GHSA: Task Scheduling in Heterogeneous Cloud
07 Aug 2018-Vol. 26, pp 1202-1207
TL;DR: A combination of Gravitational search algorithm and Harmony search is used and created a new hybrid algorithm called Gravitational Harmony Search algorithm (GHSA) which produced enormous improvement over other scheduling algorithms.
Abstract: Cloud Environments provides affords effective distribution of resource on need, which makes depart from others providing splendid performance, scalability, cost efficient and less maintenance. Task Scheduling increases the dynamic allocation of resource to increase performance and decrease the cost. A solution considering makespan and cost, are used as constraints for the optimization problem. A combination of Gravitational search algorithm (GSA) and Harmony search (HS) is used and created a new hybrid algorithm called Gravitational Harmony Search algorithm (GHSA) which produced enormous improvement over other scheduling algorithms. The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters. The proposed algorithm works superior over The simulation is proposed in a cloudsim programming environment and results proved the effectiveness of the cost minimizing and makespan parameters.
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01 May 2019
TL;DR: A detailed study on huge algorithms which are explained to resolve the common issues taking place in various scheduling of resources in cloud computing.
Abstract: Cloud computing is considered to be a predominant technology in the course of events occurring and has an enchanting contribution in software and hardware setup. In cloud environment the performance improvement is highly dependent on features like load balancing and task scheduling. The major issue in cloud computing is task scheduling which leads to reduction in the performance of the system. Efficient resource scheduling algorithm is required in order to resolve this low performance issue. Through which the clients and users can demand services based on pay-as-you-go basis. There are numerous algorithms proposed especially for explaining load balancing and task scheduling. Since cloud infrastructure is based on huge client’s requirement, appropriate decision is required for each and every scheduled job. This paper illustrates a detailed study on huge algorithms which are explained to resolve the common issues taking place in various scheduling of resources.
1 citations
Cites background from "GHSA: Task Scheduling in Heterogene..."
...In [9], distributing resources effectively in an dynamic manner is affordable by the cloud environments by providing perfection in performance, extensibility and efficient cost usage....
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15 May 2019
TL;DR: A systematic survey on various task scheduling approaches in cloud computing has been carried out and the same has been presented herewith.
Abstract: Cloud computing, with its huge potential has started evolving as a new age technology. The varied applications of cloud that includes QoS, Charging based on usage, virtualization, offering self-services that are in demand, elasticity etc. make it a viable option for businesses in today’s IT industry. Allocating the optimal task over the available set of resources is termed to be task scheduling. The task scheduling must be done effectively for the sake of maximizing the usage of cloud computing. For this purpose various task scheduling strategies have been adopted. In this paper, a systematic survey on various task scheduling approaches in cloud computing has been carried out and the same has been presented herewith.
1 citations
01 Jan 2021
28 Apr 2022
TL;DR: In this paper , the authors used machine learning technology for determining the best crop suitable for the climatic condition, soil condition from the data set for addressing the issue of crop yield prediction.
Abstract: Agriculture is an important job in the development of the nation's economy. The climate along with other natural variations has to turn out to be a significant risk in the agricultural field. Machine Learning technology can be used for determining the best crop suitable for the climatic condition, soil condition from the data set for addressing this issue. Crop Yield Prediction includes anticipating the yield of the crop from accessible verifiable information like climate parameter, soil parameter, and notable yield. Such huge numbers of individuals are doing fertile agriculture by developing the yield on ill-advised soil. To actualize the application to recognize the sorts of oil, water wellspring of that land whether that land depends on downpour or bore water. Furthermore, recommend what of the crop is reasonable for that dirt. So, through this application to the individual to think about agriculture. It tends to be improved by the utilization of many mechanical assets, device, and methods. Find the kind of crop that is appropriate for that specific soil.
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01 Jan 2021TL;DR: In this paper, the Gaussian mixture model (GMM) is used in the sample image to process information in its pixels to identify malignant growth in each stage, RGB2LAB shading transformation, entropy estimation, and classification are performed.
Abstract: Cervical malignancy is perhaps the deadliest disease perceived and is likewise a key research territory in image processing. It is a threatening illness that is created in the cells of the cervix or the neck of the uterus. The fundamental issue with this malignancy is that it cannot be recognized easily, as it does not show any side effects until the last stages. To identify malignant growth in each stage, RGB2LAB shading transformation, entropy estimation, and classification are to be performed. In this paper, the Gaussian mixture model (GMM) is used in the sample image to process information in its pixels. Neural networks (NN) are a directed learning strategy which examines information and perceives designs utilized for factual characterization and relapse examination. In a probabilistic neural network (PNN), the activities are composed into a multilayered feed forward system. A four-layer neural system of the sort can outline input example to any number of groupings. Coming to the realization, there has therefore been progress in the dissemination of the screening test equipment for the timely identification of the precancerous cervical cells. The proposed calculation removes highlights from the pictures extra precisely.
