Energy-Efficient Resource Allocation in Software ... - IEEE Xplore

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS): IECCO: Integrating Edge Computing, Caching, and Offloading in Next Generation Networks

Energy-Efficient Resource Allocation in Software-Defined Mobile Networks with Mobile Edge Computing and Caching Chengchao Liang∗ , Ying He† , F. Richard Yu∗ , and Nan Zhao† ∗ Depart.

† School

of Systems and Computer Eng., Carleton University, Ottawa, ON, Canada of Inform. and Commun. Eng., Dalian University of Technology, Dalian, Liaoning, P. R. China

Abstract—In this paper, we study the energy-efficient resource allocation in software-defined mobile networks with mobile edge computing and caching. With the introduction of caching and computing functions in mobile networks, content sources need to be selected according to the distribution of contents in caches, the capability of computational resources and the status of networks. Moreover, the network needs to provision bandwidth on each link for data flows from the source to the destination by allocating backhaul and radio resources. In this framework, we formulate a novel optimization problem to jointly consider bandwidth provisioning and content source selection. To solve this problem efficiently, firstly the content source selection problem is decoupled from the bandwidth provisioning problem by deploying dual-decomposition method. Additionally, based on alternating direction method of multipliers, we develop decentralized schemes to solve the decoupled problems across links and base stations coordinated by a central controller. Simulation results are presented to show the performance of the proposed scheme. Index Terms—Energy efficiency, mobile edge computing, in network caching, dual decomposition

I. I NTRODUCTION Software-defined mobile networks (SDMNs) are proposed to fully support software-defined networking (SDN) design in wireless networks, which enable the programmability in mobile networks so that the complexity and the cost of networks can be reduced [1]–[4]. With the programmability, SDN is considered as a promising candidate to enhance traffic engineering, which is the key component in communication networks [5], [6]. Traffic engineering is used to optimize the network and provision services with requirements by directing traffic in networks [5], [7]. The success of the utilizing SDMNs for traffic engineering depends critically on our ability to jointly provision the backhaul and radio access networks (RANs) for the traffic [7], [8]. Mobile-edge computing (MEC), as an extension of cloud computing on mobile networks, has attracted great interest in 5G recently [9]. As the local cache is proposed to be implemented at the MEC server [10], an emerging technology, in-network caching [11], can be enabled and enhanced in the mobile edge. To improve the performance of delivering content, popular contents can be cached at network nodes that close to users to avoid the duplicated transmission of data. It has shown that access delays, traffic loads, and network

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costs can be significantly reduced by caching content at the mobile edge [11]–[13]. To benefit from both computing and caching at the mobile edge, distinct features of computing and caching should be carefully considered in the optimization of next generation mobile networks [14]. Meanwhile, energy-efficient techniques are significant topics in next generation mobile networks, as energy consumption has become a primary concern in the design and operation of wireless communication systems [15]–[17]. As MEC and in-network caching are key components of 5G, energy efficiency has become a main concern for the design of those mechanisms. Energy-efficient MEC has been investigated from the aspect of computation offloading (e.g., [18]). However, to the best of our knowledge, energy-efficient resource allocation that considers the utilization of both MEC and in-network caching has been largely ignored in the existing research. Thus, to address this challenge, in this paper, we propose to minimize the energy consumption when jointly considering the utilization of mobile-edge computing and caching (MECC) and the bandwidth provisioning in traffic engineering for services on both backhaul and radio links with limited network resources. The distinctive technical features of this paper are listed as follows: •

•

•

In this paper, we investigate the joint design of bandwidth provisioning and content source selection in a MECCenabled SDMN to improve the network performance as well as to reduce energy consumption. With the objective of minimizing energy consumption of the considered network, an optimization problem is formulated. We deploy dual-decomposition method to solve the formulated problem to cope with the coupling of selecting nodes and allocating resources. Due to the dynamic change of network status and drawbacks caused by the frequent exchange of information, we design decentralized schemes based on alternating direction method of multipliers (ADMM) to solve the decoupled problems.

