Konstantinos M. Giannoutakis

CLOUDLIGHTNING: A Framework for a Self-organising and Self-managing Heterogeneous Cloud

As clouds increase in size and as machines of different types are added to the infrastructure in order to maximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting different architectures poses significant challenges. To efficiently access heterogeneous resources and, at the same time, to exploit these resources to reduce application development effort, to make optimisations easier and to simplify service deployment, requires a re-evaluation of our approach to service delivery. We propose a novel cloud management and delivery architecture based on the principles of self-organisation and self-management that shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. Our goal is to address inefficient use of resources and consequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems particularly in mind. The framework is general but also endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service provision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.

Solving finite difference linear systems on GPUs: CUDA based Parallel Explicit Preconditioned Biconjugate Conjugate Gradient type Methods

During the last decades, explicit approximate inverse preconditioning methods have been used for efficiently solving sparse linear systems on multiprocessor systems. The effectiveness of explicit approximate inverse preconditioning schemes relies on the use of efficient preconditioners that are close approximants to the coefficient matrix and are fast to compute in parallel. A new parallel computational technique is proposed for the parallelization of the explicit preconditioned conjugate gradient type method on a Graphics Processing Unit (GPU). The proposed parallel methods have been implemented using Compute Unified Device Architecture (CUDA) developed by NVIDIA. The inherently parallel operations between vectors and matrices involved in the explicit preconditioned biconjugate conjugate gradient type schemes exhibit significant amounts of loop-level parallelism because of the matrix–vector and the vector–vector products that can lead to high performance gain on the GPU systems, specifically designed for such computations. Finally, numerical results for the performance of the explicit preconditioned biconjugate conjugate gradient type method for solving characteristic two dimensional boundary value problems, using the finite difference method, on a massive multiprocessor interface on a GPU are presented. The CUDA implementation issues of the proposed method are also discussed.

High performance finite element approximate inverse preconditioning

Abstract A new parallel normalized optimized approximate inverse algorithm, based on the concept of the “fish bone” computational approach satisfying an antidiagonal data dependency, for computing classes of explicit approximate inverses, is introduced for symmetric multiprocessor systems. The parallel normalized explicit approximate inverses are used in conjunction with parallel normalized explicit preconditioned conjugate gradient square schemes, for the efficient solution of finite element sparse linear systems. The parallel design and implementation issues of the new proposed algorithms are discussed and the parallel performance is presented, using OpenMP.

Energy modeling in cloud simulation frameworks

Abstract There is a quite intensive research for Cloud simulators in the recent years, mainly due to the fact that the need for powerful computational resources has led organizations to use cloud resources instead of acquiring and maintaining private servers. In order to test and optimize the strategies that are being used on cloud resources, cloud simulators have been developed since the simulation cost is substantially smaller than experimenting on real cloud environments. Several cloud simulation frameworks have been proposed during the last years, focusing on various components of the cloud resources. In this paper, a survey on cloud simulators is conducted, in order to examine the different models that have been used for the hardware components that constitute a cloud data center. Focus is given on the energy models that have been proposed for the prediction of the energy consumption of data center components, such as CPU, memory, storage and network, while experiments are performed in order to compare the different power models used by the simulation frameworks. The following cloud simulation frameworks are considered: CloudSched, CloudSim, DCSim, GDCSim, GreenCloud and iCanCloud.

On the performance of parallel approximate inverse preconditioning using Java multithreading techniques

In this paper a parallel shared memory Java multithreaded design and implementation of the explicit approximate inverse preconditioning is presented for solving efficiently arrow-type linear systems on symmetric multiprocessor systems. A new parallel algorithm for computing a class of optimized approximate inverse matrix is introduced. The performance on a symmetric multiprocessor system, using Java multithreading, is investigated by solving characteristic arrow-type linear systems and numerical results are given, considering the parallel performance of the construction of the optimized approximate inverse and the explicit preconditioned generalized conjugate gradient square scheme.

On the rate of convergence and complexity of finite element normalized explicit approximate inverse preconditioning

Publisher Summary This chapter introduces recent normalized approximate inverse finite-element matrix techniques, which is based on normalized approximate factorization procedures. Explicit preconditioned conjugate gradient schemes in conjunction with normalized approximate inverses are presented for the efficient solution of sparse systems. Theoretical results on the rate of convergence of the normalized explicit preconditioned conjugate gradient scheme and estimates of the required computational work are also presented.

Parallel approximate finite element inverse preconditioning on distributed systems

New parallel Normalized Explicit Preconditioned Conjugate Gradient-type methods are introduced for solving finite element systems on distributed memory MIMD systems. The performance and applicability of the proposed methods implemented in message passing interface, is discussed by solving a characteristic two dimensional boundary value problem and numerical results are given.

A Two-Phase Cyclic Nonhomogeneous Markov Chain Performability Evaluation by Explicit Approximate Inverses Applied to a Replicated Database System

The need for a more accurate modeling of the performance of systems whose functioning mainly dependant on external time parameters such as the number of requests during a particular time phase, led us to a novel approach, taking into account the time parameters involved. This is achieved through the evaluation of a performability indicator modeled by means of a two-phase cyclic nonhomogenous Markov chain considering periodical time-dependant arrival request probabilities and applied to a replicated database system. The computation of the performability indicator modeled by cyclic nonhomogeneous Markov chain requires the use of efficient computational methods by using explicit approximate inverse preconditioning methods.

Parallel and systolic solution of normalized explicit approximate inverse preconditioning

A new class of normalized approximate inverse matrix techniques, based on the concept of sparse normalized approximate factorization procedures are introduced for solving sparse linear systems derived from the finite difference discretization of partial differential equations. Normalized explicit preconditioned conjugate gradient type methods in conjunction with normalized approximate inverse matrix techniques are presented for the efficient solution of sparse linear systems. Theoretical results on the rate of convergence of the normalized explicit preconditioned conjugate gradient scheme and estimates of the required computational work are presented. Application of the new proposed methods on two dimensional initial/boundary value problems is discussed and numerical results are given. The parallel and systolic implementation of the dominant computational part is also investigated.

An ontology-based mechanism for automatic categorization of web services

The addition of semantic information into Web services (WS) results in more accurate search and retrieval in service registries. The key issue to facilitate organization of services, taking into account their semantics, is the development of automatic mechanisms that generate appropriate mappings between Web service elements and their semantics‐enabled counterparts. In this paper, we introduce an ontology‐based mechanism for automatic semantic categorization of WS and their structural components. The presented approach, as opposed to similar ones, takes into account the lexicographic, structural, and data type characteristics of WS. Moreover, a software tool that implements the proposed service categorization mechanism is presented, and a benchmark process is executed that reveals outstanding performance of the developed mechanism in comparison with a relevant state‐of‐the‐art approach. Copyright