CSL-driven and energy-efficient resource scheduling in cloud data center

Li, Hongjian; Zhao, Yanan; Fang, Shuyong

doi:10.1007/s11227-019-03036-9

Cited by 12 publications

(6 citation statements)

References 32 publications

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“…To further prove the performance and feasibility of the proposed approach, it is compared with some of the latest existing mechanisms in literature. On comparing the proposed method with the CDEERS framework proposed by Li et al, 27 it has been identified that the framework reduced the SLAV to a great extent along with the optimization of energy. This has been achieved due to the prediction of future SLAV and adapting to the scenario.…”

Section: Simulation Analysismentioning

confidence: 99%

“…To attain customer satisfaction, Li et al 27 presented a customer satisfaction level (CSL)‐driven and energy efficient resource scheduling (CDEERS) framework to achieve resource scheduling in cloud with optimized energy efficiency. Three scheduling strategies (SSs) were built such as SS for minimizing energy consumption (SS‐MEC), SS for maximizing CSL (SS‐MCSL) and SS for minimizing CSL per energy (SS‐MCPE).…”

Section: Related Workmentioning

confidence: 99%

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Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Saif¹,

Aradhya²,

Murshed

et al. 2023

Concurrency and Computation

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Cloud multi-tenancy has a variant requirement, due to its resource sharing nature, satisfying such requirements and maintaining a balance between the resources and the business workloads of multiple tenants is a challenging task, and also the communication between scheduled containers leads to high power consumption. To address these issues, this article proposes an autonomic approach to ensure the elasticity of BPM in multi-tenant cloud. Where it employs the autonomic computing capabilities for scheduling the containers into the available servers then regulates the communication between the containers using multi-path routing. For the container scheduling, a multi-objective crow search optimization algorithm is proposed to schedule the containers into appropriate servers. Then, the discrete wolf search algorithm based multipath routing is proposed to route the communication flows between the containers by finding the optimal path with an objective to minimize the energy consumption.The optimal path is constructed as a multi-tenancy graph with bandwidths determining the shortest distance between the servers and containers. The overall simulations shows that the proposed algorithm outperformed the other compared approaches in terms of make-span, resource utilization, execution cost, execution time, and energy consumption.

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Section: Simulation Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Saif¹,

Aradhya²,

Murshed

et al. 2023

Concurrency and Computation

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“…In [45], the energyaware asset allocation approach has been investigated to improve the energy productivity of a server farm without SLA negotiations. An asset scheduling strategy with a hereditary method has been proposed to improve the usage of assets and save the expense of energy in distributed computing [46,47]. It utilizes a migration approach dependent on 3 load degrees (CPU usage, the throughput of organization, and pace of circle I/O).…”

Section: Green Data Centrementioning

confidence: 99%

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

Mekala

Patan

Islam

et al. 2021

Security and Communication Networks

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The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches.

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“…They proposed an initial virtual machine allocation mechanism, which predicts virtual machine resource utilization patterns, coordinates the needs of different resources based on prediction results, and integrates complementary virtual machines in the same physical machine 37 . They design a CSL‐driven energy‐efficient scheduling framework to optimize energy efficiency in cloud data center 38 . To reduce the energy consumption, a hybrid dynamic voltage and frequency scaling (DVFS) scheduling based on Q‐learning was proposed by combining three DVFS techniques 39 …”

Section: Related Workmentioning

confidence: 99%

An energy efficient and resource‐constrained scheduling framework for smart city application

Jing

Miao

Song

et al. 2020

Trans Emerging Tel Tech

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Summary With the development of smart Internet of things devices, intelligent applications are expected to lead further innovation in smart city. However, although cloud computing infrastructure can be used to meet traditional challenges, the scheduling model for new big data intelligent application has still not matured. In this work, we proposed a two‐stage scheduling framework for smart city intelligent application. In the first stage, we propose a virtual machine selection algorithm for edge computing to enhance relative migration benefits. The algorithm defines the invalid virtual machine migration and relative migration benefits from the change in the overall computing resources of the cloud data center after the virtual machine migration. In the second stage, we proposed an energy efficient and resource‐constrained scheduling framework for edge computing. The historical data of the cloud and edge computing workload of the computing node are processed in a sliding window manner, and the median absolute deviation of the historical data is used as the base of the physical reserved resource constraint when the base also changes as the workload changes. The experimental results show that energy‐efficient and resource‐constrained can make the computer resource provide high‐quality services for users in a low‐energy state.

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CSL-driven and energy-efficient resource scheduling in cloud data center

Cited by 12 publications

References 32 publications

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

Multi‐objective container scheduling and multi‐path routing for elastic business process management in autonomic multi‐tenant cloud

DAWM: Cost-Aware Asset Claim Analysis Approach on Big Data Analytic Computation Model for Cloud Data Centre

An energy efficient and resource‐constrained scheduling framework for smart city application

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