Mostafa Ghobaei-Arani received the PhD Degree in Software Engineering from Islamic Azad University, Science andResearch Branch, Tehran, Iran. He is Assistant Professor ofComputer Engineering Department, Qom Branch, Islamic Azad University, Qom, Iran. He has published more than 50 journal and conference papers in the area of distributed computing. His research interests include distributed computing, cloud computing, autonomic computing, edge/fog computing, exascale computing, soft computing, and the IoT.
The ever increasing demand for the cloud services requires more data centres. The power consumpti... more The ever increasing demand for the cloud services requires more data centres. The power consumption in the data centres is a challenging problem for cloud computing, which has not been considered properly by the data centre developer companies. Especially, large data centres struggle with the power cost and the Greenhouse gases production. Hence, employing the power efficient mechanisms are necessary to optimise the mentioned effects. Moreover, virtual machine (VM) placement can be used as an effective method to reduce the power consumption in data centres. In this paper by grouping both virtual and physical machines, and taking into account the maximum absolute deviation during the VM placement, the power consumption as well as the service level agreement (SLA) deviation in data centres are reduced. To this end, the best-fit decreasing algorithm is utilised in the simulation to reduce the power consumption by about 5% compared to the modified best-fit decreasing algorithm, and at the same time, the SLA violation is improved by 6%. Finally, the learning automata are used to a trade-off between power consumption reduction from one side, and SLA violation percentage from the other side.
In cloud computing environment, resources can be dynamically provisioned on deman for cloud serv... more In cloud computing environment, resources can be dynamically provisioned on deman for cloud services The amount of the resources to be provisioned is determined during runtime according to the workload changes. Deciding the right amount of resources required to run the cloud services is not trivial, and it depends on the current workload of the cloud services. Therefore, it is necessary to predict the future demands to automatically provision resources in order to deal with fluctuating demands of the cloud services. In this paper, we propose a hybrid resource provisioning approach for cloud services that is based on a combination of the concept of the autonomic computing and the reinforcement learning (RL). Also, we present a framework for autonomic resource provisioning which is inspired by the cloud layer model. Finally, we evaluate the effectiveness of our approach under two real world workload traces. The experimental results show that the proposed approach reduces the total cost by up to 50%, and increases the resource utilization by up to 12% compared with the other approaches.
Maintenance process generally plays a vital role to achieve more benefits to the enterprises. Und... more Maintenance process generally plays a vital role to achieve more benefits to the enterprises. Undoubtedly, this process has a high value-added in oil and gas industries. Process owner expectations and new technology acquisition have been changing the mindset of domain experts to the new maintenance approaches and different newer methods such as condition-based maintenance models for improving the reliability and decreasing the cost of maintenance. Because of the high dynamic behavior of the gas and the instability of the input parameters, the need to apply a model with self-healing behavior is a serious demand in the gas industry. However, to the best of our knowledge, despite its importance, there is not any comprehensive study in the literature. In this paper, we present a new neuro-fuzzy model and a self-management control loop using real world data to meet the mentioned targets for a specified control valve in a gas refinery. ANFIS model is employed for the reasoning process which has six inputs (Inlet/outlet Pressures, temperature, flow rate, controller output and valve rod displacement), and one output that is a type of failure of the control valve and the most failures are considered based on domain expert knowledge. A suitable control loop is used to unceasingly monitor, analyze, plan and finally execute the process of prediction of failures. Due to undertaken improvement, there is a considerable change in reliability and financial indices. Moreover, the proposed approach is compared with two different methods. The results show that our proposed model comprehensively improves accuracy by 24%.
In recent years, novel mobile applications such as augmented reality, virtual reality, and three-... more In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature , offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource het-erogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.
