Scheduling Independent Parallel Jobs in Cloud Computing: A Survey

The impressive and rapid development of the internet and wireless networks leads to growing of users in the last decade. Therefore, the limited resources of these systems are now more evident than in the past. Cloud computing is the latest technology to handle the limitation of resources for users. Type of jobs play the main role in the design of scheduling algorithms. A job can be run simultaneously by multi-processor called parallel job, while the job can run by a single processor called serial job. In addition, based on dependency of jobs to each other, the jobs can be divided into dependent and independent jobs. Scheduling the independent parallel jobs is one of important challenges in cloud computing. Hence, in this paper, we classified the existing algorithms of scheduling independent parallel jobs into two main categories including Non-Layer and Two-Layer. This division is performed based on the number of jobs running on a processor simultaneously. Furthermore, the existing scheduling algorithms belong to each categories are divided into two subcategories based on their solving techniques including heuristic and metaheuristic. Then, the algorithms belong to each category are described in detail. After that, these algorithms are compared to each other based on their different attributes. Our analysis show that the existing Two-Layer scheduling algorithms focus on cost parameter to increase the performance of scheduling algorithms by reducing the waste time of CPU through simultaneous assigning more than one job to each physical machine, while Non-Layer scheduling algorithms didn't pay attention to this issue and only employ techniques to manage the scheduling queue in order to improve the different parameters such as cost, energy, load balancing and deadline

Simulació i modelat d'entorns cloud per a experimentació amb machine learning

Nowadays Cloud computing has emerged as one of the most promising computer paradigms. The idea of selling software as a service has promoted IT enterprises to bet for this new paradigm. Cloud computing aims to power the next generation data centers not only offering software as a service but virtual services like hardware, data storage capacity or application logic. The increasing use of Cloud-based applications will also increase the power dedicated to the data centers that support this Clouds. Research in Cloud computing requires solutions that have to be tested in real environments. It is difficult and expensive to set up suitable test-beds for large scale cluster applications. Simulation can fulfill the needs that we find in Cloud computing experimentation. A large data center simulator can save lots of time and effort in Cloud investigation. This project presents the design and development process of some extensions to an existing virtualized data center simulator for Cloud computing research. It is able to reproduce the behaviour of a real Cloud framework and the information that it offers of the execution makes it suitable for testing and investigation purposes. The final idea of this project was to extend the heterogeneity of the tests that can be run by the simulator to use it as a test-bed for machine learning experimentation

Power Analysis and Optimization Techniques for Energy Efficient Computer Systems

Reducing power consumption has become a major challenge in the design and operation of to-day’s computer systems. This chapter describes different techniques addressing this challenge at different levels of system hardware, such as CPU, memory, and internal interconnection network, as well as at different levels of software components, such as compiler, operating system and user applications. These techniques can be broadly categorized into two types: Design time power analysis versus run-time dynamic power management. Mechanisms in the first category use ana-lytical energy models that are integrated into existing simulators to measure the system’s power consumption and thus help engineers to test power-conscious hardware and software during de-sign time. On the other hand, dynamic power management techniques are applied during run-time, and are used to monitor system workload and adapt the system’s behavior dynamically to save energy

Algorithms and architectures for the multirate additive synthesis of musical tones

In classical Additive Synthesis (AS), the output signal is the sum of a large number of independently controllable sinusoidal partials. The advantages of AS for music synthesis are well known as is the high computational cost. This thesis is concerned with the computational optimisation of AS by multirate DSP techniques. In note-based music synthesis, the expected bounds of the frequency trajectory of each partial in a finite lifecycle tone determine critical time-invariant partial-specific sample rates which are lower than the conventional rate (in excess of 40kHz) resulting in computational savings. Scheduling and interpolation (to suppress quantisation noise) for many sample rates is required, leading to the concept of Multirate Additive Synthesis (MAS) where these overheads are minimised by synthesis filterbanks which quantise the set of available sample rates. Alternative AS optimisations are also appraised. It is shown that a hierarchical interpretation of the QMF filterbank preserves AS generality and permits efficient context-specific adaptation of computation to required note dynamics. Practical QMF implementation and the modifications necessary for MAS are discussed. QMF transition widths can be logically excluded from the MAS paradigm, at a cost. Therefore a novel filterbank is evaluated where transition widths are physically excluded. Benchmarking of a hypothetical orchestral synthesis application provides a tentative quantitative analysis of the performance improvement of MAS over AS. The mapping of MAS into VLSI is opened by a review of sine computation techniques. Then the functional specification and high-level design of a conceptual MAS Coprocessor (MASC) is developed which functions with high autonomy in a loosely-coupled master- slave configuration with a Host CPU which executes filterbanks in software. Standard hardware optimisation techniques are used, such as pipelining, based upon the principle of an application-specific memory hierarchy which maximises MASC throughput

