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УНИВЕРСИТЕТ ИТМО
НАУЧНО-ТЕХНИЧЕСКИИ ВЕСТНИК ИНФОРМАЦИОННЫХ ТЕХНОЛОГИИ, МЕХАНИКИ И ОПТИКИ июль-август 2021 Том 21 № 4 http://ntv.ifmo.ru/
SCIENTIFIC AND TECHNICAL JOURNAL OF INFORMATION TECHNOLOGIES, MECHANICS AND OPTICS July-August 2021 Vol. 21 No 4 http://ntv.ifmo.ru/en/
ISSN 2226-1494 (print) ISSN 2500-0373 (online)
ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ, МЕХАНИКИ И ОПТИКИ
КОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ
COMPUTER SCIENCE
doi: 10.17586/2226-1494-2021-21-4-463-472
Nature-inspired metaheuristic scheduling algorithms in cloud:
a systematic review Sandeep Kumar Bothra1 Sunita Singhal2^
1>2 Manipal University Jaipur, Jaipur, Rajasthan, 303007, India
1 bothrajain@gmail.comH, http://orcid.org/0000-0003-0555-569X
2 sunita.smghal@jaipur.manipal.eduH, http://orcid.org/0000-0003-2462-8102
Abstract
Complex huge-scale scientific applications are simplified by workflow to execute in the cloud environment. The cloud is an emerging concept that effectively executes workflows, but it has a range of issues that must be addressed for it to progress. Workflow scheduling using a nature-inspired metaheuristic algorithm is a recent central theme in the cloud computing paradigm. It is an NP-complete problem that fascinates researchers to explore the optimum solution using swarm intelligence. This is a wide area where researchers work for a long time to find an optimum solution but due to the lack of actual research direction, their objectives become faint. Our systematic and extensive analysis of scheduling approaches involves recently high-cited metaheuristic algorithms like Genetic Algorithms (GA), Whale Search Algorithm (WSA), Ant Colony Optimization (ACO), Bat Algorithm, Artificial Bee Colony (ABC), Cuckoo Algorithm, Firefly Algorithm and Particle Swarm Optimization (PSO). Based on various parameters, we do not only classify them but also furnish a comprehensive striking comparison among them with the hope that our efforts will assist recent researchers to select an appropriate technique for further undiscovered issues. We also draw the attention of present researchers towards some open issues to dig out unexplored areas like energy consumption, reliability and security for considering them as future research work. Keywords
genetic algorithm, literature review, nature inspired algorithm, metaheuristic scheduling algorithm, swarm intelligence For citation: Bothra S.K., Singhal S. Nature-inspired metaheuristic scheduling algorithms in cloud: a systematic review. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2021, vol. 21, no. 4, pp. 463-472. doi: 10.17586/2226-1494-2021-21-4-463-472
УДК 004.75
Биоинспирированные метаэвристические алгоритмы построения расписаний в облаке: систематический обзор
Сандип Кумар Ботра1И, Сунита Сингхал2И
!>2 Университет Манипала в Джайпуре, Джайпур, Раджастан, 303007, Индия
1 bothrajain@gmail.comH, http://orcid.org/0000-0003-0555-569X
2 sunita.smghal@jaipur.manipal.eduH, http://orcid.org/0000-0003-2462-8102
Аннотация
Применение сложных крупномасштабных научных приложений упрощается в случае их обработки в облачной среде. Дальнейшее развитие облачных технологий связано с решением ряда новых проблем. Центральной темой парадигмы облачных вычислений является планирование рабочих процессов с использованием биоинспирированных метаэвристических алгоритмов. NP-полная задача (NP-completeness) привлекает исследователей к поиску оптимального решения с использованием роевого интеллекта. В работе представлены систематизированный анализ и оценка метаэвристических алгоритмов, таких как генетический (Genetic Algorithms, GA), китовый (Whale Search Algorithm, WSA), муравьиный (Ant Colony Optimization, ACO), летучих мышей (Bat Algorithm, BA), пчелиный (Artificial Bee Colony, ABC), кукушкин поиск (Cuckoo Algorithm, CA), светлячковый (Firefly Algorithm, FA), оптимизация роем частиц (Particle Swarm Optimization, PSO). Представлены параметры алгоритмов, дана их классификация, приведено подробное сравнение. Уделено внимание нерешенным проблемам, таким как потребление энергии, надежность и безопасность. Представленные результаты позволят исследователям выбрать подходящие решения возможных новых проблем в облачных вычислениях.
© Bothra S.K., Singhal S., 2021
Ключевые слова
генетический алгоритм, обзор, биоинспирированный алгоритм, метаэвристический алгоритм, роевой интеллект
Ссылка для цитирования: Ботра Сандип Кумар, Сингхал Сунита. Биоинспирированные метаэвристические алгоритмы построения расписаний в облаке: систематический обзор // Научно-технический вестник информационных технологий, механики и оптики. 2021. Т. 21, № 4. С. 463-472 (на англ. яз.). ёо1: 10.17586/2226-1494-2021-21-4-463-472
Introduction
In the last few years, the distributed computing paradigm has become a buzzword due to its robust features like reliability, elasticity, scalability, and sharing ability. Due to the pay-as-you-go (PAYG) and dynamic scalable nature Cloud Computing is an emerging technology of the distributed computing paradigm [1]. Scheduling is a process used to allocate resources among a set of tasks in a distributed environment in order to achieve Quality of Service (QoS) within a time frame, otherwise end-users will be hesitant to pay the service provider, despite the service provider's promises to users via Service Level Agreement (SLA) [2, 3]. Optimum resource scheduling is one of the central themes in the cloud, which is NP-complete. We have not had such an algorithm till now that generates an optimal solution within the polynomial time for the NP-complete problem [4]. Due to the local optimum nature of the heuristic approach, researchers are moving towards meta-heuristic techniques. The global optimum result can be achieved by the nature-inspired algorithm which is meta-heuristic in flavor. Nature-inspired algorithms may be biotic and abiotic phenomena. The bio-inspired algorithm is biotic, whereas the algorithm based on physical and chemical properties is abiotic. Bio-inspired algorithms are mostly based on the behavior of plants and animals, like flower pollination algorithm, Strawberry Plant Algorithm, Dolphin echolocation algorithm, etc. but not all. Some bio-inspired algorithms are not dependent upon the behavior of animals, like queen-bee evolution etc. Mostly bio-inspired algorithms are swarm-intelligence based like ant colony optimization etc. Physical and chemical Properties-based algorithms are black hole algorithm etc. An overview of nature-inspired metaheuristic algorithms is illustrated by (Fig. 1).
Scheduling strategies are classified as optimal or suboptimal [5]. To achieve an optimal solution to the NP-complete problem is very expensive, so it is better to find an approximate solution, i.e. sub-optimal. This is the reason why researchers focus on resolving such problems through metaheuristic techniques. Heuristic techniques are problem-specific and thus they cover small domain areas. Because of their problem independence, metaheuristic techniques attract researchers. Nowadays researchers attempt to solve such problems using a hybrid technique that combines heuristic and metaheuristic techniques.
Following a review of the literature, an extract of a comparative analysis on optimization techniques is shown in (Table 1).
The Metaheuristic Algorithm is defined by literature [6] as "an iterative master process that guides and modifies the operations of subordinate heuristics to efficiently produce high-quality solutions. It may manipulate a complete (or incomplete) single solution or a collection of solutions per iteration. The subordinate heuristics may be high (or low) level procedures, or a simple local search, or just a construction method". The authors [7] define a metaheuristic algorithm as "an iterative generation process that guides a subordinate heuristic by intelligently combining different concepts for exploring and exploiting the search space, learning strategies are used to structure information in order to find efficiently near-optimal-solutions".
