Redundant Task Research Articles

Multi-objective redundancy hardening with optimal task mapping for independent tasks on multi-cores

The rate of transient faults has increased significantly as the technology scales up. The tolerance of transient faults has become an important issue in the system design. Dual modular redundancy (DMR) and triple modular redundancy (TMR) are two commonly used techniques that can achieve fault detection and masking through executing redundant tasks. As DMR and TMR have different time and cost overheads, we must carefully determine which one should be used for each task (i.e., task hardening) to achieve the optimal system design. Furthermore, for multi-core systems, the system-level design includes the allocation of cores for the tasks (i.e., task mapping) as well. This paper aims at task hardening and mapping simultaneously for independent tasks on multi-cores with heterogeneous performances, in order to minimize the maximum completion time of all tasks (i.e., makespan). We demonstrate that once task hardening is given, task mapping of independent tasks can be achieved by employing min–max-weight perfect matching with a polynomial time complexity. Besides, as there is a trade-off between cost and time performance, we propose a multi-objective memetic algorithm (MOMA)-based task hardening method to obtain a set of solutions with different numbers of cores (i.e., costs), so the designer can choose different solutions according to different requirements. The key idea of the MOMA is to incorporate problem-specific knowledge into the global search of evolutionary algorithms. Our experimental studies have demonstrated the effectiveness of the proposed method and have shown that by combining the results of MOMA and MOEA we can provide a designer with a highly accurate set of solutions within a reasonable amount of time.

Minimizing Makespan in Embedded Real-Time Systems with Failure Rate Requirements

Scheduling of real-time tasks in embedded real-time systems with quantitative reliability requirements, such as failure rate, is one of the important issues in system design. In this paper, we present a real-time scheduling heuristic that minimise makespan and satisfy reliability requirements for systems that are subject to processor and communication faults. The heuristic is based on a cost function to minimise makespan and on task redundancy to meet reliability requirements. Our approach is dedicated to heterogeneous architectures with multiple processors linked by several shared buses. It is based on active redundancy and data fragmentation strategies, which allow fast error detection and error handling. Thanks to the above-mentioned strategies, we are able to show with simulation results that our approach can generally reduce the run-time overhead.

Managing Response Time Tails by Sharding

Matrix analytic methods are developed to compute the probability distribution of response times (i.e., data access times) in distributed storage systems protected by erasure coding, which is implemented by sharding a data object into N fragments, only K <; N of which are required to reconstruct the object. This leads to a partial-fork-join model with a choice of canceling policies for the redundant N − K tasks. The accuracy of the analytical model is supported by tests against simulation in a broad range of setups. At increasing workload intensities, numerical results show the extent to which increasing the redundancy level reduces the mean response time of storage reads and significantly flattens the tail of their distribution; this is demonstrated at medium-high quantiles, up to the 99th. The quantitative reduction in response time achieved by two policies for canceling redundant tasks is also shown: for cancel-at-finish and cancel-at-start, which limits the additional load introduced whilst losing the benefit of selectivity amongst fragment service times.

A Control Method for Joint Torque Minimization of Redundant Manipulators Handling Large External Forces

In this paper, a control method is developed for minimizing joint torque on a redundant manipulator where an external force acts on the end-effector. Using null space control, the redundant task is designed to minimize the torque required to oppose the external force, and reduce the dynamic torque. Furthermore, the joint motion can be weighted to factor in physical constraints such as joint limits, collision avoidance, etc. Conventional methods for joint torque minimization only consider the internal dynamics of the manipulator. If external forces acting on the end-effector are inadvertently implemented in to these control methods this could lead to joint configurations that amplify the resulting joint torque. The proposed control method is verified through two different case studies. The first case study involves simulation of high-pressure blasting. The second is a simulation of a manipulator lifting and moving a heavy object. The results show that the proposed control method reduces overall joint torque compared to conventional methods. Furthermore, the joint torque is minimized such that there is potential for a manipulator to execute certain tasks beyond its nominal payload capacity.

