On Evaluating Commercial Cloud Services: A Systematic Review

Background: Cloud Computing is increasingly booming in industry with many competing providers and services. Accordingly, evaluation of commercial Cloud services is necessary. However, the existing evaluation studies are relatively chaotic. There exists tremendous confusion and gap between practices and theory about Cloud services evaluation. Aim: To facilitate relieving the aforementioned chaos, this work aims to synthesize the existing evaluation implementations to outline the state-of-the-practice and also identify research opportunities in Cloud services evaluation. Method: Based on a conceptual evaluation model comprising six steps, the Systematic Literature Review (SLR) method was employed to collect relevant evidence to investigate the Cloud services evaluation step by step. Results: This SLR identified 82 relevant evaluation studies. The overall data collected from these studies essentially represent the current practical landscape of implementing Cloud services evaluation, and in turn can be reused to facilitate future evaluation work. Conclusions: Evaluation of commercial Cloud services has become a world-wide research topic. Some of the findings of this SLR identify several research gaps in the area of Cloud services evaluation (e.g., the Elasticity and Security evaluation of commercial Cloud services could be a long-term challenge), while some other findings suggest the trend of applying commercial Cloud services (e.g., compared with PaaS, IaaS seems more suitable for customers and is particularly important in industry). This SLR study itself also confirms some previous experiences and reveals new Evidence-Based Software Engineering (EBSE) lessons

Scheduling Real-time Divisible Loads in Cluster Computing Environment

The significance of cluster computing in solving massively parallel workloads is tremendous. Divisible Load Theory has proven to be very successful in optimizing the usage of the system resources by partitioning the arbitrarily divisible loads adequately among the cluster nodes. Arbitrarily divisible loads have significant real-world applications in high energy and particle physics. In this thesis, various algorithms for a cluster computing environment are studied including the ones dealing with divisible load theory confirming DLT based algorithms performing better in most cases. The loads that are considered in this thesis are hard real-time tasks with associated deadlines. Specifically, a comparison is made between clusters with one where the head node doesn't participate in processing of the work-loads with the other where the head node does participate in processing of the work-loads. A new mathematical formula is derived for the task execution time corresponding to the new scenario of head node possessing front-end processing capability. The existing algorithms corresponding to Real-Time Divisible Load Theory are then implemented using this new formula to examine the scheduling performance in this new scenario compared to the conventional scenario where the head node lacks front-end processing capability

Hadoop performance modeling and job optimization for big data analytics

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonBig data has received a momentum from both academia and industry. The MapReduce model has emerged into a major computing model in support of big data analytics. Hadoop, which is an open source implementation of the MapReduce model, has been widely taken up by the community. Cloud service providers such as Amazon EC2 cloud have now supported Hadoop user applications. However, a key challenge is that the cloud service providers do not a have resource provisioning mechanism to satisfy user jobs with deadline requirements. Currently, it is solely the user responsibility to estimate the require amount of resources for their job running in a public cloud. This thesis presents a Hadoop performance model that accurately estimates the execution duration of a job and further provisions the required amount of resources for a job to be completed within a deadline. The proposed model employs Locally Weighted Linear Regression (LWLR) model to estimate execution time of a job and Lagrange Multiplier technique for resource provisioning to satisfy user job with a given deadline. The performance of the propose model is extensively evaluated in both in-house Hadoop cluster and Amazon EC2 Cloud. Experimental results show that the proposed model is highly accurate in job execution estimation and jobs are completed within the required deadlines following on the resource provisioning scheme of the proposed model. In addition, the Hadoop framework has over 190 configuration parameters and some of them have significant effects on the performance of a Hadoop job. Manually setting the optimum values for these parameters is a challenging task and also a time consuming process. This thesis presents optimization works that enhances the performance of Hadoop by automatically tuning its parameter values. It employs Gene Expression Programming (GEP) technique to build an objective function that represents the performance of a job and the correlation among the configuration parameters. For the purpose of optimization, Particle Swarm Optimization (PSO) is employed to find automatically an optimal or a near optimal configuration settings. The performance of the proposed work is intensively evaluated on a Hadoop cluster and the experimental results show that the proposed work enhances the performance of Hadoop significantly compared with the default settings.Abdul Wali Khan University Marda

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure

Enhanced face detection framework based on skin color and false alarm rejection

Fast and precise face detection is a challenging task in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as recognition tracking, and image database management. In the applications, face objects often come from an inconsequential part of images that contain variations namely different illumination, pose, and occlusion. These variations can decrease face detection rate noticeably. Besides that, detection time is an important factor, especially in real time systems. Most existing face detection approaches are not accurate as they have not been able to resolve unstructured images due to large appearance variations and can only detect human face under one particular variation. Existing frameworks of face detection need enhancement to detect human face under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework was proposed to improve detection rate based on skin color and provide a validity process. A preliminary segmentation of input images based on skin color can significantly reduce search space and accelerate the procedure of human face detection. The main detection process is based on Haar-like features and Adaboost algorithm. A validity process is introduced to reject non-face objects, which may be selected during a face detection process. The validity process is based on a two-stage Extended Local Binary Patterns. Experimental results on CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. As a conclusion, the proposed enhanced face detection framework in color images with the presence of varying lighting conditions and under different poses has resulted in high detection rate and reducing overall detection time

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms