Energy-Efficient Resource Allocation for Cache-Assisted Mobile Edge Computing

Cui, Ying; We, He; Ni, Chun Chun; Guo, Chengjun; Liu, Zhi

doi:10.1109/lcn.2017.112

Cited by 75 publications

(51 citation statements)

References 13 publications

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“…Moreover, for MEC applications, in [27], the authors explored the fundamental tradeoffs between caching, computing, and communication for VR/AR applications. Finally, the work in [28] proposed a joint caching and offloading mechanism that considers task uploading and executing, computation output downloading, multi-user diversity, and multi-casting.…”

Section: Joint Caching and Communication (2c)mentioning

confidence: 99%

Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing

Ndikumana

Tran

et al. 2020

IEEE Trans. on Mobile Comput.

226

147

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The concept of multi-access edge computing (MEC) has been recently introduced to supplement cloud computing by deploying MEC servers to the network edge so as to reduce the network delay and alleviate the load on cloud data centers. However, compared to a resourceful cloud, an MEC server has limited resources. When each MEC server operates independently, it cannot handle all of the computational and big data demands stemming from the users' devices. Consequently, the MEC server cannot provide significant gains in overhead reduction due to data exchange between users' devices and remote cloud. Therefore, joint computing, caching, communication, and control (4C) at the edge with MEC server collaboration is strongly needed for big data applications. In order to address these challenges, in this paper, the problem of joint 4C in big data MEC is formulated as an optimization problem whose goal is to maximize the bandwidth saving while minimizing delay, subject to the local computation capability of user devices, computation deadline, and MEC resource constraints. However, the formulated problem is shown to be non-convex. To make this problem convex, a proximal upper bound problem of the original formulated problem that guarantees descent to the original problem is proposed. To solve the proximal upper bound problem, a block successive upper bound minimization (BSUM) method is applied. Simulation results show that the proposed approach increases bandwidth-saving and minimizes delay while satisfying the computation deadlines.

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Section: Joint Caching and Communication (2c)mentioning

confidence: 99%

Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing

Ndikumana

Tran

et al. 2020

IEEE Trans. on Mobile Comput.

226

147

View full text Add to dashboard Cite

show abstract

“…The use of caching to dynamically store the program and/or task data at the MEC system has been recently recognized as a cost-effective method to reduce computation delay, energy consumption, and bandwidth cost [3], [10]. Here, we refer to the techniques to cache the input and/or output of computation tasks at the server/user side as computation content caching (such as in [3]- [7]). On one hand, content caching reduces the data exchange between the edge servers and MUs if the required input data can be found in the cache.…”

Section: A Motivations and Summary Of Contributionsmentioning

confidence: 99%

“…Integrating content caching into MEC system design can effectively reduce computation delay, energy consumption, and bandwidth cost. In particular, an edge server can cache task output data [3], task input data [4], and intermediate task computation results that are potentially useful for future task executions [5]. Meanwhile, content caching can also be implemented at the MU side to minimize the offloading (downloading) traffic to (from) the edge server [6].…”

Section: B Related Workmentioning

confidence: 99%

Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

Huang

Zhang

2020

IEEE Trans. Wireless Commun.

216

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In mobile edge computing (MEC) systems, edge service caching refers to pre-storing the necessary programs for executing certain computation tasks at MEC servers, which is effective to reduce the real-time delay/bandwidth cost on acquiring and installing the programs. Due to the limited caching space at resource-constrained edge servers, it calls for careful design of caching placement to determine which programs to cache over time. This is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. In this paper, we consider an edge server assisting a mobile user (MU) in executing a sequence of computation tasks. In particular, the MU can upload and run its customized programs at the edge server, while the server can selectively cache the previously generated programs for future service reuse. To minimize the computation delay and energy consumption of the MU, we formulate a mixed integer non-linear programming (MINLP) that jointly optimizes the service caching placement, computation offloading decisions, and system resource allocation (e.g., CPU processing frequency and transmit power of MU). To tackle the problem, we first derive the closed-form expressions of the optimal resource allocation solutions, and subsequently transform the MINLP into an equivalent pure 0-1 integer linear programming (ILP) that optimizes only the binary caching placement and offloading decisions. To further reduce the complexity in solving a large-size ILP, we exploit the underlying graphical structures in caching causality and task dependency models, and accordingly devise a reduced-complexity alternating minimization technique to iteratively update either the caching placement or offloading decision by fixing the other. Extensive simulations show that the proposed joint optimization techniques achieve substantial resource savings of the MU compared to other representative benchmark methods considered.

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“…x k,n f,j = x k f,j , f ∈ F , j ∈ {1, 2, 3, 4}, k ∈ K, n ∈ N , (32) where x = (x k f,j ) f ∈F ,j∈{1,2,3,4},k∈K in constraints (21), (22) and (23) is replaced with {x n } n∈N (x k,n f,j ) f ∈F ,j∈{1,2,3,4},k∈K,n∈N in constraints (29), (30) and (31).…”

Section: B Optimal Policy Design For α >mentioning

confidence: 99%

Bandwidth Gain From Mobile Edge Computing and Caching in Wireless Multicast Systems

Sun

Chen

Tao

et al. 2020

IEEE Trans. Wireless Commun.

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In this paper, we present a novel mobile edge computing (MEC) model where the MEC server has the input and output data of all computation tasks and communicates with multiple caching-and-computing-enabled mobile devices via a shared wireless link. Each task request can be served from local output caching, local computing with input caching, local computing or MEC computing, each of which incurs a unique bandwidth requirement of the multicast link. Aiming to minimize the transmission bandwidth, we design and optimize the local caching and computing policy at mobile devices subject to latency, caching, energy and multicast transmission constraints. The joint policy optimization problem is shown to be NP-hard. When the output data size is smaller than the input data size, we reformulate the problem as minimization of a monotone submodular function over matroid constraints and obtain the optimal solution via a strongly polynomial algorithm of Schrijver. On the other hand, when the output data size is larger than the input data size, by leveraging sample approximation and concave convex procedure together with the alternating direction method of multipliers, we propose a lowcomplexity high-performance algorithm and prove it converges to a stationary point. Furthermore, we theoretically reveal how much bandwidth gain can be achieved from computing and caching resources at mobile devices or the multicast transmission for symmetric case. Our results indicate that exploiting the computing and caching resources at mobile devices as well as multicast transmission can provide significant bandwidth savings.

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Energy-Efficient Resource Allocation for Cache-Assisted Mobile Edge Computing

Cited by 75 publications

References 13 publications

Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing

Joint Communication, Computation, Caching, and Control in Big Data Multi-Access Edge Computing

Joint Optimization of Service Caching Placement and Computation Offloading in Mobile Edge Computing Systems

Bandwidth Gain From Mobile Edge Computing and Caching in Wireless Multicast Systems

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