Execution Time Of Process Research Articles

A Real-Time Performance Monitoring Tool for Dual Redundant and Resource Augmented Framework of Cruise Control System

The computing resources used in safety-critical systems have stringent timing requirements due to mission critical nature of their tasks. A fault in these systems could lead to mission failure and catastrophic consequences. To avoid this various redundancy schemes are built in to mission critical applications to ensure the overall success of the system. The usual industrial practice is to employ fault tolerance using hardware redundancy where costs are highly exorbitant depending on the mission. In this paper, a prototype tool has been designed and developed for testing and evaluation of a framework for adaptive fault tolerance on an existing dual hardware redundancy with resource augmentation. This proposed model gives enhanced resource management and improved system performance under normal runtime and provides minimal safe functionality under permanent fault condition. It has been implemented with a practical case study of Cruise Control System using NXP LPC2148 processors. The results demonstrate the better performance and process speedup (execution time of process) vis-a-vis over a traditional dual redundant processor system and the high performance that can accrue by applying this model to an m-processor redundancy model.

Decentralized Enactment of BPEL Processes

This article presents ${\ssr BPELcube}$ , a framework comprising a scalable architecture and a set of distributed algorithms, which support the decentralized enactment of BPEL processes. In many application domains, BPEL processes are long-running, involve the exchange of voluminous data with external Web services, and are concurrently accessed by large numbers of users. In such context, centralized BPEL process execution engines pose considerable limitations in terms of scalability and performance. To overcome such problems, a scalable hypercube peer-to-peer topology is employed by ${\ssr BPELcube}$ in order to organize an arbitrary number of nodes, which can then collaborate in the decentralized execution and monitoring of BPEL processes. Contrary to traditional clustering approaches, each node does not fully take charge of executing the whole process; rather, it contributes to the overall process execution by running a subset of the process activities and maintaining a subset of the process variables. Hence, the hypercube-based infrastructure acts as a single execution engine, where workload is evenly distributed among the participating nodes in a fine-grained manner. An experimental evaluation of ${ \ssr BPELcube}$ and a comparison with centralized and clustered BPEL engine architectures demonstrate that the decentralized approach yields improved process execution times and throughput.

Agent-Based Distributed Computing for Dynamic Networks

Dynamic networks of personal computers are characterized by sudden, unexpected failures of computational nodes, as well as the addition of new computers at any given moment. This paper presents a new agent-oriented system for distributed computing, named ADiS, specifically designed for operating in these kinds of environments. The system relies on the agent technology in order to adapt to dynamic changes in a computational net-work. Originally envisioned as a distributed system for distance matrix calculations, ADiS has recently been redesigned as a general-purpose, extensible architecture for arbitrary computing in a distributed environment. Experimental results demonstrate that the usage of ADiS has a significant positive impact onto the execution time of lengthy processes. DOI: http://dx.doi.org/10.5755/j01.itc.43.1.4588

Design, Analysis and Implementation of Improved Adaptive Fault Tolerant Model for Cruise Control Multiprocessor System

or mission critical applications have to recover from an error within an acceptable time window or it may potentially lead to disastrous effects or higher costs. The usual industrial practice is to employ fault tolerance using hardware redundancy where costs are highly exorbitant depending on the mission. In this paper, we present a framework for adaptive fault tolerance on the commonly used hardware redundancy. This proposed model gives enhanced resource management and improved system performance under normal runtime and provides minimal safe functionality under error conditions. A new scheduling method, a combination of dynamic planning and dynamic best effort approach has been designed for joint scheduling of periodic and aperiodic tasks which also include online reconfiguration for error management. This fault recovery technique allows all critical tasks to meet their deadlines and the system continues functioning with minimal safe functionality upon errors. This model has been analyzed and evaluated on a practical case study of a Cruise Control System vis- a-vis a traditional redundancy scheme with simulation and validated with appropriate performance metrics. The results demonstrate the high performance throughput and process speedup (Execution time of process) that can be gained by applying this model to an m-processor redundancy model and the advantages can be accrued specially in the field of avionics in terms of fuel/weight ratio.

