Execution Time Of Process Research Articles

Business Process Configuration According to Data Dependency Specification

Configuration techniques have been used in several fields, such as the design of business process models. Sometimes these models depend on the data dependencies, being easier to describe what has to be done instead of how. Configuration models enable to use a declarative representation of business processes, deciding the most appropriate work-flow in each case. Unfortunately, data dependencies among the activities and how they can affect the correct execution of the process, has been overlooked in the declarative specifications and configurable systems found in the literature. In order to find the best process configuration for optimizing the execution time of processes according to data dependencies, we propose the use of Constraint Programming paradigm with the aim of obtaining an adaptable imperative model in function of the data dependencies of the activities described declarative.

A New Adaptive Filter Approach for Acoustic Echo Canceller in Teleconference Systems

A flexible Frequency domain Block Recursive Least Squares (FBRLS) algorithm using the Multi-Delay Filter (MDF) is presented throughout this paper. In term of performances, the MDF-FBRLS adaptive filter introduces smaller block delay and is usually faster and suitable for ideal time-varying system such as an acoustic echo in a teleconference room. The implementation of the FBRLS algorithm using MDF adaptive filter allows reducing the FFT size and consequently optimizing the hardware implementation that could be performed using standard DSP chips. These good performances are achieved by using smaller block size and updating frequently the weight vectors which will reduce the total execution time of the adaptive process. Simulation results show that the MDF-FBRLS algorithm is better than the FBRLS algorithm in terms of the total execution time and the efficiency of the computational complexity.

An Improved Page Replacement Algorithm Using Block Retrieval of Pages

The computer programmer write programming codes of any length without keeping in mind the available primary memory. This is possible if we use the concept of virtual memory. As the name suggests, virtual memory is a concept of executing a programming code of any size even having a primary memory of smaller size than the size of program to be executed. The virtual memory can be implemented using the concept of paging. The operating system allocates a number of memory frames to each program while loading into the memory. The programming code is equally divided into pages of same size as frame size. The size of pages and memory frames are retained equal for the better utilization of the memory. During the execution of program, every process is allocated limited number of memory frames; hence there is a need of page replacements. To overcome this limitation, a number of page replacement techniques had suggested by the researchers. In this paper, we have proposed an modified page replacement technique, which is based on the concept of block reading of pages from the secondary storage. The disc access is very slow as compared to the access from primary memory. Whenever there is a page fault, the required page is retrived from the secondary storage. The numerous page faults increase the execution time of process. In the proposed methodology, a number of pages, which is equal to the allotted memory frames, are read every time when there is a page fault instead of reading a single page at a time. If a block of pages has fetched from secondary storage, it will definitely increases the possibilities of page hit and as a result, it will improve the hit ratio for the processes.

Simple estimation and energy‐aware migration models of virtual machines in a server cluster

SummaryIn order to realize green society, it is critical to reduce electric energy consumed by servers in clusters. In our previous studies, these types of algorithms are proposed to select an energy‐efficient server to perform an application process issued by a client. In this paper, we newly discuss a migration approach to reducing the electric energy consumption of servers where virtual machines with application processes migrate to more energy‐efficient servers. We propose a new algorithm called ISEAM2 to reduce the electric energy consumption of servers. Here, a pair of a host server and a virtual machine on the host server are first selected to perform a process issued by an application. In addition, a target virtual machine on a host server and a guest server to which the target virtual machine migrates are selected so that the electric energy consumption of the host and guest servers can be minimized. Thus, virtual machines with application processes migrate to more energy‐efficient servers. We also propose a simple way to estimate the termination time of every process on each server. In the evaluation, we show the total electric energy consumption and total active time of servers, and the average execution time of processes can be reduced in the ISEAM2 algorithm compared with other algorithms.

