Time Deadline Research Articles

Optimization Approach for Resource Allocation on Cloud Computing for IoT

Combinatorial auction is a popular approach for resource allocation in cloud computing. One of the challenges in resource allocation is that QoS (Quality of Service) constraints are satisfied and provider's profit is maximized. In order to increase the profit, the penalty cost for SLA (Service Level Agreement) violations needs to be reduced. We consider execution time constraint as SLA constraint in combinatorial auction system. In the system, we determine winners at each bidding round according to the job's urgency based on execution time deadline, in order to efficiently allocate resources and reduce the penalty cost. To analyze the performance of our mechanism, we compare the provider's profit and success rate of job completion with conventional mechanism using real workload data.

Dysfunctional behavior of external auditors the collision of time budget and time deadline evidence from a developing country

The general goal behind this research is to gain a better understanding of factors leading to dysfunctional behavior of auditors. The role of auditors has been a controversial issue especially after the recent accounting scandals (WorldCom, Enron, Paramalat SpA, Waste Management Inc., Xerox Corporation, and Société Générale). In relation to these scandals and lawsuits, a question is frequently asked “where were the auditors?” The survey we offer up for research is made up of 34 questions that are designed to analyse the perception of auditors and the cause of dysfunctional behavior. The object of this research is comprised of auditors positioned and employed at the Big Four audit firms in Kuwait. The selected sample is comprised from auditors at different levels ranging from audit trainees to audit partners. The so-called stakeholder theory talks about the relationship between audit behavior and audit firm practices with time deadlines and time budgets involved. It is generally accepted that the more auditors align their interests with firms’ measures and practices, the more a firm is capable of meeting general goals and objectives. Consequently, this may result in a less dysfunctional workplace conduct. Dysfunctional behavior (DB) is analysed against two signalling proxies of dysfunctional behavior; premature sign-off and under reporting of chargeable time. DB is analysed against time budget and time deadline pressures. The results suggest that the general belief among auditors is that the profession of accountancy predetermines their tendency to commit certain patterns of dysfunctional behavior. The results indicate that auditors understand that dysfunctional behavior is exercised by the majority of auditors. Dysfunctional behaviour is exercised with intention and it is not committed out of ignorance. One of the interesting findings is that auditors, at all levels, are concerned about their reputation. Thus, dysfunctional behavior is exercised, to a certain extent, without harming the quality of the audit work. Having our investigation conducted at the Big Four audit firms, we have come to the conclusion that there is a general difference in behavior patterns among perceptions of dysfunctional behavior and normal skeptic professional behavior. Audit firms should continuously conduct workshop and training events related to dysfunctional behavior. It is revealed that some auditors are not aware that certain practices are considered dysfunctional behavior or not. This research work can be used to aid audit practitioners to mitigate audit dysfunctional behavior. It highlights certain type of practices that is exercised in an improper way.

Adaptive Cost-Based Task Scheduling in Cloud Environment

Task execution in cloud computing requires obtaining stored data from remote data centers. Though this storage process reduces the memory constraints of the user’s computer, the time deadline is a serious concern. In this paper, Adaptive Cost-based Task Scheduling (ACTS) is proposed to provide data access to the virtual machines (VMs) within the deadline without increasing the cost. ACTS considers the data access completion time for selecting the cost effective path to access the data. To allocate data access paths, the data access completion time is computed by considering the mean and variance of the network service time and the arrival rate of network input/output requests. Then the task priority is assigned to the removed tasks based data access time. Finally, the cost of data paths are analyzed and allocated based on the task priority. Minimum cost path is allocated to the low priority tasks and fast access path are allocated to high priority tasks as to meet the time deadline. Thus efficient task scheduling can be achieved by using ACTS. The experimental results conducted in terms of execution time, computation cost, communication cost, bandwidth, and CPU utilization prove that the proposed algorithm provides better performance than the state-of-the-art methods.

A Hierarchical Multi-Constrained Cost Efficient Qos-Driven Scheduling Heuristic for Computational Tasks in Grid

In a Grid environment, a plenty of heterogeneous resources are shared among the users to solve complex problems that requires massive computational capability. In this view, scheduling algorithms plays a vital role in provisioning a suitable resource for executing a complex computational task. Also, the scheduling algorithms should address the issues that arise when an allocated resource fails. Resource allocation is an important phenomenon where several issues should be taken for consideration such as balancing the load of resources, handling resource failure, satisfying the user's demand and finally the cost for task execution. In this paper, we propose a scheduling heuristic (HMC-QoS) that addresses all these scheduling issues and performs better resource allocation. This heuristic works at three different levels of hierarchy and finds an appropriate resource for a task. It considers the factors like execution time, resource load, communication time, cost, and task completion deadline and failure history of resources for scheduling at each level. The proposed HMC-QoS heuristic is implemented and its performance is measured based on various performance metrics which involves makespan, task deadline hit, resource utilization, cost, and communication time and fault tolerance. The analysis shows that the proposed heuristic results in improved makespan and other metrics when compared with other existing algorithms.

Energy-Efficient Opportunistic Transmission Scheduling for Sparse Sensor Networks with Mobile Relays

Wireless sensing devices have been widely used in civilian and military applications over the past decade. In some application scenarios, the sensors are sparsely deployed in the field and are costly or infeasible to have stable communication links for delivering the collected data to the destined server. A possible solution is to utilize the motion of entities that are already present in the environment to provide opportunistic relaying services for sensory data. In this paper, we design and propose a new scheduling scheme that opportunistically schedules data transmissions based on the optimal stopping theory, with a view of minimizing the energy consumption on network probes for data delivery. In fact, by exploiting the stochastic characteristics of the relay motion, we can postpone the communication up to an acceptable time deadline until the best relay is found. Simulation results validate the effectiveness of the derived optimal strategy.

Improvement in symptoms, processing speed and cognition in a 59-year-old stroke patient with Wernicke's aphasia

Event Abstract Back to Event Improvement in symptoms, processing speed and cognition in a 59-year-old stroke patient with Wernicke's aphasia Matthew M. Antonucci1, 2, 3* and Freddys X. Garcia1, 3 1 Carrick Institute, Clinical Neuroscience, United States 2 Plasticity Brain Centers, Neurorehabilitation, United States 3 NeuroSynergy Associates, PA, Neurorehabilitation, United States Background: A 59-year-old, male, with a history of a left-sided MCA stroke, presented with right hand and face numbness, right hand weakness, incomplete Wernicke's receptive aphasia, and confusion. His Graded Symptom Checklist (GSC) severity was 78/162. Standard Assessment of Cognition (SAC) was 0/30. Trails A (TA) was unable to be completed within the two-minute time deadline. Processing Speed (PS) coding test was unable to be performed due to delays between each symbol. Methods: A five-day multimodal program of receptor based neurological rehabilitation was administered three times per day, one hour per session. Each session consisted of electrical somatosensory stimulation on the second branch of his trigeminal nerve on the right and bilaterally on the third branch of his trigeminal nerve, vestibular rehabilitation exercises, therapeutic exercises, hand-eye coordination exercises, Carrick eye exercises, and off-vertical axis rotations. Results: At the end of five days of treatment there were significant resolutions in GSC (-92.3%), improvement of SAC from 0/30 to 4/30, TA time from unable to be performed to completion in 69.4 seconds and processing speed of 15 symbols matched correctly from being unable to perform. Conclusion: Short duration, multimodal, intensive programs of receptor based neurological rehabilitation may be a viable method to improve neurological integrity and performance in individuals with Stroke. The authors suggest further investigation into short duration, multi-modal, intensive approaches to restoring neurological function in individuals suffering from acquired brain injuries, such as stroke. Keywords: Stroke, Aphasia, Wernicke’s aphasia, vestibular rehabilitaiton, non-invasive nerve stimulation, Off-vertical axis rotation Conference: International Symposium on Clinical Neuroscience: Clinical Neuroscience for Optimization of Human Function, Orlando, United States, 7 Oct - 9 Oct, 2016. Presentation Type: Poster Presentation Topic: Abstracts ISCN 2016 Citation: Antonucci MM and Garcia FX (2016). Improvement in symptoms, processing speed and cognition in a 59-year-old stroke patient with Wernicke's aphasia. Front. Neurol. Conference Abstract: International Symposium on Clinical Neuroscience: Clinical Neuroscience for Optimization of Human Function. doi: 10.3389/conf.fneur.2016.59.00075 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Sep 2016; Published Online: 07 Sep 2016. * Correspondence: Dr. Matthew M Antonucci, Carrick Institute, Clinical Neuroscience, Cape Canaveral, United States, drnucci@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Matthew M Antonucci Freddys X Garcia Google Matthew M Antonucci Freddys X Garcia Google Scholar Matthew M Antonucci Freddys X Garcia PubMed Matthew M Antonucci Freddys X Garcia Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

