Energy efficiency for cloud computing system based on predictive optimization

Bui, Dinh-Mao; Yoon, Yongik; Huh, Eui‐Nam; Jun, Sungik; Lee, Sungyoung

doi:10.1016/j.jpdc.2016.11.011

Cited by 67 publications

(24 citation statements)

References 11 publications

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“…Several works paid attention to the predictive frameworks, and variety of host overloading methods [20]- [22], [25] have been proposed. Z. Xiao et al [21] proposed the dynamic resources allocation using VMs in cloud data center.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Chang

Luo

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYVirtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combination BS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.

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Section: Related Workmentioning

confidence: 99%

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Chang

Luo

et al. 2018

IEICE Trans. Inf. & Syst.

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“…SaaS VMs Server Group1 Server hosting VMs Server Group 2 Predictive Optimization-based Energy efficiency for cloud computing system (POEE) [54] has been proposed to optimize energy consumption and maintain system performance, firstly by predicting the resource usage of upcoming period using a Gaussian process regression approach for resource orchestration in cloud computing-based IoT. An appropriate numbers of physical servers are computed using a convex optimization technique for every monitoring window.…”

Section: Cloud Management Systemmentioning

confidence: 99%

“…Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems (DREAM) [52] has been proposed by invoking Lyapunov optimization to solve jointly three dynamic problems: first, determining the use of local CPU or cloud resources, second, task allocation to transmit across the network and processing in local CPU and third, CPU clock speed and network Predictive Optimization-based Energy efficiency for cloud computing system (POEE) [54] has been proposed to optimize energy consumption and maintain system performance, firstly by predicting the resource usage of upcoming period using a Gaussian process regression approach for resource orchestration in cloud computing-based IoT. An appropriate numbers of physical servers are computed using a convex optimization technique for every monitoring window.…”

Section: Energy Efficient Lyapunov Optimization Techniquesmentioning

confidence: 99%

“…The cloud provides sharable resources among multiple users, while making accessible large amounts of information gathered from IoT devices for analysis and processing [53]. In the case of gathering large amount of data from devices it creates multiple effects such as more energy consumption [54]. As a result new approaches and research can be introduced for reducing the device interactivity to minimize energy consumption in IoT applications [63][64][65][66].…”

Section: Interactivity Limitationsmentioning

confidence: 99%

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Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

Kumar

Kaiwartya

et al. 2017

Energies

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Abstract:Energy efficiency is a significant characteristic of battery-run devices such as sensors, RFID and mobile phones. In the present scenario, this is the most prominent requirement that must be served while introducing a communication protocol for an IoT environment. IoT network success and performance enhancement depend heavily on optimization of energy consumption that enhance the lifetime of IoT nodes and the network. In this context, this paper presents a comprehensive review on energy efficiency techniques used in IoT environments. The techniques proposed by researchers have been categorized based on five different layers of the energy architecture of IoT. These five layers are named as sensing, local processing and storage, network/communication, cloud processing and storage, and application. Specifically, the significance of energy efficiency in IoT environments is highlighted. A taxonomy is presented for the classification of related literature on energy efficient techniques in IoT environments. Following the taxonomy, a critical review of literature is performed focusing on major functional models, strengths and weaknesses. Open research challenges related to energy efficiency in IoT are identified as future research directions in the area. The survey should benefit IoT industry practitioners and researchers, in terms of augmenting the understanding of energy efficiency and its IoT-related trends and issues.

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“…The MCC technology is mainly comprised of three components, which are mobile internet, mobile computing, and cloud computing [3] [4]. Cloud computing is notably becoming the primary computing service delivery platform, while mobile computing is becoming the enduser device of selection, specifically for sustaining integrated exterior services [5] [27].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

Mohammed¹,

Țăpuș²

2017

STUD INFORM CONTROL

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Abstract:The Mobile Cloud Computing (MCC) technology is a growing technology that aids in improving the quality of mobile services. The resources in MCC are dynamically allocated to the users based on their needs. The users pay for the resources consumed by their programs, but the drawbacks of process failures and knapsack problems of resource allocation still exist in MCC. Furthermore, the scheduling of energy consumption and computational cost is very high. To solve these issues, an optimized energy efficiency resource management technique is set forth in this study. The recommended method holds two stages: a) the initial stage, when the task loss, transmission probability, delay, utilization and reputation for every single task is individually measured and the enthalpy was calculated, and b) the second stage, when the enthalpy-related Cuckoo Search Optimization (CSO) algorithm was used to optimize and prioritize the resources to the powerful resource management. The execution of the recommended method automatically reduced the knapsack issue, energy and cost. A formal analysis of the resource management framework has ensured that the provider executes resources based on the power consumption. The performance of the suggested algorithm was benchmarked against the performance of other conventional algorithms.

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Energy efficiency for cloud computing system based on predictive optimization

Cited by 67 publications

References 11 publications

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Energy Efficient Resource Selection and Allocation Strategy for Virtual Machine Consolidation in Cloud Datacenters

Cross-Layer Energy Optimization for IoT Environments: Technical Advances and Opportunities

A Novel Approach of Reducing Energy Consumption by Utilizing Enthalpy in Mobile Cloud Computing

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