HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Sharma, Mohan; Garg, Ritu

doi:10.1016/j.jestch.2019.03.009

Cited by 38 publications

(26 citation statements)

References 16 publications

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“…The solicitations assigned to the VMs organization on resting workers are replaced and migrated to a few selected Virtual Machine VMs on active workers. The consolidation technique is seen in many executed approaches [15]. The writers of [16] have taken care to consider the dependability of physical jobs while enticing consolidation options.…”

Section: Efficacy Of the Force Based On The Restructuring Methodsmentioning

confidence: 99%

A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

Vaitheeswari¹,

Krishnaveni²

2021

JUSST

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Matrix structure was one of the most important devices for finding data from big data. Here you’ll find data produced by current applications using cloud computing. However, moving big data using such a system in a performance computer or through virtual machines is still inefficient or impossible. Furthermore, big data is often gathered data from a variety of data sources and stored on a variety of machines using scheduling algorithms. As a result, such data usually bear solid shifted commotion. Growing circulated matrix deterioration is necessary and beneficial for big data analysis. Such a plan should have a good chance of succeeding. Represent the diverse clamor and deal with the correspondence problem in a disseminated manner. In order to do this, we used a Bayesian matrix decay model (DBMD) for big data mining and grouping. Only three approaches to disseminated computation are considered: 1) accelerate slope drop, 2) alternating path method of multipliers (ADMM), and 3) observable derivation. We look at how these approaches could be mixed together in the future. To deal with the commotion’s heterogeneity, we suggest an ideal module weighted norm that reduces the assessment’s differentiation. Finally, a comparison was made between these approaches in order to understand the differences in their outcomes.

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Section: Efficacy Of the Force Based On The Restructuring Methodsmentioning

confidence: 99%

A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

Vaitheeswari¹,

Krishnaveni²

2021

JUSST

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“…As a result, one of the main concerns in cloud computing is how to decrease energy consumption and related costs while keeping execution performance. Minimizing energy consumption improves overall efficiency and also increases system reliability and availability [17]. In other words, minimizing energy consumption while ensuring the user's QoS preferences is critical to achieving maximu m profit for service providers and ensuring the user's service level agreement (SLA).…”

Section: Task Schedulingmentioning

confidence: 99%

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

2022

IJE

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Cloud computing provides computing resources like software and hardware as a service by the network for several users. Task scheduling is one of the main problems to attain cost-effective execution. The main purpose of task scheduling is to allocate tasks to resources so that it can optimize one or more criteria. Since the problem of task scheduling is one of the Nondeterministic Polynomial-time (NP)-hard problems, meta-heuristic algorithms have been widely employed for solving task scheduling problems. One of the new bio-inspired meta-algorithms is Seagull Optimization Algorithm (SOA). In this paper, we proposed an energy-aware and cost-efficient SOA-based T ask Scheduling (SOAT S) algorithm. The aims of proposed algorithm to make a trade-off between five objectives (i.e., energy consumption, makespan, cost, waiting time, and load balancing) using a fewer number of iterations. The experiment results by comparing with several meta-heuristic algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Whale Optimization Algorithm (WOA)) prove that the proposed technique performs better in solving task scheduling problem. Moreover, we compared the proposed algorithm with well-known scheduling methods: Cost -based Job Scheduling (CJS), Moth Search Algorithm based Differential Evolution (MSDE), and Fuzzy -GA (FUGE). In the heavily loaded environment, the SOAT S algorithm improved energy consumption and cost saving by 10 and 25%, respectively.

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“…It should be remembered that the PSO population's heuristic initialization produces initial particles that all begin the search process from a single starting place. Sharma et al [77] suggested that the Harmony-Inspired Genetic Algorithm (HIGA) reduces the amount of iteration waste in local or global regions to consider task preparation. The algorithm was evaluated by using a well-known Cloud-SIM cloud environment emulator for simulation purposes.…”

Section: Literature Reviewmentioning

confidence: 99%

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

Yahia

Zeebaree

Sadeeq

et al. 2021

AJRCoS

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Many applications in the real world include optimizing specific targets, such as cost minimization, energy conservation, climate, and maximizing production, efficiency, and sustainability. The optimization problem is strongly non-linear with multifunctional landscapes under several dynamic, non-linear constraints in some instances. It is challenging to address those issues. Also, with the increasing strength of modern computers, simplistic brute force methods are still inefficient and unwanted. Practical algorithms are also vital for these implementations whenever possible. Cloud computing has become an essential and popular emerging computing environment that supports on-demand services and provides internet-based services. Cloud computing allows a range of services and tools to be easily accessed from anywhere in the world. Since cloud computing has global access to its services, there will always be threats and challenges facing its servers and services, such as; task scheduling, security, energy efficiency, network load, and other challenges. In the research area, many algorithms have been addressed to solve these problems. This paper investigates relevant analysis and surveys on the above topics, threats, and outlooks. This paper offers an overview of nature-inspired algorithms, their applications, and valuation, emphasizing cloud computing problems. Many problems in science and engineering can be viewed as optimization problems with complex non-linear constraints. Highly nonlinear solutions typically need advanced optimization algorithms, and conventional algorithms can have difficulty addressing these issues. Because of its simplicity and usefulness, nature-inspired algorithms are currently being used. There are nevertheless some significant concerns with computing and swarming intelligence influenced by evolution.

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HIGA: Harmony-inspired genetic algorithm for rack-aware energy-efficient task scheduling in cloud data centers

Cited by 38 publications

References 16 publications

A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

An Efficient Task Scheduling Based on Seagull Optimization Algorithm for Heterogeneous Cloud Computing Platforms

Comprehensive Survey for Cloud Computing Based Nature-Inspired Algorithms Optimization Scheduling

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