A novel energy efficiency algorithm in green mobile networks with cache
© Yao et al. 2015
Received: 25 February 2015
Accepted: 30 April 2015
Published: 20 May 2015
With more devices and emerging data-intensive services, mobile data traffic grows explosively, which makes the energy efficiency issue in current and future wireless networks be a growing concern. Recently, the advantages of bringing caching scheme in wireless networks have been widely investigated. Although additional power consumption is incurred by deploying caching equipments, the traffic loading balance of radio access network (RAN) and total network delay will be improved. In this paper, we study the energy efficiency problem in future wireless network. Particularly, we give an energy-delay tradeoff algorithm by using the sleeping control and power matching scheme. In the proposed algorithm, we consider a N-based sleeping control scheme, where the base station (BS) is in the sleep state whenever there is no arriving user, and works again when N users arrive. Moreover, the proposed algorithm is suitable for multi-BS scenario; to simplify the analysis, we mainly consider the single case. Simulation results shows that the proposed algorithm can obviously reduce the network power and delay in heterogeneous network conditions. Moreover, we find that a larger cache size does not always obtain a less network cost, because more cache power is consumed as cache size increases.
KeywordsFuture wireless network Caching scheme Sleeping control Power matching Energy-delay tradeoff
The growing popularity of smart phones, machine to machine devices place an increasing demand for differentiated services from wireless networks. The number of wireless devices is predicted to reach 7 trillion by 2020. With more devices and emerging data-intensive services, mobile data traffic is estimated to increase by 1000-fold in 2010-2020 . Moreover, by use of the wireless networks, M2M communications are rapidly developing based on the large diversity of machine type terminals, including sensors, mobile phones, consumer electronics, utility metering, vending machines, and so on. With the dramatic penetration of embedded devices, M2M communications will become a dominant communication paradigm in the communication network, which currently concentrates on machine-to-human or human-to-human information production, exchange, and processing .With the explosively grows of mobile data traffic, it brings a lot of challenges in designing and deployment of future wireless networks, including the architecture, complexity control scheme, energy assumption, etc. One of the most important challenges is the energy efficiency problem . Moreover, because of the centralized architecture of current wireless networks, the bandwidth as well as the wireless link capacity of the RAN and the backhaul network cannot practically deal with the explosive growth in mobile traffic .
Recently, more and more researchers have proved the advantages of caching scheme in wireless networks, which can speed up content distribution and improve network resource utilization. In , a comprehensive overview of the recently proposed in-network caching mechanisms for information-centric networking (ICN) is proposed. In , an embedding cache in wireless networks is analyzed to achieve significant reduction in response time and thus provide exceptional wireless user experience. In , the authors present a comprehensive survey of state-of-art techniques aiming to address the challenges to ICN caching technologies, with particular focus on reducing cache redundancy and improving the availability of cached content, and also points out several interesting yet challenging research directions in the caching study area. In , the authors propose an economic model that can be used to analyze the cost saving and benefit when cache function is place at different places of mobile network, and a real mobile network is studied according to the proposed model. In , the potential of forward caching in 3G cellular networks is explored, and the authors develop a caching cost model to realize the tradeoffs between deploying forward caching at different levels in the 3G network hierarchy. In , the authors put forward a link quality-based cache replacement technique in mobile ad hoc network. In particular, the source obtains multiple paths to the destination through multipath routing, and he acquired paths are stored in route cache. The cache replacement technique estimates the link quality using received signal strength (RSS) value. Links that possess low RSS value are removed from the route cache. In , the authors demonstrate the feasibility and effectiveness of using micro-caches at the base-stations of the RAN, coupled with new caching policies based on video preference of users in the cell and a new scheduling technique that allocates RAN backhaul bandwidth in coordination with requesting video clients.
