Comprehensive survey on energy-aware server consolidation techniques in cloud computing

Chaurasia, Nisha; Kumar, Mohit; Chaudhry, Rashmi; Verma, Om Prakash

doi:10.1007/s11227-021-03760-1

Cited by 43 publications

(20 citation statements)

References 104 publications

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“…Furthermore, the authors in [29]- [31] investigate the cost efficiency and cost-effective real-time analysis to overcome the application running cost during task scheduling. These studies aim to enhance the performance of mobile cloud applications and provide effective workflow categories to execute the tasks efficiently.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

Ali

Iqbal²

2022

IEEE Access

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The number of smartphone users and mobile devices has increased significantly. The Mobile Cloud Applications based on cloud computing have also been increased. The mobile apps can be used in Augmented Reality, E-Transportation, 2D/3-D Games, E-Healthcare, and Education. The modern cloudbased frameworks provide such services on Virtual Machines. The existing frameworks worked well, but these suffered the problems such as overhead, resource utilization, lengthy boot-time, and cost of running Mobile Applications. This study addresses these problems by proposing a Dynamic Decision-Based Task Scheduling Technique for Microservice-based Mobile Cloud Computing Applications (MSCMCC). The MSCMCC runs delay-sensitive applications and mobility with less cost than existing approaches. The study focused on Task Scheduling problems on heterogeneous Mobile Cloud servers. We further propose Task Scheduling and Microservices based Computational Offloading (TSMCO) framework to solve the Task Scheduling in steps, such as Resource Matching, Task Sequencing, and Task Scheduling. Furthermore, the experimental results elaborate that the proposed MSCMCC and TSMCO enhance the Mobile Server Utilization. The proposed system effectively minimizes the cost of healthcare applications by 25%, augmented reality by 23%, E-Transport tasks by 21%, and 3-D games tasks by 19%, the average boot-time of microservices applications by 17%, resource utilization by 36%, and tasks arrival time by 16%.INDEX TERMS Cloud computing, mobile cloud computing, task offloading, task sequencing, task scheduling, microservices.

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Section: Literature Reviewmentioning

confidence: 99%

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

Ali

Iqbal²

2022

IEEE Access

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“…Companies will be able to better manage their business using IT systems that can adopt and implement directives based on business strategy and can make alterations in response to changing surroundings, according to the higher-order advantages. Server consolidation is another benefit of using AI-based autonomic computing, since it reduces the cost and human labour required to maintain huge server farms [30]. Management of computer systems should be made easier using AI for autonomous computing.…”

Section: Benefits Of Ai/ml-integrated Next Generation Computingmentioning

confidence: 99%

AI for Next Generation Computing: Emerging Trends and Future Directions

Gill,

Xu,

Ottaviani

et al. 2022

Preprint

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Autonomic computing investigates how systems can achieve (user) specified "control" outcomes on their own, without the intervention of a human operator. Autonomic computing fundamentals have been substantially influenced by those of control theory for closed and open-loop systems. In practice, complex systems may exhibit a number of concurrent and inter-dependent control loops. Despite research into autonomic models for managing computer resources, ranging from individual resources (e.g., web servers) to a resource ensemble (e.g., multiple resources within a data center), research into integrating Artificial Intelligence (AI) and Machine Learning (ML) to improve resource autonomy and performance at scale continues to be a fundamental challenge. The integration of AI/ML to achieve such autonomic and self-management of systems can be achieved at different levels of granularity, from full to human-in-the-loop automation. In this article, leading academics, researchers, practitioners, engineers, and scientists in the fields of cloud computing, AI/ML, and quantum computing join to discuss current research and potential future directions for these fields. Further, we discuss challenges and opportunities for leveraging AI and ML in next generation computing for emerging computing paradigms, including cloud, fog, edge, serverless and quantum computing environments.

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“…26 The results of this method indicate that GC has an acceptable performance in energy consumption optimization. 27 The threshold limit is exploited in this study so that overhead hosts be specified. The determination of the threshold value has to be precisely conducted since the amount of SLA violation declines by decreasing the threshold, and the energy consumption enhances, and vice versa.…”

Section: Literature Reviewmentioning

confidence: 99%

“…GC implements the dynamic VM consolidation using Sercon and ecoCloud 26 . The results of this method indicate that GC has an acceptable performance in energy consumption optimization 27 . The threshold limit is exploited in this study so that overhead hosts be specified.…”

Section: Literature Reviewmentioning

confidence: 99%

Confidence interval‐based overload avoidance algorithm for virtual machine placement

et al. 2022

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Virtualization plays an essential role in decreasing energy consumption and optimizing resource utilization by enabling the creation of virtual machines (VM) and their consolidation through live migration. Excessive migrations and a lack of required VMs are two critical factors in QoS degradation. The current consolidation approaches impose an intensive time complexity and cannot be used in large data centers with hundreds of hosts. This article proposes a framework for dynamic consolidation divided into a QoS‐aware algorithm for overload avoidance and a power‐aware algorithm for VM placement. To compute a safe zone criterion for any VM, relations were suggested by applying an interval estimate with a confidence level. By employing this criterion, the offered algorithm could guarantee the quality of service (QoS), particularly for specific VMs, while avoiding overhead. The VM placement algorithm is developed based on the maximum utilization of active hosts. It provides the capability to control the number of active hosts for the data center manager. The simulation results with real workloads revealed that the proposed framework could decline the amount of service level agreement violations by 78% and the number of migrations by 74%, and energy consumption by up to 13% in comparison with the best results of the benchmark algorithms. Hence, the application of this framework upgrades the QoS of data centers and declines their energy costs.

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Comprehensive survey on energy-aware server consolidation techniques in cloud computing

Cited by 43 publications

References 104 publications

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

A Cost and Energy Efficient Task Scheduling Technique to Offload Microservices Based Applications in Mobile Cloud Computing

AI for Next Generation Computing: Emerging Trends and Future Directions

Confidence interval‐based overload avoidance algorithm for virtual machine placement

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