The rest of this paper is organized as follows. Section II introduces the system model and formulates the presented problem. Section III describes the proposed algorithms and the corresponding analysis. Simulation results are discussed

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power allocation schemes. Thus, by using the Shannon bound, the spectrum efficiency of the wireless link l is defined as ! gil jl pjl P (1) γl = log 1 + σ0 + l0 ∈Lr /l gil jl0 pjl0 where gil jl is the large-scale channel gain that includes pathloss and shadowing between the transmission node jl (the source of the link l) and the receiving node il (the destination of the link l). We deploy the same model used in [24] to calculate the pathloss and apply shadowing. pjl (Watt/Hz) is the normalized transmission power on link l. The fixed equal power allocation mechanism is used, which means transmission power pjl is the same for all frequencies. σ0 is the P power spectrum density of additive white Gaussian noise. 0 l0 ∈Lr /l gil jl0 pjl is the aggregated received interference. Accordingly, the achievable data rate capacity of the link l is Rl = xl Wjl γl Fig. 1: The architecture of a cache-enabled SDMN with MEC.

in Section IV. Finally, we conclude this study in Section V. II. S YSTEM M ODEL AND P ROBLEM F ORMULATION In this section, we present the system model of the network, the caching, services and the related assumptions. A. System Model Considering the downlink transmission case in a SDNenabled heterogeneous network (HetNet) comprised of a set of N nodes and a set of directed links L := {1, ..., l, ...L}. il ∈ N and jl ∈ N are used to denote the destination node and the source node of the link l, respectively. A set J ⊂ N of Macro BSs (MBSs), Small BSs (SBSs) and the core network (the core router) n0 are connected by wired backhaul links Lw ⊂ L. A set I ⊂ N of user equipments (UEs) can be served by multiple SBSs and MBSs by wireless links Lr ⊂ L. Similar to [8], a set F := {1, ..., f, ...F } of flows are running in the considered network, where each flow has a required data rate rf , and rfl denotes the rate on the link l. We assume multiple flows may reach to one user (multiple services) and one flow can reach to the destination through multiple wireless links. This multi-BSs association is enabled by techniques, such as the multistream carrier aggregation [19] and the BS cooperation [8]. For simplicity, user mobility [20] and handover [21]–[23] are not considered in this paper. Moreover, a central SDN controller is deployed to steer the traffic engineering by conducting the bandwidth provisioning and the resource allocation. If link l is a wired link, it is assumed to provide a fixed bandwidth capacity Bl . If link l is a wireless link, the capacity depends on the ratio of the radio resource that the network allocates to this link. In this paper, to simply our analysis, we do not consider any advanced interference management and

(2)

where xl ∈ [0, 1] is the allocated ratio of the radio resource for the link l from its source node jl . Wjl is the total available spectrum bandwidth of its source node jl . We assume that a node n ∈ J is equipped with caching function and stores Sn popular contents. In this proposed scheme, total S content files are stored at the content server (cloud centers or the Internet source) and each content has the normalized size of 1. This assumption is reasonable because we can slice the content into chunks with the same length. As nodes in mobile networks only have limited storage capability, Sn is much smaller than the cloud center (Sn S, ∀n ∈ J ). If the content carried by the flow f can be found in node n, a hitting indicator hfn = 1 and n can be considered as a potential source node of flow f ; otherwise, hfn = 0. If a node n ∈ n0 ∪J 1 is selected as the source node of flow f , the data needs to be processed so that they can be transferred to appropriate formats (e.g., trans-coding, compression, and coding) and transmitted by the required data rate rf . However, unlike the powerful computing resource at the source server (e.g., the data center), due to the computing resource at each node, limited tasks can be activated at the same time. We assume the maximum scheduled CPU computing frequency at the BS n is Cn . To process a bit information, cn CPU cycles are required, which means cn rf (cycles/s) is the minimum requirement to support the flow f to start from the BS n. If node n is selected as the content source, a variable sfn ∈ {0, 1} is set to 1; otherwise sfn = 0. Let dfn ∈ {0, 1} denote the destination node of flow f , which means dfn = 1 shows n ∈ I is the destination of flow f . The consumed energy to support F flows in the networks includes two main part, namely the node operation energy and the transmission energy. The node operation energy E C (sfn ) shown below depends on the fixed power consumption p0n 1 Note that we use n to denote the content source server and the CN 0 because the CN can be considered as the link from the BS to the content server.

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(e.g, circuit, control signals, content caches)2 and the power of computational consumption pC (Watts/Cycle).   X X p0n + E C (sfn ) = t0 (3) sfn cn rf pC  , f ∈F

n∈J

where t0 seconds is the operation time. Moreover, we ignore the energy used at the content server as our purpose is to minimize the energy of the considered system. The transmission energy depends on wireless transmission power Pjrl and backhaul transmission power Plw shown as X E T (xl , rlf ) = el (xl , rlf ), (4) l∈L

where el (xl , rlf ) =

t0 xl Pjrl , if l ∈ Lr , P f w w t0 Pl f ∈F rl , if l ∈ L

Thus, the total energy consumption is ηEE = E T (xl , rlf ) + E C (sfn )

(5)

(6)

B. Problem Formulation As this study focuses on developing an energy-efficient solution to provisioning bandwidth and utilizing MECC, the joint energy-efficient problem can be mathematically presented as min ηEE (7a) R,S,X

subject to xl , rlf ∈