A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

Gholipour, Niloofar; Arianyan, Ehsan; Buyya, Rajkumar

doi:10.1016/j.simpat.2020.102127

Cited by 74 publications

(34 citation statements)

References 18 publications

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“…Recent research on green computing in cloud data centers includes energy-aware approaches that utilize several techniques to reduce energy consumption [44,45]. In [44], a hybrid framework was proposed to improve the efficiency of consuming electrical energy in cloud data centers.…”

Section: Optimization Methodologiesmentioning

confidence: 99%

“…The framework includes several algorithms: scheduling algorithm to schedule requests, a consolidation algorithm for servers, and a migration algorithm for transferring migrated virtual machines to new servers [44]. In [45], A novel energy-aware technique is introduced to address the green resource management problem in container-based cloud data centers. The proposed approach considers multiple objectives: violation of service level agreements, energy consumption, and the number of both container and VM migrations.…”

Section: Optimization Methodologiesmentioning

confidence: 99%

See 1 more Smart Citation

Green Computing Approaches - A Survey

Dhaini

Jaber

Fakhereldine

et al. 2021

IJCAI

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Section: Optimization Methodologiesmentioning

confidence: 99%

Section: Optimization Methodologiesmentioning

confidence: 99%

Green Computing Approaches - A Survey

Dhaini

Jaber

Fakhereldine

et al. 2021

IJCAI

View full text Add to dashboard Cite

“…Gholipour [9] demonstrated a task-level scheduling algorithm and used Dynamic Programming (DP) to develop a budget-driven scheduling system. They know their algorithms as Global Optimal Scheduling (GOS) and Global Greedy Budget (GGB).…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment

Sridevi¹,

Midhunchakkravarthy²

2020

ISI

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As the size of the cloud-based applications and its tasks are increasing exponentially, it is necessary to estimate the load balancing metrics in the real-time cloud computing environments. Hybrid load balancing framework play a vital role in the cloud-based applications and tasks monitoring and resource allocation. Most of the conventional load balancing metrics are dependent on limited number of cloud metrics and type of virtual machines. Also, these models require high computational memory and time on large number of tasks. In this paper, an advanced multi-level statistical load balancer-based parameters estimation model is designed and implemented on the real-time cloud computing environment. In this model, a novel statistical load balancing data collector is used to find the best metrics for the load balance computation. In this model, different types of tasks are simulated under different virtual machine types such as small, medium and large instances. Experimental results show that the proposed multi-level based statistical load balancing collector has better efficiency than the conventional models in terms of memory utilization, CPU utilization, runtime and reliability are concerned.

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“…Server consolidation techniques in virtualized data centers have been also reviewed in [33]. A joint VM and container consolidation approach was utilized in [17] for energy-aware resource management in cloud data centers. EMC2 was proposed in [20] as an energy-efficient and multi-resource fairness VM consolidation in cloud data centers.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

Fard

Sohrabi

Ghods

2021

ITC

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With the expansion and enhancement of cloud data centers in recent years, increasing the energy consumptionand the costs of the users have become the major concerns in the cloud research area. Service quality parametersshould be guaranteed to meet the demands of the users of the cloud, to support cloud service providers,and to reduce the energy consumption of the data centers. Therefore, the data center's resources must be managedefficiently to improve energy utilization. Using the virtual machine (VM) consolidation technique is animportant approach to enhance energy utilization in cloud computing. Since users generally do not use all thepower of a VM, the VM consolidation technique on the physical server improves the energy consumption andresource efficiency of the physical server, and thus improves the quality of service (QoS). In this article, a serverthreshold prediction method is proposed that focuses on the server overload and server underload detectionto improve server utilization and to reduce the number of VM migrations, which consequently improves theVM's QoS. Since the VM integration problem is very complex, the exponential smoothing technique is utilizedfor predicting server utilization. The results of the experiments show that the proposed method goes beyondexisting methods in terms of power efficiency and the number of VM migrations.

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A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers

Cited by 74 publications

References 18 publications

Green Computing Approaches - A Survey

Green Computing Approaches - A Survey

A Hybrid Multi-level Statistical Load Balancer-Based Parameters Estimation Model in Realtime Cloud Computing Environment

Energy-Aware and Proactive Host Load Detection in Virtual Machine Consolidation

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