A heuristic for maximizing investigation effectiveness of digital forensic cases involving multiple investigators

Digital forensic investigation refers to the use of science and technology in the process of investigating a crime scene so as to maximize the effectiveness of proving the perpetrator has committed crime in a court of law. Evidences are considered to be the building block of any crime scene investigation (CSI) procedure including those involving cyber crimes. Selecting the right set of evidence and assigning the appropriate investigator for the selected evidence is vital in time critical forensic cases, in which results have to be finalized within a specified time deadline. Not doing this may lead to the scope creep problem, which is a significant issue in digital forensics. Therefore, major challenges with respect to digital forensic investigation are to determine the right set of evidences to be assigned to each of the available multiple investigators and allocate appropriate investigation time for the selected evidences to maximize the effectiveness of the investigation effort. A mixed integer linear programming (MILP) model is developed to analyze and solve the problem of evidence selection and resource allocation in a digital crime scene investigation. In view of the problem being NP-hard, a heuristic algorithm with polynomially bounded computational complexity is proposed to solve the problem. Results of extensive computational experiments to empirically evaluate its effectiveness to find an optimal or near-optimal solution are reported. Finally, this paper concludes with a summary of findings and some fruitful directions for future research.

클라우드 컴퓨팅을 위한 VM 스팟 인스턴스 입찰 최적화 전략

The cloud computing service provides physical IT resources to VM instances to users using virtual technique and the users pay cost of VM instances to service provider. The auction model based on cloud computing provides available resources of service provider to users through auction mechanism. The users bid spot instances to process their a job until its deadline time. If the bidding price of users is higher than the spot price, the user will be provided the spot instances by service provider. In this paper, we propose a new bidding strategy to minimize the total cost for job completion. Typically, the users propose bidding price as high as possible to get the spot instances and the spot price get high. we lower the spot price using proposed strategy and minimize the total cost for job completion. To evaluate the performance of our strategy, we compare the spot price and the total cost for job completion with real workload data.

Energy Efficient Transmission Approach for WBAN Based on Threshold Distance

Energy efficiency is a key concern for wireless sensor nodes, especially for wireless body area network (WBAN) in which sensors operate in close vicinity to, on or even inside a human body. In this paper, we first present a system-level energy consumption model associated with transmission distance $d$ and transmission data rate over on-body wireless communication link. Then, based on the analysis of tradeoff between circuit energy and transmission energy on distance, a threshold distance $d_{\rm th}$ which is responsible for the proportion of transmission energy and circuit energy is derived for energy saving in WBAN. With the case of $d\leq d_{\rm th}$ , since circuit energy is comparable with transmission energy consumption, the total energy consumption can be saved by optimizing the transmission data rate $R$ . Simulation results show that a 59.77% or even more energy saving is achievable using the optimized scheme, compared with baseline scheme. With $d>d_{\rm th}$ , since the total energy consumption is monotonically decreasing with respect to time $t$ , an offline algorithm is applied to energy saving by prolonging transmission time within the deadline time. In addition, on the basis of the offline algorithm, a battery-aware transmission approach is presented for WBAN using battery electrochemical property. Experimental results show that, using the presented battery-aware approach, 71.05% and 60.81% energy saving can be obtained, in comparison with the baseline and offline schemes, respectively.

Routing optimization for multi-type containerships in a hub-and-spoke network

Abstract This paper considers the ship routing optimization problem in a hub-and-spoke network. A routing optimization model for multi-type containerships with time deadlines is established, and the target is to minimize the total cost, which consists of the total travelling cost, total service cost and total waiting cost. The model is set up through an improved genetic algorithm. The study data are from the Pearl River Delta region of China, which include 1 hub port and 29 feeder ports and have a population of 30 million. Result shows that when the iteration time reaches 190, the total cost comes to 521 thousand yuan near the optimal value. There are 6 routes, including 3 containerships of 100 TEU, 2 containerships of 150 TEU and 1 containership of 200 TEU. At the same time, in the single-type containerships case, there are 7 routes, and when the iteration time reaches 120, the total cost comes to 573 thousand yuan, which is close to the optimal value. Comparing the two cases, it shows that the model for multi-type containerships with time deadlines is reasonable, and the algorithm is practicable. In the last, three factors, which may affect the total cost to carry out sensitivity analysis are chosen. It shows that time deadline, containership capacity and cargo handling capacity of each port have significant influence on the total cost. It is also shown that the total cost for multi-type containerships is always less than that for the single-type containerships.

A Hybrid Algorithm for the Vehicle Routing Problem with Soft Time Windows and Hierarchical Objectives

This paper presents an algorithm for the vehicle routing problem with soft time windows (VRPSTW). It involves serving a set of customers, with earliest and latest time deadlines, which may be violated if a penalty is paid, and a constant service time at the customer site. Customer demands are served by capacitated vehicles. The purpose of this research is to develop a hybrid algorithm that includes an insertion heuristic, a local search algorithm and a meta-heuristic algorithm to solve VRPSTW problems with more than one objective. The first priority aims to find the minimum number of vehicles required and the second priority aims to search for the solution that minimizes the total travel time. Performance of the algorithmic approach is measured by two criteria: solution quality and run time. A set of well-known benchmark data and a genetic algorithm are used to compare the solution quality and running time of the algorithm. Results show a trade-off can be made between total cost and service when considering soft time windows. Running time results display that the hybrid algorithm has a higher performance than the genetic algorithm when the number of customers is less than 25.

A Weighted Routing Scheme for Industrial Wireless Sensor Networks

The WSNs replace the medium of communication from wired to wireless in industrial environment. This offer several advantages that includes easy and fast installation, low-cost maintenance and energy saving. In industrial monitoring and control application, the sensory measures should be delivered to control center in predefined deadline time, so the necessary actions may timely initiated. The geographical routing as reactive routing protocol plays a massive role for real-time packet delivery. The proposed routing protocol follows path discovery on demand basis to reduce the path discovery overhead. Moreover, the routing protocol follows weighted forwarding node selection process. This selects the shorter path over speedy reliable links for smaller deadline time and distributes the traffic over energy efficient node for larger deadline time. Through simulation, the authors demonstrate, compared to existing routing protocol the proposed routing protocol improves the packet delivery ratio along with enhanced network life while maintaining the high energy efficiency and low delivery latency.

THERMOBASE - A new Medical Device to Aid the Control and Temperature of Liquid Meals for Post Bariatric Surgery Patients

Bariatric surgery is a surgical procedure in order to promote the isolation of the stomach and thereby reduce the health risks arising from obesity. The meal in the first month after surgery, which may extend for a longer period according to experts, is based only on a liquid diet. During this period of rehabilitation, the patient ingests 20 ml of fluid every 10 minutes, and should be followed strictly for proper hydration throughout the day. Liquids like soups , juices and waters , are part of diet and needing to be stored in a container such as a bottle , which generally can do not store them in the correct temperature to facilitate the intake for nutritional education caused by diet on postoperative of bariatric surgery throughout the day. This document aims to present a project to improve the quality of life of people undergoing surgery to reduce the stomach, through the development of a device called adjustable THERMOBASE, wherein the patient is able to control the temperature of the heating fluid or cooling the machine contact surface, which should be in contact with a container containing the liquid to be consumed according to the model proposed by nutritionists that to monitor post operative diet. Thus, by means of physical property of thermal conduction, through the Peltier sensor, the soup can be heated to the boiling point or fresh juice to its freezing point, as examples of the extreme draft potential. The THERMOBASE tells you how many drinks were consumed during the day providing valuable nutritional information for nutrition professionals who follow the patient postoperatively, and audible, visual or vibration alarm, indicating the next meal time deadline.

Collaborative Task Execution in Mobile Cloud Computing Under a Stochastic Wireless Channel

This paper investigates collaborative task execution between a mobile device and a cloud clone for mobile applications under a stochastic wireless channel. A mobile application is modeled as a sequence of tasks that can be executed on the mobile device or on the cloud clone. We aim to minimize the energy consumption on the mobile device while meeting a time deadline, by strategically offloading tasks to the cloud. We formulate the collaborative task execution as a constrained shortest path problem. We derive a one-climb policy by characterizing the optimal solution and then propose an enumeration algorithm for the collaborative task execution in polynomial time. Further, we apply the LARAC algorithm to solving the optimization problem approximately, which has lower complexity than the enumeration algorithm. Simulation results show that the approximate solution of the LARAC algorithm is close to the optimal solution of the enumeration algorithm. In addition, we consider a probabilistic time deadline, which is transformed to hard deadline by Markov inequality. Moreover, compared to the local execution and the remote execution, the collaborative task execution can significantly save the energy consumption on the mobile device, prolonging its battery life.

The Preprocessing and Probing Technique of Open Capacitated Vehicle Routing Problem with Split and Time Deadline (OCVRP-St) Model In Rubbish Transportation Problem

The Preprocessing and Probing Technique of Open Capacitated Vehicle Routing Problem with Split and Time Deadline (OCVRP-St) Model In Rubbish Transportation Problem

The Preprocessing and Probing Technique of Open Capacitated Vehicle Routing Problem with Split and Time Deadline (OCVRP-St) Model In Rubbish Transportation Problem

The activity of rubbish transportation in Palembang is one of the applications of Vehicle Routing Problem (VRP) in transporting rubbish in Sako Palembang by applying preprocessing and probing techniques to obtain simplest OCVRP model. The solution is conducted by using LINDO software. The results show that the optimal routes in Sukarami before and after applying the tehniques are the same routes. In addition, we obtain the reduction of constraints and variables, the reduction of iteration numbers and the optimal value did not change.

Performance analysis of backup-task scheduling with deadline time in cloud computing

In large-scale parallel job processing for cloud computing, a huge task is divided into subtasks, which are processed independently on a cluster of machines called workers. Since the task processing lasts until all the subtasks are completed, a slow worker machine makes the overall task-processing time long, degrading the task-level throughput. In order to alleviate the performance degradation, MapReduce conducts backup execution, in which the master node schedules the remaining in-progress subtasks when the whole task operation is close to completion. In this paper, we investigate the effect of backup tasks on the task-level throughput. We consider the backup-task scheduling in which a backup subtask for a worker starts when the subtask-processing time of the worker reaches the deadline time. We analyze the task-level processing-time distribution by considering the maximum subtask-processing time among workers. The task throughput and the amount of all the workers' processing times are derived when the worker-processing-time (WPT) follows a hyper-exponential, Weibull, and Pareto distribution. We also propose an approximate method to derive performance measures based on extreme value theory. The approximations are validated by Monte Carlo simulation. Numerical examples show that the performance improvement by backup tasks significantly depends on workers' processing time distribution.

