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List of subject articles Cloud computing


    • Open Access Article

      1 - Hybrid Task Scheduling Method for Cloud Computing by Genetic and PSO Algorithms
      Amin Kamalinia Ali Ghaffari
      Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cl Full Text
      Cloud computing makes it possible for users to use different applications through the internet without having to install them. Cloud computing is considered to be a novel technology which is aimed at handling and providing online services. For enhancing efficiency in cloud computing, appropriate task scheduling techniques are needed. Due to the limitations and heterogeneity of resources, the issue of scheduling is highly complicated. Hence, it is believed that an appropriate scheduling method can have a significant impact on reducing makespans and enhancing resource efficiency. Inasmuch as task scheduling in cloud computing is regarded as an NP complete problem; traditional heuristic algorithms used in task scheduling do not have the required efficiency in this context. With regard to the shortcomings of the traditional heuristic algorithms used in job scheduling, recently, the majority of researchers have focused on hybrid meta-heuristic methods for task scheduling. With regard to this cutting edge research domain, we used HEFT (Heterogeneous Earliest Finish Time) algorithm to propose a hybrid meta-heuristic method in this paper where genetic algorithm (GA) and particle swarm optimization (PSO) algorithms were combined with each other. The results of simulation and statistical analysis of proposed scheme indicate that the proposed algorithm, when compared with three other heuristic and a memetic algorithms, has optimized the makespan required for executing tasks. Manuscript Document
    • Open Access Article

      2 - Toward an Enhanced Dynamic VM Consolidation Approach for Cloud Datacenters Using Continuous Time Markov Chain
      Monireh Hosseini Sayadnavard Abolfazl Toroghi Haghighat
      Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirement Full Text
      Dynamic Virtual Machine (VM) consolidation is an effective manner to reduce energy consumption and balance the resource load of physical machines (PMs) in cloud data centers that guarantees efficient power consumption while maintaining the quality of service requirements. Reducing the number of active PMs using VM live migration leads to prevent inefficient usage of resources. However, high frequency of VM consolidation has a negative effect on the system reliability and we need to deal with the trade-off between energy consumption and system reliability. In recent years many research work has been done to optimize energy management using power management techniques. Although these methods are very efficient from the point of view of energy management, but they ignore the negative impact on the system reliability. In this paper, a novel approach is proposed to achieve a reliable VM consolidation method. In this way, a Markov chain model is designed to determine the reliability of PMs and then it has been prioritized PMs based on their CPU utilization level and reliability status. Two algorithms are presented to determining source and destination servers. The efficiency of our proposed approach is validated by conducting extensive simulations. The results of the evaluation clearly show that the proposed approach significantly improve energy consumption while avoiding the inefficient VM migrations. Manuscript Document
    • Open Access Article

      3 - Reliable resource allocation and fault tolerance in mobile cloud computing
      Zahra Najafabadi Samani Mohammad Reza  Khayyam Bashi
      By switching the computational load from mobile devices to the cloud, Mobile Cloud Computing (MCC) allows mobile devices to offer a wider range of functionalities. There are several issues in using mobile devices as resource providers, including unstable wireless connec Full Text
      By switching the computational load from mobile devices to the cloud, Mobile Cloud Computing (MCC) allows mobile devices to offer a wider range of functionalities. There are several issues in using mobile devices as resource providers, including unstable wireless connections, limited energy capacity, and frequent location changes. Fault tolerance and reliable resource allocation are among the challenges encountered by mobile service providers in MCC. In this paper, a new reliable resource allocation and fault tolerance mechanism is proposed in order to apply a fully distributed resource allocation algorithm without exploiting any central component. The objective is to improve the reliability of mobile resources. The proposed approach involves two steps: (1) Predicting device status by gathering contextual information and applying TOPSIS to prevent faults caused by volatility of mobile devices, and (2) Adapting replication and checkpointing methods to fault tolerance. A context-aware reliable offloading middleware is developed to collect contextual information and manage the offloading process. To evaluate the proposed method, several experiments are run in a real environment. The results indicate improvements in success rates, completion time, and energy consumption for tasks with high computational load Manuscript Document
    • Open Access Article