Improvements in the Scalability of the NASA Goddard Multiscale Modeling Framework for Hurricane Climate Studies

Improving our understanding of hurricane inter-annual variability and the impact of climate change (e.g., doubling CO2 and/or global warming) on hurricanes brings both scientific and computational challenges to researchers. As hurricane dynamics involves multiscale interactions among synoptic-scale flows, mesoscale vortices, and small-scale cloud motions, an ideal numerical model suitable for hurricane studies should demonstrate its capabilities in simulating these interactions. The newly-developed multiscale modeling framework (MMF, Tao et al., 2007) and the substantial computing power by the NASA Columbia supercomputer show promise in pursuing the related studies, as the MMF inherits the advantages of two NASA state-of-the-art modeling components: the GEOS4/fvGCM and 2D GCEs. This article focuses on the computational issues and proposes a revised methodology to improve the MMF's performance and scalability. It is shown that this prototype implementation enables 12-fold performance improvements with 364 CPUs, thereby making it more feasible to study hurricane climate

Parallel solution of power system linear equations

At the heart of many power system computations lies the solution of a large sparse set of linear equations. These equations arise from the modelling of the network and are the cause of a computational bottleneck in power system analysis applications. Efficient sequential techniques have been developed to solve these equations but the solution is still too slow for applications such as real-time dynamic simulation and on-line security analysis. Parallel computing techniques have been explored in the attempt to find faster solutions but the methods developed to date have not efficiently exploited the full power of parallel processing. This thesis considers the solution of the linear network equations encountered in power system computations. Based on the insight provided by the elimination tree, it is proposed that a novel matrix structure is adopted to allow the exploitation of parallelism which exists within the cutset of a typical parallel solution. Using this matrix structure it is possible to reduce the size of the sequential part of the problem and to increase the speed and efficiency of typical LU-based parallel solution. A method for transforming the admittance matrix into the required form is presented along with network partitioning and load balancing techniques. Sequential solution techniques are considered and existing parallel methods are surveyed to determine their strengths and weaknesses. Combining the benefits of existing solutions with the new matrix structure allows an improved LU-based parallel solution to be derived. A simulation of the improved LU solution is used to show the improvements in performance over a standard LU-based solution that result from the adoption of the new techniques. The results of a multiprocessor implementation of the method are presented and the new method is shown to have a better performance than existing methods for distributed memory multiprocessors

3D parallel computations of turbofan noise propagation using a spectral element method

A three-dimensional code has been developed for the simulation of tone noise generated by turbofan engine inlets using computational aeroacoustics. The governing equations are the linearized Euler equations, which are further simplified to a set of equations in terms of acoustic potential, using the irrotational flow assumption, and subsequently solved in the frequency domain.Due to the special nature of acoustic wave propagation, the spatial discretization is performed using a spectral element method, where a tensor product of the nth-degree polynomials based on Chebyshev orthogonal functions is used to approximate variations within hexahedral elements. Non-reflecting boundary conditions are imposed at the far-field using a damping layer concept. This is done by augmenting the continuity equation with an additional term without modifying the governing equations as in PML methods.Solution of the linear system of equations for the acoustic problem is based on the Schur complement method, which is a nonoverlapping domain decomposition technique. The Schur matrix is first solved using a matrix-free iterative method, whose convergence is accelerated with a novel local preconditioner. The solution in the entire domain is then obtained by finding solutions in smaller subdomains.The 3D code also contains a mean flow solver based on the full potential equation in order to take into account the effects of flow variations around the nacelle on the scattering of the radiated sound field.All aspects of numerical simulations, including building and assembling the coefficient matrices, implementation of the Schur complement method, and solution of the system of equations for both the acoustic and mean flow problems are performed on multiprocessors in parallel using the resources of the CLUMEQ Supercomputer Center. A large number of test cases are presented, ranging in size from 100 000-2 000 000 unknowns for which, depending on the size of the problem, between 8-48 CPU's are used.The developed code is demonstrated to be robust and efficient in simulating acoustic propagation for a large number of problems, with an excellent parallel performance