Solving Multidimensional 0-1 Knapsack Problem with Time-Free Tissue P Systems

Tissue P system is a class of parallel and distributed model; a feature of traditional tissue P system is that the execution time of certain biological processes is very sensitive to environmental factors that might be hard to control. In this work, we construct a family of tissue P systems that works independently from the values associated with the execution times of the rules. Furthermore, we present a time-free efficient solution to multidimensional 0-1 knapsack problem by timed recognizer tissue P systems.

Improved Stochastic Random Walker Segmentation based on Gaussian Filtering

Image segmentation is the process to capture the object from the background and it is a difficult task when a vision of the object is in stochastic region. Here introduce in this paper extension of stochastic random walker segmentation method. In stochastic random walker segmentation, a weighted graph is built from the image, where the each pixel considered as a node and edge weights depend on the image gradient between the pixels. For given seed regions, the probability are evaluated for a stochastic random walk on this graph starting at a pixel to end in one of the seed regions. The problem associated with existing method is the number of random variable (gray-level value in random order) in stochastic images. These random variables increase the graph sizes of stochastic images. If the graph size will increase, consequently execution time would also increase. To overcome these problems, the proposed “Improved stochastic random walker segmentation based on Gaussian filtering” for stochastic image segmentation. In proposed method Gaussian filter has been used for the removal of uncertain gray level and which in turn reduce the noise level and the resultant graph size of corresponding stochastic image, then apply stochastic random walker segmentation method which may help to decrease the execution time of the segmentation process.

Energy-Efficient Redundant Execution of Processes in a Fault-Tolerant Cluster of Servers

This paper investigates into fault tolerance of cluster of servers and their energy efficiency to realize a reliable and energy aware server cluster system. A client issues a request to one server in a server cluster and the server sends a reply to the client in information systems. Once the server stops by fault, the client does not receive a reply of the request. Even if the request is performed on another server on detection of fault of the server, some QoS requirements like response time may not be satisfied. Hence, each request has to be redundantly performed on multiple servers to be tolerant of server faults. The redundant power consumption laxity-based (RPCLB) algorithm is discussed where multiple servers are selected to redundantly and energy-efficiently perform a request process in our previous studies. Since each application process is redundantly performed on more than one server, the larger amount of electric power is consumed. In this paper, we propose a novel and improved RPCLB (IRPCLB) algorithm to reduce the power consumption of servers, where once a process successfully terminates on one server, meaningless redundant processes are forced to terminate on the other servers. In the evaluation, we show the total power consumption of servers and total execution time of processes are reduced in homogeneous and heterogeneous types of clusters by the IRPCLB algorithm than the RPCLB and RR algorithms.

Power consumption and processing models of servers in computation and storage based applications

The electric power consumption of information systems has to be reduced in addition to increasing the performance and fault tolerance in order to realize green societies. Here, servers mainly consume electric power to perform processes requested by clients compared with clients. We consider computation (CP) and storage (ST) types of application processes on servers. In CP processes, CPU is mainly consumed as in scientific computation. In ST processes, files in storage drives are mainly manipulated. First, we present the storage and computation based power consumption (SCBPC) model of a server to perform ST and CP processes. In the SCBPC model, the power consumption rate of a server depends on the types of processes concurrently performed and is independent of the number of processes. Then, we discuss the storage and computation based processing (SCBP) model to perform CP and ST processes on a server. Here, the execution times of a CP process and an ST process depend on the number of CP processes and the number of CP and ST processes concurrently performed, respectively. By using the SCBPC and SCBP models, we discuss an energy-aware (EA) algorithm to select a server for a process so that not only the execution time but also the power consumption can be reduced. We evaluate the EA algorithm in terms of total power consumption of servers and total execution time of processes. We show not only the total power consumption but also the total execution time can be reduced in the EA algorithm.

Modifications in Lamport Algorithm for Distributed Computing System

the current scenario, distributed approach of computing is very popular over the centralized approach of computing due to faster execution of processes; cut off the execution time of processes and cost. In the year 1978, Lamport (6) has proposed an approach for synchronization of processes under distributed environment which has the limitations for reordering and executing the events of the processes by using time, ordering of events and clock conditions. The important limitation is that the algorithm does not cover the process execution in reflexive, symmetric and transitive manners when the unidirectional or bidirectional ring is appearing in the distributed network for executing the processes and sharing the common resources under distributed environment. The present work will focus on these aspects and processor can execute the events of processes either on its node called as computer system in the reflexive manner and if the current node is busy for other tasks then it can use the next promising node under the defined topology and can be executed by using symmetric property and if further second node is busy then events can be transmitted to next promising node and these are executed in the transitive manner and the output is transferred to the first node. These aspects are demonstrated by proposing a new kind of topology called as step topology in which numbers of computer systems are attached in the distributed network. Algorithms are designed for all these three cases by considering the definitions of process and thread. Since one computer system can interact with another computer system with message passing technique under distributed environment, therefore, message complexities in all these cases are also measured and compared with Lamport and other similar kinds of algorithms available for distributed computing system.

Dynamic Data-Driven Genetic Algorithm for forest fire spread prediction

This work represents the first step towards a Dynamic Data-Driven Application System (DDDAS) for wildland fire prediction. Our main efforts are focused on taking advantage of the computing power provided by High Performance Computing systems and to propose computational data-driven steering strategies to overcome input data uncertainty. In doing so, prediction quality can be enhanced significantly. On the other hand, these proposals reduce the execution time of the overall prediction process in order to be of use during real-time crisis. In particular, this work describes a Dynamic Data-Driven Genetic Algorithm (DDDGA) used as steering strategy to automatically adjust highly dynamic input data values of forest fire simulators taking into account the underlying propagation model and real fire behaviour.

THE DEVELOPMENT OF THE QUALITY MANAGEMENT SYSTEM IN TERMS OF KNOWLEDGE MANAGEMENT / KOKYBĖS VADYBOS SISTEMŲ TOBULINIMAS ŽINIŲ VADYBOS ASPEKTU

The interaction of quality management and knowledge man­agement are analyzed quite widely in scientific literature and discussed with reference to various models integrating these two management theories. However, there are not enough precise tools that could provide a possibility of improving the quality of products and services through knowledge management. To this end, the article examines the already existing models for the interaction between quality management and knowledge management. The results of a survey on Lithuanian industry show what organizational knowledge is the most important while implementing quality management principles aimed at improving quality. The correlation analysis method showed the relationship between quality management principles (process approach, continual improvement, management, facts) and the factors defining the product and process quality (the extent of determining process execution time, process resources, costs of process stages, indicators for products and/or services, the dura­tion of the process, the extent of measuring indicators, the extent of collecting information on indicators, the extent of collecting information about the costs of the process). Santrauka Kokybės vadybos ir žinių vadybos sąveika mokslinėje literatūroje analizuojama gana plačiai, aptariami įvairūs sąveikos modeliai integruojant šias dvi vadybos teorijas. Tačiau nepakankamai nagrinėjamos konkrečios priemonės, kuriomis būtų galima pagerinti produktų ir paslaugų kokybę pasitelkiant žinių vadybą. Tuo tikslu straipsnyje analizuojami jau egzis­tuojantys kokybės vadybos ir žinių vadybos sąveikos modeliai bei pateikiami Lietuvos pramonės įmonių tyrimo rezultatai, parodantys, kokios organizacinės žinios svarbiausios įgyvendinant kokybės vadybos principus, skirtus kokybei gerinti. Taikant koreliacinės analizės metodą nustatyta, kad egzistuoja ryšys tarp kokybės vadybos principų (procesinio požiūrio, nuolatinio gerinimo, valdymo remiantis faktais) įgyvendinimo lygio ir veiksnių, apibūdinančių produktų ir procesų kokybę (procesų įvykdymo laiko nustatymas, procesų išteklių nustatymas, procesų etapų išlaidų nustatymas, produktų ir (ar) paslaugų rodiklių nustatymas, rodiklių matavimas, rodiklių informacijos rinkimas, informacijos apie proceso trukmę nustatymas ir informacijos apie proceso išlaidas rinkimas).

A multi‐resolution multi‐classifier system for speaker verification

AbstractThis paper describes a Speaker Verification System based on the use of multi resolution classifiers in order to cope with performance degradation due to natural variations of the excitation source and of the vocal tract. The different resolution representations of the speaker are obtained by considering multiple frame lengths in the feature extraction process and from these representations a single Pseudo‐Multi Parallel Branch (P‐MPB) Hidden Markov Model is obtained. In the verification process, different resolution representations of the speech signal are classified by multiple P‐MPB systems: the final decision is obtained by means of different combination techniques. The system based on the Weighted Majority Vote technique considerably outperforms baseline systems: improvements are between 15% and 38%. The execution time of the verification process is also evaluated and it proves to be very acceptable, thus allowing the use of the approach for applications in real time systems.

