Grid Task Scheduling Based on Adaptive Ant Colony Algorithm

Liu, Aihong; Wang, Zhengyou

doi:10.1109/icmecg.2008.50

Cited by 23 publications

(11 citation statements)

References 1 publication

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“…Aihong Liu et al [8] proposed an improved version of basic ant colony optimization algorithm (ACO) for task scheduling in grid, they have implemented adaptive algorithm in basic ACO which gives optimal value of evaporation rate and thereby it increases its efficiency and load balancing rate. Results proved better than traditional ACO but for getting widespread acceptability it is an important factor to compare an improved version with other existing scheduling algorithms that particular part is missing in their article.…”

Section: Related Workmentioning

confidence: 99%

“…minimum use of cloud which ultimately indicates economic use. As given in equation (8) above mathematically it can be written as = max{ } where Fi denotes the finishing time of task i Performance Improvement (PI) percentage: It can be defined as improvement in makespan for technique under consideration i with respect to other existing technique k. Percentage performance improvement is given by the equation…”

Section: Experimentationmentioning

confidence: 99%

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Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Gade¹,

Mundukur²,

Thakare³

2019

ISI

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Scheduling is a heart of cloud computing as without appropriate scheduling it is impossible to get the desired results. Primary focus of this article is to focus on minimization of makespan, minimum utilization of resources and make the cloud services economic for an independant task. Out of the various task scheduling strategies, in last few years meta-heuristic algorithms have gained recognition in successful operation of task scheduling algorithms. League Championship based Algorithm (LCA) is fascinated from sports leagues through which best team/task in this case can be find out for scheduling. Task scheduling using Adaptive League Championship Algorithm (ALCA) is employed in this article and thereby it shrinks makespan, cloud utilization and cost. ALCA is implemented with cloudsim simulator using java as a programming tool and scheduling has followed the non-premptive approach. Implementation of ALCA results reducation in makespan by 32.95 %, 20.99 % and 7.29 % against customary Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Global League Championship Algorithm (GBLCA) respectively. ALCA also reduces significantly cloud utilization value and improves economy of scale. ALCA may serve as preferred choice for cloud broker as it proved to be multipurpose in the area of makespan, resource utilization and economy.

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Section: Related Workmentioning

confidence: 99%

Section: Experimentationmentioning

confidence: 99%

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Gade¹,

Mundukur²,

Thakare³

2019

ISI

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“…Grid scheduling problems effectively solved using this approach. Liu ALA et al [14] presented an adaptive ant colony algorithm in grid infrastructure. In the proposed algorithm, the value of evaporation rate p adaptively changed, and a minimum value p assigns.…”

Section: Related Workmentioning

confidence: 99%

“…In the following section, static, dynamic, and evolution based soft computing techniques are reviewed in detail. The rest of the part organized as follows, Section 2 exhibits the survey on resource provisioning approaches based on ACO [7][8][9][10][11][12][13][14][15][16], GA [17][18][19][20][21][22][23], PSO [24][25][26][27][28][29][30][31][32], (FCFS, SJF, RR) [33][34][35][36][37][38][39][40][41], respectively. Section 3 exhibits the input parameters IaaS, PaaS, SaaS model.…”

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confidence: 99%

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

2019

IJITEE

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In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.

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“…Based on the pheromone evaporation rate Modified pheromone updation policy [20] Load Balancing, Total simulation time…”

Section: Selfgoverningmentioning

confidence: 99%

Theoretical Analysis of Bio-Inspired Load Balancing Approach in Cloud Computing Environment

Tiwari¹,

Singh²,

Sharma³

2017

IJDTA

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Grid Task Scheduling Based on Adaptive Ant Colony Algorithm

Cited by 23 publications

References 1 publication

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

Theoretical Analysis of Bio-Inspired Load Balancing Approach in Cloud Computing Environment

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