In recent years, cloud computing paradigm has emerged as an internet-based technology to realize ... more In recent years, cloud computing paradigm has emerged as an internet-based technology to realize the utility model of computing for serving compute-intensive applications. In the cloud computing paradigm, the IT and business resources, such as servers, storage, network, and applications, can be dynamically provisioned to cloud workloads submitted by end-users. Since the cloud workloads submitted to cloud providers are heterogeneous in terms of quality attributes, management and analysis of cloud workloads to satisfy Quality of Service (QoS) requirements can play an important role in cloud resource management. Therefore, it is necessary for the provisioning of proper resources to cloud workloads using clustering of them according to QoS metrics. In this paper, we present a hybrid solution to handle the resource provisioning issue using workload analysis in a cloud environment. Our solution utilized the Imperialist Competition Algorithm (ICA) and K-means for clustering the workload submitted by end-users. Also, we use a decision tree algorithm to determine scaling decisions for efficient resource provisioning. The effectiveness of the proposed approach under two real workloads traces is evaluated. The simulation results demonstrate that the proposed solution reduces the total cost by up to 6.2%, and the response time by up to 6.4%, and increases the CPU utilization by up to 13.7%, and the elasticity by up to 30.8% compared with the other approaches.
With the recent advancements in Internet-based computing models, the usage of cloud-based applica... more With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user's QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.
The ever increasing demand for the cloud services requires more data centres. The power consumpti... more The ever increasing demand for the cloud services requires more data centres. The power consumption in the data centres is a challenging problem for cloud computing, which has not been considered properly by the data centre developer companies. Especially, large data centres struggle with the power cost and the Greenhouse gases production. Hence, employing the power efficient mechanisms are necessary to optimise the mentioned effects. Moreover, virtual machine (VM) placement can be used as an effective method to reduce the power consumption in data centres. In this paper by grouping both virtual and physical machines, and taking into account the maximum absolute deviation during the VM placement, the power consumption as well as the service level agreement (SLA) deviation in data centres are reduced. To this end, the best-fit decreasing algorithm is utilised in the simulation to reduce the power consumption by about 5% compared to the modified best-fit decreasing algorithm, and at the same time, the SLA violation is improved by 6%. Finally, the learning automata are used to a trade-off between power consumption reduction from one side, and SLA violation percentage from the other side.
In cloud computing environment, resources can be dynamically provisioned on deman for cloud serv... more In cloud computing environment, resources can be dynamically provisioned on deman for cloud services The amount of the resources to be provisioned is determined during runtime according to the workload changes. Deciding the right amount of resources required to run the cloud services is not trivial, and it depends on the current workload of the cloud services. Therefore, it is necessary to predict the future demands to automatically provision resources in order to deal with fluctuating demands of the cloud services. In this paper, we propose a hybrid resource provisioning approach for cloud services that is based on a combination of the concept of the autonomic computing and the reinforcement learning (RL). Also, we present a framework for autonomic resource provisioning which is inspired by the cloud layer model. Finally, we evaluate the effectiveness of our approach under two real world workload traces. The experimental results show that the proposed approach reduces the total cost by up to 50%, and increases the resource utilization by up to 12% compared with the other approaches.
Maintenance process generally plays a vital role to achieve more benefits to the enterprises. Und... more Maintenance process generally plays a vital role to achieve more benefits to the enterprises. Undoubtedly, this process has a high value-added in oil and gas industries. Process owner expectations and new technology acquisition have been changing the mindset of domain experts to the new maintenance approaches and different newer methods such as condition-based maintenance models for improving the reliability and decreasing the cost of maintenance. Because of the high dynamic behavior of the gas and the instability of the input parameters, the need to apply a model with self-healing behavior is a serious demand in the gas industry. However, to the best of our knowledge, despite its importance, there is not any comprehensive study in the literature. In this paper, we present a new neuro-fuzzy model and a self-management control loop using real world data to meet the mentioned targets for a specified control valve in a gas refinery. ANFIS model is employed for the reasoning process which has six inputs (Inlet/outlet Pressures, temperature, flow rate, controller output and valve rod displacement), and one output that is a type of failure of the control valve and the most failures are considered based on domain expert knowledge. A suitable control loop is used to unceasingly monitor, analyze, plan and finally execute the process of prediction of failures. Due to undertaken improvement, there is a considerable change in reliability and financial indices. Moreover, the proposed approach is compared with two different methods. The results show that our proposed model comprehensively improves accuracy by 24%.