Dynamic Allocation of CPUs in Multicore Processor for Performance Improvement in Network Security Applications

Problem statement: Multicore and multithreaded CPUs have become the new approach for increase in the performance of the processor based systems. Numerous applications benefit from use of multiple cores. Increasing performance of the system by increasing the number of CPUs of the multicore processor for a given application warrants detailed experimentation. In this study, the results of the experimentation done by dynamic allocation/deallocation of the CPU based on the workload conditions for the packet processing for security application are analyzed and presented. Approach: This evaluation was conducted on SunfireT1000 server having Sun UltraSPARC T1 multicore processor. OpenMP tasking feature is used for scheduling the logical CPUs for the parallelized application. Dynamic allocation of a CPU to a process is done depending on the workload characterization. Results: Execution time for packet processing was analyzed to arrive at an effective dynamic allocation methodology that is dependant on the hardware and the workload. Conclusion/Recommendations: Based on the analysis, the methodology and the allocation of the number of CPUs for the parallelized application are suggested.

Hypergraph Partitioning-Based Fill-Reducing Ordering for Symmetric Matrices

A typical first step of a direct solver for the linear system $Mx=b$ is reordering of the symmetric matrix $M$ to improve execution time and space requirements of the solution process. In this work, we propose a novel nested-dissection-based ordering approach that utilizes hypergraph partitioning. Our approach is based on the formulation of graph partitioning by vertex separator (GPVS) problem as a hypergraph partitioning problem. This new formulation is immune to deficiency of GPVS in a multilevel framework and hence enables better orderings. In matrix terms, our method relies on the existence of a structural factorization of the input $M$ matrix in the form of $M=AA^T$ (or $M=AD^2A^T$). We show that the partitioning of the row-net hypergraph representation of the rectangular matrix $A$ induces a GPVS of the standard graph representation of matrix $M$. In the absence of such factorization, we also propose simple, yet effective structural factorization techniques that are based on finding an edge clique cover of the standard graph representation of matrix $M$, and hence applicable to any arbitrary symmetric matrix $M$. Our experimental evaluation has shown that the proposed method achieves better ordering in comparison to state-of-the-art graph-based ordering tools even for symmetric matrices where structural $M=AA^T$ factorization is not provided as an input. For matrices coming from linear programming problems, our method enables even faster and better orderings.

Parallel ordinary kriging interpolation incorporating automatic variogram fitting

This work introduces a methodology for reducing the execution time of the kriging interpolation method without losing the quality of the model results, as occurs in simplified moving neighborhood solutions. The proposed solution distributes the computation applying parallel programming using MPI (Message Passing Interface) libraries in a HPC (High Performance Computing) environment. For the solution to be automatic and adaptable to different spatial patterns the variogram was automatically fitted; this preliminary modeling step is usually interactive in this interpolation method. The experimental results show the validity of the implemented solution, as it significantly reduces (in one of the examples the execution time decreases from 2 h 38 min to only 3 min) the final execution time of the entire process. The proposed solution is not exclusive to a particular architecture or operating system and can be applied in various environments and spatial resolutions of the generated raster model as well as at different magnitudes of the data to be interpolated.

A distributed service-oriented architecture for business process execution

The Business Process Execution Language (BPEL) standardizes the development of composite enterprise applications that make use of software components exposed as Web services. BPEL processes are currently executed by a centralized orchestration engine, in which issues such as scalability, platform heterogeneity, and division across administrative domains can be difficult to manage. We propose a distributed agent-based orchestration engine in which several lightweight agents execute a portion of the original business process and collaborate in order to execute the complete process. The complete set of standard BPEL activities are supported, and the transformations of several BPEL activities to the agent-based architecture are described. Evaluations of an implementation of this architecture demonstrate that agent-based execution scales better than a non-distributed approach, with at least 70% and 120% improvements in process execution time, and throughput, respectively, even with a large number of concurrent process instances. In addition, the distributed architecture successfully executes large processes that are shown to be infeasible to execute with a nondistributed engine.

