Concurrent Execution Of Tasks Research Articles

Decentralized Scheduling for Concurrent Tasks in Mobile Edge Computing via Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising solution to enhance the computing capability of resource-limited networks. A fundamental problem in MEC is efficiently offloading tasks from user devices to edge servers. However, there still exists a gap to deploy in real-world environments: 1) traditional centralized approaches needs complete information of edge network, ignoring the communication costs generated by synchronization, 2) previous works do not consider concurrent computation on edge servers, which may cause dynamic changes in the environment, and 3) the scheduling algorithm should deliver individualized decisions for different users independently and with high efficiency To solve this mismatch, we studied a multi-user task offloading problem where user devices make offloading decisions independently. We consider the concurrent execution of tasks and formulate a non-divisible and delay-aware task offloading problem to jointly minimize the dropped task ratio and long-term latency. We propose a decentralized task scheduling algorithm based on DRL that makes offloading decisions without knowing the information of other user devices. We employ Double-DQN, Dueling-DQN, Prioritized Replay Memory, and Recurrent Neural Network (RNN) techniques to improve the algorithm's performance. The results of simulation experiments show that our method can significantly reduce the long-term latency and dropped task ratio compared to the baseline algorithms.

Integration of Efficient Techniques Based on Endpoints in Solution Method for Lifelong Multiagent Pickup and Delivery Problem

We investigate the integration of several additional efficient techniques that improve a solution method for the lifelong multiagent pickup-and-delivery (MAPD) problem to reduce the redundancy in the concurrent task execution and space usage of a warehouse map. The lifelong MAPD problem is an extended class of iterative multiagent pathfinding problems where a set of shortest collision-free travel paths of multiple agents is iteratively planned. This problem models a system in automated warehouses with robot-carrier agents that are allocated to pickup-and-delivery tasks generated on demand. In the task allocation to agents, several solution methods for lifelong MAPD problems consider the endpoints of the agents’ travel paths to avoid the deadlock situations among the paths due to the conflict of the endpoints. Since redundancies are found in the problem settings themselves and the concurrency of allocated tasks, several additional techniques have been proposed to reduce them in solution methods. However, there should be opportunities to investigate the integration of additional techniques with improvements for more practical solution methods. As analysis and an improved understanding of the additional solution techniques based on endpoints, we incrementally integrate the techniques and experimentally investigate their contributions to the quality of task allocation and the paths of the agents. Our result reveals significant complementary effects of the additionally integrated techniques and trade-offs among them in several different problem settings.

A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud

Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost.

Meta-heuristic-based offloading task optimization in mobile edge computing

With the recent advancements in communication technologies, the realization of computation-intensive applications like virtual/augmented reality, face recognition, and real-time video processing becomes possible at mobile devices. These applications require intensive computations for real-time decision-making and better user experience. However, mobile devices and Internet of things have limited energy and computational power. Executing such computationally intensive tasks on edge devices either leads to high computation latency or high energy consumption. Recently, mobile edge computing has been evolved and used for offloading these complex tasks. In mobile edge computing, Internet of things devices send their tasks to edge servers, which in turn perform fast computation. However, many Internet of things devices and edge server put an upper limit on concurrent task execution. Moreover, executing a very small size task (1 KB) over an edge server causes increased energy consumption due to communication. Therefore, it is required to have an optimal selection for tasks offloading such that the response time and energy consumption will become minimum. In this article, we proposed an optimal selection of offloading tasks using well-known metaheuristics, ant colony optimization algorithm, whale optimization algorithm, and Grey wolf optimization algorithm using variant design of these algorithms according to our problem through mathematical modeling. Executing multiple tasks at the server tends to provide high response time that leads to overloading and put additional latency at task computation. We also graphically represent the tradeoff between energy and delay that, how both parameters are inversely proportional to each other, using values from simulation. Results show that Grey wolf optimization outperforms the others in terms of optimizing energy consumption and execution latency while selected optimal set of offloading tasks.

OODIDA: On-Board/Off-Board Distributed Real-Time Data Analytics for Connected Vehicles

A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing them efficiently. Our solution to this problem is On-board/Off-board Distributed Data Analytics (OODIDA), which is a platform that tackles both task distribution to connected vehicles as well as concurrent execution of tasks on arbitrary subsets of edge clients. Its message-passing infrastructure has been implemented in Erlang/OTP, while the end points use a language-independent JSON interface. Computations can be carried out in arbitrary programming languages. The message-passing infrastructure of OODIDA is highly scalable, facilitating the execution of large numbers of concurrent tasks.

