Energy-efficient scheduling for moldable real-time tasks on heterogeneous computing platforms

Zahaf, Houssam-Eddine; Benyamina, Abou El Hassen; Olejnik, Richard; Lipari, Giuseppe

doi:10.1016/j.sysarc.2017.01.002

Cited by 60 publications

(25 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing works that focus on the energy-aware task mapping problem aim at minimizing the energy consumption or the execution time of tasks (makespan) un-der system resource and application constraints. In multicore task mapping scenarios [3,[7][8][9][10][11], Mixed-Integer Non-Linear Programming (MINLP) is a popular method to formulate the problems of mapping independent tasks [9] or dependent tasks [7,8] onto the multicore platforms. To efficiently solve these complex problems, in [9] the MINLP problem is relaxed to a convex problem by replacing the binary variables with the continuous variables, and, then, the relaxed problem is solved using polynomial-time methods.…”

Section: Energy-aware Task Mappingmentioning

confidence: 99%

“…In [10], the independent task mapping problem on homogeneous multicore (e.g., ARM big.LITTLE) is formulated as Linear Programming (LP) as the platform supports task migration. When tasks are dependent, the task mapping problem is formulated as Integer Non-Linear Programming (INLP) in [3] and solved using heuristic. Similar problem is studied in [11], but the problem is formulated as Integer Linear Programming (ILP), and, thus, this problem can be optimally solved using Benders Decomposition (BD) [21].…”

Section: Energy-aware Task Mappingmentioning

confidence: 99%

“…For instance, in target tracking application, the sensor nodes analyze the captured images on-site by extracting the important features, and send only the features for further processing to the base station or actuator nodes. This model of computation is known as "Fog/Edge-computing" [3]. In this context, task mapping plays an essential role in in-network processing by solving the matching problem between tasks and nodes subject to application requirements.…”

Section: Introductionmentioning

confidence: 99%

“…Table 1 provides a summary of some representative papers from the literature. Task mapping is a well-known problem in embedded system community, no matter the tasks are precise or imprecise, and the multicore platforms are homogeneous [4,7,8,[12][13][14][15] or heterogeneous [3,[9][10][11]16]. However, the work deals with task mapping problem on networked system is rare, and most of them consider precise tasks [2,6,[17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Mapping imprecise computation tasks on cyber-physical systems

Mo¹,

Kritikakou²

2019

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

By allocating a set of tasks onto a set of nodes and adjusting the execution time of tasks, task mapping is an efficient approach to realize distributed computing. Cyber-Physical Systems (CPS), as a particular case of distributed systems, raise new challenges in task mapping, because of the heterogeneity and other properties traditionally associated with Wireless Sensor and Actuator Networks (WSAN), including shared sensing, acting and real-time computing. In addition, many of the real-time tasks of CPS can be executed in an imprecise way. Such systems accept an approximate result as long as the baseline Quality-of-Service (QoS) is satisfied and they can execute more computations to yield better results, if more system resources is available. These systems are typically considered under the Imprecise Computation (IC) model, achieving a better tradeoff between QoS and limited system resources. However, determining a QoS-aware mapping of these real-time IC-tasks onto the nodes of a CPS creates a set of interesting problems. In this paper, we firstly propose a mathematical model to capture the dependency, energy and real-time constraints of IC-tasks, as well as the sensing, acting, and routing in the CPS. The problem is formulated as a Mixed-Integer Non-Linear Programming (MINLP) due to the complex nature of the problem. Secondly, to efficiently solve this problem, we provide a linearization method that results in a Mixed-Integer Linear Programming (MILP) formulation of our original problem. Finally, we decompose the transformed problem into a task allocation subproblem and a task adjustment subproblem, and, then, we find the optimal solution based on subproblem iteration. Through the simulations, we demonstrate the effectiveness of the proposed method.

show abstract

Section: Energy-aware Task Mappingmentioning

confidence: 99%

Section: Energy-aware Task Mappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping imprecise computation tasks on cyber-physical systems

Mo¹,

Kritikakou²

2019

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

show abstract

“…The frequency step is 0.1GHz. We use the models of Exynos 5422 in [15] to model how computation time and dynamic power consumption of tasks change with frequency.…”

Section: Simulation Setupmentioning

confidence: 99%

System-Level Modeling and Simulation of MPSoC Run-Time Management Using Execution Traces Analysis

Yang

Nours

Real

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Dynamic management of modern Multi-Processors System on Chip (MPSoC) become mandatory for optimization purpose. Evaluation of these run-time resource management strategies in MPSoCs is essential early in the design process to guarantee a reduced design cycle. However, most of the existing system-level simulation-based evaluation frameworks consider static application mapping and do not allow runtime management effects to be evaluated. In this paper, we present a modeling and simulation approach that allows integration of run-time management strategies in multicore system simulation. We have integrated the proposed approach in an industrial modeling and simulation framework. A case-study with seven applications (85 running tasks in total) on a heterogeneous multicore platform is considered and different management strategies are evaluated according to latency and power consumption criteria.

show abstract

Intelligent admission control manager for fog‐integrated cloud: A hybrid machine learning approach

Sham

Vidyarthi

2021

Concurrency and Computation

View full text Add to dashboard Cite

Internet of Things (IoT) and other smart devices produce data that are large in volume, variety, and velocity. Cloud not only helps in data analysis, but also provides storage and computation facility to these data. It has been experienced that for many time-critical applications, by the time request traverses back and forth to the cloud for analysis/execution, the opportunity to act upon it may get over. Therefore, time sensitivity and priority for such applications greatly matter. Fog computing, an upcoming computing infrastructure, complements the cloud and overcomes this limitation by supporting time-sensitive and priority-based applications by provisioning the computation, bandwidth, and storage. However, adopting fog-integrated cloud introduces newer resource management challenges requiring a new request scheduling scheme with appropriate quality of service/experience (QoS/QoE). In this work, an intelligent admission control manager is being proposed for placing the request based on the parameters such as CPU, memory, storage besides few other categorical parameters, for example, job priority and time sensitivity. The proposed work applies machine intelligence techniques, clustering for labeling the applications' requests followed by a decision tree, using the labeled requests, to classify the incoming requests. The proposed methodology is demonstrated in terms of accuracy, execution time, precision, recall, variation in accuracy, and execution time by introducing noise in multiple size batches to avoid the generalization error and fault tolerance. A comparative study with few well-known classifiers has also been performed to ascertain the effectiveness of the proposed model. The proposed model is light enough to be placed appropriately on the fog node.

show abstract

Energy-efficient scheduling for moldable real-time tasks on heterogeneous computing platforms

Cited by 60 publications

References 9 publications

Mapping imprecise computation tasks on cyber-physical systems

Mapping imprecise computation tasks on cyber-physical systems

System-Level Modeling and Simulation of MPSoC Run-Time Management Using Execution Traces Analysis

Intelligent admission control manager for fog‐integrated cloud: A hybrid machine learning approach

Contact Info

Product

Resources

About