Literature Review
We have studied various scheduling approaches based on metaheuristic algorithms like Artificial Bee Colony (ABC), Whale Search Algorithm (WSA), Bat Algorithm, Cuckoo Algorithm, Firefly Algorithm, Genetic Algorithm
Fig. 1. Representation of Nature-Inspired Metaheuristic Algorithms
Table 1. Comparative Study of Optimization Techniques
Heuristic Algorithm Metaheuristic Algorithm Hybrid Algorithm
Problem dependent Problem independent Mix mode
Cover specific area of problem Cover large area due to independent nature Cover huge area of problem
Rule-based solution Framework based solution Mix mode
Processing time — low Processing time — high Processing time — medium
Function nature — white-box Function nature — black-box Function nature — mix mod
Domain area — small Domain area — large Domain area — very large
(GA), Particles Search Optimization (PSO), and Ant Colony Optimization (ACO). The summary of all these is represented in (Fig. 2).
The authors improved the particle swarm optimization to schedule the workflow. They began by using a nonlinear reducing method of inertia weight to manage the global and local performance of particles; followed by a perfect scheduling plan to achieve the shortest possible time and cost, but they ignored the dynamic feature of the cloud computing environment [8]. A multi-objective algorithm to schedule workflow is presented by authors based on the PSO approach. To achieve their goals, they include two parameters, makespan and resource utilization, as well as a rigorous encoding scheme, in their novel algorithm.
Although their experimental result illustrates that their approach is more robust than the baseline approaches, they ignore the balancing of VMs [9]. The authors of [10] included a simulated annealing algorithm with PSO to escape sinking into local optima and enhance the convergence speed of the algorithm. Their main goal was to reduce the execution time of tasks as well as efficiently utilize the cloud's resources, but they did not focus on dynamic scheduling of workflow and security concepts.
In the paper [11], the authors tried to reduce the execution time and cost of the workflow by applying the two ant colonies approach and focused on executing maximum tasks parallel in ACO. To achieve the global optimum objective, they designed a new technique to update the pheromone. A complementary heuristic strategy (CHS) and an elite study strategy (ESS) are applied to achieve multiple objectives of the algorithm. Allocation of underutilized virtual machines by Pareto distribution is applied by authors [12] to minimize execution cost and execution time in ACO. They also adopt the approach of minimum migration of virtual machines to boost
the performance of their approach in the assessment of execution time and cost of workflow, but their practical approach is based on a very small size of the workflow, so the performance of the algorithm is not reliable.
The authors of [13] used the ACO technique to minimize the makespan by grouping the ordered tasks, but they did not consider cost, security, or load balance.
The paper [14] introduced a hybrid approach to schedule workflow by applying Artificial Bee Colony (ABC) with PSO. Their approach showed better results due to exploring the wider area of a solution space. The study [15], proposes an Artificial Bee Colony (ABC) based algorithm, in which the authors emphasize on the quality of service policies and crucial security concepts. To minimize the execution cost, execution time, migration of task, and load-balance of VMs, a hive table is maintained in a data center. Only the ABC approach is not enough to handle all these parameters, so they had to develop a hybrid technique.
The authors make an attempt to schedule the workflow using the firefly algorithm (FA), taking into account reliability, makespan, and resource utilization while maintaining a balanced load among various virtual machines. To select the proper virtual machine, they applied a rule-based strategy. They did not focus on booting time and termination delay of VMs, which impacted their algorithm's objective [16]. This dimness is removed in [17], where authors proposed a cost-effective approach using the firefly algorithm (FA) to schedule the scientific workflow under deadline constraint while considering performance variation of CPU as well as termination delay. To design the humpback whale optimization algorithm, intelligent techniques should be applied to enhance the performance.
By applying the Cuckoo Optimization Algorithm (COA) with a harmony search approach, the authors improved the scheduling performance in a cloud environment [18] where
Fig. 2. Prominent Meraheuristic Algorithm
they included cost, energy consumption, penalty factors and utilization of memory but they did not concern about load balance among the processors, which is improved in the work [19].
Vocalization of humpback whale optimization algorithm [20] is proposed to minimize the execution cost and time. This approach minimizes energy consumption to protect the environment. The authors presented a multi-objective deadline constraint-based workflow scheduling algorithm based on whale social behavior, in which they attempted to minimize makespan by considering load balance among virtual machines, but they failed to consider the dynamic nature of cloud computing, which plays an important role in the scheduling process [21]. This is solved in [22], where authors considered the dynamic behavior of cloud in their grouping whale's optimization algorithm. The first population is arranged in ascending order, then it is divided into several groups and a member is selected randomly from each group to encircle the prey section to minimize the time of response as well as execution and enhance the throughput in a cloud computing environment.
An algorithm based on the BAT optimization strategy to schedule the workflow was proposed to optimize time
and reliability in the cloud [23]. The authors applied the greedy approach to minimize the cost and execution time by improving the reliability under budget constraint, but there is no awareness about the energy consumption etc. To remove this weakness, the authors [24] gave more emphasis on energy consumption in their approach, although they included execution time and throughput but they did not consider communication time, which is an important factor in minimize the execution time and enhancing the throughput. Load balance among various VMs was also not considered by them. The work [25] proposes to use the Bat algorithm to balance the load on the various VMs, where authors tried to improve the resource allocation for VMs.
The papers [26, 27] provide meticulous information regarding metaheuristic algorithms. After reviewing various genetic algorithms [28-52], we present our deep investigation in brief (Tables 2, 3 and 4) and a comparative analysis on some metaheuristic algorithms are depicted in (Table 5).
We have analyzed various articles [53-57] and can conclude that in a cloud environment there are various issues required to be resolved.
Table 2. Study of Various Genetic Algorithms in Reverse Chronological Order
Reference Publication Year Objective Resource Type Nature of Input (Independent Task/Workflow) Workflow Type Experimental Environment
[28] 2020 Multi-Objective Heterogeneous Workflow Scientific CloudSim, JAVA
[29] 2020 Single-Objective Heterogeneous Independent — CloudSim
[30] 2020 Multi-Objective Homogeneous Workflow Scientific WorkflowSim
[31] 2020 Multi-Objective Heterogeneous Workflow Scientific CloudSim
[32] 2019 Multi-Objective Heterogeneous Independent — CloudSim
[33] 2019 Multi-Objective Heterogeneous Workflow Scientific jMetal Tool
[34] 2019 Multi-Objective Heterogeneous Workflow Scientific WorkflowSim
[35] 2019 Single-Objective Homogeneous Workflow Scientific C++
[36] 2018 Multi-Objective Heterogeneous Workflow & Independent Simple JAVA
[37] 2018 Single-Objective Heterogeneous Independent — MATLAB
[38] 2018 Single-Objective Heterogeneous Independent — CloudSim
[39] 2018 Single-Objective Heterogeneous Workflow Scientific CloudSim
[40] 2017 Single-Objective Heterogeneous Workflow Scientific WorkflowSim
[41] 2017 Multi-Objective Heterogeneous Workflow Scientific MATLAB
[42] 2016 Single-Objective Heterogeneous Workflow Scientific CloudSim
[43] 2016 Single-Objective Heterogeneous Workflow Scientific WorkflowSim
[44] 2015 Single-Objective Heterogeneous Workflow Simple No Mention
[45] 2014 Single-Objective Heterogeneous Workflow Simple C# Language
[46] 2014 Single-Objective Heterogeneous Workflow Simple In Real Cloud
[47] 2014 Single-Objective Heterogeneous Independent — MATLAB
[48] 2013 Single-Objective Homogeneous Independent — CloudAnalyst
[49] 2012 Multi-Objective Homogeneous Independent — CloudSim
[50] 2012 Single-Objective Heterogeneous Workflow Scientific No Mention
[51] 2011 Single-Objective Homogeneous Workflow Simple CloudSim
[52] 2011 Multi-Objective Homogenous Independent — No Mention
Table 3. Comparison of Various Scheduling Approaches based on GA
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Reference Scheduling Approach Initialization Selection Crossover Mutation
[28] GA with HEFT First by HEFT and remaining using efficient routine Roulette Wheel method Single-point, Double-points, Triple-points Single-point and Double-point with swap
[29] GA with Greedy Selection Technique Randomly using Binary code Roulette Wheel method Double-points Double-point with new method
[30] GA with HEFT Initialization using HEFT Tournament Selection Two-point crossover Single-point simple swap
[31] GA (For each parameter 1st get best solution, after that super best selected) Randomly Roulette Wheel method Clustered crossover operator Single-point simple swap
[32] GA with EDA Use EDA to initialize the probability then set to 1/m to ensure the randomness of the initial population. Roulette Wheel method One-point crossover Double-point simple swap
[34] Multi-populated GA Heuristic in the generation No Mention Random Random
[35] GA with PSO First generate randomly then apply PSO 1 st particle by Gbest and 2nd by random Single- point crossover Single-point simple swap
[36] GA with Gap Search Algorithm Randomly Tournament Based Single- point crossover Task Ti is mutated by transferring it from VMm to VMn
[37] Parallel GA with Priorities Strategy Randomly Roulette Wheel method Single- point crossover Single-point simple swap
[39] GA with Multi- Population and PSO Best Sec Operator Based Initialization Tournament Based Three crossover (cross-Uniform, cross- Average, cross - BLX) Two mutation (mut-Strong, mutLimit) operators
[40] GA with PEFT and PGA First Chromosome by PEFT and rest Randomly Tournament Based Single- point crossover Single-point simple swap
[41] NSGA-III Randomly and new method Niche-Preservation Operation is applied Single point -Simulated Binary Crossover (SBX) Gaussian Mutation
[42] GA with some part of JIT-C First randomly then JIT approach Tournament Based Two-point crossover Single-point simple swap
[44] GA No Mention Roulette wheel Method Two-point crossover Single-point simple swap
[45] GA with HEFT By 3 heuristic rank policies No Mention One-point crossover with new technique Single-point with their new technique
[46 GA with Best Fit and Round Robin Approach Based on Best Fit and Round Robin Approach No Mention Randomly Gene Selection First randomly select gene then replace its resource by less loaded resource having better failure rate
[47] GA with job spanning time and load balancing Greedy Approaches By Applying Greedy approach Rotating Selection One Point Local Search
[48] GA Randomly Randomly Single-point Toggled from 1 to 0 or 0 to 1
[49] Energy consumption with ETU GA and ETDF GA Randomly Elitist Generational Strategy and Roulette wheel method Single-point Single-point simple swap
[51] Genetic Algorithm with Markov Decision Process Randomly Roulette Wheel method Single-point Single-point simple swap
[52] GA with Feitelson's Parallel Workload Archive Greedy method and Random method Tournament Strategy Two-point crossover Single-point simple swap
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Table 4. Matrix Considered to Schedule Workflow using GA
Reference Cost Makespan Deadline Load Balance Security Energy
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
[51]
[52]
An extract from a comparative analysis on some metaheuristic algorithms is illustrated in (Table 5).
There are several available survey articles on metaheuristic algorithms, however in this study, we comprehensively covered the most recent metaheuristic techniques and focused on their pros and cons. In fact,
the current scenario necessitates the security of sensitive data/tasks and awareness of energy consumption. As per our survey, the major security components of the cloud include authentication services, integrity services, and confidentiality services. As there has been a little bit of research in these areas we have attempted to draw the
Table 5. Strength and Limitation of GA, PSO and ACO
Algorithm Strength Limitation
GA Other techniques can be combined easily. Encoding scheme is complex.
Search space can be explored in various directions simultaneously. Convergence rate is low.
Manipulation of various parameters can be done at the same time. Crossover and Mutation rates depend on stability.
An efficient global optimum solution can be achieved for various problems.
Able to resolve complex optimization problem of various types.
PSO Low level of dependency during initial point. Convergence rate is very low.
There are few parameters to adjust. Trapping into local optima.
Performs good global search. Capacity of local search is weak.
ACO Graph based complex problem can be solve easily. Theoretical analysis is very complex.
Able to solve problem related to the dynamic nature. Initialization of parameters is based on trial and errors.
attention of current researchers to it. Our observations, which are based on this survey, focus on a variety of technical issues and will guide present researchers in their decision for selection of an appropriate metaheuristic technique and lighting the path of research.
Observations and Discussion
Our observation based on the above described surveys is as follows:
Using local search approaches to build the initial population can increase the quality of resolutions obtained by metaheuristic algorithms which are based on the population. The elite solutions, which are derived from previous generations' greatest answers, can also be utilized to populate future generations' beginning populations. If these elites are strengthened before becoming a part of the following generation, they can produce better results than the initial elites.
Combining a metaheuristic algorithm with another metaheuristic algorithm which is based on population or a local search-based metaheuristic method, can improve solution quality or convergence speed.
The transition operators employed in metaheuristic algorithms have been modified by researchers. It is beneficial to improve the consistency of the solution by changing the transfer operator.
Virtual Machine placement optimization, Virtual Machine consolidation, and Dynamic Voltage and Frequency Scaling strategies are commonly used for energy conservation. The most significant disadvantage of this method is that frequency and voltage can only be altered to a limited extent.
Service providers should agree to a dual Service Level Agreement with the consumer, with the second SLA being optional and selected only when the cloud customer requires the "Green mode". The term "green mode" refers to a mode in which the primary purpose is to save energy at the expense of production.
The majority of energy-aware scheduling research has used metaheuristic strategies with the goal of lowering energy consumption. Computation resources produce a lot of heat, which makes execution more error-prone and, as a result, can reduce machine efficiency and shorten computer life spans.
In order to solve large-scale combinatorial and multimodal problems, exact optimization algorithms
are ineffective. An exhaustive search for the algorithms is impractical for dealing with these problems because the search space grows exponentially with the size of the problem. As a result, a large number of researchers have used meta-heuristic algorithms to solve the issues. Metaheuristic algorithms have various advantages:
They are not designed to solve a particular problem and can be used to solve multimodal complex problems.
They are easy to use in parallel processing and are adaptable to changing situations and environments.
They can include mechanisms to prevent them from being stuck in local optima.
Because of their discovery and extraction capabilities, these algorithms are able to identify promising regions in a reasonable amount of time.
Although the listed algorithms have shown satisfactory results in a variety of fields, they do not guarantee that an ideal solution can ever be found, and they do have some inevitable drawbacks like consuming long execution time, trapping in local optima, low convergence speed, several parameter tuning, complicated encoding scheme, and only decent output in real or binary search spaces. As a result, it appears that improving the performance of previous meta-heuristics, or even introducing new ones, is fruitful for current researchers.
Conclusion
We analyzed the most famous bio-inspired metaheuristic algorithms like ABC, ACO, BAT, Cuckoo, Firefly, GA, PSO, and Whale Optimization algorithms. We illustrated the classification of metaheuristic algorithms based on various factors and presented the comparison among the latest approaches with the hope that our efforts will give directions to current researchers to select the appropriate technique which meets their objective.
We discovered that most researchers are concerned with minimizing execution cost, execution time, response time, makespan, and increasing throughput, even though current researchers are becoming more concerned with protecting the environment by reducing energy consumption and carbon-dioxide emissions without affecting the Service Level Agreement (SLA). We also draw attention to unresolved issues as trapping in local optima, low convergence speed, several parameters tuning, complicated encoding scheme etc. and recent challenges in areas like reliability, security, and privacy for future research work.
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Authors
Sandeep Kumar Bothra—M.Tech., Researcher, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India, http://orcid.org/0000-0003-0555-569X, bothrajain@gmail.com
Sunita Singhal — PhD, Associate Professor, Associate Professor, Manipal University Jaipur, Jaipur, Rajasthan, 303007, India, gg 57194063002, http://orcid.org/0000-0003-2462-8102, sunita.singhal@jaipur.manipal.edu
Received 25.06.2021
Approved after reviewing 05.07.2021
Accepted 30.07.2021
43. Liu L., Zhang M., Buyya R., Fan Q. Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing // Concurrency and Computation: Practice and Experience. 2017. V. 29. N 5. P. e3942. https://doi.org/10.1002/cpe.3942
44. Chen Z.G., Du K.J., Zhan Z.H., Zhang J. Deadline constrained cloud computing resources scheduling for cost optimization based on dynamic objective genetic algorithm // Proc. 2015 IEEE Congress on Evolutionary Computation (CEC). 2015. P. 708-714. https://doi.org/10.1109/cec.2015.7256960
45. Xu Y., Li K., Hu J., Li K. A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues // Information Sciences. 2014. V. 270. P. 255-287. https://doi.org/10.1016/jj.ins.2014.02.122
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47. Wang T., Liu Z., Chen Y., Xu Y., Dai X. Load balancing task scheduling based on genetic algorithm in cloud computing // Proc. 12th International Conference on Dependable, Autonomic and Secure Computing (DASC). 2014. P. 146-152. https://doi.org/10.1109/DASC.2014.35
48. Dasgupta K., Mandal B., Dutta P., Mandal J.K., Dam S. A genetic algorithm (GA) based load balancing strategy for cloud computing // Procedia Technology. 2013. V. 10. P. 340-347. https://doi.org/10.1016/jj.protcy.2013.12.369
49. Ying C.-T., Yu J. Energy-aware genetic algorithms for task scheduling in cloud computing // Proc. 7th ChinaGrid Annual Conference (ChinaGrid 2012). 2012. P. 43-48. https://doi.org/10.1109/chinagrid.2012.15
50. Zhao E.-D., Qi Y.-Q., Xiang X.-X., Chen Y. A data placement strategy based on genetic algorithm for scientific workflows // Proc. 8th International Conference on Computational Intelligence and Security (CIS 2012). 2012. P. 146-149. https://doi.org/10.1109/cis.2012.40
51. Barrett E., Howley E., Duggan J. A learning architecture for scheduling workflow applications in the cloud // Proc. 9th European Conference on Web Services (ECOWS 2011). 2011. P. 83-90. https://doi.org/10.1109/ecows.2011.27
52. Kessaci Y., Melab N., Talbi E.-G. A pareto-based GA for scheduling HPC applications on distributed cloud infrastructures // Proc. of the International Conference on High Performance Computing and Simulation (HPCS 2011). 2011. P. 456-462. https://doi.org/10.1109/hpcsim.2011.5999860
53. Singhal S., Patel J. Load balancing scheduling algorithm for concurrent workflow // Computing and Informatics. 2018. V. 37. N 2. P. 311-326. https://doi.org/10.4149/cai_2018_2_311
54. Bansal S., Hota C. Efficient Algorithm on heterogeneous computing system // Proc. of the International Conference on Recent Trends in Information Systems (ReTIS 2011). 2011. P. 57-61. https://doi.org/10.1109/retis.2011.6146840
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Авторы
Сандип Кумар Ботра — магистр технологий, исследователь, Университет Манипала в Джайпуре, Джайпур, Раджастан, 303007, Индия, http://orcid.org/0000-0003-0555-569X, bothrajain@gmail.com Сунита Сингхал — PhD, доцент, доцент, Университет Манипала в Джайпуре, Джайпур, Раджастан, 303007, Индия, gQ 57194063002, http://orcid.org/0000-0003-2462-8102, sunita.singhal@jaipur.manipal.edu
Статья поступила в редакцию 25.06.2021 Одобрена после рецензирования 05.07.2021 Принята к печати 30.07.2021
