Solving the Multi-Commodity Flow Problem using a Multi-Objective Genetic Algorithm

Farrugia, Noel; Briffa, Johann A.; Buttigieg, Victor

doi:10.1109/cec.2019.8790160

Cited by 6 publications

(6 citation statements)

References 14 publications

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“…This is very different from setting biases on the objectives before the algorithm even starts, which requires very deep knowledge of the area to get such biases right. More information on this can be found in [28,33] with examples of how the provision of multiple results is beneficial in a network setting can be found in [30]. Third, the convergence rate is improved as shown in can be observed that the Hybrid algorithm found a number of solutions that dominate those found by the standard ERA.…”

Section: Plos Onementioning

confidence: 97%

“…The ERA presented here is based on [30,31] by the same authors and uses the NSGA-II algorithm [32]. It shares the chromosome design, crossover operator, total network flow objective, and the excess removal algorithm with the algorithm given in [30]. For completeness, a summary of these components is included in this text.…”

Section: Routing Solution 2: Evolutionary Routing Algorithmmentioning

confidence: 99%

“…This process is terminated when no over-capacity links remain. For a detailed explanation, refer to [28,30].…”

Section: Plos Onementioning

confidence: 99%

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Solving the multicommodity flow problem using an evolutionary routing algorithm in a computer network environment

2023

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The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that take into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Network (SDN) controller. The designed routing algorithm uses Per-Packet multipath routing to make the best use of the network’s resources. Per-Packet multipath is known to have adverse affects when used with TCP, so we propose modifications to the Multipath TCP (MPTCP) protocol to overcome this. Network simulations are performed on a real world network model with 41 nodes and 60 bidirectional links. Results for the EA routing solution with the modified MPTCP protocol show a 29% increase in the total network Goodput, and a more than 50% average reduction in a flow’s end-to-end delay, when compared to OSPF and standard TCP under the same network topology and flow request conditions.

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Section: Plos Onementioning

confidence: 97%

Section: Routing Solution 2: Evolutionary Routing Algorithmmentioning

confidence: 99%

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Solving the multicommodity flow problem using an evolutionary routing algorithm in a computer network environment

2023

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“…Determining the global optimal route/schedule for the flows can be reduced to the classic multi-commodity flow problem [220][221][222]. Hence, it cannot be solved in a reasonable amount of time or scales to more GPUs.…”

Section: Need For Adaptive Routingmentioning

confidence: 99%

“…UMJ on eight GPUs is even slower than on a single GPU. Overall, the results clearly demonstrate that existing partitioned hash join implementations the classic multi-commodity flow problem, which cannot be solved in a reasonable amount of time, even for eight GPUs [220][221][222].…”

Section: Introductionmentioning

confidence: 99%

In-memory analytical query processing on GPUs

Paul¹

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The high global memory bandwidth and the large number of parallel cores available in modern Graphics Processing Units (GPUs) make them ideal for highperformance Online Analytical Processing (OLAP) systems. However, it is challenging to design efficient high-performance query processing systems for GPUs due to the following reasons: 1) the rapid evolution of GPU hardware in the last decade, 2) the significant differences in the hardware architecture of GPUs when compared to CPUs, 3) the high overhead of moving the data between the CPU and GPU and 4) the small global memory size of a single GPU that necessitates the access of remote data over PCIe or NVLink interconnects when processing large data sets. In this thesis, we study existing query processing systems for GPUs and propose techniques to improve query execution efficiency on GPUs.We begin by studying the performance of hash join, which is one of the most compute and memory intensive relational operator in OLAP systems. Specifically, we first revisit the major GPU hash join implementations in the past decade that were designed to execute on a single GPU. We then detail how these implementations take advantage of different GPU architecture features and conduct a comprehensive evaluation of their performance and cost-efficiency using different generations of GPUs. This helps shed light on the impact of different GPU architecture features on the performance of the hash join operation and identify the factors guiding the choice of these features. We further study how data characteristics like skew and match rate impact the performance of GPU hash join implementations. Novel techniques to improve the performance of the hash join operation when joining input relations with severe skew or high match rate were also proposed as part of this study. Our evaluation finds that the proposed techniques help avoid any performance degradation when joining skewed input relations and achieve up to 2.5x better performance when joining input relations with high match rate.Next, we extend our study on the hash join operation to modern multi-GPU architectures. The recent scale-up of GPU hardware through the integration of multiple xi Contentsxii GPUs into a single machine and the introduction of higher bandwidth interconnects like NVLink 2.0 has enabled new opportunities of relational query processing on multiple GPUs. However, due to the unique characteristics of GPUs and the interconnects, existing hash join implementations spend up to 66% of their execution time moving the data between the GPUs and achieve lower than 50% utilization of the newer high bandwidth interconnects. This leads to extremely poor scalablity of hash join performance on multiple GPUs, which can be slower than the performance on a single GPU. In this thesis, we propose MG-Join, a scalable partitioned hash join implementation on multiple GPUs of a single machine. In order to effectively improve the bandwidth utilization, we develop a novel multi-hop routing for cross-GPU communication that adaptively chooses...

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The multicommodity network flow problem: state of the art classification, applications, and solution methods

Salimifard

Bigharaz

2020

Oper Res Int J

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Solving the Multi-Commodity Flow Problem using a Multi-Objective Genetic Algorithm

Cited by 6 publications

References 14 publications

Solving the multicommodity flow problem using an evolutionary routing algorithm in a computer network environment

Solving the multicommodity flow problem using an evolutionary routing algorithm in a computer network environment

In-memory analytical query processing on GPUs

The multicommodity network flow problem: state of the art classification, applications, and solution methods

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