Optimizing Assessment Tasks for Institutional and Program-Level Accreditations: A Case Study of Accreditation Requirements of MSCHE and ABET

Abstract Academic institutions in the United States often engage in challenging tasks of accreditation activities at both the institutional and program levels. Although the institutional and program level accreditations are not quite similar in scope, they do have a number of common accreditation requirements. The assessment tasks for those common accreditation requirements can be arranged to complement each other, thereby minimizing nearly redundant tasks and optimizing the overall process for an institution. This work aims to demonstrate an instance of such optimization by exploiting the commonalities in the accreditation standards and consolidating the pertinent assessment tasks between the Middle States Commission of Higher Education (MSCHE), a key institutional accreditor, and Accreditation Board for Engineering and Technology (ABET), the leading engineering program accreditor. This paves the way for academic institutions nationwide to optimize their activities involving several other accreditation bodies at both the institutional and program levels.

Organizational network analysis: A study of a university library from a network efficiency perspective

A library is a particular kind of organization. It plays a valuable role and is dedicated mainly to the development and growth of society. Analyzing a library from the perspective of a network of relations and ties, which exist between social and technical network nodes, contributes to a more nuanced assessment of effectiveness. Building on social network analysis and going beyond human relations in a library, this study examines perceptions related to knowledge and skills, resources, and tasks, identified through a survey conducted at the university library in Warsaw. Overall, the analyzed library is characterized by redundancy and congruence of knowledge, resources, and tasks required at the library (organizational) level and at the particular node (employee) level. Analyzing the network efficiency of a library is a new and valuable research design which uses a unique network measurement that should attract more interest in the future. This form of analysis gives managers the tools to dynamize relations and understand the flow, use, and sharing of resources or knowledge within a library context. However, more studies in the public sector would be invaluable in order to formulate new theories or conclusions.

Discovering Foreign Keys on Web Tables with the Crowd

Foreign-key relationship is one of the most important constraints between two tables. Previous works focused on detecting inclusion dependencies (INDs) or foreign keys in relational database. To discover foreign-key relationship is obviously helpful for analyzing and integrating data in web tables. However, because of poor quality of web tables, it is difficult to discover foreign keys by existing techniques based on checking basic integrity constraints. In this paper, we propose a hybrid human-machine framework to detect foreign keys on web tables. After discovering candidates and evaluating their confidence of being true foreign keys by machine algorithm, we verify those candidates leveraging the power of the crowd. To reduce the monetary cost, a dynamical task selection technique based on conflict detection and inclusion dependency is proposed, which could eliminate redundant tasks and assign the most valuable tasks to workers. Additionally, to make workers complete tasks more effectively and efficiently, sampling strategy is applied to minimize the number of tuples posed to the crowd. We conducted extensive experiments on real-world datasets and results show that our framework can obviously improve foreign key detection accuracy on web tables with lower monetary cost and time cost.

