scholarly journals Estimation Accuracy on Execution Time of Run-Time Tasks in a Heterogeneous Distributed Environment

Sensors ◽  
2016 ◽  
Vol 16 (9) ◽  
pp. 1386 ◽  
Author(s):  
Qi Liu ◽  
Weidong Cai ◽  
Dandan Jin ◽  
Jian Shen ◽  
Zhangjie Fu ◽  
...  
Keyword(s):  
Execution Time ◽  
Estimation Accuracy ◽  
Distributed Environment ◽  
Run Time

Dynamic Service Identification in A Distributed Environment

1999 ◽  
Vol 3 (5) ◽  
pp. 401-408 ◽  
Author(s):  
E.Damiani ◽  
◽  
M.G.Fugini ◽  
Keyword(s):  
Data Model ◽  
Fuzzy Data ◽  
Query Execution ◽  
Functional Information ◽  
Distributed Environment ◽  
System A ◽  
Service Identification ◽  
Fuzzy Query ◽  
Run Time

Dynamic invocation of services in O-O execution environments requires identification of server objects which are "fit" to a certain task on the basis of available functional and non-functional information. CORBA-compliant environments store such information in a Trader system which can be browsed or queried by client objects. In this paper a fuzzy data model is proposed as the basis of the design of a Trader system. A fuzzy query algebra is described allowing for deriving operator definitions (and, therefore, query execution mechanisms) at run time, on the basis of user-selected semantics.

scholarly journals Performance evaluation of MapReduce using full virtualisation on a departmental cloud

2011 ◽  
Vol 21 (2) ◽  
pp. 275-284 ◽  
Author(s):  
Horacio González-Vélez ◽  
Maryam Kontagora
Keyword(s):  
Parallel Computing ◽  
Performance Evaluation ◽  
Execution Time ◽  
Programming Model ◽  
Peak Performance ◽  
Distributed Environment ◽  
Maximum Peak ◽  
Distributed Parallel Computing

Performance evaluation of MapReduce using full virtualisation on a departmental cloudThis work analyses the performance of Hadoop, an implementation of the MapReduce programming model for distributed parallel computing, executing on a virtualisation environment comprised of 1+16 nodes running the VMWare workstation software. A set of experiments using the standard Hadoop benchmarks has been designed in order to determine whether or not significant reductions in the execution time of computations are experienced when using Hadoop on this virtualisation platform on a departmental cloud. Our findings indicate that a significant decrease in computing times is observed under these conditions. They also highlight how overheads and virtualisation in a distributed environment hinder the possibility of achieving the maximum (peak) performance.

scholarly journals A Design-Time/Run-Time Application Mapping Methodology for Predictable Execution Time in MPSoCs

2018 ◽  
Vol 17 (5) ◽  
pp. 1-25 ◽  
Author(s):  
Andreas Weichslgartner ◽  
Stefan Wildermann ◽  
Deepak Gangadharan ◽  
Michael Glaß ◽  
Jürgen Teich
Keyword(s):  
Execution Time ◽  
Application Mapping ◽  
Design Time ◽  
Run Time

Colored Petri Nets Based Fault Diagnosis in Service Oriented Architecture

2018 ◽  
Vol 15 (4) ◽  
pp. 1-28 ◽  
Author(s):  
Guru Prasad Bhandari ◽  
Ratneshwer Gupta ◽  
Satyanshu K. Upadhyay
Keyword(s):  
Fault Diagnosis ◽  
Petri Nets ◽  
Execution Time ◽  
Service Oriented Architecture ◽  
Service Providers ◽  
Black Box ◽  
Colored Petri Nets ◽  
Service Oriented ◽  
Run Time ◽  
Software Services

Diagnosing faults in a service-oriented architecture (SOA) is a difficult task due to limited accessibility of software services. Probabilistic approaches of diagnostic faults may be insufficient due to the black-box nature of services. In SOA, software services may be obtained by different service providers and get composed at run-time. This is the reason why there are diagnosis faults at execution time, and is a costly affair. The authors have demonstrated a Color Petri Nets (CPN)-based approach to model different faults that may occur at execution time. Some heuristics are proposed to diagnose faults from the CPN modeling. CPN behavioral properties have also been used for fault diagnosis. The model may be helpful for dependability enhancement of an SOA-based systems.

scholarly journals Uniform Sampling Methodology to Construct Projection Matrices for Angle-of-Arrival Estimation Applications

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1386 ◽  
Author(s):  
Mohammed A. G. Al-Sadoon ◽  
Marcus de Ree ◽  
Raed A. Abd-Alhameed ◽  
Peter S. Excell
Keyword(s):  
Covariance Matrix ◽  
Execution Time ◽  
Degrees Of Freedom ◽  
Signal To Noise Ratio ◽  
Low Complexity ◽  
Projection Matrix ◽  
Estimation Accuracy ◽  
Time Signal ◽  
Angle Of Arrival ◽  
Uniform Sampling

