P. Boominathan

Bio: P. Boominathan is an academic researcher from VIT University. The author has contributed to research in topics: Virtualization & Cloud computing. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

Comparison Among Cloud Technologies and Cloud Performance

Abstract: The cloud is the best method used for the utilization and organization of data. The cloud provides many resources for us via the internet. There are many technologies used in cloud computing systems; each one uses a different kind of protocols and methods. Many tasks can execute on different servers per second, which cannot execute on their computer. The most popular technologies used in the cloud system are Hadoop, Dryad, and another map reducing framework. Also, there are many tools used to optimize the performance of the cloud system, such as Cap3, HEP, and Cloudburst. This paper reviews in detail the cloud computing system, its used technologies, and the best technologies used with it according to multiple factors and criteria such as the procedure cost, speed cons and pros. Moreover, A comprehensive comparison of the tools used for the utilization of cloud computing systems is presented.

Secure Sharing of Robot and Manufacturing Resources in the Cloud for Research and Development.

Abstract: Cloud systems are nowadays more and more present in industry, being part of Industry 4.0 solutions; this can be seen at production level, in manufacturing systems and in robotics. Cloud services are used to improve and interconnect the manufacturing shop-floor processes with the higher-level enterprise components (enterprise resource planning systems, logistics, supply chains and other operational systems). The paper presents a solution for cloud – manufacturing system integration where the cloud system is used to remotely access and control a manufacturing system for research, development and training purpose. The access to the manufacturing system is configured in the cloud as a service, the connection to the manufacturing system being implemented through a set of virtual machines which are deployed and custom configured in cloud. The paper describes the architecture of the system, the deployment scenarios and presents the limitations and performances of the system obtained during the tests.

Adapting scientific computing algorithms to distributed computing frameworks

Abstract: Scientific computing applies computational methods to solve problems in genetics, biology, material science, chemistry etc., where complex real life processes need to be modeled and simulated or where a large amount of data needs to be analyzed. It is strongly associated with parallel programming and high-performance computing (HPC) as it typically requires utilization of a large amount of computer resources from local clusters and grids to supercomputers. Public clouds can provide these resources on-demand and in real–time but they are often built on commodity hardware and it’s not simple to design applications that can efficiently utilize their resources en masse and in fault tolerant manner. Frameworks based on distributed computing models such as MapReduce can significantly simplify this work by providing near automatic parallelization and fault recovery. Our first research task was to investigate the suitability of Hadoop MapReduce for more complex scientific computing algorithms and to identify what algorithm characteristics affect the parallel efficiency of the results. Hadoop MapReduce could easily handle more simple, embarrassingly parallel algorithms, such as trial division or Monte Carlo methods. However, it had serious issues with more complex and especially iterative algorithms, such as the conjugate gradient method. To be able to exploit MapReduce advantages (such as near automatic parallelization and fault recovery) for more complex algorithms, we proposed and investigated three different approaches. The first approach was to reduce the number of iterations by restructuring the algorithms or using alternative methods that might be less efficient, but would suit the MapReduce model better. The second approach was evaluating alternative MapReduce frameworks (such as Twister, HaLoop or Spark) that are specifically designed for iterative algorithms and analyzing whether they provide the same aforementioned advantages as Hadoop.

High-performance Cloud Computing for Symbolic Computation Domain

Abstract: We investigate the potential benefits of high performance cloud computing (HPCC) for novel application domain namely symbolic computation, and specifically for computational algebra as provided by systems like Maple, Mathematica or GAP. HPCCs potentially offer the computational power of a large number of hosts, flexible configuration, and ease of access to a specialized, high-performance configuration. Computational algebra deals with the symbolic manipulation of mathematical problems, often and many algebraic computations are time consuming and therefore promising candidates for parallelism. However, the nature of these computations is fundamentally different to classic high-performance scientific computation: many are highly dynamic, use complex recursive data structures, exhibit high degrees of irregularity, generate large intermediate data structures and primarily use arbitrary precision scalars rather 1 School of Mathematical and Computer Sciences Heriot-Watt University, Edinburgh, UK. E-mail: i.s.ibrahim@hw.ac.uk 2 School of Mathematical and Computer Sciences Heriot-Watt University, Edinburgh, UK. E-mail: H.W.Loidl@hw.ac.uk 3 School of Computer Science Glasgow University, Glasgow, UK. E-mail: Phil.Trinder@glasgow.ac.uk Article Info: Received : September 23, 2015. Revised : December 2, 2015. Published online : March 1, 2016. 108 High-performance Cloud Computing for Symbolic Computation Domain than floating point values. We present an initial study on how to use existing HPCC frameworks for computational algebra. We port a C+MPI parallel implementation of a representative computational algebra application, the parallel determinisation of a non-deterministic finite state automaton, to an HPCC and evaluate the performance on several cloud infrastructures. The key issies for the HPCC implementation are the efficient management of massive intermediate data structures, up to 1.1TB, and fast access to the file system in order to store such big data structures.

Cloud-Enabled Modeling of Sensor Networks in Educational Settings

Abstract: Interdisciplinarity is an important challenge for nowadays engineering studies and this can be achieved through systematic integration of concepts and technologies. The chapter presents an approach that gives an inner view on conceiving modeling languages with specific applications to sensor networks, supported by configurable tools enabled by cloud. The system is used by students to model the characteristics of the sensors and the network architecture, but also to introduce their extensions through programs that interpret such models. The modeling environment uses Windows as a host operating system and is deployed in a customizable Infrastructure as a Service cloud, based on IBM Service Delivery Manager. For achieving an easily deployable educational solution, a virtual machine is associated with each student who works to accomplish a task for a given laboratory class, or during the entire semester. The provisioning process and experimental results for several test scenarios are also described.