End-user's SLA-aware consolidation in cloud data centers

Maiyza, Aya I.; Hassan, Hanan A.; Sheta, Walaa M.; Sadek, Nayera; Mokhtar, Mohamed

doi:10.1109/isspit.2017.8388641

Cited by 2 publications

(6 citation statements)

References 16 publications

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“…The other four physical servers were used as compute servers running OpenStack Nova, as well as the data collectors and local managers of OpenStack Neat. Additional details about the testbed configuration can be found in the literature [6]. All setup parameters of the framework were set to their default values (with the exception of the setup parameters of the tested algorithm) according to each experiment.…”

Section: Methodsmentioning

confidence: 99%

“…The main disadvantage of the original MMTMC algorithm is that it does not consider the SLA of the end-user when some VMs suffer from frequent migration, whereas others are slightly offloaded. Therefore, two VM selection algorithms have been proposed previously [6]. These algorithms are modified versions of the MMTMC algorithm [7].…”

Section: Vm Selection Algorithmmentioning

confidence: 99%

“…For instance, the low growth rate decreases the number of migrations and hence reduces the network traffic; by contrast, the low growth rate increases the overload time of the server. More details about these two algorithms are given in [6].…”

Section: Vm Selection Algorithmmentioning

confidence: 99%

“…The results demonstrated that a significant improvement in the end-user SLA was achieved using these two algorithms. In addition, there was a considerable improvement in energy consumption and network traffic using the MMTMC2 algorithm [6]. Therefore, in the current study, the performance of these two algorithms was evaluated compared to several overload detection and placement algorithms.…”

Section: Vm Selection Algorithmmentioning

confidence: 99%

“…However, to reduce energy consumption in CDCs, the number of servers hosting VMs needs to be limited. (ii) Two VM selection algorithms [6] are tested using proactive overload detection algorithms and a robust local regression (LRR) algorithm, a traditional predictive algorithm that was used in a previous study [7], to assess their performance separately and jointly. These VM selection algorithms improved end-user QoS by avoiding frequent migration.…”

Section: Introductionmentioning

confidence: 99%

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Integrated resource management pipeline for dynamic resource-effective cloud data center

2020

Self Cite

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Cloud computing is a popular emerging computing technology that has revolutionized information technology through flexible provisioning of computing resources. Therefore, efforts to develop an effective resource management approach have found that implementing efficient resource sharing among multiple customers that considers power saving, service-level agreements, and network traffic simultaneously is difficult. This paper proposes a practical integrated pipeline that can use various algorithms. The performance of each algorithm is evaluated independently to obtain the combination of algorithms that guarantees a resource-effective cloud data center framework. This integrated resource management pipeline approach would optimize performance based on several key performance indicators, such as power saving, network traffic, and service-level agreements, for either the whole system or the end-user. The performance of the proposed resource management framework was evaluated using a real testbed. The results demonstrated that the proactive double exponential smoothing algorithm prevents unnecessary migrations, the MMTMC2 VM selection algorithm improved the quality of service for end-users and reduced overall energy consumption and network traffic, and the medium-fit placement algorithm provided load balancing between active servers and decreased service level agreement violations. The performance comparison illustrated that the combination of these algorithms was considered to be the best solution toward a dynamic resource-effective cloud data center. Our results showed that energy consumption and the total number of migrations decreased by 16.64% and 49.44%, respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Vm Selection Algorithmmentioning

confidence: 99%

Section: Vm Selection Algorithmmentioning

confidence: 99%

Section: Vm Selection Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrated resource management pipeline for dynamic resource-effective cloud data center

2020

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A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

Singh,

Walia

2023

IEEE Access

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In the last decade, users can access their applications, data, and services via the cloud from any location with an internet connection. The scale of heterogeneous cloud environments is continuously growing due to the development of computing-intensive smart devices. A data center is the central processing unit of any cloud environment, and it is made up of hardware-oriented machines known as Physical Machines (PMs) or server and software-oriented machines known as Virtual Machines (VMs). The deployment of a huge number of physical servers as a result of the exponential development in demand for cloud services has resulted in high energy consumption and ineffective resource usage. Efficient resource utilization and minimizing power consumption by any data center have become crucial challenges. Virtual machine consolidation(VMC) is a method of optimizing computing resources by consolidating multiple VMs onto a reduced number of PMs. By consolidating VMs and running fewer physical servers, VM consolidation can lead to reducing power consumption and efficient resource utilization. This review paper presents a comprehensive analysis of cloud computing virtual machine consolidation, exploring various strategies, benefits, challenges, and future trends in this domain. By examining a wide range of literature from the year 2015 to 2023, this review attempts to provide insight into the current state of VM consolidation and its possible effects on the performance and sustainability of cloud computing. The main flaw in the articles is that the various authors focused on different assessment metrics while the emphasis should have been on improving energy efficiency and quality of service for cloud systems. Future research can be aimed at developing a multiobjective system that emphasizes minimizing cloud energy usage without sacrificing service quality, preventing service level agreements with cloud users from being compromised.

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End-user's SLA-aware consolidation in cloud data centers

Cited by 2 publications

References 16 publications

Integrated resource management pipeline for dynamic resource-effective cloud data center

Integrated resource management pipeline for dynamic resource-effective cloud data center

A Comprehensive Review of Cloud Computing Virtual Machine Consolidation

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