PISCES: An environment for parallel scientific computation

The parallel implementation of scientific computing environment (PISCES) is a project to provide high-level programming environments for parallel MIMD computers. Pisces 1, the first of these environments, is a FORTRAN 77 based environment which runs under the UNIX operating system. The Pisces 1 user programs in Pisces FORTRAN, an extension of FORTRAN 77 for parallel processing. The major emphasis in the Pisces 1 design is in providing a carefully specified virtual machine that defines the run-time environment within which Pisces FORTRAN programs are executed. Each implementation then provides the same virtual machine, regardless of differences in the underlying architecture. The design is intended to be portable to a variety of architectures. Currently Pisces 1 is implemented on a network of Apollo workstations and on a DEC VAX uniprocessor via simulation of the task level parallelism. An implementation for the Flexible Computing Corp. FLEX/32 is under construction. An introduction to the Pisces 1 virtual computer and the FORTRAN 77 extensions is presented. An example of an algorithm for the iterative solution of a system of equations is given. The most notable features of the design are the provision for several granularities of parallelism in programs and the provision of a window mechanism for distributed access to large arrays of data

Analysis and Development of Efficient Task Scheduling Strategies in Heterogeneous Cloud Environment

In recent years, Cloud computing has become the integral part of information technology. Lots of research is being done from academic level to industry level. Cloud computing provides service to the users through internet and other distributed network environment on pay as you use basis and user demand basis. It provides an virtual environment of computing resources which can be utilized by cloud users and cloud applications. Scheduling in cloud systems is one of the biggest challenge. An efficient task scheduler is that which is flexible according to the changing environment of clouds and complexity of the submitted tasks. Efficient use of system and getting highest performance of the system is the primary goal of any task scheduling algorithm. Cloud service providers always struggles with problems such as load balancing, Task completion time and wastage of resources. This thesis basically focuses on Task completion time of tasks submitted to the virtual Machines (VMs). Multiple experiments has been performed in CloudSim 3.0.3 simulation toolkit. All the experimental results have been obtained from CloudSim by using base classes and libraries provided in toolkit. Without using any single physical machine CloudSim library gives an full environment for development and research the different techniques for simulation and modelling. Few most generic task scheduling strategies have been studied for this thesis. Based on the study a new strategy has been proposed. This new strategy is named as SCHFMC algorithm, it’s description and study has been provided in chapter 4. SCHFMC algorithm helps in allocating the tasks to the virtual machines (VMs) with varying processing capacity. It has an efficient way to utilise the full processing power of machine so that system can be active and alive without any failure. This algorithm reduces the total completion time of all tasks submitted to the virtual machines. This algorithm has performed better than generic task scheduling meth

Grid-enabling FIRST: Speeding up simulation applications using WinGrid

The vision of grid computing is to make computational power, storage capacity, data and applications available to users as readily as electricity and other utilities. Grid infrastructures and applications have traditionally been geared towards dedicated, centralized, high performance clusters running on UNIX flavour operating systems (commonly referred to as cluster-based grid computing). This can be contrasted with desktop-based grid computing which refers to the aggregation of non-dedicated, de-centralized, commodity PCs connected through a network and running (mostly) the Microsoft Windowstrade operating system. Large scale adoption of such Windowstrade-based grid infrastructure may be facilitated via grid-enabling existing Windows applications. This paper presents the WinGridtrade approach to grid enabling existing Windowstrade based commercial-off-the-shelf (COTS) simulation packages (CSPs). Through the use of a case study developed in conjunction with Ford Motor Company, the paper demonstrates how experimentation with the CSP Witnesstrade and FIRST can achieve a linear speedup when WinGridtrade is used to harness idle PC computing resources. This, combined with the lessons learned from the case study, has encouraged us to develop the Web service extensions to WinGridtrade. It is hoped that this would facilitate wider acceptance of WinGridtrade among enterprises having stringent security policies in place

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres