Constraint Programming based Virtual Cloud Resources Allocation Model

Zhang, Long; Zhuang, Yi; Zhu, Wei

doi:10.14257/ijhit.2013.6.6.30

Cited by 23 publications

(11 citation statements)

References 15 publications

(14 reference statements)

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“…To meet the goals of QoS and to reduce the cost of resource usage. Zhang, Zhuang and Zhu (2013) proposed a constraint programming-based model for cloud resource allocation. It takes into account heterogeneous workload, and when simulated, the model showed a reduction in QoS violations as well as a reduction in the cost of resource utilization.Particle Swarm Optimization (PSO)…”

Section: Constraint Programmingmentioning

confidence: 99%

An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres

Bose

Nag

2022

APJMT

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Increased energy consumption in Cloud Data Centres (CDCs) increases the carbon footprint. Efficiency of the data centres thus needs to be improved through server consolidation using effective virtual machine (VM) placement and migration techniques and minimizing the number of active physical machines (PMs). One of the problems is how to operationally allocate the VMs to PMs. These allocations have both operational costs and energy consumption issues. To achieve the aim of ‘Green Computing’ a number of state-of-the-art machine learning algorithms have been proposed for the VM placement. The authors of this paper have provided a detailed discussion and comparison of some of the current research works on energy efficiency. and cons of each of these techniques have been discussed. Some future research prospects in this field have also been mentioned at the end.

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Section: Constraint Programmingmentioning

confidence: 99%

An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres

Bose

Nag

2022

APJMT

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“…The objective of this approach is to minimize the number of active PMs to improve the resource utilization and power consumption. Similarly, Zhang and others developed a resource allocation approach using constraint programming with the objective of reducing the resource usage cost and improving the quality of service 30.…”

Section: Related Workmentioning

confidence: 99%

Resource‐efficient load‐balancing framework for cloud data center networks

2020

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Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource‐management approach. In this paper, we present a novel load‐balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics‐based VM placement approaches.

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“…Nevertheless, the approach does not consider nodes at the network edge and other QoS parameters except CPU and disk resources. Zhang et al 13 propose the virtual cloud resources allocation model based on a constraint programming (VCRA‐CP) approach which recommends the reduction of resource usage (and therefore the associated cost) during provisioning in order to achieve the desired QoS requirements. Given this model, it reduces QoS violations and proposes lower resource usage costs.…”

Section: Related Workmentioning

confidence: 99%

Quality of Service‐aware matchmaking for adaptive microservice‐based applications

Štefanič

Kochovski

Rana

et al. 2020

Concurrency and Computation

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Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi-tier applications along edge, fog, and cloud tiers by considering resource properties and non-functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud-To-Things infrastructure based on a subgraph pattern matching (P-Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud-native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above.

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Constraint Programming based Virtual Cloud Resources Allocation Model

Abstract: Abstract

Cited by 23 publications

References 15 publications

An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres

An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres

Resource‐efficient load‐balancing framework for cloud data center networks

Quality of Service‐aware matchmaking for adaptive microservice‐based applications

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