Although existing excellent works have been successfully done on caching in the cellular networks, some basic questions still remain unanswered. The most important one is energy-delay tradeoff problem. In this paper, we systematically study the energy efficiency problem in future wireless network. Particularly, we give an energy-delay tradeoff algorithm by using the sleeping control and power matching scheme. In the proposed algorithm, we consider a N-based sleeping control scheme, where the BS is in the sleep state whenever there is no arriving user, and works again when N users arrive. Moreover, the proposed algorithm is suitable for multi-BS scenario; to simplify the analysis, we mainly consider the single case. We also evaluate the proposed algorithm in different network conditions and compare it with the scenario without caching Scheme. Simulation results reveal that the proposed model obviously reduces the network power and delay, and makes an energy-delay tradeoff. In addition, we find that a large cache size does not always mean a less network cost because of the more cache power consumption.
The rest of the paper is organized as follows. Section 2 gives a brief description of the system model and formulates the problems. Section 3 depicts the details the N-based sleeping control with power matching with caching scheme. Our simulation results are given in Section 4. Section 5 concludes the paper.
2 System model
In the proposed scenario, the wireless terminals can send requests to obtain their interested contents, and the BS can satisfy parts of the requests by using the contents buffered in the BS cache. Moreover, we assume that the packet error has been guaranteed by the physical layer technique, and the retransmission is not considered .
2.1 Content popularity model
2.2 Wireless Devices Distribution Model
2.3 Power model
In the proposed scenario, we assume that the wireless devices arrive according to a Poisson process with arrival rate r. A random amount of downlink service with average length l bits is required by each device, e.g., non-real-time file download with average file size l, and then the user leaves after being served [3, 12]. Assuming the arrival rate can be well estimated [12, 15], with time varying traffic intensity in practice, we just need to operate according to the current arrival rate. In the following, we use an energy-proportional model  to model the power consumption in the wireless network with caching scheme.
2.3.1 Cache power
We assume the cache in the future wireless network has active mode and sleep mode, just like the traditional BS. Thus, cache scheme may be modeled as a binary hypothesis problem:
H 0 : The cache is active.
H 1 : The cache is sleep.
2.3.2 Base station power
2.4 Delay model
2.5 System cost
The positive weighting factor β indicates the relative importance of the average delay over the average power which can be thought of as a Lagrange multiplier on an average delay constraint [12, 23, 24]. The “delay” we consider in this paper is the average response time from the user’s service request arriving at the BS until this request is finished . From the Little’s Law, we know that the mean delay is directly related to the average queue length.
3 Sleeping control with power matching
4 Simulation results
In this section, we use computer simulations to evaluate the performance of the proposed energy-delay tradeoff model. We first describe the simulation settings and then compare it with the traditional network cost model, which does not have a cache.
4.1 Simulation settings
The simulation is assumed to be carried out in a single urban micro-cell scenario. According to the ITU test environments  and the related introduction in , the system bandwidth B = 10 MHz, the maximum transmit power P max t = 15 W, and the channel gain γ = 10(dB) in the simulation. We take the micro BS energy consumption parameters P 0 = 100 W and ΔP = 7 . Moreover, users arrive according to a Poisson process with arrival rate λ = 4, and each user requests exactly one file whose size is exponentially distributed with mean l = 2 MB from the BS and leaves the system after being served [3, 12]. The system operates in a time-slotted fashion, and the BS schedules users in a round robin way, serving one user in each time slot. Moreover, it is assumed that the average user arrival rate x is 70 Mbps.
In the simulation, it is also assumed that there are 10,000 different contents in the system, where values of skewness factor α range from 0.6  to 1.5 . We abstract the cache size for a BS as a proportion that the cache size is defined as the relative size to the total amount of different contents in the network, which varies from 0.1 to 5 % .
4.2 Performance evaluation results
In this paper, we have studied the sleeping control and power matching problem for energy-delay tradeoffs in the context of single BS, which has a cache capacity to buffer the contents through it. In the proposed model, an N-based sleeping control scheme is considered. Simulation results reveal that, by introducing the cache, the network power and delay can be obviously reduced in different network conditions compared to the scenario without a cache. In addition, we find that a large cache size does not always mean a less network cost because of the more cache power consumption.
This work was supported by NSFC (61471056), China Jiangsu Future Internet Research Fund (BY2013095-3-1), and BUPT Youth Research and Innovation Plan (2014RC0103).
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