Mixed neighborhood topology cross decoded patterns for image-based face recognition

Face recognition becomes an important task performed routinely in our daily lives. This application is encouraged by the wide availability of powerful and low-cost desktop and embedded computing systems, while the need comes from the integration in too much real world systems including biometric authentication, surveillance, human-computer interaction, and multimedia management. Moreover, face recognition technology is now adopted in new intelligent systems and devices like smart-phones, which impose some constraints related to the complexity and execution time of the recognition process. This fact brings new challenges and gives much more area to extend the ongoing researches. This research field experienced the development of many methods and architectures aiming at producing face recognition systems which are efficient in terms of precision, robustness and computation time. In the same context, this article proposes a new feature descriptor referred to as Mixed Neighborhood Topology Cross Decoded Patterns (MNTCDP) as an effective face descriptor, The proposed handcrafted descriptor fulfills the needs of current face recognition applications and can be integrated in different platforms, requiring simple, robust and computationally low algorithms. Instead of heuristic code constructions, MNTCDP is built using new neighborhood topology and new pattern encoding scheme, which have high ability to extract discriminative and stable face representation. The adopted face recognition system consists of three stages: (1) face detection and alignment to normalize the input images to a common form if needed; (2) feature extraction using the proposed MNTCDP descriptor and (3) face recognition through a supervised image classification task using the simple K-Nearest Neighbors classifier. Simulated experiments on ORL, YALE, Extended Yale B, FERET and AR datasets acquired under different illumination conditions or facial expressions show that the proposed MNTCDP descriptor presents high performance ability in classifying face images. MNTCDP demonstrates superior performance than a large number of recent state-of-the-art LBP variants and deep learning methods, as well as recent most promising works of the literature.

Fuzzy Logic-Based Robust and Autonomous Safe Landing for UAV Quadcopter

Recent years, unmanned aerial vehicles (UAVs) have been used to perform various tasks such as surveillance, monitoring, rescue, photography, and security. Quadcopter configuration of UAV is most common due to its simplicity, stability, and versatile controllability. Quadcopter landing is most crucial part of the overall operating process. It needs a lot of practice and effort for a safe landing. Therefore, a fuzzy logic-based safe landing system is proposed in this paper. This paper includes a landing system based on laser rangefinder, Arduino Mega MCU, and Pixhawk flight controller. The lookup table technique is used to implement fuzzy logic inside Arduino Mega. This technique takes very small execution time for data processing in fuzzy logic, which is essential for high-speed data processing and updating. Autonomous landing process can be triggered and override at any time using remote controller. Furthermore, various tests are performed on a quadcopter to verify the feasibility of proposed algorithm.

Performance analysis of software maintenance process using stochastic Petri nets

Software maintenance is a time taking activity in the real world. Execution time of software maintenance process may get increased due to interdepartmental communication, thus, increasing the cost and decreasing the performance of the process. We suggested performance evaluation of software maintenance process through the transformation of activity diagram into generalised stochastic Petri nets (GSPN). For this study, execution time and cost of maintenance process are defined as performance measures, and the role-based approach is used to understand the flow of software maintenance activities in a software organisation. Activity diagram is constructed to be transformed into GSPN. We used PIPE2 to analyse the GSPN. PIPE2 calculates average number of tokens on a place in the GSPN, throughput of timed transition and state space analysis. State space involves calculation of the reachability of the GSPN net that shows whether a GSPN holds the property of safeness, boundness and is deadlock free.
 
 Keywords: Generalised stochastic Petri nets (GSPN), GSPN analysis, performance evaluation, performance modelling, software maintenance process, stochastic Petri nets.

Fault and timing analysis in critical multi-core systems: A survey with an avionics perspective

Abstract With more functionality added to future safety-critical avionics systems, new platforms are required to offer the computational capacity needed. Multi-core processors offer a potential that is promising, but they also suffer from two issues that are only recently being addressed in the safety-critical contexts: lack of methods for assuring timing determinism, and higher sensitivity to permanent and transient faults due to shrinking transistor sizes. This paper reviews major contributions that assess the impact of fault tolerance on worst-case execution time of processes running on a multi-core platform. We consider the classic approach for analyzing the impact of faults in such systems, namely fault injection. The review therefore explores the area in which timing effects are studied when fault injection methods are used. We conclude that there are few works that address the intricate timing effects that appear when inter-core interferences due to simultaneous accesses of shared resources are combined with fault tolerance techniques. We assess the applicability of the methods to currently available multi-core processors used in avionics. Dark spots on the research map of the integration problem of hardware reliability and timing predictability for multi-core avionics systems are identified.

