Software Defined Networking, Caching, and Computing for Green Wireless Networks

Huo, Ru; Yu, F. Richard; Huang, Tao; Xie, Renchao; Liu, Jiang; Leung, Victor C. M.; Liu, Yunjie

doi:10.1109/mcom.2016.1600485cm

Cited by 83 publications

(45 citation statements)

References 13 publications

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“…Since MEC can enhance the computational capabilities of edge nodes, in [25], the authors formulated a computation offloading decision, resource allocation and data caching framework as an optimization problem in which the total revenue of the network is considered. Furthermore, in [26], the authors proposed an energy-efficient framework that considers joint networking, caching, and computing whose goal is to meet the requirements of the next generation of green wireless networks. Moreover, for MEC applications, in [27], the authors explored the fundamental tradeoffs between caching, computing, and communication for VR/AR applications.…”

Section: Joint Caching and Communication (2c)mentioning

confidence: 99%

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

Ndikumana

Tran

et al. 2020

IEEE Trans. on Mobile Comput.

245

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%

“…This process of serving cached content after computation was not considered in [19]- [23]. Finally, the works in [16], [24]- [26] do not take into account any user deadlines for performing computations, which can be impractical.…”

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.

245

147

View full text Add to dashboard Cite

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“…The disciplined convex programming (DCP) problem (18) can be easily solved by using the available optimization toolboxes, such as CVX [37]. The CVX package with SeDuMi and SDPT3 solvers can be used to solve the DCP problems [37].…”

Section: ) Initializationmentioning

confidence: 99%

Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets

Rezvani

Mokari

Javan

et al. 2020

IEEE Trans. on Mobile Comput.

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Recently, wireless edge caching has been emerged as a promising technology for future wireless networks to cope with exponentially increasing demands for high data rate and low latency multimedia services by proactively storing contents at the network edge. Here, we aim to design efficient cache placement and delivery strategies for an orthogonal frequency division multiple access (OFDMA)based cache-enabled heterogeneous cellular network (C-HetNet) which operates in two separated phases: caching phase (CP) and delivery phase (DP). Since guaranteeing fairness among mobile users (MUs) is not well investigated in cache-assisted wireless networks, we first propose two delay-based fairness schemes called proportional fairness (PF) and min-max fairness (MMF). The PF scheme deals with minimizing the total weighted latency of MUs while MMF aims at minimizing the maximum latency among them. In the CP, we propose a novel proactive fairness and transmission-aware cache placement strategy (CPS) corresponding to each target fairness scheme by exploiting the flexible wireless access and backhaul transmission opportunities. Specifically, we jointly perform the allocation of physical resources as storage and radio, and user association to improve the flexibility of the CPSs. Moreover, In the DP of each fairness scheme, an efficient delivery policy is proposed based on the arrival requests of MUs, CSI, and caching status. Numerical assessments demonstrate that our proposed CPSs outperform the total latency of MUs up to 27% compared to the conventional baseline popular CPSs.

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“…Authors formulate the virtual resource allocation strategy as a joint optimization problem by considering both virtualization and caching and computing at the edge. A novel framework which jointly considers networking, caching, and computing techniques to support energy-efficient information retrieval and computing services is presented in [185]. This framework integrates SDN, MEC and ICN to enable the dynamic orchestration of different resources in next generation green wireless networks.…”

Section: Information Centric Networkingmentioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

647

328

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The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

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Software Defined Networking, Caching, and Computing for Green Wireless Networks

Cited by 83 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

Fairness and Transmission-Aware Caching and Delivery Policies in OFDMA-Based HetNets

Survey on Multi-Access Edge Computing for Internet of Things Realization

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