Managing overloaded hosts for energy-efficiency in cloud data centers

Yadav, Rahul; Zhang, Weizhe; Li, Keqin; Liu, Chuanyi; Laghari, Asif Ali

doi:10.1007/s10586-020-03182-3

Cited by 53 publications

(28 citation statements)

References 26 publications

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“…From one hand, minimizing total energy costs requires limiting the number of VMs, and from another hand, respecting the SLA requires additional VMs. Add to that, the authors in reference 21 have developed a minimum size and upper threshold calculation algorithm for respectively managing the SLA and the energy consumption. That is performed through a dynamic consolidation of VMs from the determined hosts.…”

Section: Selection Of Cazsmentioning

confidence: 99%

Predictive BPaaS management with quantum and neural computing

Hedhli

Mezni

Saïd

2022

J Software Evolu Process

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With the increasing adoption of cloud computing, the deployment and management of business processes over cloud environments have become an essential operation for most enterprises, leading to the emergence of BPaaS (Business Process as a Service) as a new cloud service model. This SaaS-like service, like its ancestors, should be strategically distributed and managed over multiple cloud zones, while taking into account several constraints and conditions (e.g., sensitivity of BPaaS fragments, insecure and untrusted cloud zones, lack of resources, and workload changes). However, current BPaaS approaches are static, which means that they are no longer suitable to manage such enterprise-oriented cloud service model and to deal with the uncertain and dynamic nature of cloud availability zones. To fill this gap, we adopt a predictive BPaaS management strategy by proposing a model that forecasts the next-short time overload of cloud zones. These latter, as hosting environments for the managed BPaaS, are categorized as overloaded or underloaded, which triggers the migration of BPaaS fragments to high-performance cloud zones. The proposed neural network prediction model (called QGA-NN) is enhanced with a quantum genetic algorithm to optimize the prediction of cloud zones' overload. QGA-NN is evaluated using a BPaaS placement algorithm, which we defined as a triggered management operation. Experimental results have proved the accuracy and effectiveness of our predictive approach, compared with state-ofthe-art solutions.

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Section: Selection Of Cazsmentioning

confidence: 99%

Predictive BPaaS management with quantum and neural computing

Hedhli

Mezni

Saïd

2022

J Software Evolu Process

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“…They mainly investigate CPU, RAM, and BW utilization where require some improvement in VM consolidation. Yadav et al [8] proposed an algorithm, namely GradCent (Stochastic Gradient Descent), which evaluates the CPU workload using an upper threshold value. It is applicable for predicting the overloaded hosts only.…”

Section: Load Balancing Approachmentioning

confidence: 99%

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Devi¹,

Kumar²

2022

INDJCSE

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The advent development of cloud computing is an on-demand adaptive technology for many IT organizations due to its dynamic scalability and virtualization resources as a global service. There are numerous energy-conscious approaches in existence that attempt to minimize energy consumption and Service Level Agreements (SLAs) degradation of the host. In this paper, we proposed a novel load balancing approach that identifies overloaded or underloaded hosts, and selects the VMs (Virtual Machines) for allocation to the host based on the predicted load. We consider CPU utilization parameter for evaluating host loading which is far enough to compute all the characteristics of Physical Machines (PM) provided to the Linear Adam Algorithm for Overloaded detection followed by Interquartile Range (IQR) method for underloaded detection. The Power-Aware Best Fit Decreasing (PABFD) algorithm sorts the VM in descending order based on CPU utilization. Then those filtered VM is allocated to the corresponding host thereby reducing the energy consumption and SLAs degradation. We evaluate the proposed algorithm through CloudSim-3.0.3 simulation and random datasets with different workloads on a real Planet Lab. After the evaluation, the simulation result of the proposed method indicates a significant performance by reducing the metric parameters such as SLA degradation, number of VM migration, the total number of host shutdowns, and reduce energy consumption.

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“…The QoS can be a measure of how well a service is moving ahead. If the service is attractive to a customer the number of customers will increase and thus, the rate of request arrival also increases which affects the customer satisfaction [3]. PoS is decided by the service provider.…”

Section: Related Workmentioning

confidence: 99%

Advanced Mechanism to Achieve QoS and Profit Maximization of Brokers in Cloud Computing

Sathish¹,

Dsouza²,

Ballal³

et al. 2021

EAI Endorsed Transactions on Cloud Systems

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Cloud computing is a widely used technology today and thus, large number of applications are being stored in the cloud. To cater to the needs of the consumers an intermediate role known as cloud broker has been introduced. It helps to cut down overall expenses of the cloud users. The proposed system concentrates on configuration of the cloud broker to improve profit. This maximization scheme depends upon various factors like customer request, selling price of the resource, purchasing price of the resource, intensity of the request and so on. The system developed, aims at maximizing the profit of the cloud broker who services consumer requirements by providing cloud infrastructure at a lower cost from an infrastructure vendor. Availing payas-you-go schemes or on-demand payment has proven to be useful. The complex cloud landscape along with various billing schemes paves a way for a profit maximization model that incorporates temporal multiplexing by a middleman entity for a better economic upfront. The resource multiplexing is further enhanced by incorporating M/D/c queueing model and short-term and long-term renting schemes to optimize the resource allocation. The entire application has been implemented using Java especially the NetBeans IDE 8.2. Different algorithms were compared to further enhance the pricing scheme selection. Finally, Hill-Climbing algorithm is used to allocate the resources dynamically. The system further notifies the cloud broker on resource shortage through dynamic disk utilization graph. The proposed system considers quality of service and price of service as the determining factors in maximizing the net profit of the cloud broker.

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Managing overloaded hosts for energy-efficiency in cloud data centers

Cited by 53 publications

References 26 publications

Predictive BPaaS management with quantum and neural computing

Predictive BPaaS management with quantum and neural computing

Novel Load Balancing Approach Towards Cloud Computing by Reducing Sla Degradation and Energy Consumption

Advanced Mechanism to Achieve QoS and Profit Maximization of Brokers in Cloud Computing

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