Selective bypassing and mapping for heterogeneous applications on GPGPUs

Modern GPGPU supports executing multiple tasks with different run time characteristics and resource utilization. Having an efficient execution and resource management policy has been shown to be a critical performance factor when handling the concurrent execution of tasks with different run time behavior. Previous policies either assign equal resources to disparate tasks or allocate resources based on static or standalone behavior profiling. Treating tasks equally cannot efficiently utilize the system resources, while the standalone profiling ignores the correlated impact when running tasks concurrently and could hint incorrect task behavior. This paper addresses the above drawbacks and proposes a heterogeneity aware Selective Bypassing and Mapping (SBM) to manage both computing and cache resources for multiple tasks in a fine-grain manner. The light-weight run time profiling of SBM properly characterizes the disparate behavior of the concurrently executed multiple tasks, and selectively applies suited cache management and workgroup mapping policies to each task. When compared with the previous coarse-grained policies, SBM can achieve an average of 138% and up to 895% performance enhancement. When compared with the state-of-art fine-grained policy, SBM can achieve an average of 58% and up to 378% performance enhancement.

Enabling Failure-Resilient Intermittent Systems Without Runtime Checkpointing

Self-powered intermittent systems typically adopt runtime checkpointing as a means to accumulate computation progress across power cycles and recover system status from power failures. However, existing approaches based on the checkpointing paradigm normally require system suspension and/or logging at runtime. This paper presents a design which overcomes the drawbacks of checkpointing-based approaches, to enable failure-resilient intermittent systems. Our design allows accumulative execution and instant system recovery under frequent power failures while enforcing the serializability of concurrent task execution to improve computation progress and ensuring data consistency without system suspension during runtime, by leveraging the characteristics of data accessed in hybrid memory. We integrated the design into FreeRTOS running on a Texas Instruments device. Experimental results show that our design can still accumulate progress when the power source is too weak for checkpointing-based approaches to make progress, and improves the computation progress by up to 43% under a relatively strong power source, while reducing the recovery time by at least 90%.

A Framework Prototype for Multithreaded Implementation Over Micro-Controllers

Multithreading is pervasive in embedded system applications development. The applications requirements are becoming more rigorous, demanding the execution of concurrent tasks that must also take into account modularity and flexibility. An important part of the operating systems development concerns the implementation of scheduling algorithms. In an embedded system context, it is essential to consider that the scheduling algorithm heavily influences application behavior. Due to restricted and finite hardware resources, it is important to evaluate the use of flexible algorithms to guarantee efficiency. Currently, projects for embedded operating systems do exist for microcontrollers’ devices that implement scheduling algorithms, however, the developer cannot change or add new scheduling policies without implementing kernel tweaks and modifications. The alternatives are not flexible when choosing the scheduling algorithm according to the application needs. This imposes restrictions to many systems, forcing them to run specific static scheduling algorithms because no other options are available. The objective of this work concerns the design and development of a framework that implements a microkernel with a modular scheduler unit, allowing the execution of tailored algorithms according to the application profile. The idea is to provide a flexible platform to conveniently select the most appropriated algorithm. We have employed low capacity hardware to implement multithreading patterns corresponding to sets of concurrent tasks, demonstrating the strengths of adopting our approach. Our results show that the use of modern techniques that combine modularity, multithreading, and scheduling methods for embedded systems yield best executions when compared to its sequential counterparts.

Adopting new technologies in the LHCb Gauss simulation framework

The increase in luminosity foreseen in the future years of operation of the Large Hadron Collider (LHC) creates new challenges in computing efficiency for all participating experiment. For Run 3 of the LHC, the LHCb collaboration needs to simulate about two orders of magnitude more Monte Carlo events to exploit the increased luminosity and trigger rate. Therefore, the LHCb simulation framework (Gauss) will go through a significant renovation, mostly driven by the upgraded core software framework (Gaudi) and the availability of a multithreaded version of Geant4. The upgraded Gaudi framework replaces single-threaded processing by a multithreaded approach, allowing concurrent execution of tasks with a single event as well as multiple events in parallel. A major task of the required overhaul of Gauss is the implementation of a new interface to the multithreaded version of Geant4.

Optimization of data flow execution in a parallel environment

Although the modern data flows are executed in parallel and distributed environments, e.g. on a multi-core machine or on the cloud, current cost models, e.g., those considered by state-of-the-art data flow optimization techniques, do not accurately reflect the response time of real data flow execution in these execution environments. This is mainly due to the fact that the impact of parallelism, and more specifically, the impact of concurrent task execution on the running time is not adequately modeled in current cost models. The contribution of this work is twofold. Firstly, we propose an advanced cost model that aims to reflect the response time of a data flow that is executed in parallel more accurately. Secondly, we show that existing optimization solutions are inadequate and develop new optimization techniques targeting the proposed cost model. We focus on the single multi-core machine environment provided by modern business intelligence tools, such as Pentaho Kettle, but our approach can be extended to massively parallel and distributed settings. The distinctive features of our proposal is that we model both time overlaps and the impact of concurrency on task running times in a combined manner; the latter is appropriately quantified and its significance is exemplified. Furthermore, we propose extensions to current optimizers that decide on the exact ordering of flow tasks taking into account the new optimization metric. Finally, we evaluate the new optimization algorithms and show up to 59% response time improvement over state-of-the-art task ordering techniques.

Dual task performance: a comparison between healthy elderly individuals and those with Parkinson’s disease

Introduction The dual tasks (DT) is learned during the whole life and a prerequisite in functional performance in different activities of daily living. Healthy elderly have reduced ability to perform motor activities and cognitive tasks simultaneously, compared to young adults. Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly and classic motor symptoms coexist with prejudice in cognitive domains. Objective To compare balance, gait and performance in dual tasks of individuals with Parkinson’s disease and healthy elderly. Material and method Transversal study consisted of 21 individuals with PD, classified between 1.5 to 3 in Hoehn and Yahr scale and 21 healthy individuals. To evaluate the performance on simple tasks and dual tasks the participants were submitted to five simple tasks (motor) and each was associated with a cognitive task, featuring a DT. To balance and gait evaluation was used the following instruments: Berg Balance Scale, Tinetti Scale and Dynamic Gait Index. Results In respect to gait and performance in dual tasks, there was a statistically significant difference with the worst performance for the group of individuals with PD. Conclusion It was found that the group of elderly people with PD has lower performance in the execution of concurrent tasks when compared with healthy elderly, so the DT can be introduced in rehabilitation programs to improve the performance of these patients.

Autonomic fault tolerant scheduling approach for scientific workflows in Cloud computing

Autonomic fault tolerant scheduling is now a mandatory approach for the execution of performance-motivated Cloud applications such as scientific workflows. Since concurrent engineering is strongly associated with scientific workflows, an efficient scheduling for scientific workflows can have major impact on the performance of concurrent systems and engineering applications in Cloud computing. To facilitate the execution of concurrent tasks in scientific workflows, Cloud providers entail efficient scheduling heuristics and fault tolerant approaches. The work presented in this article formulates an effort focusing on this research problem to design an autonomic fault tolerant scheduling approach for scientific workflow applications. First, hybrid heuristic has been proposed to schedule scientific workflows effectively. Second, fault tolerant technique has been implemented using virtual machine migration approach that migrates the virtual machine automatically in case of task failure occurrences due to the overutilization of resources. Furthermore, the proposed approach has been validated through performance evaluation parameters using CloudSim and WorkflowSim toolkits. The simulation results demonstrate the effectiveness of the proposed approach to improve the performance of scientific workflows by appreciably reducing total mean execution time, standard deviation time, and makespan.

One step toward bridging the gap between theory and practice in moldable task scheduling with precedence constraints

SummaryBecause of the increasing number of cores of current parallel machines and the growing need for a concurrent execution of tasks, the problem of parallel task scheduling is more relevant than ever, especially under the moldable task model, in which tasks are allocated to a fixed number of processors before execution. Much research has been conducted to develop efficient scheduling algorithms for moldable tasks, both in theory and practice. The problem is that theoretical and practical approaches expose shortcomings, for example, many approximation algorithms only guarantee bounds under assumptions, which are unrealistic in practice, or most heuristics have not been rigorously compared with competing approximation algorithms. In particular, it is often assumed that the speedup function of moldable tasks is either non‐decreasing, sub‐linear, or concave. In practice, however, the resulting speedup of parallel programs on current hardware with deep memory hierarchies is most often neither non‐decreasing nor concave. We present a new algorithm for the problem of scheduling moldable tasks with precedence constraints for the makespan objective and for arbitrary speedup functions. We show through simulation that the algorithm not only creates competitive schedules for moldable tasks with arbitrary speedup functions but also outperforms other published heuristics and approximation algorithms for non‐decreasing speedup functions. Copyright © 2014 John Wiley & Sons, Ltd.

Reducing Peak Power Consumption inMulti-Core Systems without ViolatingReal-Time Constraints

The potential of multi-core chips for high performance and reliability at low cost has made them ideal computing platforms for embedded real-time systems. As a result, power management of a multi-core chip has become an important issue in the design of embedded real-time systems. Most existing approaches have been designed to regulate the behavior of average power consumption, such as minimizing the total energy consumption or the chip temperature. However, little attention has been paid to the worst-case behavior of instantaneous power consumption on a chip, called chip-level peak power consumption, an important design parameter that determines the cost and/or size of chip design/packaging and the underlying power supply. We address this problem by reducing the chip-level peak power consumption at design time without violating any real-time constraints. We achieve this by carefully scheduling real-time tasks, without relying on any additional hardware implementation for power management, such as dynamic voltage and frequency scaling. Specifically, we propose a new scheduling algorithm FPΘ that restricts the concurrent execution of tasks assigned on different cores, and perform its schedulability analysis. Using this analysis, we develop a method that finds a set of concurrent executable tasks, such that the design-time chip-level peak power consumption is minimized and all timing requirements are met. We demonstrate via simulation that the proposed method not only keeps the design-time chip-level peak power consumption as low as the theoretical lower bound for trivial cases, but also reduces the peak power consumption for non-trivial cases by up to 12.9 percent compared to the case of no restriction on concurrent task execution.

