Distributed acceleration of mobile radio network optimisation algorithms

Ye, Yuanzhou; Megson, Graham M.

doi:10.1109/wts.2010.5479627

Cited by 2 publications

(8 citation statements)

References 4 publications

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“…The design of distributed system adopts the server-client model from its counterpart of the distributed Intelligent MNO system in our early work [12]. A computer cluster is utilized, encompassing a server compute node alongside a collection of client compute nodes that are "linked" via Windows Sockets.…”

Section: A Design Of the Distributed Systemmentioning

confidence: 99%

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Distributed parallelization of greedy Mobile Network Optimization algorithms

Cadenas

Megson

2013

2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013)

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The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

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Section: A Design Of the Distributed Systemmentioning

confidence: 99%

“…The detailed IPC diagram is given in our prior paper [12]. Nevertheless, data transferred through IPC is substantially differentiated and the flow of ParaController and optimizer Fig.…”

Section: B Inter-process Communications Mechanismmentioning

confidence: 99%

Distributed parallelization of greedy Mobile Network Optimization algorithms

Cadenas

Megson

2013

2013 21st International Conference on Software, Telecommunications and Computer Networks - (SoftCOM 2013)

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“…The two and only decisions (accepted, rejected) create the potential for parallelization. More details of the algorithm can be found in [11].…”

Section: B Mobile Network Optimization Algorithmsmentioning

confidence: 99%

“…Previously we have designed and implemented a distributed (cluster based) predictive binary tree based parallel solution [7], a basic diagram of which is depicted in Fig.2. Since there are two, and only two, states (accepted and rejected), for a fixed number of trial moves, all state combinations can be predicted and fit a novel predictive binary tree pattern.…”

Section: Basics Of the "Synchronous" Distributed Parallel Solutionmentioning

confidence: 99%

“…ariesoACP is built upon intelligent optimization algorithms that fine-tune antenna settings with learning capabilities and simulate the network changes to find the optimal network solution. Previously we have designed and implemented a distributed (cluster based) parallel solution to these sequential MNO algorithms as presented by [7] and achieved a substantial speed gain of 6.1 on a 16-core distributed system. The goal of this paper is to further enhance the algorithm behaviour and move the DNO realization forward such that the optimization phase of DNO becomes more feasible on a sub-hourly basis.…”

Section: Introductionmentioning

confidence: 99%

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Asynchronous distributed parallelization of mobile network optimization algorithms

Megson

Cadenas

2013

Wireless VITAE 2013

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It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

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Distributed acceleration of mobile radio network optimisation algorithms

Cited by 2 publications

References 4 publications

Distributed parallelization of greedy Mobile Network Optimization algorithms

Distributed parallelization of greedy Mobile Network Optimization algorithms

Asynchronous distributed parallelization of mobile network optimization algorithms

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