List of Articles virtual machine

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  1 - A Near Optimal Approach in Choosing The Appropriate Physical Machines for Live Virtual Machines Migration in Cloud Computing
  Seyedeh Roudabeh Hosseini Sepideh Adabi Reza Tavoli
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  2 - Task Scheduling Algorithm Using Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in Cloud Computing
  Ghazaal Emadi Amir Masoud Rahmani Hamed Shahhoseini
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  3 - A review of methods for resource allocation and operational framework in cloud computing
  Hadi Moei Emamqeysi Nasim Soltani Masomeh Robati Mohamad Davarpanah
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  4 - A Version Numbering Scheme for Informational Objects Used in VM Live Migration
  Majid Tajamolian Mohammad Ghasemzadeh
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  5 - Study on load balancing using fuzzy adaptive algorithm
  Zahra Dehghani S. J Mirabedini علی Harounabadi
  Cloud computing is one of the newest developments in information technology and has been widespread in the industry and different educational sectors over time. Cloud computing is not a new technology, but it is also a new method for providing services via the Internet. More

  Cloud computing is one of the newest developments in information technology and has been widespread in the industry and different educational sectors over time. Cloud computing is not a new technology, but it is also a new method for providing services via the Internet. Cloud computing is a new concept as a storage of virtualized resources and it increases the efficiency, saves the hardware resources, and enhances the computing power. The load balancing is one of the main concerns in cloud computing space; and it can increase the speed, efficiency, customer satisfaction and reduce the response time in the case of proper performance. This paper also studies the problems of load balancing in cloud computing and introduces several load balancing algorithms. Finally, an algorithm is proposed and evaluated to improve the load balancing in cloud computing space. Our proposed algorithm utilizes the combination of fuzzy and min-max algorithms; and we have found that our algorithm has better performance than the non-fuzzy algorithm in most of the cases. Manuscript profile
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  6 - A MAPE-K Loop Based Model for Virtual Machine Consolidation in Cloud Data Centers
  Negin Najafizadegan Eslam Nazemi Vahid Khajehvand
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  7 - ‎An Artificial Intelligence Framework for Supporting Coarse-Grained Workload Classification in Complex Virtual Environments
  Alfredo Cuzzocrea Enzo Mumolo Islam Belmerabet Abderraouf Hafsaoui
  10.30495/tfss.2023.1990572.1079
  We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the “next” workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combines the effectiveness of More

  We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the “next” workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combines the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. The so- called workload categorization problem plays a critical role in improving the efficiency and reliability of Cloud-based big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate in the distributed classification approach on top of virtual machines, which represent classical “commodity” settings for Cloud-based big data applications. Given a number of known reference workloads, and an unknown workload, in this paper we deal with the problem of finding the reference workload which is most similar to the unknown one. The depicted scenario turns out to be useful in a plethora of modern information system applications. We name this problem as coarse-grained workload classification, because, instead of characterizing the unknown workload in terms of finer behaviors, such as CPU, memory, disk, or network intensive patterns, we classify the whole unknown workload as one of the (possible) reference workloads. Reference workloads represent a category of workloads that are relevant in a given applicative environment. In particular, we focus our attention on the classification problem described above in the special case represented by virtualized environments. Today, Virtual Machines (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms. In virtualization frameworks, workload classification is very useful for accounting, security reasons, or user profiling. Hence, our research makes more sense in such environments, and it turns out to be very useful in a special context like Cloud Computing, which is emerging now. In this respect, our approach consists of running several machine learning-based classifiers of different workload models, and then deriving the best classifier produced by the Dempster-Shafer Fusion, in order to magnify the accuracy of the final classification. Experimental assessment and analysis clearly confirm the benefits derived from our classification framework. The running programs which produce unknown workloads to be classified are treated in a similar way. A fundamental aspect of this paper concerns the successful use of data fusion in workload classification. Different types of metrics are in fact fused together using the Dempster-Shafer theory of evidence combination, giving a classification accuracy of slightly less than 80%. The acquisition of data from the running process, the pre-processing algorithms, and the workload classification are described in detail. Various classical algorithms have been used for classification to classify the workloads, and the results are compared. Manuscript profile
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  8 - Communication-Aware Traffic Stream Optimization for Virtual Machine Placement in Cloud Datacenters with VL2 Topology
  Sara Farzai Mirsaeid Hosseini Shirvani Mohsen Rabbani
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