Benchmarks have historically played a key role in guiding the progress of computer science systems research and development, But the areas such as availability have been neglected. For cloud computing, the areas such as availability are critically important.In this a new methodology to measure the availability by measuring the service availability is introduced.Service availability deals with the amount of services under process at an instant of time. Since we are measuring the amount of services under process and not whether the system is up or not, this will give a clear cut idea about the availability of cloud for the users.
Keywords: - Availability,Cloud , Benchmarks.
[1] Mohammed Hussain and Hanady Abdulsalam, Secaas: Security as a servicefor cloud-based applications, IEEE (2012).
[2] Smith E Ramgovind S, Eloff MM, The management of security in cloud computing, IEEE (2010).
[3] John C. Roberts II,Who Can You Trust in the Cloud? A Review of Security Issues Within Cloud Computing,Information Security Curriculum Development Conference (2011).
[4] Ertaul, L. and Singhal, S. 2009. Security Challenges in Cloud Computing, California State University, East Bay,Academic paper
[5] Aaron Brown and David A. Patterson,Towards Availability Benchmarks: A Case Study of Software RAID Systems,University of California at Berkeley,Academic paper
This paper derives mathematical formulae for the estimation of critical loads for rigid sway frames with pinned or fixed base restraints. This is informed by understanding that the critical loads of sway frames have values, which always fall within those of a pinned ended strut and a cantilever strut zero depending on the beam to column stiffness. Based on this, interpolation formulae that rely on the stiffness of the sway frame are developed using numerical integration. The results of the method have been found to be striking the result of the exact method. The method can be extended to multi-bay, multi-storey sway frames with ease.
Keywords: - Sway frame, critical load, stability functions, and Euler load
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This paper presents a efficient facial image recognition based on multi scale local binary pattern (LBP) texture features .It's a fast and simple for implementation, has shown its superiority in face recognition. To extract representative features, "uniform" LBP was proposed and its effectiveness has been validated. However, all "non-uniform" patterns are clustered into one pattern, so a lot of useful information is lost. In this study, propose to build a hybrid multiscale LBP histogram for an image. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The useful information of "non-uniform" patterns at large scale is dug out from its counterpart of small scale, The performance of the proposed method is that it can fully utilize LBP information while it does not need any training step, which may be sensitive to training samples assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed.
Index Terms: - Facial image representation, local binary pattern, multiscale, component based face recognition, texture features, face misalignment
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