Current Issue : October - December Volume : 2016 Issue Number : 4 Articles : 5 Articles
ABE has been widely applied for secure data protection in cloud computing. In ABE, userââ?¬â?¢s private keys are\ngenerated by attribute authority, thus, attribute authority has the ultimate privileges in the system and can\nimpersonate any users to forge valid signatures. Once the attribute authority become dishonest or be invaded in\ncloud systems, the systemââ?¬â?¢s security will be at risk. To better solve the problem mentioned above, in this paper, we\npropose a key-policy attribute based signature scheme with untrusted authority and traceability (KP-ABS-UT). In our\nscheme, the signerââ?¬â?¢s private key is composed by two components: one part is distributed by attribute authority and\nthe other part is chosen privately by the signerââ?¬â?¢s self. Thus attribute authority cannot forge any signatures which\nshould be signed by legal users. Besides, our scheme introduces an entity ââ?¬Å?tracerââ?¬Â, which can trace the identity of\nsigner when necessary. By security analysis and efficiency comparison, we prove our KP-ABS-UT scheme meets the\nrequirements of unforgeability as well as lower computation cost....
Energy consumption for datacenter has grown significantly and the trend is still growing due to the increasing\npopularity of cloud computing. Datacenter networks (DCNs), however, are starting to consume a greater portion\nof overall energy in comparison to servers used in datacenters due to advanced virtualization techniques. On the\nother hand, devices in a DCN often remain under-utilized. There are various DCN architectures. This paper proposes an\napproach called Green Spine Switch Management System (GSSMS) for Spine-Leaf topology based DCNs. The objective\nof the approach is to reduce energy consumption used by the network for a Spine-Leaf topology-based\ndatacenter. The primary idea of GSSMS is to monitor the dynamic workload and only keep Spine switches that\nare necessary for handling the current network traffic. We have developed an adaptive management system to\ncontrol the number of Spine switches in a Spine-Leaf DCN for efficient energy consumption. Further, we have\nperformed extensive simulation using CloudSim for a number of scenarios. The simulation results demonstrate\nthat our proposed GSSMS can effectively save energy by as much as 63 % of the energy consumed by a\ndatacenter comprising a fixed static set of Spine switches....
The rapid growth in the demand for cloud computing data presents a performance challenge for both software\nand hardware architects. It is important to analyze and characterize the data processing performance for a given\ncloud cluster and to evaluate the performance bottlenecks in a cloud cluster that contribute to higher or lower\ncomputing processing time. In this paper, we implement a detailed performance analysis and characterization for\nHadoop K-means iterations by scaling different processor micro-architecture parameters and comparing performance\nusing Intel and AMD processors. This leads to the analysis of the underlying hardware in a cloud cluster servers to\nenable optimization of software and hardware to achieve maximum performance possible. We also propose a\nperformance estimation model that estimates performance for Hadoop K-means iterations by modeling different\nprocessor micro-architecture parameters. The model is verified to predict performance with less than 5 % error margin\nrelative to a measured baseline....
Data compression is an area that needs to be given almost attention in text quality assessment. Different methodologies have been defined for this purpose. Hence choosing the best machine learning algorithm is really important. In addition to different compression technologies and methodologies, selection of a good data compression tool is most important. There is a complete range of different data compression techniques available both online and offline working such that it becomes really difficult to choose which technique serves the best. Here comes the necessity of choosing the right method for text compression purposes and hence an algorithm that can reveal the best tool among the given ones. A data compression algorithm is to be developed which consumes less time while provides more compression ratio as compared to existing techniques. In this paper we represent a hybrid approach to compress the text data. This hybrid approach is combination of Dynamic Bit reduction method and Huffman coding....
To meet the increasing demand of computational power, at present IT service providers� should choose cloud\nbased services for its flexibility, reliability and scalability. More and more datacenters are being built to cater\ncustomers� need. However, the datacenters consume large amounts of energy, and this draws negative\nattention. To address those issues, researchers propose energy efficient algorithms that can minimize energy\nconsumption while keeping the quality of service (QoS) at a satisfactory level. Virtual Machine consolidation is\none such technique to ensure energy-QoS balance. In this research, we explore fuzzy logic and heuristic\nbased virtual machine consolidation approach to achieve energy-QoS balance. A Fuzzy VM selection method\nis proposed in this research. It selects VM from an overloaded host. Additionally, we incorporate migration\ncontrol in Fuzzy VM selection method that will enhance the performance of the selection strategy. A new\noverload detection algorithm has also been proposed based on mean, median and standard deviation of\nutilization of VMs. We have used CloudSim toolkit to simulate our experiment and evaluate the performance\nof the proposed algorithm on real-world work load traces of Planet lab VMs. Simulation results demonstrate\nthat the proposed method is most energy efficient compared to others....
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