References
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TL;DR: This paper proposes a resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand.
Abstract: Public clouds provide Infrastructure as a Service (IaaS) to users who do not own sufficient compute resources. IaaS achieves the economy of scale by multiplexing, and therefore faces the challenge of scheduling tasks to meet the peak demand while preserving Quality-of-Service (QoS). Previous studies proposed proactive machine purchasing or cloud federation to resolve this problem. However, the former is not economic and the latter for now is hardly feasible in practice. In this paper, we propose a resource allocation framework in which an IaaS provider can outsource its tasks to External Clouds (ECs) when its own resources are not sufficient to meet the demand. This architecture does not require any formal inter-cloud agreement that is necessary for the cloud federation. The key issue is how to allocate users' tasks to maximize the profit of IaaS provider while guaranteeing QoS. This problem is formulated as an integer programming (IP) model, and solved by a self-adaptive learning particle swarm optimization (SLPSO)-based scheduling approach. In SLPSO, four updating strategies are used to adaptively update the velocity of each particle to ensure its diversity and robustness. Experiments show that, SLPSO can improve a cloud provider's profit by 0.25%-11.56% compared with standard PSO; and by 2.37%-16.71% for problems of nontrivial size compared with CPLEX under reasonable computation time.
275 citations
TL;DR: Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.
Abstract: For task-scheduling problems in cloud computing, a multi-objective optimization method is proposed here. First, with an aim toward the biodiversity of resources and tasks in cloud computing, we propose a resource cost model that defines the demand of tasks on resources with more details. This model reflects the relationship between the user’s resource costs and the budget costs. A multi-objective optimization scheduling method has been proposed based on this resource cost model. This method considers the makespan and the user’s budget costs as constraints of the optimization problem, achieving multi-objective optimization of both performance and cost. An improved ant colony algorithm has been proposed to solve this problem. Two constraint functions were used to evaluate and provide feedback regarding the performance and budget cost. These two constraint functions made the algorithm adjust the quality of the solution in a timely manner based on feedback in order to achieve the optimal solution. Some simulation experiments were designed to evaluate this method’s performance using four metrics: 1) the makespan; 2) cost; 3) deadline violation rate; and 4) resource utilization. Experimental results show that based on these four metrics, a multi-objective optimization method is better than other similar methods, especially as it increased 56.6% in the best case scenario.
265 citations
TL;DR: A CLOUD Resource Broker (CLOUDRB) for efficiently managing cloud resources and completing jobs for scientific applications within a user-specified deadline is designed and integrated with a Deadline-based Job Scheduling and Particle Swarm Optimization-based Resource Allocation mechanism.
117 citations
TL;DR: A fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs and showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degrees of imbalance, and makespan.
Abstract: Cloud computing has attracted significant attention from research community because of rapid migration rate of Information Technology services to its domain. Advances in virtualization technology has made cloud computing very popular as a result of easier deployment of application services. Tasks are submitted to cloud datacenters to be processed on pay as you go fashion. Task scheduling is one the significant research challenges in cloud computing environment. The current formulation of task scheduling problems has been shown to be NP-complete, hence finding the exact solution especially for large problem sizes is intractable. The heterogeneous and dynamic feature of cloud resources makes optimum task scheduling non-trivial. Therefore, efficient task scheduling algorithms are required for optimum resource utilization. Symbiotic Organisms Search (SOS) has been shown to perform competitively with Particle Swarm Optimization (PSO). The aim of this study is to optimize task scheduling in cloud computing environment based on a proposed Simulated Annealing (SA) based SOS (SASOS) in order to improve the convergence rate and quality of solution of SOS. The SOS algorithm has a strong global exploration capability and uses fewer parameters. The systematic reasoning ability of SA is employed to find better solutions on local solution regions, hence, adding exploration ability to SOS. Also, a fitness function is proposed which takes into account the utilization level of virtual machines (VMs) which reduced makespan and degree of imbalance among VMs. CloudSim toolkit was used to evaluate the efficiency of the proposed method using both synthetic and standard workload. Results of simulation showed that hybrid SOS performs better than SOS in terms of convergence speed, response time, degree of imbalance, and makespan.
65 citations
TL;DR: Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.
Abstract: Task scheduling in cloud computing environments is a multi-objective optimization problem, which is NP hard. It is also a challenging problem to find an appropriate trade-off among resource utilization, energy consumption and Quality of Service (QoS) requirements under the changing environment and diverse tasks. Considering both processing time and transmission time, a PSO-based Adaptive Multi-objective Task Scheduling (AMTS) Strategy is proposed in this paper. First, the task scheduling problem is formulated. Then, a task scheduling policy is advanced to get the optimal resource utilization, task completion time, average cost and average energy consumption. In order to maintain the particle diversity, the adaptive acceleration coefficient is adopted. Experimental results show that the improved PSO algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.
57 citations