Parallel Control: A Method for Data-Driven and Computational Control

摘要: 本文简述平行控制的理念、概念及基本方法与应用, 主要强调虚实互动的平行扩展方式与同时计算的并行划分方式的不同之处. 平行控制方法是ACP理论在控制领域中的具体应用, 其核心是利用人工系统进行建模和表示、通过计算实验进行分析和评估、最后借助平行执行实现对复杂系统的控制和管理. 这一控制方法是反馈控制, 特别是自适应控制方法向复杂系统问题扩展的自然结果, 是一种迈向数据驱动控制和计算控制的必然且有效途径. 关键词: 平行控制 / 复杂系统 / ACP方法 / 自适应控制 / 计算控制 / 数据驱动

Task-execution scheduling schemes for network measurement and monitoring

Measurement is a required process in high performance networks for efficient quality-of-service (QoS) provisioning and service verification. Active measurement is an attractive approach because the measurement traffic injected into the network can be controlled and the measurement tasks can be distributed throughout the network. However, the execution of measurement tasks in common parts of a network may face contention for resources, such as computational power, memory, and link bandwidth. This contention could jeopardize measurement accuracy and affect network services. This contention for limited resources defines a conflict between measurement tasks. Furthermore, we consider two sets of measurement tasks, those used to monitor network state periodically, called periodic tasks, and those for casual measurements issued as needed, called on-demand measurement tasks. In this paper, we propose a novel scheduling scheme to resolve contention for resources of both periodic and on-demand measurement tasks from graph coloring perspective, called ascending order of the sum of clique number and degree of tasks. The scheme selects tasks according to the ascending order of the sum of clique number and conflict task degree in a conflict graph and allows concurrent execution of multiple measurement tasks for high resource utilization. The scheme decreases the average waiting time of all tasks in periodic measurement tasks scheduling. For on-demand measurement tasks, the proposed scheme minimizes the waiting time of inserted on-demand tasks while keeping time space utilization high. In other words, the total time spent on finishing all the tasks is shortened. We evaluate our proposed schemes under different measurement task assignment scenarios through computer simulations, and compare the performance of this scheme with others that also allow concurrent task execution. The simulation results show that the proposed scheme produces effective contention resolution and low execution delays.

PRECEDENCE-CONSTRAINED TASK ALLOCATION IN DISTRIBUTED COMPUTING SYSTEMS

A distributed computing system (DCS) provides a platform for concurrent execution of tasks consisting of various modules. The problem of task allocation becomes quite difficult to solve when the precedence constraint is considered along with other constraints such as memory, network topology, etc. Various solutions have been proposed, considering one or the other constraint, in the literature. The present work discusses a comprehensive task allocation policy that can promise to provide an optimal solution to the problem. An algorithm, considering the precedence relation among the modules of a task, is proposed for allocation. The algorithm is used to show the allocation for some interconnection topologies and task graphs.

Modeling of Concurrent Task Execution in a Distributed System for Real-Time Control

In a distributed system that implements real-time control, computational tasks are distributed over different nodes for execution to improve response time and system reliability. To model system behavior, tasks in each node are first decomposed into activities. The activities and precedence constraints among them are then modeled by a generalized stochastic Petri net (GSPN). Finally, a sequence of homogeneous continuous-time Markov chains (CTMC's) is built from the GSPN to model the concurrent task execution in the system.

Modeling the Ada task system by Petri nets

Multitasking is one of the most novel aspects of Ada. However, the combination of language primitives for concurrent execution of tasks, synchronization, termination, abortion, exception handling, etc. make Ada programs difficult to understand and analyze. This is partly due to the inherent complexity of the language and partly to the lack of a rigorous definition of its semantics. The Ada Reference Manual describes semantics in informal English prose; as a result, it is often verbose and ambiguous. The goal of this paper is not to provide a complete formal semantics of Ada multitasking. Rather, we illustrate the use of a semi-formal approach based on (timed) Petri nets which support a rigorous description of the language. The approach is described by stepwise refinements and is used to describe several cases of task interactions, ranging from simple to complex ones. The proposed approach can easily be applied in the description of other multitasking problems not covered in the paper.