This manuscript firstly proposes a reduced size, low-complexity Angle of Arrival (AoA) approach, called Reduced Uniform Projection Matrix (RUPM). The RUPM method applies a Uniform Sampling Matrix (USM) criterion to sample certain columns from the obtained covariance matrix in order to efficiently find the directions of the incident signals on an antenna array. The USM methodology is applied to reduce the dependency between the adjacent sampled columns within a covariance matrix; then, the sampled matrix is used to construct the projection matrix. The size of the obtained projection matrix is reduced to minimise the computational complexity in the searching grid stage. A theoretical analysis is presented to demonstrate that the USM methodology can increase the Degrees of Freedom (DOFs) with the same aperture size and number of sampled columns compared to the classical sampling criterion. Then, a polynomial root is constructed as an alternative efficient computational solution of the UPM method in a one-dimensional (1D) array spectrum peak searching problem. It is found that this distribution increases the number of produced nulls and enhances noise immunity. The advantage of the RUPM method is that it is appropriate to apply for any array configuration while the Root-UPM offers better estimation accuracy with less execution time under a uniform linear array condition. A computer simulation based on various scenarios is performed to demonstrate the theoretical claims. The proposed direction-finding methods are compared with several AoA methods in terms of the required execution time, Signal-to-Noise Ratio (SNR) and different numbers of data measurements. The results verify that the new methods can achieve significantly better performance with reduced computational demands.

scholarly journals Self-Adaptive Run-Time Variable Floating-Point Precision for Iterative Algorithms: A Joint HW/SW Approach

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2209
Author(s):  
Noureddine Ait Said ◽  
Mounir Benabdenbi ◽  
Katell Morin-Allory
Keyword(s):  
Power Consumption ◽  
Execution Time ◽  
Energy Savings ◽  
Iterative Algorithms ◽  
Time Variable ◽  
Floating Point ◽  
Double Precision ◽  
Time Saving ◽  
Variable Precision ◽  
Run Time

Using standard Floating-Point (FP) formats for computation leads to significant hardware overhead since these formats are over-designed for error-resilient workloads such as iterative algorithms. Hence, hardware FP Unit (FPU) architectures need run-time variable precision capabilities. In this work, we propose a new method and an FPU architecture that enable designers to dynamically tune FP computations’ precision automatically at run-time called Variable Precision in Time (VPT), leading to significant power consumption, execution time, and energy savings. In spite of its circuit area overhead, the proposed approach simplifies the integration of variable precision in existing software workloads at any level of the software stack (OS, RTOS, or application-level): it only requires lightweight software support and solely relies on traditional assembly instructions, without the need for a specialized compiler or custom instructions. We apply the technique on the Jacobi and the Gauss–Seidel iterative methods taking full advantage of the suggested FPU. For each algorithm, two modified versions are proposed: a conservative version and a relaxed one. Both algorithms are analyzed and compared statistically to understand the effects of VPT on iterative applications. The implementations demonstrate up to 70.67% power consumption saving, up to 59.80% execution time saving, and up to 88.20% total energy saving w.r.t the reference double precision implementation, and with no accuracy loss.

scholarly journals Flexible Mixed-Criticality Scheduling with Dynamic Slack Management

2021 ◽  
Vol 30 (10) ◽  
pp. 2150306
Author(s):  
Xinyang Dong ◽  
Gang Chen ◽  
Mingsong Lv ◽  
Weiguang Pang ◽  
Wang Yi
Keyword(s):  
Quality Of Service ◽  
Execution Time ◽  
Time Behavior ◽  
Task Execution ◽  
Early Completion ◽  
Research Attention ◽  
The Past ◽  
Run Time ◽  
Mixed Criticality

Mixed-criticality (MC) system has attracted a lot of research attention in the past few years for its resource efficiency. Recent work attempted to provide a new MC model, the so-called Flexible Mixed-Criticality (FMC) task model, to relax the pessimistic assumptions in classic MC scheduling. However, in FMC, the behavior of MC tasks is still analyzed in offline stage. The run-time behavior such as dynamic slack has not yet been studied in FMC scheduling framework. In this paper, we present a utilization-based slack scheduling framework for FMC tasks. In particular, we monitor task execution on run time and collect dynamic slacks generated by task early completion. And these slacks can then be used either by high-criticality tasks to reduce mode-switches, or by low-criticality tasks so that less suspensions are triggered with more execution time, and thus quality of service is improved. We evaluate our approach with extensive simulations, and experiment results demonstrate the effectiveness of the proposed approaches.

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