

the next frame, so it is kept in the BS buffer. While processing the unicast data, which need to be scheduled in the current frame, MSBS scheduled them by earliest deadline first. In frame j + 1, we combine the data packets of MSS 3 and schedule them in the integrated scheduling interval. In this way, MSBS scheduling scheme can reduce the number of state transitions by scheduling unicast data in the integrated scheduling interval and combining unicast data from different frame.

4

Performance study

In this section, we study the performance of our MSBS scheduling algorithm by simulation. We consider a single cell with one BS and a varying number of MSSs. The simulation parameters have been given in Table 2. The traffic generation rate of unicast data in BS follows the Poisson distribution, and BS decides the number of multicast services for each MSS by random access. The total energy of an MSS is 1 000 000 units. We compare the performance of our MSBS scheduling scheme with that of the SSBIS in terms of energy efficiency and average delay. Fig. 8 shows the operation time and the remaining energy of an MSS for both MSBS and SSBIS scheduling scheme. The SSBIS scheme does not consider the high cost of mode switch in unicast data scheduling, so the energy consumption rate in SSBIS scheme is higher than that in MSBS scheme. For the SSBIS scheme, the total operation time of the MSS is about 16 600 units, while for MSBS scheme, the same is about 20 000 units. The energy usage in our proposed approach shows better performance when there is only one connection in an MSS. However, since we considered the mode switches when scheduling the packets, the proposed algorithm will choose the flexible unicast data and bring it to the next frame to reduce the number of mode switches. We use average energy efficiency (AEE) [17], which is defined as the ratio of energy consumed in transmitting data to the overall energy consumed, as a measure of energy efficiency of scheduling schemes in a multicast super frame. AEE =

Energy used to transmit packets Overall energy consumed

(5)

According to [13], we assume the average power consumptions in the idle state, then each state transition will cost two time slots unit of energy. Fig. 9 shows the AEE as a function of the number of MSS. There are two broadcast groups in the system. We compare the AEE of MSBS with that of SSBIS. We can see that MSBS has better AEE performance than SSBIS. The reason is that the number of state transitions in MSBS is less than that in SSBIS, so the AEE in case of MSBS is more. Table 2 Parameters for numerical calculation and simulation Parameter

Fig. 6 MSBS scheduling algorithm IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

number of MSS frame duration, ms time slots of a frame multicast scheduling interval of a frame unicast scheduling interval of a frame power consumption for receiving energy for state transition

Value 20 5 256 16 240 1 2

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Fig. 7 a b c d e

Example of MSBS

Packets in BS queue Unicast data in frame j Packet scheduling in frame j Unicast data in frame j + 1 Packet scheduling in frame j + 1

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IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

www.ietdl.org Fig. 10 compares the average delay of the packets against mean data rate for MSBS and SSBIS scheduling scheme. The delay of a packet is defined as the time interval from the time the packet arrives at the BS to the time when it is scheduled for transmission. The simulation results show a tradeoff between the power consumption and delay. When

traffic load is low, the average delay of MSBS scheme is higher than that of the SSBIS scheme, but according to the QoS demand of the MSSs, this delay time can be accepted.

5

Conclusion

In this paper, we propose an MSBS algorithm, which improves energy efficiency in an integrated scheduling scheme. We defined a new scheduling model; it can reduce the idle time generated in adjacent interval of multicast scheduling periods. MSBS improves energy efficiency of MSSs by reducing the number of mode switch in the multicast transmission scheme and enhances the QoS requirements of each service class. Simulation result shows that MSBS can result in a significant energy saving in our scheme.

6

Acknowledgments

This work was supported in part by the National Science Council, Taiwan, under grant NSC 101-2221-E-036-040 and Tatung University, under grant B101-N02-051. Fig. 8 Remaining energy of an MSS with one and four connections

Fig. 9 Comparison of AEE in transmission of DL frames against mean data rate

Fig. 10 Comparison of the average delay against mean data rate IET Commun., 2013, Vol. 7, Iss. 3, pp. 255–262 doi: 10.1049/iet-com.2011.0325

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