Acoustic Ray Tracing Comparisons in the Context of Geodetic Precise off-shore Positioning Experiments

ABSTRACTThe GNSS-Acoustics (GNSS-A) method couples acoustics with GNSS to allow the precise localization of a seafloor reference in a global frame. This method can extend on-shore GNSS networks and allows the monitoring of hazardous oceanic tectonic phenomena. The goal of this study is to test the influence of both acoustics ray tracing techniques and spatial heterogeneities of acoustic wave speed on positioning accuracy. We test three different ray tracing methods: the eikonal method (3D sound speed field), the Snell-Descartes method (2D sound speed profile), and an equivalent sound speed method. We also compare the processing execution time. The eikonal method is compatible with the Snell-Descartes method (by up to 10 ppm in term of propagation time difference) but takes approximately a thousand times longer to run. We used the 3D eikonal ray tracing to characterize the influence of a lateral sound speed gradient on acoustic ray propagation and positioning accuracy. For a deep water (⩾ 3,000 m) situation, frequent in subduction zones such as the Lesser Antilles, not accounting for lateral sound speed gradients can induce an error of up to 5 cm in the horizontal positioning of a seafloor transponder, even when the GNSS-A measurements are made over the barycenter of a seafloor transponder array.

Comparative evaluation of genetic algorithm-based test case optimization

Software testing is a crucial phase in software development process although it consumes more time and cost of software development. Researchers have proposed several approaches focusing on helping software testers to reduce the execution time and cost of the testing process. Test case optimization is a multi-objective approach that has become one of the best solutions to overcome these problems. Test case optimization focusing on reducing the number of test cases in the test suite that may reduce the overall testing time, cost and effort of software testers especially in regression testing. This paper presents the comparative evaluation between test case optimization techniques that are based on Genetic Algorithm (GA). The evaluation is based on five criteria i.e. technique objectives, applied fitness function, contributions, the percentage of the reduced test cases, fault detection capability, and technique limitations. The evaluation results able identify the gaps in the existing GA-based test case optimization approaches and provide insight in determining the potential research directions in this area.

Simple algorithms for selecting an energy-efficient server in a cluster of servers

Electric energy consumed by computers, especially servers in scalable clusters has to be reduced to realise eco society. In this paper, we first propose a multi-level power consumption (MLPCM) model which gives how much electric power a server consumes to perform application processes. We also propose an multi-level computation (MLCM) model which shows the expected execution time of an application process performed on a server. By using the MLPCM and MLCM models, we propose a pair of simple locally energy-aware (SLEA) and simple globally energy-aware (SGEA) algorithms to select a host server to perform an application process issued by a client. In the SLEA algorithm, a host server is selected, which is expected to consume the minimum electric energy to perform the process. In the SGEA algorithm, a host server is selected, where the total expected electric energy consumption of not only the host server but also the other servers is minimum. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the SGEA algorithm compared with other algorithms.

Dynamic migration of virtual machines to reduce energy consumption in a cluster

Virtual machines are widely used to support applications with virtual service in server clusters. Here, a virtual machine can migrate from a host server to a guest server. In this paper, we consider a cluster where virtual machines are dynamically created and dropped depending on the number of processes. We propose a dynamic virtual machine migration (DVMM) algorithm to reduce the total electric energy consumption of servers. If an application issues a process to a cluster, the most energy-efficient host server is first selected and then the process is performed on a virtual machine of the server. Then, a virtual machine migrates from a host server to a guest server so that total electric energy consumption of servers can be reduced. In the evaluation, we show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced in the DVMM algorithm.

Recovery of business intelligence systems: Towards guaranteed continuity of patient centric healthcare systems through a matrix-based recovery approach