Towards a dynamic rule-based business process

Making a business process more dynamic is an open issue, and we think it is feasible if we decompose the business process transitions and activities in a set of rules defined as Event Condition Action (ECA) rules. The goal of a dynamic rule-based business process is to change the implementation of a process instance at runtime. We are proposing a way for representing a Dynamic Business Process in terms of rules based on pattern identification. In this paper, we also discuss characteristics related to business process execution time and effective business process modification support. In particular, we analyse YAWL and Bonita Workflow in order to compare them with our approach and discuss their strengths and weaknesses.

Multi-Source Multi-Sensor Image Fusion Based on Bootstrap Approach and SEM Algorithm

Bootstrap approach and Stochastic EM algorithm combination applied for the improvement of the multisource and multi-sensor image fusion process; was presented in this research. Improvement concerned not only image quality and reducing processing execution time as mentioned in our previous Bootstrap EM algorithm (BEM), but also regarding ini- tialization dependence as well as fixed classes' number. Such interesting fusion algorithm for multisource and multi- sensor image using one stochastic phase, i.e. SEM algorithm, preceded by Bootstrap procedure was successfully imple- mented and tested for several prototype images. Targeted images were firstly split by an unsupervised Bayesian segmenta- tion approach in order to determine a joint region map for the fused image. The Bootstrap approach was then applied to the targeted multisource image in conjunction with the SEM algorithm, forming hence one Bootstrap SEM algorithm called BSEM. The procedure of such algorithm involved both statistical parameters' estimation from one representative Bootstrap sample of each source or sensor images.

A scalable, parallel algorithm for maximal clique enumeration

The problem of maximal clique enumeration (MCE) is to enumerate all of the maximal cliques in a graph. Once enumerated, maximal cliques are widely used to solve problems in areas such as 3-D protein structure alignment, genome mapping, gene expression analysis, and detection of social hierarchies. Even the most efficient serial MCE algorithms require large amounts of time to enumerate the maximal cliques in networks arising from these problems that contain hundreds, thousands, or larger numbers of vertices. The previous attempts to provide practical solutions to the MCE problem through parallel implementation have had limited success, largely due to a number of challenges inherent to the nature of the MCE combinatorial search space. On the one hand, MCE algorithms often create a backtracking search tree that has a highly irregular and hard-or-impossible to predict structure; therefore, almost any static decomposition of the search tree by parallel processors results in highly unbalanced processor execution times. On the other hand, the data-intensive nature of the MCE problem often makes naive dynamic load distribution strategies that require extensive data movement prohibitively expensive. As a result, good scaling of the overall execution time of parallel MCE algorithms has been reported for only up to a couple hundred processors. In this paper, we propose a parallel, scalable, and memory-efficient MCE algorithm for distributed and/or shared memory high performance computing architectures, whose runtime scales linearly for thousands of processors on real-world application graphs with hundreds and thousands of nodes. Its scalability and efficiency are attributed to the proposed: (a) representation of the search tree decomposition to enable parallelization; (b) parallel depth-first backtracking search to both constrain the search space and minimize memory requirement; (c) least stringent synchronization to minimize data movement; and (d) on-demand work stealing intelligently coupled with work stack splitting to minimize computing elements’ idle time. To the best of our knowledge, the proposed parallel MCE algorithm is the first to achieve a linear scaling runtime using up to 2048 processors on Cray XT machines for a number of real-world biological networks.

Artificial intelligence approaches to achieve strategic control over project cash flows

The ability over the course of a construction project to make reliable predictions regarding cash flows enhances project cost management. This paper uses artificial intelligence (AI) approaches to predict cash flow trends for such projects in order to develop appropriate strategies that apply factors such as float, process execution time, construction rate and resource demand to project cash flow control. AI approaches involved in this paper include K-means clustering, genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). K-means clustering is employed to categorize similar projects, while the other approaches are used to develop the Evolutionary Fuzzy Neural Inference Model (EFNIM), a knowledge learning model. FL and NN are employed in the EFNIM to develop a neural-fuzzy model that can deal with uncertainties and knowledge mapping. GA is used to optimize the membership functions of FL and NN parameters globally. The major target of this AI learning is to address sequential cash flow trends. This trained result is furthermore applied to a strategic project cash flow control. This cash flow control affects project performance within the banana envelope of the S-curve for project management.