Halting the Comeback of “Kiddie Packs”: Defeating an Invisible Enemy

Background and context: “Kiddie packs”–packs of 10 or fewer cigarettes–have been banned in Malaysia in 2010. In August 2017, however, tobacco control civil society organizations (CSOs) were informed that the tobacco industry had approached the Malaysian government on this issue, claiming that a “return” would increase the government’s tobacco tax revenue. Unfortunately, this news was not formalised and CSOs were not allowed to reveal the source. We thus could not implement direction action organizing, and the media is much less likely to respond to “smoke without fires”. Aim: To stop the return of kiddie packs. Strategy/Tactics: Through discussions on a WhatsApp group, CSOs launched a “divide and conquer” battle. After a CSO took the risk and leaked the information to media (without revealing its source), many groups built on the voice, and “smoked out” the enemy. A few CSOs tackled a different front, including using social media to gather voices, rallying the support of policymakers, other ministers, and nonhealth civil societies, and coordinating a visit to the government. Program/Policy process: After a CSO disclosed the “kiddie pack” news to the largest selling daily newspaper, many groups followed up with interviews, media statements and letters. Meantime, another CSO reached out to its network of policymakers and nonhealth CSOs for support, and sent briefing points or draft “letters to the government” with key messages that tailored to the senders´ specialty (human rights, environment, etc.) When a protobacco retailer announced it had 3200 signatures from their members, one CSO launched an online petition on its Facebook page, shared the effects of kiddie packs on adolescents, and gathered 10 times the signature within a month. The final effort was a visit to the government - a tactic used by the e-cigarette industry previously - to submit the signatures and statement. Outcomes: The “leak” resulted in a front page coverage, allowing the Health Minister to state his views, and other CSOs to use it as a platform to voice their protest. It was also publicised in mainstream as well as online news in different languages. Within two weeks, the tobacco industry finally revealed its intention, and coordinated its media responses through its supporters. Each of these responses were met - point by point–by CSOs, resulting in at least 20 published “letters”. Nearly 40,400 (online and offline) signatures were gathered, representing 57 CSOs. To date, kiddie packs have not made a “return”. What was learned: Instant messaging applications and social media tools can replace meetings and on-the-ground efforts, especially when CSOs lack funds, time, and human resources. Also, great campaigns should consist of general (e.g., writing to the media) as well as specialized (social media, networking with policymakers) efforts. This allows different CSOs to focus their strengths, avoid redundant tasks or “working in silos”, and have every contribution count.

AutoCRUD: Automatic generation of CRUD specifications in interaction flow modelling language

Data-intensive Web applications are usually developed by means of specific Web application frameworks, which provide, among other features, the proper tools to automate redundant tasks, e.g. the implementation of CRUD operations. However, although Model-Driven Web Engineering (MDWE) approaches have defined specific languages and tools to significantly reduce development efforts, the provision of such automation tools has been traditionally neglected. This paper presents AutoCRUD, a WebRatio plug-in automating the generation of CRUD operations in OMG Interacting Flow Modelling Language (IFML). It has been developed as a joint effort by academia and industry to assess its validity in a real scenario. • A MDWE tool (WebRatio plugin) to automatically generate CRUD operations specification in IFML by pattern instantiation. • New patterns may be defined to enlarge its default repository. • For teaching purposes, a step-by-step generation of IFML may be performed. • Easy and guided visual instantiation of patterns. • It may significantly increase development team productivity.

DF‐DTM: Dynamic Task Memoization and reuse in dataflow

SummaryInstruction Reuse is a technique adopted in Von Neumann architectures that improves performance by avoiding redundant execution of instructions when the result to be produced can be obtained by searching an input/output memoization table for such instruction. Trace reuse can be applied to traces of instructions in a similar fashion. However, those techniques are yet to be studied in the context of the Dataflow model, which has been gaining traction in the high performance computing community due to its inherent parallelism. Dataflow programs are represented by directed graphs where nodes are instructions or tasks and edges denote data dependencies between tasks. This work presents Dataflow Dynamic Task Memoization (DF‐DTM), a technique that allows the reuse of both nodes and subgraphs in dataflow, which are analogous to instructions and traces, respectively. The potential of DF‐DTM is evaluated by a series of experiments that analyze the behavior of redundant tasks in five relevant benchmarks, where up to 99.70% of the instantiated tasks could be reused. Moreover, this paper evaluates how reuse rates can be affected by limiting subgraph size, memoization table size, task granularity, and problem size, showing that DF‐DTM can yield good reuse rates in more realistic environments.

An Uncontrolled Manifold Analysis of Arm Joint Variability in Virtual Planar Position and Orientation Telemanipulation.

In teleoperated robot-assisted tasks, the user interacts with manipulators to finely control remote tools. Manipulation of robotic devices, characterized by specific kinematic and dynamic proprieties, is a complex task for the human sensorimotor system due to the inherent biomechanical and neuronal redundancies that characterize the human arm and its control. We investigate how master devices with different kinematics structures and how different task constraints influence users capabilities in exploiting arm redundancy. A virtual teleoperation workbench was designed and the arm kinematics of seven users was acquired during the execution of two planar virtual tasks, involving either the control of position only or position-orientation of a tool. Using the uncontrolled manifold analysis of arm joint variability, we estimated the logarithmic ratio between the task irrelevant and the task relevant manifolds ( Rv). The Rv values obtained in the position-orientation task were higher than in the position only task, while no differences were found between the master devices. A modulation of Rv was found through the execution of the position task and a positive correlation was found between task performance and redundancy exploitation. Users exploited additional portions of arm redundancy when dealing with the tool orientation. The Rv modulation seems influenced by the task constraints and by the users possibility of reconfiguring the arm position. This paper advances the general understanding of the exploitation of arm redundancy in complex tasks, and can improve the development of future robotic devices.

Challenges and Prospects of Enterprise Resource Planning (ERP) Systems in the Newly Chartered Public Universities in Kenya

Enterprise resource planning (ERP) systems have been the most popular business management systems in the 21st century, providing benefits of real-time capabilities, enhancing organizational competencies and seamless communication for business in both small and large organizations. Today, most universities have begun replacing their legacy systems with ERP initiatives to improve management and administration. Though they are credited with major prospects of re-engineering the work processes, these initiatives present some challenges to the management of these universities. This study focused on the challenges and prospects of ERP systems in the newly chartered public universities in Kenya. A review of previous studies on the challenges and prospects of putting in place this framework in public sector including universities was done. A descriptive survey research design was used to select the staff in all the 24 newly chartered universities in Kenya, while simple random sampling method was used in choosing 120 respondents who were heads of departments. A five point Likert scale questionnaire was used for data collection. A reliability estimate of 0.69 was realized for the instrument using Cronbach reliability coefficient. Data collected was analysed using descriptive statistics and presented in tables and charts. The study established that management support is vital for any organization to be able to successfully implement and use ERP systems effectively. In addition, inadequate preparation by the universities to manage change, failure to involve end-users during the implementation stage, failure by the system vendors to modify the system to conform to the requirements of these universities, failure by the institutions to re-engineer their business processes to match ERP requirements and lack of effective end-user training were also cited as the challenges facing the success of the ERP systems in the newly chartered universities. The findings further indicated successful implementation of the system will eliminate redundant tasks and lead to an overall reduction of operational cost. It will also lead to easier access to reliable data and information, improved customer relationship and the ability to produce better reports and information are some of the prospects that majority of the respondents believe the ERP system will provide. This study may be useful to public universities management by helping them gain better understanding on how to utilize information technologies. It also provides information that will assist them in making informed decisions on the best ERP implementation strategies to be employed. Lastly, the study will creates a forum for discussions on ways of solving ERP challenges currently being faced by the newly chartered public universities in Kenya.

High variability impairs motor learning regardless of whether it affects task performance.

Motor variability plays an important role in motor learning, although the exact mechanisms of how variability affects learning are not well understood. Recent evidence suggests that motor variability may have different effects on learning in redundant tasks, depending on whether it is present in the task space (where it affects task performance) or in the null space (where it has no effect on task performance). We examined the effect of directly introducing null and task space variability using a manipulandum during the learning of a motor task. Participants learned a bimanual shuffleboard task for 2 days, where their goal was to slide a virtual puck as close as possible toward a target. Critically, the distance traveled by the puck was determined by the sum of the left- and right-hand velocities, which meant that there was redundancy in the task. Participants were divided into five groups, based on both the dimension in which the variability was introduced and the amount of variability that was introduced during training. Results showed that although all groups were able to reduce error with practice, learning was affected more by the amount of variability introduced rather than the dimension in which variability was introduced. Specifically, groups with higher movement variability during practice showed larger errors at the end of practice compared with groups that had low variability during learning. These results suggest that although introducing variability can increase exploration of new solutions, this may adversely affect the ability to retain the learned solution. NEW & NOTEWORTHY We examined the role of introducing variability during motor learning in a redundant task. The presence of redundancy allows variability to be introduced in different dimensions: the task space (where it affects task performance) or the null space (where it does not affect task performance). We found that introducing variability affected learning adversely, but the amount of variability was more critical than the dimension in which variability was introduced.

Fault-Tolerant Dynamic Task Mapping and Scheduling for Network-on-Chip-Based Multicore Platform

In Network-on-Chip (NoC)-based multicore systems, task allocation and scheduling are known to be important problems, as they affect the performance of applications in terms of energy consumption and timing. Advancement of deep submicron technology has made it possible to scale the transistor feature size to the nanometer range, which has enabled multiple processing elements to be integrated onto a single chip. On the flipside, it has made the integrated entities on the chip more susceptible to different faults. Although a significant amount of work has been done in the domain of fault-tolerant mapping and scheduling, existing algorithms either precompute reconfigured mapping solutions at design time while anticipating fault(s) scenarios or adopt a hybrid approach wherein a part of the fault mitigation strategy relies on the design-time solution. The complexity of the problem rises further for real-time dynamic systems where new applications can arrive in the multicore platform at any time instant. For real-time systems, the validity of computation depends both on the correctness of results and on temporal constraint satisfaction. This article presents an improved fault-tolerant dynamic solution to the integrated problem of application mapping and scheduling for NoC-based multicore platforms. The developed algorithm provides a unified mapping and scheduling method for real-time systems focusing on meeting application deadlines and minimizing communication energy. A predictive model has been used to determine the failure-prone cores in the system for which a fault-tolerant resource allocation with task redundancy has been performed. By selectively using a task replication policy, the reliability of the application, executing on a given NoC platform, is improved. A detailed evaluation of the performance of the proposed algorithm has been conducted for both real and synthetic applications. When compared with other fault-tolerant algorithms reported in the literature, performance of the proposed algorithm shows an average reduction of 56.95% in task re-execution time overhead and an average improvement of 31% in communication energy. Further, for time-constrained tasks, deadline satisfaction has also been achieved for most of the test cases by the developed algorithm, whereas the techniques reported in the literature failed to meet deadline in about 45% test cases.

Efficient Redundancy Techniques for Latency Reduction in Cloud Systems

In cloud computing systems, assigning a task to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers and reduce latency. But adding redundancy may result in higher cost of computing resources, as well as an increase in queueing delay due to higher traffic load. This work helps in understanding when and how redundancy gives a cost-efficient reduction in latency. For a general task service time distribution, we compare different redundancy strategies in terms of the number of redundant tasks and the time when they are issued and canceled. We get the insight that the log-concavity of the task service time creates a dichotomy of when adding redundancy helps. If the service time distribution is log-convex (i.e., log of the tail probability is convex), then adding maximum redundancy reduces both latency and cost. And if it is log-concave (i.e., log of the tail probability is concave), then less redundancy, and early cancellation of redundant tasks is more effective. Using these insights, we design a general redundancy strategy that achieves a good latency-cost trade-off for an arbitrary service time distribution. This work also generalizes and extends some results in the analysis of fork-join queues.

Task-space separation principle: a force-field approach to motion planning for redundant manipulators

In this work, we address human-like motor planning in redundant manipulators. Specifically, we want to capture postural synergies such as Donders’ law, experimentally observed in humans during kinematically redundant tasks, and infer a minimal set of parameters to implement similar postural synergies in a kinematic model. For the model itself, although the focus of this paper is to solve redundancy by implementing postural strategies derived from experimental data, we also want to ensure that such postural control strategies do not interfere with other possible forms of motion control (in the task-space), i.e. solving the posture/movement problem.The redundancy problem is framed as a constrained optimization problem, traditionally solved via the method of Lagrange multipliers. The posture/movement problem can be tackled via the separation principle which, derived from experimental evidence, posits that the brain processes static torques (i.e. posture-dependent, such as gravitational torques) separately from dynamic torques (i.e. velocity-dependent). The separation principle has traditionally been applied at a joint torque level. Our main contribution is to apply the separation principle to Lagrange multipliers, which act as task-space force fields, leading to a task-space separation principle. In this way, we can separate postural control (implementing Donders’ law) from various types of tasks-space movement planners.As an example, the proposed framework is applied to the (redundant) task of pointing with the human wrist. Nonlinear inverse optimization (NIO) is used to fit the model parameters and to capture motor strategies displayed by six human subjects during pointing tasks. The novelty of our NIO approach is that (i) the fitted motor strategy, rather than raw data, is used to filter and down-sample human behaviours; (ii) our framework is used to efficiently simulate model behaviour iteratively, until it converges towards the experimental human strategies.

Task Scheduling Based Redundant Task Allocation Method for the Multi-Core Systems with the DTTR Scheme

Task Scheduling Based Redundant Task Allocation Method for the Multi-Core Systems with the DTTR Scheme

Intelligent information system to ensure quality in higher education institutions, towards an automated e-university

Despite great efforts to assure quality in higher education institutions, the ambiguity of its related concepts and requirements constitute a big challenge when trying to implement it as an intelligent information system. The present work introduces a framework for an intelligent information system that manages the quality assurance in higher education's institutions. The aim of designing such a system is to provide an automation tool that avoids unnecessary and redundant tasks associated to quality in higher education institutions. In addition, the proposed system helps all higher education stockholders to handle and monitor their tasks. Moreover, it aims to help the quality assurance units in a higher education institution to apply their quality's standards and to make sure that they are being maintained and enhanced. This information system contains a core module and 18 sub-modules, which are described in detail. Finally, the characteristics and components of each of these sub-modules are also discussed.

EtherCAT-integrated Processing Machine with Full Local Task Redundancy

A numerically controlled processing machine, integrated over EtherCAT with full local redundancy in the axes- to task-space mapping has been designed and built in a laboratory. The redundancy arises from a set of slow, long-ranging base axes manipulating a set of fast, short-ranging tool axes, which again holds and manipulates the tool. The principle of the machine is time-efficient in manufacturing applications with high task detail and where the tool process is faster than the long-ranging axes. This paper will give an overview of construction of the machine and experimental trajectory planning.

Adaptability of stride-to-stride control of stepping movements in human walking

Humans continually adapt their movements as they walk on different surfaces, avoid obstacles, etc. External (environmental) and internal (physiological) noise-like disturbances, and the responses that correct for them, each contribute to locomotor variability. This variability may sometimes be detrimental (perhaps increasing fall risk), or sometimes beneficial (perhaps reflecting exploration of multiple task solutions). Here, we determined how humans regulated stride-to-stride fluctuations in walking when presented different task goals that allowed them to exploit inherent redundancies in different ways. Fourteen healthy adults walked on a treadmill under each of four conditions: constant speed only (SPD), constant speed and stride length (LEN), constant speed and stride time (TIM), or constant speed, stride length, and stride time (ALL). Multiple analyses tested competing hypotheses that participants might attempt to either equally satisfy all goals simultaneously, or instead adopt systematic intermediate strategies that only partly satisfied each individual goal. Participants exhibited similar average stepping behavior, but significant differences in variability and stride-to-stride serial correlations across conditions. Analyses of the structure of stride-to-stride fluctuation dynamics demonstrated humans resolved the competing goals presented not by minimizing errors equally with respect to all goals, but instead by trying to only partly satisfy each goal. Thus, humans exploit task redundancies even when they are explicitly removed from the task specifications. These findings may help identify when variability is predictive of, or protective against, fall risk. They may also help inform rehabilitation interventions to better exploit the positive contributions of variability, while minimizing the negative.