With the intensive use of the internet, patient centric healthcare systems shifted away from paper-based records towards a computerized format. Electronic patient centric healthcare databases contain information about patients that should be kept available for further reference. Healthcare databases contain potential data that makes them a goal for attackers. Hacking into these systems and publishing their contents online exposes them to a challenge that affects their continuity. Any denial of this service will not be tolerated since we cannot know when we need to retrieve a patient’s record. Denial of service affects the continuity of the healthcare system which in turn threatens patients’ lives, decreases the efficiency of the healthcare system and increases the operating costs of the attacked healthcare organization. Although there are many defensive security methods that have been devised, nonetheless malicious transactions may find a way to penetrate the secured safeguard and then modify critical data of healthcare databases. When a malicious transaction modifies a patient record in a database, the damage may spread to other records through valid transactions. Therefore, recovery techniques are required. The efficiency of the data recovery algorithm is substantial for e-healthcare systems. A patient cannot wait too long for his/her medical history to be recovered so that the correct medication be prescribed. Nevertheless, in order to have fast data recovery, an efficient damage assessment process should precede the recovery stage. The damage assessment must be performed as the intrusion detection system detects the malicious activity. The execution time of the recovery process is a crucial factor for measuring the performance because it is directly proportional to the denial of service time of any healthcare system. This paper presents a high performance damage assessment and recovery algorithm for e-healthcare systems. The algorithm provides fast damage assessment after an attack by a malicious transaction to keep the availability of the e-healthcare database. Reducing the execution time of recovery is the key target of our algorithm. The proposed algorithm outperforms the existing algorithm. It is about six times faster than the most recent proposed algorithm. In the worst case, the proposed algorithm takes 8.81 ms to discover the damaged part of the database; however, the fastest recent algorithm requires 50.91 ms. In the best case, the proposed algorithm requires 0.43 ms, which is 86 times faster than the fastest recent work. This is a significant reduction of execution time compared with other available approaches. Saving the damage assessment time means shorter denial of service periods, which in turn guarantees the continuity of the patient centric healthcare system.

An adaptive rule-based approach to resolving real-time VoIP wholesale billing disputes

The Voice over Internet Protocol (VoIP) industry has grown immensely since its inception, and is predicted to grow at double the rate in the coming years. The growth of the VoIP industry has made significant contributions to the economy, and has also increased the volume of data, which is a challenge for processing. VoIP security vulnerabilities and lack of appropriate tools and infrastructure can lead to billing disputes and fraud attacks, impacting VoTP wholesalers' profits. To reduce economic losses, these challenges need to be addressed in a comprehensive and efficient way. This study proposes an intelligent and adaptive rule based reconciliation process to resolve real-time billing disputes with minimal revenue loss. Real-time disputed Call Detail Records are analyzed to generate adaptive rules to cater for dynamic data sources. These rules are used to classify the Call Detail Record into six categories. A summarized report is generated at the end of the analysis that can be used to come to a better resolution during the billing dispute negotiation process. The complexity and volume of data affects the execution time of reconciliation processes. Spark, a distributed processing framework, is used to reduce execution times. The distributed processing solution has reduced execution times by 81.8% on average as compared to non-distributed solutions. The performance of the proposed solution is evaluated against the CALLS Dispute management system (an Aiztek Technologies solution), and the proposed solution has detected 38% more billing disputes in less time as compared to the existing solution.

Model-based thermal system design optimization for the James Webb Space Telescope

Spacecraft thermal model validation is normally performed by comparing model predictions with thermal test data and reducing their discrepancies to meet the mission requirements. Based on thermal engineering expertise, the model input parameters are adjusted to tune the model output response to the test data. The end result is not guaranteed to be the best solution in terms of reduced discrepancy and the process requires months to complete. A model-based methodology was developed to perform the validation process in a fully automated fashion and provide mathematical bases to the search for the optimal parameter set that minimizes the discrepancies between model and data. The methodology was successfully applied to several thermal subsystems of the James Webb Space Telescope (JWST). Global or quasiglobal optimal solutions were found and the total execution time of the model validation process was reduced to about two weeks. The model sensitivities to the parameters, which are required to solve the optimization problem, can be calculated automatically before the test begins and provide a library for sensitivity studies. This methodology represents a crucial commodity when testing complex, large-scale systems under time and budget constraints. Here, results for the JWST Core thermal system will be presented in detail.

Indoor Positioning System Based on a PSD Detector, Precise Positioning of Agents in Motion Using AoA Techniques.

Here, we present an indoor positioning system (IPS) for detecting mobile agents based on a single Position Sensitive Device sensor (PSD) sited in the environment and InfraRed Emitter Diode (IRED) located on mobile agents. The main goal of the work is to develop an alternative IPS to other sensing technologies, cheaper, easier to install and with a low computational load to obtain a high rate of measurements per second. The proposed IPS has the capacity to accurately determine 3D position from the angle of arrival (AoA) of the signal received at the PSD sensor. In this first approach to the method, the agents are considered to move along a plane. We propose two alternatives for determining position: in one, tones are emitted on a frequency unique to each transmitter, while in the other, sequences are emitted.The paper proposes and set up a very simple and easy to deploy system capable of performing 3D positioning with a single analog sensor, obtaining a high accurate positioning and a reduced execution time for the signal processing. The low computational load of the IPS makes it possible to obtain a very high position update rate (more than 100 times per second), yielding millimetric accuracies.

An approach to embed image in video as watermark using a mobile device

Evolution in communication media and Internet technology has made information access easier and faster. At the same time there has been a major concern towards the security of various multimedia objects. Multimedia objects are files that contain images, video clips etc. These are the most popular shared objects over the internet through various transferrable devices. One of the way to secure the content is digital watermarking. Digital watermarking is the method to embed the secret data within text, image or video for ownership verification. In this work related to bitstream video watermarking technique, a study of spatial and temporal domain watermarking is carried out. An experiment is conducted to analyze the execution time of insertion and extraction process of bitstream watermarking on videos. Bit error count and power consumed by mobile device are computed for analysis of the bitstream video watermarking technique. As a proof of concept, the embedding of image on video is implemented using a computer and then the same logic is implemented for execution on a mobile phone. Experimental results demonstrate that, the method is feasible to be implemented using a computer and mobile phone. Image that is inserted on the video is extracted with an acceptable PSNR value. Execution time and power consumed in a mobile phone are within the acceptable limits. This ascertains the application’s feasibility for a mobile phone.

Progressive Mining of Sequential Patterns Based on Single Constraint

Data that were appeared in the order of time and stored in a sequence database can be processed to obtain sequential patterns. Sequential pattern mining is the process to obtain sequential patterns from database. However, large amount of data with a variety of data type and rapid data growth raise the scalability issue in data mining process. On the other hand, user needs to analyze data based on specific organizational needs. Therefore, constraint is used to impose limitation in the mining process. Constraint in sequential pattern mining can reduce the short and trivial sequential patterns so that the sequential patterns satisfy user needs. Progressive mining of sequential patterns, PISA, based on single constraint utilizes Period of Interest (POI) as predefined time frame set by user in progressive sequential tree. Single constraint checking in PISA utilizes the concept of anti monotonic or monotonic constraint. Therefore, the number of sequential patterns will decrease, the total execution time of mining process will decrease and as a result, the system scalability will be achieved.

An integrated access control and lighting configuration system for smart buildings

This article presents an integrated access control and lighting configuration system for smart buildings. The system uses two-factor authentication, one based on face recognition and other on RFID TAG, and identifies the user inside a room and performs an automatic lighting configuration based on user’s behavior. The communication among the devices is performed by Radio Frequency using the low to medium frequency spectrum (LMRF), without providing direct Internet access, and, hence, avoiding known Internet security issues. This system can be easily deployed on meeting rooms or offices in business or government buildings. Through the evaluations we observe an acceptable processing execution time, an acceptable communication time and the robustness of the system.

Method for a Parallel Solution of a Combined Economic Emission Dispatch Problem

The power system simulation software tools are traditionally designed for serial codes and optimized using single-processor computers. They are inadequate in terms of computational efficiency and execution time for the ever-increasing complexity of the power grid. Due to the above-mentioned sequential computing demerits, this paper used MATLAB data parallelism message passing interface software to execute the Lagrange's and Particle Swarm Optimization (PSO) algorithms in parallel with multiple processor units with different and large data sets for the solution of the Combined Economic Emission Dispatch (CEED) problem. The two important advantages of using parallel computing approach to solve the power system economic dispatch problem are 1) to increase the efficiency (solution quality) and 2) to reduce the execution time (speed-up) of the parallelization process for the CEED problem solution. The comparison between the Lagrange's and PSO data-parallel solution quality and execution time is presented for the CEED problem for Institute of Electrical and Electronic Engineers (IEEE) 30 bus and IEEE 118 bus systems. The paper contributes to the on-line real-time market analyses of the deregulated power system, which need improved solution quality and a fast computation process to solve the power system energy management (CEED) problems for proper discussion and decision making at the control center level.