Current Issue : January - March Volume : 2018 Issue Number : 1 Articles : 5 Articles
Infrastructure as a Service (IaaS) cloud provides resources as a service from a pool of compute, network, and storage resources.\nCloud providers can manage their resource usage by knowing future usage demand from the current and past usage patterns of\nresources. Resource usage prediction is of great importance for dynamic scaling of cloud resources to achieve efficiency in terms\nof cost and energy consumption while keeping quality of service.The purpose of this paper is to present a real-time resource usage\nprediction system. The system takes real-time utilization of resources and feeds utilization values into several buffers based on\nthe type of resources and time span size. Buffers are read by R language based statistical system. These buffers� data are checked\nto determine whether their data follows Gaussian distribution or not. In case of following Gaussian distribution, Autoregressive\nIntegratedMovingAverage (ARIMA) is applied; otherwiseAutoregressiveNeuralNetwork (AR-NN) is applied. In ARIMA process,\na model is selected based on minimum Akaike Information Criterion (AIC) values. Similarly, in AR-NN process, a network with\nthe lowest Network Information Criterion (NIC) value is selected.We have evaluated our system with real traces of CPU utilization\nof an IaaS cloud of one hundred and twenty servers....
The management strategy of a data center needs access to sufficient resources in order to handle different\nrequests of applications, while minimizing the consumed energy. With regard to high and varying resource\ndemands, Virtual Machines (VM) management necessitates dynamic strategies. Dynamic management of\nVMs includes VM placement and VM migration. The management approach presented in this paper aimed\nto reduce the energy consumption and the violation of service level agreements (SLA) simultaneously in\ndata centers. The simulation results indicate that proposed approach improved the VM management 40%\ncompared to the previous single-goal approaches based on the energy consumption and SLA violation rates....
With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to\nthe fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is\nexposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware\nfailure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate\nverifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering,\nwhich is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which\nis based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based\non the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and\nthus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query\nvector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the\nproposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively....
In cloud computing, user functional requirements are satisfied through service composition. However, due to the process of\ninteraction and sharing among SaaS services, user privacy data tends to be illegally disclosed to the service participants. In this\npaper, we propose a privacy data decomposition and discretization method for SaaS services. First, according to logic between\nthe data, we classify the privacy data into discrete privacy data and continuous privacy data. Next, in order to protect the user\nprivacy information, continuous data chains are decomposed into discrete data chain, and discrete data chains are prevented from\nbeing synthesized into continuous data chains. Finally, we propose a protection framework for privacy data and demonstrate its\ncorrectness and feasibility with experiments....
The widespread use of digital images has led to a new challenge in digital image forensics. These images can be\nused in court as evidence of criminal cases. However, digital images are easily manipulated which brings up the\nneed of a method to verify the authenticity of the image. One of the methods is by identifying the source camera.\nIn spite of that, it takes a large amount of time to be completed by using traditional desktop computers. To tackle\nthe problem, we aim to increase the performance of the process by implementing it in a distributed computing\nenvironment. We evaluate the camera identification process using conditional probability features and Apache\nHadoop. The evaluation process used 6000 images from six different mobile phones of the different models and\nclassified them using Apache Mahout, a scalable machine learning tool which runs on Hadoop. We ran the source\ncamera identification process in a cluster of up to 19 computing nodes. The experimental results demonstrate\nexponential decrease in processing times and slight decrease in accuracies as the processes are distributed across\nthe cluster. Our prediction accuracies are recorded between 85 to 95% across varying number of mappers....
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