Efficient Ubiquitous Big Data Storage Strategy for Mobile Cloud Computing over HetNet

Siddavaatam, Richa; Woungang, Isaac; Carvalho, Glaucio H. S.; Anpalagan, Alagan

doi:10.1109/glocom.2016.7841784

Cited by 7 publications

(17 citation statements)

References 13 publications

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“…This paper extends our previous work [6,7] by considering the case of a multiple users scenario, operating in a dense HetNet equipped with MCC and MEC technologies. The problem of radio resource and data transmission strategy in such network is then formulated as a combinatorial optimisation problem, and solved using an ant colony optimisation (ACO)-based energy-aware algorithm consisting of a data transmission management scheme, a physical resource blocks (PRBs)/bandwidth allocation scheme, and a data split multiple UEs algorithm (DSMU).…”

Section: Introductionmentioning

confidence: 54%

“…12, where it can be observed that the energy transmitted by our proposed algorithm for complete file transfer is significantly lower compared with the amount of energy transmitted by the GA and DSMU algorithm. This improvement in performance is attributed to the fact that the energy consumed by the network is part of the objective function and is among the constraints of the optimisation problem described in (1)- (6). The optimal solution also aimed at minimising this energy consumption.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…The path loss ε is calculated by using (18)-(21), depending on the location of the UE and the type of BS. All these calculations are inherited from our previous work [6,7,24] for the same purpose P = P m − ϵ (dB) (for macro base station),…”

Section: Simulation Settingsmentioning

confidence: 99%

“…The DSMU algorithm is an augmented version of an algorithm proposed in [6], wherein the network model consists of multiple concurrently active UEs. Typically, in this version, each UE has multiple data links, i.e.…”

Section: Dsmu Algorithmmentioning

confidence: 99%

“…In our earlier work [6, 7], we have investigated the problem of mobile cloud storage for big data transmission, with the goal to minimise the latency while intelligently exploiting the overlapping nature of a dense 5G wireless network to expedite the big data transmissions from a mobile device to the MEC. In this sense, the solution proposed in [6] was to divide the big data file into multiple chunks along with two data correction syndromes and to concurrently transmit them over the available data paths.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Joint optimisation of radio and infrastructure resources for energy‐efficient massive data storage in the mobile cloud over 5G HetNet

Siddavaatam

Woungang

Anpalagan

2019

IET wirel. sens. syst.

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This study formulates the problem of radio resource and data transmission strategy in a dense HetNet enhanced with mobile cloud computing and mobile edge computing technologies as a combinatorial optimisation problem. An ant colony optimisation (ACO)-based energy-aware algorithm is proposed as solution approach, which consists of a data transmission management scheme and a physical resource blocks/bandwidth allocation scheme. Simulation results show that compared with baseline approaches [(greedy and data split multiple user equipment (UEs) algorithms]), the proposed ACO-based algorithm achieves: (i) about 99% of reduction in terms of transmission delay under varying data file sizes (resp. varying number of UEs in the network); (ii) about 99% of reduction in terms of transmission energy dissipated by an UE during transmission of a file under varying data file sizes (resp. varying number of UEs in the network); (iii) improved performance with an increase in the number of small base stations in the network, (iv) significant reduction in the energy transmitted (by the UE) during transmission of big data files to the cloud storage; and (v) some stability irrespective of the variation in network conditions such as increasing the number of active UEs or increasing the number of UEs in the network.

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Section: Introductionmentioning

confidence: 54%

Section: Simulation Resultsmentioning

confidence: 99%