      4 - BSFS: A Bidirectional Search Algorithm for Flow Scheduling in Cloud Data Centers
      حسبیه ناصری Sadoon Azizi Alireza Abdollahpouri
      To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data cent Full Text
      To support high bisection bandwidth for communication intensive applications in the cloud computing environment, data center networks usually offer a wide variety of paths. However, optimal utilization of this facility has always been a critical challenge in a data center design. Flow-based mechanisms usually suffer from collision between elephant flows; while, packet-based mechanisms encounter packet re-ordering phenomenon. Both of these challenges lead to severe performance degradation in a data center network. To address these problems, in this paper, we propose an efficient mechanism for the flow scheduling problem in cloud data center networks. The proposed mechanism, on one hand, makes decisions per flow, thus preventing the necessity for rearrangement of packets. On the other hand, thanks do SDN technology and utilizing bidirectional search algorithm, our proposed method is able to distribute elephant flows across the entire network smoothly and with a high speed. Simulation results confirm the outperformance of our proposed method with the comparison of state-of-the-art algorithms under different traffic patterns. In particular, compared to the second-best result, the proposed mechanism provides about 20% higher throughput for random traffic pattern. In addition, with regard to flow completion time, the percentage of improvement is 12% for random traffic pattern Manuscript Document
    • Open Access Article

      5 - Embedding Virtual Machines in Cloud Computing Based on Big Bang–Big Crunch Algorithm
      Ali Ghaffari Afshin Mahdavi
      Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of Full Text
      Cloud computing is becoming an important and adoptable technology for many of the organization which requires a large amount of physical tools. In this technology, services are provided and presented according to users’ requests. Due to the presence of a large number of data centers in cloud computing, power consumption has recently become an important issue. However, data centers hosting Cloud applications consume huge amounts of electrical energy and contributing to high operational costs to the environment. Therefore, we need Green Cloud computing solutions that can not only minimize operational costs but also reduce the environmental impact. Live migration of virtual machines and their scheduling and embedding lead to enhanced efficiency of dynamic resources. The guarantee of service quality and service reliability is an indispensable and irrevocable requirement with respect to service level agreement. Hence, providing a method for reducing costs of power consumption, data transmission, bandwidth and, also, for enhancing quality of service (QoS) in cloud computing is critical. In this paper, a Big Bang–Big Crunch (BB-BC) based algorithm for embedding virtual machines in cloud computing was proposed. We have validated our approach by conducting a performance evaluation study using the CloudSim toolkit. Simulation results indicate that the proposed method not only enhances service quality, thanks to the reduction of agreement violation, but also reduces power consumption. Manuscript Document
    • Open Access Article

      6 - Reallocation of Virtual Machines to Cloud Data Centers to Reduce Service Level Agreement Violation and Energy Consumption Using the FMT Method
      Hojjat Farrahi Farimani Davoud  Bahrepour Seyed Reza Kamel Tabbakh reza Ghaemi
      Due to the increased use of cloud computing services, cloud data centers are in search of solutions in order to better provide the services demanded by their users. Virtual machine consolidation is an appropriate solution to the trade-off between power consumption and s Full Text
      Due to the increased use of cloud computing services, cloud data centers are in search of solutions in order to better provide the services demanded by their users. Virtual machine consolidation is an appropriate solution to the trade-off between power consumption and service level agreement violation. The present study aimed to identify low, medium, and high load identification techniques, as well as the energy consumption and SLAv to minimize. In addition to the reduced costs of cloud providers, these techniques enhance the quality of the services demanded by the users. To this end, reallocation of resources to physical hosts was performed at the medium load level using a centralized method to classify the physical hosts. In addition, quartile was applied in each medium to reduce the energy consumption parameters and violation level. The three introduced SMT - NMT and FMT methods for reallocation of resources were tested and the best results were compared with previous methods.The proposed method was evaluated using the Cloudsim software with real Planet Lab data and five times run, the simulation results confirmed the efficiency of the proposed algorithm, which tradeoff between decreased the energy consumption and service level of agreement violation (SLAv) properly. Manuscript Document