Current Issue : October - December Volume : 2017 Issue Number : 4 Articles : 5 Articles
With the development of Internet, cloud computing has emerged to provide service to data users. But, it is necessary for an auditor\non behalf of users to check the integrity of the data stored in the cloud. The cloud server alsomust ensure the privacy of the data. In\na usual public integrity check scheme, the linear combination of data blocks is needed for verification. But, after times of auditing\non the same data blocks, based on collected linear combinations, the auditor might derive these blocks. Recently, a number of\npublic auditing schemes with privacy-preserving are proposed. With blinded linear combinations of data blocks, the authors of\nthese schemes believed that the auditor cannot derive any information about the data blocks and claimed that their schemes are\nprovably secure in the random oracle model. In this paper, with detailed security analysis of these schemes, we show that these\nschemes are vulnerable to an attack from the malicious cloud server who modifies the data blocks and succeeds in forging proof\ninformation for data integrity check....
Mobile cloud computing uses features to deliver outsourcing data to remotely available mobile devices.\nHowever, the flexible nature of the mobile device is a critical challenge for the mobile cloud computing\nenvironment. The mobile phone significantly degrades the data transfer performance when initiating the\nhandover process. Thus, an energy-efficient handover process could improve the quality of service (QoS).\nHere, we introduce a secure energy-efficient and quality-of-service architecture (EEQoSA) for the handover\nprocess in the mobile cloud computing environment. The proposed architecture involves four layers:\napplication, the Internet protocol multimedia subsystem (IPMS), communication, and media with\nconnectivity layers.\nThese four layers collectively handle the energy-efficiency, security and QoS parameters. Existing serviceoriented\narchitectures designed for mobile cloud computing are based on the symmetric encryption\ncryptography to support different media services. However, this approach easily allows an adversary to expose\nthe symmetric key and gain access to private data. Thus, our proposed architecture uses the secure and\nstrong authentication (SSA) process at the IPMS layer by protecting the media services from unauthorized\nusers, as the IPMS is the central layer that could be the entry point for an adversary. Furthermore, to extend\nthe mobile lifetime during the handover process, an energy detection (ED) model is deployed at the\ncommunication layer to detect the energy level of the mobile device prior to the handover initialization\nprocess. The media with the connectivity layer supports the secure handover process using a priority\nenforcement module that allows only legitimate users to complete the\nre-registration process after initiating the handover. Finally, the architecture is tested using the CloudSim\nsimulation environment and validated by a comparison with other known service-oriented architectures....
In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources\nin the form of virtual machines are provided over the Internet. A user can rent an arbitrary number\nof computing resources to meet their requirements, making cloud computing an attractive choice\nfor executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual\nmachines is an important problem in the cloud computing environment. This paper proposes a greedy\nand a genetic algorithm with an adaptive selection of suitable crossover and mutation operations\n(named as AGA) to allocate and schedule real-time tasks with precedence constraint on heterogamous\nvirtual machines. A comprehensive simulation study has been done to evaluate the performance\nof the proposed algorithms in terms of their solution quality and efficiency. The simulation results\nshow that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of\nsolution quality....
Cloud computing technology is used in traveling wave fault location, which establishes a new technology platform\nfor multi-terminal traveling wave fault location in complicated power systems. In this paper, multi-terminal traveling\nwave fault location network is developed, and massive data storage, management, and algorithm realization are\nimplemented in the cloud computing platform. Based on network topology structure, the section connecting\npoints for any lines and corresponding detection placement in the loop are determined first. The loop is divided\ninto different sections, in which the shortest transmission path for any of the fault points is directly and uniquely\nobtained. In order to minimize the number of traveling wave acquisition unit (TWU), multi-objective optimal\nconfiguration model for TWU is then set up based on network full observability. Finally, according to the TWU\ndistribution, fault section can be located by using temporal correlation, and the final fault location point can be\nprecisely calculated by fusing all the times recorded in TWU. PSCAD/EMTDC simulation results show that the\nproposed method can quickly, accurately, and reliably locate the fault point under limited TWU with optimal\nplacement....
In a cloud computing environment, the number of virtual machines (VMs) on a single\nphysical server and the number of applications running on each VM are continuously growing.\nThis has led to an enormous increase in the demand of memory capacity and subsequent increase\nin the energy consumption in the cloud. Lack of enough memory has become a major bottleneck\nfor scalability and performance of virtualization interfaces in cloud computing. To address this\nproblem, memory deduplication techniques which reduce memory demand through page sharing\nare being adopted. However, such techniques suffer from overheads in terms of number of online\ncomparisons required for the memory deduplication. In this paper, we propose a static memory\ndeduplication (SMD) technique which can reduce memory capacity requirement and provide\nperformance optimization in cloud computing. The main innovation of SMD is that the process of\npage detection is performed offline, thus potentially reducing the performance cost, especially in\nterms of response time. In SMD, page comparisons are restricted to the code segment, which has the\nhighest shared content. Our experimental results show that SMD efficiently reduces memory capacity\nrequirement and improves performance.We demonstrate that, compared to other approaches, the cost\nin terms of the response time is negligible....
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