Dependent Tasks Research Articles

DBE‐Net: A Dual‐Branch Boundary Enhancement Network for Pathological Image Segmentation

ABSTRACTPathological image segmentation provides support for the accurate assessment of lesion area by precisely segmenting various tissues and cellular structures in pathological images. Due to the unclear boundaries between targets and backgrounds, as well as the information loss during upsampling and downsampling operations, it remains a challenging task to identify boundary details, especially in differentiating between adjacent tissues, minor lesions, or clustered cell nuclei. In this paper, a Dual‐branch Boundary Enhancement Network (DBE‐Net) is proposed to improve the sensitivity of the model to the boundary. Firstly, the proposed method includes a main task and an auxiliary task. The main task focuses on segmenting the target object and the auxiliary task is dedicated to extracting boundary information. Secondly, a feature processing architecture is established which includes three modules: Feature Preservation (FP), Feature Fusion (FF), and Hybrid Attention Fusion (HAF) module. The FP module and the FF module are used to provide original information for the encoder and fuse information from every layer of the decoder. The HAF is introduced to replace the skip connections between the encoder and decoder. Finally, a boundary‐dependent loss function is designed to simultaneously optimize both tasks for the dual‐branch network. The proposed loss function enhances the dependence of the main task on the boundary information supplied by the auxiliary task. The proposed method has been validated on three datasets, including Glas, CoCaHis, and CoNSep dataset.

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Collaborative Optimization Strategy for Dependent Task Offloading in Vehicular Edge Computing

The advancement of the Internet of Autonomous Vehicles has facilitated the development and deployment of numerous onboard applications. However, the delay-sensitive tasks generated by these applications present enormous challenges for vehicles with limited computing resources. Moreover, these tasks are often interdependent, preventing parallel computation and severely prolonging completion times, which results in substantial energy consumption. Task-offloading technology offers an effective solution to mitigate these challenges. Traditional offloading strategies, however, fall short in the highly dynamic environment of the Internet of Vehicles. This paper proposes a task-offloading scheme based on deep reinforcement learning to optimize the strategy between vehicles and edge computing resources. The task-offloading problem is modeled as a Markov Decision Process, and an improved twin-delayed deep deterministic policy gradient algorithm, LT-TD3, is introduced to enhance the decision-making process. The integration of LSTM and a self-attention mechanism into the LT-TD3 network boosts its capability for feature extraction and representation. Additionally, considering task dependency, a topological sorting algorithm is employed to assign priorities to subtasks, thereby improving the efficiency of task offloading. Experimental results demonstrate that the proposed strategy significantly reduces task delays and energy consumption, offering an effective solution for efficient task processing and energy saving in autonomous vehicles.

Prioritized Assignment With Task Dependency in Collaborative Mobile Edge Computing

Prioritized Assignment With Task Dependency in Collaborative Mobile Edge Computing

Work Sync 360

To optimize project and task management in businesses, WorkSync 360 is a stand-alone internet solution that doesn't require any additional tools. It offers a user-friendly interface for keeping track of, arranging, and enhancing work processes. Timely job distribution, deadline control, and real-time progress tracking are critical components that enhance productivity and teamwork. With its news feed, discussion boards, and tools for involving coworkers, WorkSync 360 acts as a safe haven for internal communication, encouraging transparency and information sharing. Task dependencies, connected calendars, file sharing, and other essential technologies are combined by WorkSync 360 to centralize project management operations. Teams are able to improve teamwork, achieve operational efficiency, and effectively manage projects in a single digital environment.

Dependent task offloading for air-ground integrated MEC networks: a multi-agent collaboration approach

Dependent task offloading for air-ground integrated MEC networks: a multi-agent collaboration approach

Efficient slicing scheme and cache optimization strategy for structured dependent tasks in intelligent transportation scenarios

The challenges posed by structured large-scale tasks to resource-sensitive intelligent transportation systems have been acknowledged, particularly regarding the need to reduce delay and energy consumption during the caching and offloading processes. To address these challenges and improve the quality of service for vehicular users, a cloud–edge-end collaboration caching strategy (CACCSC) based on structured task content awareness was proposed in this paper. The dependencies among task fragments were modeled through fuzzy judgment criteria. In addition, a system delay model, an energy consumption model, and an edge server load balancing model were developed, along with a multi-objective optimization model that integrates system delay, energy consumption, and edge server load balancing variance. To solve this multi-objective optimization problem, an adaptive multi-objective optimization algorithm (MDE-NSGA-III) was developed, which combines an enhanced version of the Differential Evolution algorithm with improvements to the NSGA-III algorithm. Finally, it has been demonstrated through simulation experiments that when the number of users in the system reaches 35, the system delay, energy consumption, and load balancing variance of the MDE-NSGA-III optimization scheme proposed in this paper are 6.1%, 6.6%, and 25% lower than those of the NSGA-III scheme, 15.8%, 10%, and 41.7% lower than those of the NSGA-II scheme, and 62.7%, 20.7%, and 8.3% lower than those of the PeEA scheme.

Efficient Workflow Scheduling for Minimizing Data Transfers and Enhancing Resource Utilization in Cloud IaaS Platforms

Cloud IaaS platforms readily provide access to homogeneous multi-core machines, whether they are physical ("bare metal") or virtual machines. Each of these machines can be equipped with high-performance SSD disks, enabling the distribution of workflow-generated files across multiple machines, which helps minimize the overhead associated with data transfers. In this paper, we propose a scheduling algorithm called SMDT-ERU (Scheduling for Minimizing Data Transfer - Enhancing Resource Utilization), designed to reduce the makespan of data-intensive workflows by minimizing data transfers between dependent tasks over the network. Intermediate files generated by tasks are stored locally on the disk of the machine where the tasks are executed. Through experimentation, we confirm that increasing the number of cores per machine reduces the additional costs caused by network data transfers. Real-world workflow experiments demonstrate the advantages of the proposed algorithm. Our data-driven scheduling approach significantly reduces execution time and the volume of data transferred over the network, outperforming one of the leading state-of-the-art algorithms, which we have adapted to fit our assumptions.

Optimizing the Agricultural Internet of Things (IoT) with Edge Computing and Low-Altitude Platform Stations.

Using low-altitude platform stations (LAPSs) in the agricultural Internet of Things (IoT) enables the efficient and precise monitoring of vast and hard-to-reach areas, thereby enhancing crop management. By integrating edge computing servers into LAPSs, data can be processed directly at the edge in real time, significantly reducing latency and dependency on remote cloud servers. Motivated by these advancements, this paper explores the application of LAPSs and edge computing in the agricultural IoT. First, we introduce an LAPS-aided edge computing architecture for the agricultural IoT, in which each task is segmented into several interdependent subtasks for processing. Next, we formulate a total task processing delay minimization problem, taking into account constraints related to task dependency and priority, as well as equipment energy consumption. Then, by treating the task dependencies as directed acyclic graphs, a heuristic task processing algorithm with priority selection is developed to solve the formulated problem. Finally, the numerical results show that the proposed edge computing scheme outperforms state-of-the-art works and the local computing scheme in terms of the total task processing delay.

DMITS: Dependency and Mobility-Aware Intelligent Task Scheduling in Socially-Enabled VFC Based on Federated DRL Approach

DMITS: Dependency and Mobility-Aware Intelligent Task Scheduling in Socially-Enabled VFC Based on Federated DRL Approach

REPLANTEAMIENTO DE LA PLANIFICACIÓN DE PROYECTOS MINEROS: USO DE NASA-TLX PARA MEDIR UNA NOVEDOSA INTEGRACIÓN ENTRE OTSM-TRIZ Y DSM

