Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform

Wang, Longhui; Wang, Yong; Xie, Yudong

doi:10.3390/a8030407

Cited by 17 publications

(11 citation statements)

References 9 publications

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“…All the previous studies show that parallel ACOs are faster than sequential ACOs for TSPs [14]- [17]. Although there were mismatched settings between the sequential and parallel ACO algorithms in these studies, the general trend points to the fact that the larger the TSP, the more efficient the parallel computing.…”

Section: A Computing Efficiencymentioning

confidence: 89%

“…Some previous studies simply assume that the quality of the solutions from obtained both sequential and parallel ACOs are similar [14], [16] whereas others showed parallel ACOs produced better solutions than sequential ACOs, at least among the best solutions [15], [17]. Even in the latter cases, the comparison was based on the mismatched settings between the sequential and parallel ACO algorithms.…”

Section: B Quality Of Solutionsmentioning

confidence: 99%

“…Like [14], the study reported in [16] compared the outcomes of parallel ACOs with different number of ants and clusters of computers by assuming that both sequential and parallel ACOs have the same accuracy in the quality of their solutions for the selected TSPs. In [17], some outcomes from a parallel MMAS based on Spark MapReduce applied to five cases of TSPs were reported. However, it focused on reaching the global optimal solution with a random number of ants assigned to the different cases without rationalization.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Dong¹,

Li²,

Shen³

et al. 2018

IJMLC

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Abstract-In recent years some comparative studies have explored the use of parallel ant colony optimization (ACO) algorithms over the traditionally sequential ACOs to solve the traveling salesman problem (TSP). However, these studies did not take a systematical approach to assess the performance of both algorithms on a comparable ground. In this paper, we aim to make a comparison of both the quality of the solutions and the running time as a result of the application of a sequential ACO and a parallel ACO to Eil51, Eil76 and KroA100 on a normalized and thus, comparable ground. Our study reaffirmed that the parallel algorithm is superior in computing efficiency over the sequential algorithm, particularly for larger TSPs. We also found that such a comparison could be meaningless if the size of the TSPs keeps increasing. We revealed that the worst solution among 10 repeated runs obtained from the parallel ACO was still better than the best solution among 10 repeated runs obtained from the sequential ACO, though both did not reach the global optimal solution within 300 iterations. The proposed parallel ACO has a very high consistency because at least one best solution was found within an error of 0.5% to the global optimal solution in every three repeats for all three cases.

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Section: A Computing Efficiencymentioning

confidence: 89%

Section: B Quality Of Solutionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Dong¹,

Li²,

Shen³

et al. 2018

IJMLC

View full text Add to dashboard Cite

show abstract

“…In [30] a GA is parallelized for pairwise test generation. A MAX-MIN Ant System algorithm (MMAS) is presented in [31] to solve the Traveling Salesman Problem (TSP). A coral reef (CR) algorithm is applied to the job shop scheduling problem (JSP) in [32].…”

Section: Related Workmentioning

confidence: 99%

Implementing cloud-based parallel metaheuristics: an overview

González

Pardo

Doallo

et al. 2018

JC&ST

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Metaheuristics are among the most popular methods for solving hard global optimization problems in many areas of science and engineering. Their parallel im- plementation applying HPC techniques is a common approach for efficiently using available resources to re- duce the time needed to get a good enough solution to hard-to-solve problems. Paradigms like MPI or OMP are the usual choice when executing them in clusters or supercomputers. Moreover, the pervasive presence of cloud computing and the emergence of programming models like MapReduce or Spark have given rise to an increasing interest in porting HPC workloads to the cloud, as is the case with parallel metaheuristics. In this paper we give an overview of our experience with different alternatives for porting parallel metaheuris- tics to the cloud, providing some useful insights to the interested reader that we have acquired through extensive experimentation.

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“…The second paper [11], entitled "Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform", by Wang et al, proposes to parallelize the well-known MAX-MIN Ant System (MMAS) algorithm in order to solve the annoying Traveling Salesman Problem (TSP) based on a Spark Cloud computing platform. Indeed, as authors correctly highlight, parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems.…”

mentioning

confidence: 99%

Algorithms for Managing, Querying and Processing Big Data in Cloud Environments

Cuzzocrea

2016

Algorithms

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Big data (e.g., [1][2][3]) has become one of the most challenging research topics in current years. Big data is everywhere, from social networks to web advertisements, from sensor and stream systems to bio-informatics, from graph management tools to smart cities, and so forth. Cloud computing environments (e.g., [4][5][6]) represent the "natural" context for such data, as they embed several emerging trends, both at the research level and the technological level, which comprise high-performance, high reliability, high availability, transparence, abstraction, virtualization, and so forth.At the convergence of these emerging trends, managing, querying and processing big data in Cloud environments, which have received a great deal of attention from the research community recently (e.g., [7][8][9]), plays a leading role, and algorithmic approaches to these challenges are very promising now. These approaches come from a rich variety of multi-disciplinary areas, ranging from mathematical models to approximation models, from resource-constrained paradigms to memory-bounded methods, and so forth. Furthermore, algorithms for managing big data according to a "systematic" view of the problem are gaining momentum. For instance, algorithms for efficiently managing MapReduce tasks over Clouds are a clear instance of the latter scientific area.Inspired by these exciting research challenges, this special issue "Algorithms for Managing, Querying and Processing Big Data in Cloud Environments" of Algorithms focuses the attention on topics related to the theory and practice of algorithms for managing big data in Cloud environments, the design and analysis of algorithms for managing big data in Cloud environments, the tuning and experimental evaluation of algorithms for managing big data in Cloud environments, and so forth. The aim is that of providing a significant milestone on the road of the investigated topic, to be significant for both theory and practice, as well as applications and systems that are founded on such algorithms.The special issue contains four papers which have been accepted after two rigorous review rounds. In the following, we provide an overview on these papers.The first (MMAS) algorithm in order to solve the annoying Traveling Salesman Problem (TSP) based on a Spark Cloud computing platform. Indeed, as authors correctly highlight, parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In the solution proposed by authors, MMAS is combined with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer Cluster. In addition to this, to improve the precision of the proposed solution, the local optimization strategy 2-opt is adapted in MMAS. Experimental results show that Spark has a very great accelerating effect on the ant colony algorithm when the city scale of TSP or the number of ants is relatively large. The third paper [12], entitled "A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data...

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Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform

Cited by 17 publications

References 9 publications

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Solving Traveling Salesman Problems with Ant Colony Optimization Algorithms in Sequential and Parallel Computing Environments: A Normalized Comparison

Implementing cloud-based parallel metaheuristics: an overview

Algorithms for Managing, Querying and Processing Big Data in Cloud Environments

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