Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers

Mastroianni, Carlo; Meo, Michela; Papuzzo, Giuseppe

doi:10.1109/tcc.2013.17

Cited by 167 publications

(127 citation statements)

References 30 publications

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“…However, their work only takes into account CPU usage and assumes perfect predictions about future workload behavior. Another more recent example of consolidation at cloud data centers is presented by Mastroianni et al [32]. Unlike the previous work, it does not only consider CPU resources but also RAM usage.…”

Section: Scheduling With Collocationmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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Abstract-Elasticity is a key characteristic of cloud computing that increases the flexibility for cloud consumers, allowing them to adapt the amount of physical resources associated to their services over time in an on-demand basis. However, elasticity creates problems for cloud providers as it may lead to poor resource utilization, specially in combination with other factors, such as user overestimations and pre-defined VM sizes. Admission control mechanisms are thus needed to increase the number of services accepted, raising the utilization without affecting services performance. This work focuses on implementing an autonomic risk-aware overbooking architecture capable of increasing the resource utilization of cloud data centers by accepting more virtual machines than physical available resources. Fuzzy logic functions are used to estimate the associated risk to each overbooking decision. By using a distributed PID controller approach, the system is capable of self-adapting over time -changing the acceptable level of risk -depending on the current status of the cloud data center. The suggested approach is extensively evaluated using a combination of simulations and experiments executing real cloud applications with real-life available workloads. Our results show a 50% increment at both resource utilization and capacity allocated with acceptable performance degradation and more stable resource utilization over time.

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Section: Scheduling With Collocationmentioning

confidence: 99%

An Autonomic Approach to Risk-Aware Data Center Overbooking

Tomás

Tordsson

2014

IEEE Trans. Cloud Comput.

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“…In recent years, many architectures and mathematical models have been proposed to provide more efficient use of the cloud distributed data centers to eliminate server consolidation problems [1], reduce energy consumption [2], [3] and improve total cloud performance [4]. Other research papers emphasize in the virtualization of the cloud geo-distributed data centers for optimized performance too [3], [5].…”

Section: Introductionmentioning

confidence: 99%

“…Other research papers emphasize in the virtualization of the cloud geo-distributed data centers for optimized performance too [3], [5]. In addition, there have been proposed data storage [6] and cloud quality [7] models, and cost optimization approaches for geo-distributed [8] and public [9] clouds.…”

Section: Introductionmentioning

confidence: 99%

Efficient Multimedia Transmission over Scalable IoT Architecture

Memos¹

2018

IJCNIS

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Abstract-Today the new technology concept, called "Internet of Things" (IoT), presents a significant field of research, due to the fact that it promises many advantages to the users worldwide. Besides this, multimedia file streaming tends to be a daily user habit, as a result of the evolution of the internet and the capabilities of devices, such as mobile phones and computers. This tendency has established Cloud Computing (CC) as a vital technology due to the fact that it reduces economic costs, improves accessibility and flexibility, while it increases the total network performance too. This paper demonstrates a new technological approach for IoT architecture, where a layered implementation with the use of many cloud servers can offer optimized streaming and download, because of the significant reduction of the transfer time costs and the storage space of the Main Cloud Server (MCS). Moreover, users will have the ability to choose or adapt to their devices' restrictions, between low -up toUltra-High Definition (UHD) quality for multimedia access and downloading, thanks to a new quality scalability feature which is introduced and is based on the new High-Efficiency Video Coding (HEVC) compression standard. Experimental results show the efficiency of the proposed approach with math formulas and graphs.

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“…Users submit their work requests to the cloud, to be processed and the results are delivered. Typically, the rapid growth in demand for computational power driven by modern service-oriented applications has led to the establishment of large-scale virtualized data centers [1][2][3][4]. However, data centers consume a huge amount of energy resulting in high operating costs and resulting contribution to greenhouse gas (GHG) emissions.…”

Section: Introductionmentioning

confidence: 99%

“…A major reason for this significant power consumption is the inefficiency of data center deployments, which are often underutilized. It has been estimated that only 10%-50% of the total server capacity is used on average [3,5].…”

Section: Introductionmentioning

confidence: 99%

Virtual Machine Replication on Achieving Energy-Efficiency in a Cloud

Mondal

Muppala

Machida³

2016

Electronics

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Abstract:The rapid growth in cloud service demand has led to the establishment of large-scale virtualized data centers in which virtual machines (VMs) are used to handle user requests for service. A user's request cannot be completed if the VM fails. Replication mechanisms can be used to mitigate the impact of failures. Further, data centers consume a large amount of energy resulting in high operating costs and contributing to significant greenhouse gas (GHG) emissions. In this paper, we focus on Infrastructure as a Service (IaaS) cloud where user job requests are processed by VMs and analyze the effectiveness of VM replications in terms of job completion time performance as well as energy consumption. Three different schemes: cold, warm, and hot replications are considered. The trade-offs between job completion time and energy consumption in different replication schemes are characterized through comprehensive analytical models which capture VM state transitions and associated power consumption patterns. The effectiveness of replication schemes are demonstrated through experimental results. To verify the validity of the proposed analytical models, we extend the widely used cloud simulator CloudSim and compare the simulation results with analytical solutions.

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Probabilistic Consolidation of Virtual Machines in Self-Organizing Cloud Data Centers

Cited by 167 publications

References 30 publications

An Autonomic Approach to Risk-Aware Data Center Overbooking

An Autonomic Approach to Risk-Aware Data Center Overbooking

Efficient Multimedia Transmission over Scalable IoT Architecture

Virtual Machine Replication on Achieving Energy-Efficiency in a Cloud

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