In recent years, novel mobile applications such as augmented reality, virtual reality, and three-... more In recent years, novel mobile applications such as augmented reality, virtual reality, and three-dimensional gaming, running on handy mobile devices have been pervasively popular. With rapid developments of such mobile applications, decentralized mobile edge computing (MEC) as an emerging distributed computing paradigm is developed for serving them near the smart devices, usually in one hop, to meet their computation, and delay requirements. In the literature , offloading mechanisms are designed to execute such mobile applications in the MEC environments through transferring resource-intensive tasks to the MEC servers. On the other hand, due to the resource limitations, resource het-erogeneity, dynamic nature, and unpredictable behavior of MEC environments, it is necessary to consider the computation offloading issues as the challenging problem in the MEC environment. However, to the best of our knowledge, despite its importance, there is not any systematic, comprehensive, and detailed survey in game theory (GT)-based computation offloading mechanisms in the MEC environment. In this article, we provide a systematic literature review on the GT-based computation offloading approaches in the MEC environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and to provide open issues as well. The proposed taxonomy is classified into four main fields: classical game mechanisms, auction theory, evolutionary game mechanisms, and hybrid-base game mechanisms. Next, these classes are compared with each other according to the important factors such as performance metrics, case studies, utilized techniques, and evaluation tools, and their advantages and disadvantages are discussed, as well. Finally, open issues and future uncovered or weakly covered research challenges are discussed and the survey is concluded.
In recent years, cloud computing paradigm has emerged as an internet-based technology to realize ... more In recent years, cloud computing paradigm has emerged as an internet-based technology to realize the utility model of computing for serving compute-intensive applications. In the cloud computing paradigm, the IT and business resources, such as servers, storage, network, and applications, can be dynamically provisioned to cloud workloads submitted by end-users. Since the cloud workloads submitted to cloud providers are heterogeneous in terms of quality attributes, management and analysis of cloud workloads to satisfy Quality of Service (QoS) requirements can play an important role in cloud resource management. Therefore, it is necessary for the provisioning of proper resources to cloud workloads using clustering of them according to QoS metrics. In this paper, we present a hybrid solution to handle the resource provisioning issue using workload analysis in a cloud environment. Our solution utilized the Imperialist Competition Algorithm (ICA) and K-means for clustering the workload submitted by end-users. Also, we use a decision tree algorithm to determine scaling decisions for efficient resource provisioning. The effectiveness of the proposed approach under two real workloads traces is evaluated. The simulation results demonstrate that the proposed solution reduces the total cost by up to 6.2%, and the response time by up to 6.4%, and increases the CPU utilization by up to 13.7%, and the elasticity by up to 30.8% compared with the other approaches.
With the recent advancements in Internet-based computing models, the usage of cloud-based applica... more With the recent advancements in Internet-based computing models, the usage of cloud-based applications to facilitate daily activities is significantly increasing and is expected to grow further. Since the submitted workloads by users to use the cloud-based applications are different in terms of quality of service (QoS) metrics, it requires the analysis and identification of these heterogeneous cloud workloads to provide an efficient resource provisioning solution as one of the challenging issues to be addressed. In this study, we present an efficient resource provisioning solution using metaheuristic-based clustering mechanism to analyze cloud workloads. The proposed workload clustering approach used a combination of the genetic algorithm and fuzzy C-means technique to find similar clusters according to the user's QoS requirements. Then, we used a gray wolf optimizer technique to make an appropriate scaling decision to provide the cloud resources for serving of cloud workloads. Besides, we design an extended framework to show interaction between users, cloud providers, and resource provisioning broker in the workload clustering process. The simulation results obtained under real workloads indicate that the proposed approach is efficient in terms of CPU utilization, elasticity, and the response time compared with the other approaches.
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Papers by Mostafa Ghobaei-Arani