Maximizing rumor containment in social networks with constrained time

The spread of rumor or misinformation in social networks may cause bad effects among the public. Thus, it is necessary to find effective strategies to control the spread of rumor. Specifically, in our paper, we consider such a setting: initially, a subset of nodes is chosen as the set of protectors, and the influence of protector diffuses competitively with the diffusion of rumor. However, in real world, we generally have limited budget (limited number of protectors) and time to fight with rumor. Therefore, we study the problem of maximizing rumor containment within a fixed number of initial protectors and a given time deadline. Generalizing two seminal models in the field—the Independent Cascade (IC) model and the Linear Threshold (LT) model—we propose two new models of competitive influence diffusion in social networks with the following three factors: a time deadline for information diffusion, random time delay of information exchange and personal interests regarding the acceptance of information. Under these two models, we show that the optimization problems are NP-hard. Furthermore, we prove that the objective functions are submodular. As a result, the greedy algorithm is used as constant-factor approximation algorithms with performance guarantee \(1-\frac{1}{e}\) for the two optimization problems.

비정형적인 무선 센서 네트워크에서 음영지역 자가 구성 실시간 라우팅 프로토콜

The real-time data dissemination schemes exploit the spatiotemporal commuication approach which forwards data at the delivery speed calculated with the desired time deadline and the end-to-end distance in wireless sensor networks (WSNs). In practical environments, however, the performance of the real-time data dissemination might be degraded by additional and inevitable delay due to some holes. Namely, the holes lengthen the data delivery path and the spatiotemporal approach could not estimate a distance of the data delivery path. To deal with this, we propose A Hole Self-Organization Real-time Routing Protocol for Irregular Wireless Sensor Networks. In proposed protocol, nodes around holes could detect them at deploying phase. A hole is represented as a circle with center point and radius. This hole information is processed and provided as a form of location service. When a source queries a destination location, location provider replies certain points for avoiding holes as well as destination location. Thus, the source could set desired speed toward the destination via the points. Performance evaluation shows that provides better real-time service in practical environments. 논문 14-39B-05-04 The Journal of Korea Information and Communications Society '14-05 Vol.39B No.05 http://dx.doi.org/10.7840/kics.2014.39B.5.281 281 ※ 이 논문은 2012년도 정부(교육과학기술부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임(NRF-2012R1A1A2044460) First Author : Chungnam National Univ. Department of Computer Engineering, sdkim@cclab.cnu.ac.kr, 정회원 ° Corresponding Author : Chungnam National Univ. Department of Computer Engineering, shkim@cnu.ac.kr, 종신회원 * 충남대학교 컴퓨터 공학과 네트워크 연구실 ({sdkim, cykim, hccho, ybyim}@cclab.cnu.ac.kr, shkim@cnu.ac.kr) 논문번호:KICS2014-02-063, Received February 20, 2014; Revised May 8, 2014; Accepted May 8, 2014 The Journal of Korea Information and Communications Society '14-05 Vol.39B No.05

Execution Times Reconstruction in a LTI System Real-time Simulation

A Real-time System (RTS) interacts with physical world by sensors, actuators, Analogical/Digital and Digital/Analogical convertors processing its requirements through Real-time Tasks (RTT). Each RTT is formed by an instances set with at least three time constraints: arrival time, execution time and deadline time. In this sense, it is important to propose a model to describe the behavior of this time constraints; specifically the execution time. The parameter model estimator considered is a digital filter based on Instrumental Variable to use on Autoregressive Moving Average Model (ARMA) (1, 1). The execution times were measured as experimental techniques using QNX 6.5 Neutrino real-time operating system in an AMD Phenom™ 9950 processor with speed of 2.6 GHz and 4GB in RAM memory with a different execution time measurement method. This paper presents a state of the art of Real-time task model, the execution times measurements, execution time model, experimental validation of the minimal probabilistic characteristics to use the Instrumental Variable estimator, experimentation and results. The algorithm was efficient because the execution time reconstructed converges to the execution time measured.