Task Placement Research Articles

Multi-Institutional Collaboration in Human Resource Development at Madrasah Ibtidaiyah Negeri (MIN) 3 Deli Serdang

This article discusses improving the quality of education through multi-institutional collaboration with a case study at MIN 3 Deli Serdang. This study aims to analyze and describe HR management in this Islamic educational institution. This research uses a qualitative approach with a case study method. Data were collected through observation, documentation, and structured interviews. The results show that collaboration between madrasahs and the government, as well as the private sector and community, significantly improves the quality of education and human resource development (HRD). This collaboration improves teachers' professional competence and strengthens community trust in madrasahs. The study also highlights the critical role of madrasah principals in HR management, from procurement to educator development. Appropriate task placement and systematic coaching programs, such as regular meetings, training, seminars, and supervision, support the improvement of teachers' professionalism. The madrasah principal also motivates and facilitates teachers' career development and improves their welfare, especially for honorary teachers. This research provides recommendations for Islamic education institutions to strengthen multi-agency collaboration in human resource development. Effective collaboration can create a conducive environment for the growth and capacity development of educators and learners, improve the quality of teaching and learning, and increase the nation's competitiveness at the global level.

РЕАЛІЗАЦІЯ ДИФЕРЕНЦІЙОВАНОГО ПІДХОДУ ПРИ НАВЧАННІПРОГРАМУВАННЮ МОВОЮ PYTHON ЗДОБУВАЧІВЗАГАЛЬНОЇ СЕРЕДНЬОЇ ОСВІТИ

Students choose their future path starting from school. The learner can determine their abilities when they genuinely understand their potential and prospects in a specific subject or direction. A differentiated approach helps explore the limits of knowledge and encourages its expansion. The paper discusses the implementation of a differentiated approach in teaching computer science, particularly in programming. Tasks are proposed to students, emphasizing the method of solving. Each learner chooses the method that suits them best. Evaluation criteria are provided in advance, helping students identify the aspects of the task to focus on. An example task demonstrating the implementation of a differentiated approach involves calculating the recommended body weight for a woman and a man using the Broca's Formula. Depending on the complexity chosen by the student, this task can be solved using a linear structure, applying branching, or developing a widget. Widgets allow students to demonstrate the use of libraries and functions in the Python programming language. Various solutions in Python and recommendations for the placement of such tasks in the study of a specific topic are also provided. During the defense, all students become acquainted with different task implementations, allowing each student to understand various execution methods and choose a more challenging one next time. The presented example can be extended to other topics, in the study of other programming languages or programming environments. Prospects for further research include developing a system of multi-level programming tasks for the purpose of implementing differentiated learning

LATA: learning automata-based task assignment on heterogeneous cloud computing platform

A cloud computing environment is a distributed system where idle resources are accessible across a wide area network, such as the Internet. Due to the diverse specifications of these resources, computational clouds exhibit high heterogeneity. Task scheduling, the process of dispatching cloud applications onto processing nodes, becomes a critical challenge in such environments. Ensuring high utilization in this heterogeneous environment entails identifying suitable machines or virtual machines capable of efficiently executing jobs, constituting a multi-objective optimization problem. This paper proposes a dynamic Learning Automata-based Task Assignment algorithm, named LATA, to address this challenge. In the algorithm, each application is represented as a Directed Acyclic Graph, with tasks as nodes and data dependencies as edges. Initially, tasks are grouped based on their data dependencies to consolidate independent tasks into one group. Subsequently, a variable-structure learning automaton is assigned to each group of tasks to identify appropriate task-machine combinations. The primary objectives of LATA include minimizing makespan and energy consumption by facilitating efficient task placement to achieve load balance and maximize resource utilization. Additionally, an enhancement is proposed, involving the use of a different grouping policy prior to task assignment to further improve performance. Computer simulation results demonstrate the superior performance of the proposed algorithms in highly heterogeneous environments compared to state-of-the-art algorithms. Notably, total execution time and energy consumption decrease by up to 50% and 37%, respectively.

A learning-based data and task placement mechanism for IoT applications in fog computing: a context-aware approach

A learning-based data and task placement mechanism for IoT applications in fog computing: a context-aware approach

On Algorithms of Task Placement in Computing Clusters Taking into Account Task and System Topologies

On Algorithms of Task Placement in Computing Clusters Taking into Account Task and System Topologies

DDPG-based optimal task placement strategy for computation offloading in green mobile edge networks

DDPG-based optimal task placement strategy for computation offloading in green mobile edge networks

Dynamic modelling and energy-efficiency optimization in a 3-DOF parallel robot

AbstractEnergy efficiency is a challenging and relevant research field in modern manufacturing industries, where robotic systems play an essential role in the automation of several industrial operations. In this paper, we present an approach for the energy-efficiency optimization of a 3-DOF parallel robot. The proposed strategy leverages the task placement, the execution time, and the length of the robot lower arms to minimize the energy consumption for the execution of a predefined high-speed pick-and-place operation. To evaluate the actuators energy consumption, the kinematic, dynamic and electro-mechanic mathematical models, as well as an equivalent multibody model, of the parallel robot are implemented. The results of extensive numerical simulations show that the proposed strategy provides notable improvements in the energy efficiency of the parallel robot, with respect to alternative approaches. Starting from a pick-and-place task with optimal task placement with a consumption of 38.2 J (with a cycle time of 0.4 s), the energy expenditure can be reduced to 3.75 J (with a cycle time of 1.86 s), with a reduction percentage of 90.2%, by additionally optimizing the execution time, and the length of the robot lower arms. These results lead to a reduction from 5733 J/min (for 150 cycles/min) to 121 J/min (for 32 cycles/min), allowing to choose the best trade-off between robot productivity and consumed energy.

An ML‐based task clustering and placement using hybrid Jaya‐gray wolf optimization in fog‐cloud ecosystem

SummaryThe rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important challenge is the optimal task placement which is an NP‐class problem. This work applies machine learning for task clustering and addresses the task placement problem in a fog computing environment using a hybrid of two recent metaheuristics; Jaya and gray wolf optimization (GWO). The hybrid method considers optimizing the total number of active fog nodes, load balancing in fog nodes, and average response time of the tasks. The performance of the proposed method is evaluated on a real‐time LCG dataset and is compared with reinforcement learning fog scheduling (RLFS), genetic algorithm (GA), dynamic resource allocation mechanism (DRAM), load balancing and scheduling algorithm (LBSSA), and particle swarm optimization with simulated annealing (PSO‐SA) algorithms. The results demonstrate the superiority of the suggested method over the baseline techniques in terms of average improvement of 51.04% in load balance variance, 30.25% in average response time, 24.16% in execution time, and 47.10% in the number of devices used.

Online Learning Aided Decentralized Multi-User Task Offloading for Mobile Edge Computing

Mobile edge computing facilitates users to offload computation tasks to edge servers for meeting their stringent delay requirements. Previous works mainly explore task offloading when system-side information is given (e.g., server processing speed, cellular data rate), or centralized offloading under system uncertainty. But both generally fall short of handling task placement involving many coexisting users in an uncertain environment. In this paper, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-user</i> offloading framework considering <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unknown yet stochastic</i> system-side information to enable a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decentralized user-initiated</i> service placement under overlapping server coverage. Specifically, we formulate the dynamic task placement as an online multi-user multi-armed bandit process, and propose a decentralized epoch based offloading (DEBO) to optimize user rewards which are subjected under network delay. We consider both cases without and with neighboring edge feedback once users' tasks are processed, where the latter incorporates system-side information sharing among edge servers for an enhanced task placement. For both cases, we show that DEBO can gradually deduce the optimal user-server assignment during dynamic offloading, thereby achieving a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">close-to-optimal</i> service performance and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tight</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(\log _{2} T)$</tex-math></inline-formula> regret. Moreover, we generalize DEBO to various common scenarios such as unknown reward gap, dynamic entering or leaving of clients, and fair reward distribution, while further exploring when users' offloaded tasks require <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heterogeneous</i> computing resources. Particularly, we accomplish a sub-linear regret for each of these instances. Real measurements based evaluations corroborate the superiority of our offloading schemes over state-of-the-art approaches in optimizing delay-sensitive rewards.

Joint optimization of phase shift and task offloading for RIS-assisted multi-access edge computing in beyond 6G communication

Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.

UGV-awareness task placement in edge-cloud based urban intelligent video systems

UGV-awareness task placement in edge-cloud based urban intelligent video systems

Energy-focused simulation of edge computing architectures in 5G networks

While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network’s edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions.

Efficient Task-Network Scheduling With Task Conflict Metric in Time-Sensitive Networking

With the rapid development of Industrial Internet of Things (IIoT), Time-Sensitive Networking (TSN) with deterministic and real-time features has gained broad interest. However, most existing research focuses on the network scheduling with fixed task placement and computing resource allocation, restricting the scheduling space of coupled task-network. To tackle this coupling problem, an efficient task-network scheduling (ETNS) scheme is proposed in this paper for time-sensitive networking. A task-conflict metric (TCM) is established to quantify the competition degree of scheduling resources. For increasing the overall scheduling space, a TCM-aware pre-scheduling method is proposed by optimizing task placement and routing paths to reduce the potential conflicts between tasks. Integrated with the pre-scheduling method, we design a TCM-aware parallel group-scheduling algorithm by reducing the conflicts between task groups to enhance schedulability and scalability. Experiments show that our ETNS scheme significantly improves the schedulability and scalability performances compared with the existing scheduling approaches. The larger number of tasks, the higher the performance improvement.

Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration in Heterogeneous Systems

Optimizing Task Placement and Online Scheduling for Distributed GNN Training Acceleration in Heterogeneous Systems

PARING: Joint Task Placement and Routing for Distributed Training With In-Network Aggregation

PARING: Joint Task Placement and Routing for Distributed Training With In-Network Aggregation

Achieving Cost Optimization for Tenant Task Placement in Geo-Distributed Clouds

Achieving Cost Optimization for Tenant Task Placement in Geo-Distributed Clouds

Task Placement and Resource Allocation for Edge Machine Learning: A GNN-Based Multi-Agent Reinforcement Learning Paradigm

Task Placement and Resource Allocation for Edge Machine Learning: A GNN-Based Multi-Agent Reinforcement Learning Paradigm

Region-aware dynamic job scheduling and resource efficiency for load balancing based on adaptive chaotic sparrow search optimization and coalitional game in cloud computing environments

Uneven distribution of workloads represents a significant challenge in cloud data centers, negatively impacting the efficient utilization of resources. To address this problem and optimize the performance-to-profit (PP) ratio, we introduced the Regional Awareness dynamic Scheduling Algorithm (RASA) through a three-phase approach. In the initial phase, tasks are categorized based on critical parameters like CPU usage, memory utilization, and I/O operations, using a task classification model. The second phase involves estimating the CPU, memory, and I/O capacities of each node. Subsequently, a unique merge-and-split-based coalitional game-theoretic process is employed to group nodes into clusters. In the final phase, we have improved task placement by introducing an enhanced Sparrow Search Algorithm (ISSA) to overcome the slow convergence and local optimization issues found in the traditional SSA. We have incorporated the Tent chaotic mapping approach to enhance both global and local search efficiency during this stage. Additionally, we have fine-tuned fair load distribution using the Cauchy mutation process. We performed simulations using the CloudSim toolkit, and the results underscore the RASA algorithm’s superiority in terms of how quickly it converges its precision in task placement and its ability to distribute workloads fairly. Our proposed algorithm achieves a 9% reduction in latency overhead, a 14% decrease in processing time, a 15% reduction in workload imbalance, a 19% decrease in energy consumption, and a 26% decrease in idle periods. Furthermore, it enhances resource availability and efficiency by 22% and 27% respectively, while simultaneously boosting service throughput by 32%.

Constraint-Aware Federated Scheduling for Data Center Workloads

The use of data centers is ubiquitous, as they support multiple technologies across domains for storing, processing, and disseminating data. IoT applications utilize both cloud data centers and edge data centers based on the nature of the workload. Due to the stringent latency requirements of IoT applications, the workloads are run on hardware accelerators such as FPGAs and GPUs for faster execution. The introduction of such hardware alongside existing variations in the hardware and software configurations of the machines in the data center, increases the heterogeneity of the infrastructure. Optimal job performance necessitates the satisfaction of task placement constraints. This is accomplished through constraint-aware scheduling, where tasks are scheduled on worker nodes with appropriate machine configurations. The presence of placement constraints limits the number of suitable resources available to run a task, leading to queuing delays. As federated schedulers have gained prominence for their speed and scalability, we assess the performance of two such schedulers, Megha and Pigeon, within a constraint-aware context. We extend our previous work on Megha by comparing its performance with a constraint-aware version of the state-of-the-art federated scheduler Pigeon, PigeonC. The results of our experiments with synthetic and real-world cluster traces show that Megha reduces the 99th percentile of job response time delays by a factor of 10 when compared to PigeonC. We also describe enhancements made to Megha’s architecture to improve its scheduling efficiency.

DAG Scheduling in Mobile Edge Computing

In Mobile Edge Computing, edge servers have limited storage and computing resources that can only support a small number of functions. Meanwhile, mobile applications are becoming more complex, consisting of multiple dependent tasks, modeled as a Directed Acyclic Graph (DAG). When a request arrives, typically in an online manner with a deadline specified, we need to configure the servers and assign the dependent tasks for efficient processing. This work jointly considers the problem of dependent task placement and scheduling with on-demand function configuration on edge servers, aiming to meet as many deadlines as possible. For a single request, when the configuration on each edge server is fixed, we derive FixDoc to find the optimal task placement and scheduling. When the on-demand function configuration is allowed, we propose GenDoc , a novel approximation algorithm, and analyze its additive error from the optimal theoretically. For multiple requests, we derive OnDoc , an online algorithm easy to deploy in practice. Our extensive experiments show that GenDoc outperforms state-of-the-art baselines in processing 86.14% of these unique applications, and reduces their average completion time by at least 24%. The number of deadlines that OnDoc can satisfy is at least 1.9× that of the baselines.