This research introduces an integrated methodology combining OTSM-TRIZ and the Design Structure Matrix (DSM) to enhance project planning and risk management in mining projects. OTSM-TRIZ, through a Network of Problems (NoP), systematically addresses complex challenges, while DSM analyzes task dependencies, optimizing resource allocation and sequencing. Applied in a real-world Chilean mining company with over 120 workers, the methodology improved outcomes by reducing trial-and-error iterations and boosting efficiency. Workers gained valuable insights into managing project complexity. NASA-TLX was used to explore usefulness authors’ proposal. Keywords: Mining Projects, OTSM-TRIZ, DSM, NASA-TLX

Volunteer vehicle assisted dependent task offloading based on ant colony optimization algorithm in vehicular edge computing

Vehicle Edge Computing improves the Quality of Service of vehicular applications by offloading tasks to the VEC server. However, with the continuous development of computation-intensive vehicular applications, the limited resources of the VEC server will not be enough to support these applications. Volunteer Computing-Based Vehicular Ad-hoc Networking (VCBV) proposes a concept of using vehicles as resources, which is considered to be a promising solution. In this paper, we study the multi-dependent task offloading problem in order to quickly and economically handle the overload task of the requesting vehicle in VCBV. Considering both task execution delay and execution cost, we formulate the problem of offloading the multi-dependent tasks of requesting vehicles to minimize total task completion time and execution cost. Since the offloading problem is NP-hard, an improved multi-objective Ant Colony Optimization algorithm is proposed. Firstly, we use a density-based clustering algorithm to form volunteer alliances that can contribute idle resources. Secondly, based on the volunteer alliances and RSUs, we use Analytic Hierarchy Process (AHP) to initialize pheromone concentration to make better decisions. Then, we design the update strategy of the pheromone concentration and heuristic information. Finally, we introduce Pareto optimal relationship to evaluate the results. A large number of simulation results verify that our algorithm has better performance than other alternatives.

SHIELD: Security-aware Schreduling for Real-Time DAGs on Heterogeneous Systems

Many control applications in real-time cyber-physical systems are represented as Directed Acyclic Graphs ( DAGs ) due to complex interactions among their functional components, and executed on distributed heterogeneous platforms. Data communication between dependent task nodes running on different processing elements are often realized through message transmission over a public network, and are hence susceptible to multiple security threats such as snooping , alteration and spoofing . Several alternative security protocols having varying security strengths and associated implementation overheads are available in the market, for incorporating confidentiality , integrity and authentication on the transmitted messages. While message size and conceptually its associated transmission overheads may be marginally increased due to the assignment of security protocols, significant computation overheads must be incurred for securing the message at the location of its source task node and for unlocking security/message extraction at the destination. Obtained security strengths and associated computation overheads vary depending on the set of protocols chosen for a given message from an available pool of protocols. Given lower bounds on the security demands of an application's messages, selecting the appropriate protocols for each message such that a system's overall security is maximized while satisfying constraints related to the resource, task precedence and deadline, is a challenging and computationally hard problem. In this paper, we propose an efficient heuristic strategy called SHIELD for security-aware real-time scheduling of DAG-structured applications to be executed on distributed heterogeneous systems. The efficacy of the proposed scheduler is exhibited through extensive simulation-based experiments using two DAG-structured application benchmarks. Our performance evaluation results demonstrate that SHIELD significantly outperforms two greedy baseline strategies SHIELDb in terms of solution generation times (i.e., run-times) and SHIELDf in terms of achieved security utility. Additionally, a case study on the Traction Control application in automotive systems has been included to exhibit the applicability of SHIELD in real-world settings.

NUMA-aware parallel sparse LU factorization for SPICE-based circuit simulators on ARM multi-core processors

In circuit simulators that resemble the Simulation Program with Integrated Circuit Emphasis (SPICE), one of the most crucial steps is the solution of numerous sparse linear equations generated by frequency domain analysis or time domain analysis. The sparse direct solvers based on lower-upper (LU) factorization are extremely time-consuming, so their performance has become a significant bottleneck. Despite the existence of some parallel sparse direct solvers for circuit simulation problems, they remain challenging to adapt in terms of performance and scalability in the face of rapidly evolving parallel computers with multiple NUMA hardware based on ARM architecture. In this paper, we introduce a parallel sparse direct solver named HLU, which re-examines the performance of the parallel algorithm from the viewpoint of parallelism in pipeline mode and the computing efficiency of each task. To maximize task-level parallelism and further minimize the thread waiting time, HLU devises a fine-grained scheduling method based on an elimination tree in pipeline mode, which employs depth-first search (DFS-like) to iteratively search for parent tasks and then place dependent tasks in the same task queue. HLU also suggests two NUMA node affinity strategies: thread affinity optimization based on NUMA nodes topology to guarantee computational load balancing and data affinity optimization to enable effective memory placement when threads access data. The rationality and effectiveness of the sparse solver HLU are validated by the SuiteSparse Matrix Collection. In comparison with KLU and NICSLU, the experimental results and analysis show that HLU attains a speedup of up to 9.14× and 1.26x (geometric mean) on a Huawei Kunpeng 920 Server, respectively.

Workflow Scheduling in Cloud–Fog Computing Environments: A Systematic Literature Review

ABSTRACTThe Internet of Things (IoT) facilitates the connectivity of billions of physical devices for exchanging information and enabling a wide range of applications. These applications can be presented in the form of dependent tasks, as outlined in a workflow. These workflows face limitations due to constraints in IoT sensors. To address these limitations, cloud computing has emerged to offer a large capacity of computing and storing with a great capability to adjust resources according to the need. However, cloud computing might not adequately meet the low‐latency of IoT workflow requirements when scheduling a workflow composed of IoT tasks due to its centralized nature. Moreover, cloud computing is not ideal for delay‐sensitive workflows and may increase communication costs. In response to these challenges, the use of fog computing as an extension to cloud computing scheme is recommended. Fog computing aims to process workflow tasks close to IoT devices. While fog computing offers various advantages, integrating these systems into workflow scheduling remains one of the most formidable challenges in distributed environments. Indeed, significant issues arise due to the timely execution and the resource limitations. In this survey paper, we present a Systematic Literature Review (SLR) on the current state of the art in this domain. We propose a taxonomy to compare and evaluate the existing studies on workflow scheduling approaches in cloud–fog computing environments. This taxonomy encompasses various criteria, including scheduling techniques, performance metrics, workflow dependencies, scheduling policies, and evaluation tools. We highlight certain recommendations for open issues which require more investigations. Our aim is to provide valuable insights for researchers and developers interested in understanding the contributions and challenges of current workflow scheduling approaches in cloud–fog computing environments.

Two "What" Networks in the Human Brain.

Ungerleider and Mishkin, in their influential work that relied on detailed anatomical and ablation studies, suggested that visual information is processed along two distinct pathways: the dorsal "where" pathway, primarily responsible for spatial vision, and the ventral "what" pathway, dedicated to object vision. This strict division of labor has faced challenges in light of compelling evidence revealing robust shape and object selectivity within the putative "where" pathway. This article reviews evidence that supports the presence of shape selectivity in the dorsal pathway. A comparative examination of dorsal and ventral object representations in terms of invariance, task dependency, and representational content reveals similarities and differences between the two pathways. Both exhibit some level of tolerance to image transformations and are influenced by tasks, but responses in the dorsal pathway show weaker tolerance and stronger task modulations than those in the ventral pathway. Furthermore, an examination of their representational content highlights a divergence between the responses in the two pathways, suggesting that they are sensitive to distinct features of objects. Collectively, these findings suggest that two networks exist in the human brain for processing object shapes, one in the dorsal and another in the ventral visual cortex. These studies lay the foundation for future research aimed at revealing the precise roles the two "what" networks play in our ability to understand and interact with objects.

Balancing enjoyment and learning in teaching software project management with game-based learning

Despite advancements in project management education, project failures remain common, often due to insufficient experience and skills. Games offer a valuable platform for teaching practical project management skills, allowing players to learn from mistakes without significant economic costs. However, many serious games in this field lack focus on engagement and motivation. The "Software Project Management Game" introduced in this article addresses these issues by incorporating motivational game design principles, drawing inspiration from entertainment games like Game Dev Tycoon and The Sims. The game ensures a smooth flow without unnecessary interruptions, putting players in control. Players progress through five projects of increasing complexity, gradually developing their skills and learning soft skills and task dependencies. This article details the design, implementation, and evaluation of the game, focusing on gameflow, intrinsic motivation, perceived learning, and usability. The results indicate that the game successfully balances enjoyment and learning, making it an effective tool for teaching core project management skills.

Adductor Laryngeal Dystonia Versus Muscle Tension Dysphonia: Examining the Utility of Automated Acoustic Analysis to Detect Task Dependency as a Distinguishing Feature.

Differentiating adductor laryngeal dystonia (ADLD) and primary muscle tension dysphonia (pMTD) can be challenging. Unlike pMTD, ADLD is described as "task-dependent" with voiced phonemes purportedly provoking greater sign expression than voiceless phonemes. We evaluated the ability of two automated acoustic measures, the Cepstral Spectral Index of Dysphonia (CSID) and creak, to detect task dependency and to discriminate ADLD and pMTD. CSID, % creak, and listener ratings of dysphonia severity were obtained from audio recordings of patients with ADLD (n = 29) or pMTD (n = 33) reading two sentences loaded with either voiced or voiceless phonemes. Group × Sentence Type interaction effects confirmed that both "normalized" CSID and % creak detected task-dependent sign expression in ADLD (i.e., worse symptoms on the voiced- vs. voiceless-loaded sentence). However, a stepwise binary logistic regression analysis with group (ADLD vs. pMTD) as the dependent variable and % creak and normalized CSID variables (voiced, voiceless, and voiced vs. voiceless difference) as covariates revealed that the normalized CSID voiceless-laden sentence z score was the only significant predictor of group membership. Estimates of diagnostic precision from the normalized CSID voiceless sentence z scores were superior to % creak or listener ratings. Finally, the CSID possessed the strongest correlations with listener severity ratings regardless of group or sentence type. Although both normalized CSID and % creak detected task-dependent performance as a distinguishing feature of ADLD, a CSID profile wherein (a) the voiceless sentence z score was less severe than the voiced sentence and (b) the normalized voiceless sentence z score was within approximately 2 SDs (or less) of typical expectations provided the best estimates of diagnostic precision. Automated acoustic measures such as the CSID and creak provide useful information to objectively discriminate ADLD and pMTD.

Energy efficient dynamic scheduling of dependent tasks for multi‐core real‐time systems using delay techniques

SummaryOptimizing energy consumption and maximizing throughput in multi‐core real‐time architectures through dynamic task scheduling is a critical design challenge. While significant attention has been devoted to addressing this challenge in the domain of real‐time multi‐core scheduling, the focus has primarily centered on considering periodic tasks as independent. However, the existing literature notably lacks comprehensive study of scheduling methodologies on multi‐core systems that consider dependent tasks, though typical real‐time systems execute tasks that share resources. Earlier studies have predominantly examined scenarios involving random new tasks and task instances (jobs), which are executed in different power levels. Each task (and job) has distinct execution time corresponding to each power level. By considering these parameters (power levels and execution times of jobs), various combinations of energy signatures have been found to attain an optimum system state. Building upon this prior research, our paper extends the scope to encompass task scheduling in multi‐core systems with task dependencies. We introduce a novel approach that categorizes dependent tasks into ASAP (as soon as possible) and ALAP (as late as possible) groups, prioritizing task execution based on task mobility—defined as the disparity between the last cycle the task can be scheduled in and the current cycle. Furthermore, our model demonstrates an approach for efficient scheduling of sporadic and aperiodic tasks within this framework. Through experimental validation using randomized task sets, our results indicate that the proposed model achieves a minimum of 5% reduction in normalized total energy consumption compared to existing methodologies.

Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks

Asynchronous Federated Deep-Reinforcement-Learning-Based Dependency Task Offloading for UAV-Assisted Vehicular Networks