فهرس المقالات 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
  Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM m أکثر

  Migration of Virtual Machine (VM) is a critical challenge in cloud computing. The process to move VMs or applications from one Physical Machine (PM) to another is known as VM migration. In VM migration several issues should be considered. One of the major issues in VM migration problem is selecting an appropriate PM as a destination for a migrating VM. To face this issue, several approaches are proposed that focus on ranking potential destination PMs by addressing migration objectives. In this paper we propose a new hierarchal fuzzy logic system for ranking potential destination PMs for a migrating VM by considering following parameters: Performance efficiency, Communication cost between VMs, Power consumption, Workload, Temperature efficiency and Availability. Using hierarchal fuzzy logic systems which consider the mentioned six parameters which have great role in ranking of potential destination PMs for a migrating VM together, the accuracy of PMs ranking approach is increased, furthermore the number of fuzzy rules in the system are reduced, thereby reducing the computational time (which is critical in cloud environment). In our experiments, we compare our proposed approach that is named as (HFLSRPM: Hierarchal Fuzzy Logic Structure for Ranking potential destination PMs for a migrating VM) with AppAware algorithm in terms of communication cost and performance efficiency. The results demonstrate that by considering more effective parameters in the proposed PMs ranking approach, HFLSRPM outperforms AppAware algorithm. تفاصيل المقالة
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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
  The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is main أکثر

  The cloud computing is considered as a computational model which provides the uses requests with resources upon any demand and needs.The need for planning the scheduling of the user's jobs has emerged as an important challenge in the field of cloud computing. It is mainly due to several reasons, including ever-increasing advancements of information technology and an increase of applications and user needs for these applications with high quality, as well as, the popularity of cloud computing among user and rapidly growth of them during recent years. This research presents the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), an evolutionary algorithm in the field of optimization for tasks scheduling in the cloud computing environment. The findings indicate that presented algorithm, led to a reduction in execution time of all tasks, compared to SPT, LPT, and RLPT algorithms.Keywords: Cloud Computing, Task Scheduling, Virtual Machines (VMs), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) تفاصيل المقالة
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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
  The issue of management and allocation of resources in cloud computing environments, according to the breadth of scale and modern technology implementation, is a complicated issue. Issues such as: the heterogeneity of resources, resource dependencies to each other, the أکثر

  The issue of management and allocation of resources in cloud computing environments, according to the breadth of scale and modern technology implementation, is a complicated issue. Issues such as: the heterogeneity of resources, resource dependencies to each other, the dynamics of the environment, virtualization, workload diversity as well as a wide range of management objectives of cloud service providers to provide services in this environment. In this paper, first, the description of cloud computing environment and related issues have been reported. According to the performed studies, challenges such as: the absence of a comprehensive management for resources in the cloud environment, the method of predicting the resource allocation process, optimum resource allocation methods to reduce energy consumption and reducing the time to access resources and also implementation of dynamic resources allocation methods in the mobile cloud environments, have been addressed. Finally, with regard to the challenges, some recommendations to improve the process of allocation of resources in a cloud computing environment is has been proposed. تفاصيل المقالة
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  4 - A Version Numbering Scheme for Informational Objects Used in VM Live Migration
  Majid Tajamolian Mohammad Ghasemzadeh
  Various numbering schemes are used to track different versions and revisions of files, software packages, and documents. One major challenge in this regard is the lack of an all-purpose, adaptive, comprehensive and efficient standard. To resolve the challenge, this arti أکثر

  Various numbering schemes are used to track different versions and revisions of files, software packages, and documents. One major challenge in this regard is the lack of an all-purpose, adaptive, comprehensive and efficient standard. To resolve the challenge, this article presents Quadruple Adaptive Version Numbering Scheme. In the proposed scheme, the version identifier consists of four integers. These four numbers from Left to Right are called: "Release Sequence Number", "Generation Number", "Features List Number", and "Corrections List Number" respectively. In the article, special values are given for the quadruple numbers and their meanings are described. QAVNS is an "Adaptive" scheme; this means that it has the capability to track the different versions and revisions of files, software packages, project output documents, design documents, rules, manuals, style sheets, drawings, graphics, administrative and legal documents, and the other types of "Informational Objects" in different environments, without alterations in its structure. The proposed scheme has the capability to monitor changes in the types of informational objects, such as virtual machine memory, in the live migration process. The experimental and analytical results indicate the desirability and effectiveness of the proposed scheme in satisfying the desired expectations. The proposed scheme can become a common standard and successfully applied in all academic, engineering, administrative, legislative, legal, manufacturing, industrial, operational, software development, documentary, and other environments. The standardization of this scheme and its widespread usage can be a great help in improving everyone's understanding of the numbering of versions & revisions. تفاصيل المقالة
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  5 - مطالعه توازن بار به کمک الگوریتم فازی تطبیقی
  زهرا دهقانی سید جواد میرعابدینی علی هارون آبادی
  رایانش ابری یکی از جدیدترین تحولات در فناوری اطلاعات محسوب می‌شود که به مرور زمان در صنعت و بخش-های آموزشی مختلف فراگیر شده است. رایانش ابری یک فناوری جدید نیست، بلکه یک روش جدید برای ارائه سرویس از طریق اینترنت است. رایانش ابری یک مفهوم جدید به عنوان مخزنی از منابع مجا أکثر

  رایانش ابری یکی از جدیدترین تحولات در فناوری اطلاعات محسوب می‌شود که به مرور زمان در صنعت و بخش-های آموزشی مختلف فراگیر شده است. رایانش ابری یک فناوری جدید نیست، بلکه یک روش جدید برای ارائه سرویس از طریق اینترنت است. رایانش ابری یک مفهوم جدید به عنوان مخزنی از منابع مجازی‌سازی شده است، که باعث افزایش بهره‌وری، صرفه‌جویی در منابع سخت‌افزاری و بالابردن توان محاسباتی می‌شود. یکی از نگرانی‌های اصلی در محیط رایانش ابری توازن بار است که در صورتی که به صورت مناسب انجام شود می‌تواند باعث افزایش سرعت، کارایی، افزایش رضایت مشتری، کاهش زمان پاسخ می‌شود. در این مقاله نیز مشکلات توازن بار در رایانش ابری مورد بررسی قرار گرفته و تعدادی الگوریتم‌های توازن بار معرفی می‌شود.در انتها الگوریتمی جهت بهبود توازن بار در محیط رایانش ابری پیشنهاد و سپس ارزیابی می‌شود.الگوریتم پیشنهادی ما از ترکیب الگوریتمmin-max و فازی بهره گرفته است و نشان دادیم که در اکثر حالات الگوریتم ما از خالت غیرفازی بهتر رفتار می‌کند. تفاصيل المقالة
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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
  Today, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an auto أکثر

  Today, with the rise of cloud data centers, power consumption has increased and cloud infrastructure management has become more complex. On the other hand, meeting the needs of cloud users is an important goal in the cloud infrastructure. To solve such problems, an autonomous model with predictive capability is needed to do virtual machine consolidation at runtime effectively. In fact, using the feedback system of autonomous systems can make this process simpler and more optimized. The goal of this research is to propose a cloud resource management model that makes the virtual machine consolidation process autonomous, and by using a prediction method compromises between service level agreement violations and energy consumption reduction. In this research, an autonomous model is presented which detects overloaded servers in the analysis phase by a prediction algorithm. Also, at the planning phase, a multi heuristic algorithm based on learning automata is proposed to find proper servers for virtual machine placement. Cloudsim version 3.0.3 was used to evaluate the proposed model. The results show that the proposed model has reduced averagely the service level agreement violations, energy and migration counts by 67.08%, 11.61% and 70.64% respectively, compared to other methods. تفاصيل المقالة
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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 أکثر

  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. تفاصيل المقالة
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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
  By pervasiveness of cloud computing, a colossal amount of applications from gigantic organizations increasingly tend to rely on cloud services. These demands caused a great number of applications in form of couple of virtual machines (VMs) requests to be executed on dat أکثر

  By pervasiveness of cloud computing, a colossal amount of applications from gigantic organizations increasingly tend to rely on cloud services. These demands caused a great number of applications in form of couple of virtual machines (VMs) requests to be executed on data centers’ servers. Some of applications are as big as not possible to be processed upon a single VM. Also, there exists several distributed applications such as MapReduce projects which exploit much number of VMs dispersed over physical machines (PMs) attached with high speed networks. These types of VMs involve mutual traffic transferring which is completely processed as an atomic application. High volume of traffic transfer among VMs may saturate network links and leads performance bottleneck for both data center and applications which seriously threat users’ service level agreement (SLA). Furthermore, communication energy consumption increases when network devices are heavily in use. This paper addresses the virtual machine placement (VMP) problem by considering inter-VM communications on VL2 topology. This is an optimization problem with the aim of network traffic transferring minimization. Dependent VMs are tried to be co-hosted or to be placed in close neighborhoods to minimize the amount of total traffic streaming over the network. A combined meta-heuristic approach based and ACO and GA algorithms is employed to solve the problem. The results of simulations imply the superiority of our proposed approach in comparison with other state-of-the-art approaches in terms of reducing total traffic flow, saving energy, and declining resource dissipation in servers. تفاصيل المقالة