System Level Performance Evaluation of Distributed Embedded Systems

In order to evaluate the feasibility of the distributed embedded systems in different application domains at an early phase, the System Level Performance Evaluation (SLPE) must provide reliable estimates of the nonfunctional properties of the system such as end-to-end delays and packet losses rate. The values of these non-functional properties depend not only on the application layer of the OSI model but also on the technologies residing at the MAC, transport and Physical layers. Therefore, the system level performance evaluation methodology must provide functionally accurate models of the protocols and technologies operating at these layers. After conducting a state of the art survey, it was found that the existing approaches for SLPE are either specialized for a particular domain of systems or apply a particular model of computation (MOC) for modeling the communication and synchronization between the different components of a distributed application. Therefore, these approaches abstract the functionalities of the data-link, Transport and MAC layers by the highly abstract message passing methods employed by the different models of computation. On the other hand, network simulators such as OMNeT++, ns-2 and Opnet do not provide the models for platform components of devices such as processors and memories and totally abstract the application processing by delays obtained via traffic generators. Therefore the system designer is not able to determine the potential impact of an application in terms of utilization of the platform used by the device. Hence, for a system level performance evaluation approach to estimate both the platform utilization and the non-functional properties which are a consequence of the lower layers of OSI models (such as end-to-end delays), it must provide the tools for automatic workload extraction of application workload models at various levels of refinement and functionally correct models of lower layers of OSI model (Transport MAC and Physical layers). Since ABSOLUT is not restricted to a particular domain and also does not depend on any MOC, therefore it was selected for the extension to a system level performance evaluation approach for distributed embedded systems. The models of data-link and Transport layer protocols and automatic workload generation of system calls was not available in ABSOLUT performance evaluation methodology. The, thesis describes the design and modelling of these OSI model layers and automatic workload generation tool for system calls. The tools and models integrated to ABSOLUT methodology were used in a number of case studies. The accuracy of the protocols was compared to network simulators and real systems. The results were 88% accurate for user space code of the application layer and provide an improvement of over 50% as compared to manual models for external libraries and system calls. The ABSOLUT physical layer models were found to be 99.8% accurate when compared to analytical models. The MAC and transport layer models were found to be 70-80% accurate when compared with the same scenarios simulated by ns-2 and OMNeT++ simulators. The bit error rates, frame error probability and packet loss rates show close correlation with the analytical methods .i.e., over 99%, 92% and 80% respectively. Therefore the results of ABSOLUT framework for application layer outperform the results of performance evaluation approaches which employ virtual systems and at the same time provide as accurate estimates of the end-to-end delays and packet loss rate as network simulators. The results of the network simulators also vary in absolute values but they follow the same trend. Therefore, the extensions made to ABSOLUT allow the system designer to identify the potential bottlenecks in the system at different OSI model layers and evaluate the non-functional properties with a high level of accuracy. Also, if the system designer wants to focus entirely on the application layer, different models of computations can be easily instantiated on top of extended ABSOLUT framework to achieve higher simulation speeds as described in the thesis

A Survey on Compiler Autotuning using Machine Learning

Since the mid-1990s, researchers have been trying to use machine-learning based approaches to solve a number of different compiler optimization problems. These techniques primarily enhance the quality of the obtained results and, more importantly, make it feasible to tackle two main compiler optimization problems: optimization selection (choosing which optimizations to apply) and phase-ordering (choosing the order of applying optimizations). The compiler optimization space continues to grow due to the advancement of applications, increasing number of compiler optimizations, and new target architectures. Generic optimization passes in compilers cannot fully leverage newly introduced optimizations and, therefore, cannot keep up with the pace of increasing options. This survey summarizes and classifies the recent advances in using machine learning for the compiler optimization field, particularly on the two major problems of (1) selecting the best optimizations and (2) the phase-ordering of optimizations. The survey highlights the approaches taken so far, the obtained results, the fine-grain classification among different approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated quarterly here (Send me your new published papers to be added in the subsequent version) History: Received November 2016; Revised August 2017; Revised February 2018; Accepted March 2018

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed