AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Zhou, Zhou; Shojafar, Mohammad; Alazab, Mamoun; Abawajy, Jemal; Li, Fangmin

doi:10.1109/tgcn.2021.3067309

Cited by 83 publications

(38 citation statements)

References 41 publications

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“…A load of PM is calculated using the utilization of resources. 10,14,[16][17][18][19][23][24][25] In the real environment, PMs could be of different specifications, and hence for the same workload, it may be possible that PM with lower specifications is identified as overloaded with respect to PM with higher specifications. Load on VM is calculated using the total number of tasks in the queue.…”

Section: Discussionmentioning

confidence: 99%

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Load balancing in cloud using improved gray wolf optimizer

Gohil

Patel

2022

Concurrency and Computation

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Cloud computing allocates virtual resources dynamically on user's demand. The sudden rise of data storage and computation in the cloud computing environment may cause an imbalanced workload distribution. As a result, job completion time will be higher in overloaded servers than the underloaded servers in the same environment.Distributing load fairly in the cloud is a crucial challenge. Traditionally, load balancing is used to distribute the workload among multiple servers to overcome the overloading and underloading of servers. This article presents a novel load balancing approach for cloud computing using improved gray wolf optimization algorithm. We compare our approach with harmony search algorithm, artificial bee colony algorithm, particle swarm optimization, and gray wolf optimization algorithms. Results of simulation are encouraging with improved system performance and fair utilization of resources.

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Section: Discussionmentioning

confidence: 99%

“…Zhou et al 24 proposed an energy‐aware VM allocation and deployment mechanism called AFED‐EF for IoT applications to handle the fluctuation of load.…”

Section: Related Workmentioning

confidence: 99%

Load balancing in cloud using improved gray wolf optimizer

Gohil

Patel

2022

Concurrency and Computation

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“…Alo ngside this enormous energy consumption, there is low resource utilisation. Statistical reports [6], [7]suggest that resource utilisation by data centres is on average between 5% and 25%, leading to wastage of resources. In general, the energy consumption by data centres is mainly associated with servers, refrigeration systems, lighting, and other equipment.…”

Section: Introductionmentioning

confidence: 99%

IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

Zhou

Shojafar

Alazab

et al. 2022

IEEE Trans. Ind. Inf.

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The high computational capability provided by a data centre makes it possible to solve complex manufacturing issues and carry out large-scale collaborative cloud manufacturing. Accurate, real-time estimation of the power required by a data centre can help resource providers predict the total power consumption and improve resource utilisation. To enhance the accuracy of server power models, we propose a real-time energy consumption prediction method called IECL that combines the support vector machine, random forest, and grid search algorithms. The random forest algorithm is used to screen the input parameters of the model, while the grid search method is used to optimise the hyperparameters. The error confidence interval is also leveraged to describe the uncertainty in the energy consumption by the server. Our experimental results suggest that the average absolute error for different workloads is less than 1.4% with benchmark models.

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“…Delay caused by either a computing resource over-whelmed by data processing requests or a network transferring a large amount of data to different connected IoT devices is not tolerable for the latency critical applications such as IoT-based emergency response system. 3,4 Therefore, there is need to push processing, storage, analysis and decision-making near to the source of data to reduce latency, network congestion, and improve service quality. To solve these issues, many new technologies like edge computing, mobile cloud computing and fog computing have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

Fahad

Shojafar

Abbas

et al. 2022

Concurrency and Computation

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Fog computing is a novel, decentralized and heterogeneous computing environment that extends the traditional cloud computing systems by facilitating task processing near end-users on computing resources called fog nodes. These diverse and resource-constrained fog devices process a large volume of tasks generated by various fog applications. These tasks are generated by various applications, some of which may be latency-sensitive, while others may tolerate some degree of delay in their normal functions. Task scheduling determines when a task should be allocated to a computing resource and how long that task can occupy the assigned resource.The majority of task scheduling algorithms focus on prioritizing the latency-sensitive tasks only, which results in the long waiting time for the other type of tasks. Hence, these priority-based schedulers cause task starvation for less important tasks while achieving delay-optimal results for latency-sensitive tasks. As a result, in this paper, we propose MQP, a multi-queue priority-based preemptive task scheduling approach that achieves a balanced task allocation for those applications that can tolerate a certain amount of processing delay and the latency-sensitive fog applications. At run-time, the MQP algorithm categorizes tasks as short and long based on their burst time. MQP algorithm maintains a separate task queue for each task category and dynamically updates the time slot value for preemption. The proposed technique's major purpose is to reduce response time for those data-intensive applications in the fog computing environment, which include both latency-sensitive tasks and tasks which are less latency-sensitive, thereby addressing the starvation problem for less latency-sensitive tasks. A smart traffic management case study is created to model a scenario with both latency-sensitive short and less latency-sensitive long tasks. We implement the MQP algorithm using iFogSim and confirm that it reduces the service latencies for long tasks.Simulation results show that the MQP algorithm allocates tasks to a fog device more efficiently and reduces the service latencies for long tasks. The average value of percentage reduction in the latency across all experimental configurations achieved is 22.68% and 38.45% in comparison to First Come-First Serve and shortest job first algorithms.

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AFED-EF: An Energy-Efficient VM Allocation Algorithm for IoT Applications in a Cloud Data Center

Cited by 83 publications

References 41 publications

Load balancing in cloud using improved gray wolf optimizer

Load balancing in cloud using improved gray wolf optimizer

IECL: An Intelligent Energy Consumption Model for Cloud Manufacturing

A multi‐queue priority‐based task scheduling algorithm in fog computing environment

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