A heuristic task scheduling algorithm based on server power efficiency model in cloud environments

Lin, Weiwei; Wang, Weiqi; Wu, Wentai; Pang, Xiong Wen; Liu, Bo; Zhang, Ying

doi:10.1016/j.suscom.2017.10.007

Cited by 20 publications

(15 citation statements)

References 12 publications

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“…Hence this work leads to frequent load balance, even after the migration of VM. Many heuristic task scheduling algorithm was proposed for energy optimization in cloud 26 . Power factor was mathematically formulated using power efficiency and CPU utilization.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

Karthikeyan¹,

Balamurugan²

2021

Concurrency and Computation

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An emerging cloud‐Internet of Things (IoT) is a novel paradigm that brings potential benefits over a variety of applications. The pervasive use of IoT devices often struggle to meet resource requirements in cloud environment. The abundant wastage or inappropriate usage of resources leads to consume larger amount of energy and delayed response. Optimization algorithms are more popular for optimal selection, the algorithms as ant colony optimization, particle swarm optimization, genetic algorithm were majorly concentrated for the purpose of load balancing. However workload is balanced in many previous research works, however it failed to mitigate SLA violations and limitations of energy consumption. In this paper, we address both energy‐aware load balancing and satisfaction of SLA constraints. First, IoT devices submit tasks which are segregated into queues using policy‐based SLA by taking in account of SLA constraints. Second, the tasks are allocated in accordance to dynamic threshold predicting fuzzy followed by Analytical Hierarchical Process. Third, the workload is employed for energy minimization which decides whether to perform swap or migrate virtual machines (VMs). Swapping between VMs is held by novel map and consolidates processes. Then combination of fruit fly and bird swarm hybrid optimization algorithm is enabled to select an optimal VM for workload balancing. Also, the idle physical machines (PMs) are supposed to be in OFF state for diminishing unnecessary energy consumption. The outcomes of this cloud‐IoT system is experimentally evaluated and compared in terms of energy consumption, number of migrations, resource utilization, and execution time.

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Section: Related Workmentioning

confidence: 99%

Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

Karthikeyan¹,

Balamurugan²

2021

Concurrency and Computation

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“…Instead of power-off, suspending-to-RAM is used in practice to shorten the server's startup time [18]. Several studies depend only on server DSO [6], [12], [19]- [22], CPU DFS [8], [14], or the load distribution [23], [24] to optimize the cluster power. However, these methods cannot obtain the best optimization results.…”

Section: Related Studiesmentioning

confidence: 99%

“…Although [25] uses utilization as a QoS indicator, it is based on divide-and-conquer (it does not consider server DSO and CPU DFS together) and heuristic methods, so it cannot obtain the best optimization result. Reference [23] imposes punishment on the servers whose utilization is greater than 80% when calculating the power efficiency but does not ensure the server's utilization.…”

Section: Related Studiesmentioning

confidence: 99%

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

et al. 2019

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The energy conservation of application server clusters is a pressing problem. In this paper, we propose an online power-aware deployment and load distribution optimization strategy for application server clusters, whose objective is to minimize the cluster's power while ensuring that the server's CPU utilization is not higher than a preset value. The strategy includes two schemes: a mixed integer linear programming (MILP)-based scheme and a mixed integer non-linear programming (MINLP)-based scheme. The former formulates the cluster optimization problem as a MILP problem and adopts a toolkit to solve it. When the cluster scale is small, it can find the global optimal solution quickly. So, the MILP-based scheme is applicable to small-scale clusters. In the MINLP-based scheme, we first formulate the cluster optimization problem as a non-linear programming problem, and then design a method to reduce the number of variables and reformulate it as an MINLP problem. We finally propose an efficient solution method based on the flower pollination algorithm. Due to the small number of variables and the high solution efficiency, the solution method can quickly obtain a high-quality solution, so the MINLP-based scheme can be applied to largescale clusters. The experimental results demonstrate the effectiveness of our strategy.

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“…Using this storage, the data can be stored in the third party servers, using the pay-per-use model (Singh & Kumar, 2013). The Cloud Service Provider (CSP) provides the service to the users in terms of their request (Lin et al, 2018) and the user need to pay for the amount to the service they use (Al). The process of cloud computing is based on virtual machines (VMs), which are used for provisioning the user's applications into the service provider's datacenters (Mao et al, 2018, Yang et al, 2017 further extended by federated hybrid cloud model, which uses a collaborative service of various cloud service providers.…”

Section: Introductionmentioning

confidence: 99%

Minimizing the Security Risks in Hybrid Cloud Networks with the Aid of Optimal Triple Data Encryption Standard Algorithm

Gupta¹,

Kumar²

2019

IJEAT

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Security is one of the challenging problems in the hybrid cloud, because of the combination of private and public cloud. A novel algorithm has been presented in this paper, to secure the hybrid cloud networks during information storage and recovery. The algorithm consists of four modules: (i) data collection module, (ii) data de-duplication module, (iii) encrypted de-duplication module and (iv) user interface module. Initially, the document is collected using the data collection module and duplicate data is removed using the matching score mechanism. This de-duplicated data is encrypted using the Optimal Triple Data Encryption Standard (OTDES). To enhance the performance of TDES, oppositional monarch butterfly optimization algorithm (OMBOA) is used in hybrid combination with TDES and this is referred as OTDES. This encrypted data is then stored on the cloud. The stored information can be accessed through the user interface module. Performance evaluation of the proposed security system has been carried out on the basis of different evaluation metrics and have been found in good order.

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A heuristic task scheduling algorithm based on server power efficiency model in cloud environments

Cited by 20 publications

References 12 publications

Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

Energy‐aware andSLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things

Online Power-Aware Deployment and Load Distribution Optimization for Application Server Clusters

Minimizing the Security Risks in Hybrid Cloud Networks with the Aid of Optimal Triple Data Encryption Standard Algorithm

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