Mapeo estático y dinámico de tareas en sistemas multiprocesador, basados en redes en circuito integrado

RESUMEN: Las redes en circuito integrado (NoC) representan un importante paradigma de uso creciente para los sistemas multiprocesador en circuito integrado (MPSoC), debido a su flexibilidad y escalabilidad. Las estrategias de tolerancia a fallos han venido adquiriendo importancia, a medida que los procesos de manufactura incursionan en dimensiones por debajo del micrómetro y la complejidad de los diseños aumenta. Este artículo describe un algoritmo de aprendizaje incremental basado en población (PBIL), orientado a optimizar el proceso de mapeo en tiempo de diseño, así como a encontrar soluciones de mapeo óptimas en tiempo de ejecución, para hacer frente a fallos de único nodo en la red. En ambos casos, los objetivos de optimización corresponden al tiempo de ejecución de las aplicaciones y al ancho de banda pico que aparece en la red. Las simulaciones se basaron en un algoritmo de ruteo XY determinístico, operando sobre una topología de malla 2D para la NoC. Los resultados obtenidos son prometedores. El algoritmo propuesto exhibe un desempeño superior a otras técnicas reportadas cuando el tamaño del problema aumenta.ABSTARCT: Due to its scalability and flexibility, Network-on-Chip (NoC) is a growing and promising communication paradigm for Multiprocessor System-on-Chip (MPSoC) design. As the manufacturing process scales down to the deep submicron domain and the complexity of the system increases, fault-tolerant design strategies are gaining increased relevance. This paper exhibits the use of a Population-Based Incremental Learning (PBIL) algorithm aimed at finding the best mapping solutions at design time, as well as to finding the optimal remapping solution, in presence of single-node failures on the NoC. The optimization objectives in both cases are the application completion time and the network's peak bandwidth. A deterministic XY routing algorithm was used in order to simulate the traffic conditions in the network which has a 2D mesh topology. Obtained results are promising. The proposed algorithm exhibits a better performance, when compared with other reported approaches, as the problem size increases

Scheduling Problems

Scheduling is defined as the process of assigning operations to resources over time to optimize a criterion. Problems with scheduling comprise both a set of resources and a set of a consumers. As such, managing scheduling problems involves managing the use of resources by several consumers. This book presents some new applications and trends related to task and data scheduling. In particular, chapters focus on data science, big data, high-performance computing, and Cloud computing environments. In addition, this book presents novel algorithms and literature reviews that will guide current and new researchers who work with load balancing, scheduling, and allocation problems

An Enhanced Model for Job Sequencing and Dispatch in Identical Parallel Machines

This paper has developed an efficient scheduling model that is robust and minimizes the total completion time for job completion in identical parallel machines. The new model employs Genetic-Fuzzy technique for job sequencing and dispatch in identical parallel machines. It uses genetic algorithm technique to develop a job scheduler that does the job sequencing and optimization while fuzzy logic technique was used to develop a job dispatcher that dispatches job to the identical parallel machines. The methodology used for the design is the Object Oriented Analysis and Design Methodology (OOADM) and the system was implemented using C# and .NET framework. The model was tested with fifteen identical parallel machines used for printing. The parameters used in analyzing this model include the job scheduling length, average execution time, load balancing and machines utilization. The result generated from the developed model was compare with the result of other job scheduling models like First Come First Sever (FCFS) scheduling approach and Genetic Model (GA) scheduling approach. The result of the new model shows a better load balancing and high machine utilization among the individual machines when compared with the First Come First Serve (FCFS) scheduling model and Genetic Algorithm (GA) scheduling model. Keywords:  Parallel Machines, Genetic Model, Job Scheduler, Fuzzy Logic Technique, Load Balancing, Machines   Utilization DOI: 10.7176/CEIS/11-2-05 Publication date: March 31st 202

QoS-aware predictive workflow scheduling

This research places the basis of QoS-aware predictive workflow scheduling. This research novel contributions will open up prospects for future research in handling complex big workflow applications with high uncertainty and dynamism. The results from the proposed workflow scheduling algorithm shows significant improvement in terms of the performance and reliability of the workflow applications

Distributed and Lightweight Meta-heuristic Optimization method for Complex Problems

The world is becoming more prominent and more complex every day. The resources are limited and efficiently use them is one of the most requirement. Finding an Efficient and optimal solution in complex problems needs to practical methods. During the last decades, several optimization approaches have been presented that they can apply to different optimization problems, and they can achieve different performance on various problems. Different parameters can have a significant effect on the results, such as the type of search spaces. Between the main categories of optimization methods (deterministic and stochastic methods), stochastic optimization methods work more efficient on big complex problems than deterministic methods. But in highly complex problems, stochastic optimization methods also have some issues, such as execution time, convergence to local optimum, incompatible with distributed systems, and dependence on the type of search spaces. Therefore this thesis presents a distributed and lightweight metaheuristic optimization method (MICGA) for complex problems focusing on four main tracks. 1) The primary goal is to improve the execution time by MICGA. 2) The proposed method increases the stability and reliability of the results by using the multi-population strategy in the second track. 3) MICGA is compatible with distributed systems. 4) Finally, MICGA is applied to the different type of optimization problems with other kinds of search spaces (continuous, discrete and order based optimization problems). MICGA has been compared with other efficient optimization approaches. The results show the proposed work has been achieved enough improvement on the main issues of the stochastic methods that are mentioned before.Maailmasta on päivä päivältä tulossa yhä monimutkaisempi. Resurssit ovat rajalliset, ja siksi niiden tehokas käyttö on erittäin tärkeää. Tehokkaan ja optimaalisen ratkaisun löytäminen monimutkaisiin ongelmiin vaatii tehokkaita käytännön menetelmiä. Viime vuosikymmenien aikana on ehdotettu useita optimointimenetelmiä, joilla jokaisella on vahvuutensa ja heikkoutensa suorituskyvyn ja tarkkuuden suhteen erityyppisten ongelmien ratkaisemisessa. Parametreilla, kuten hakuavaruuden tyypillä, voi olla merkittävä vaikutus tuloksiin. Optimointimenetelmien pääryhmistä (deterministiset ja stokastiset menetelmät) stokastinen optimointi toimii suurissa monimutkaisissa ongelmissa tehokkaammin kuin deterministinen optimointi. Erittäin monimutkaisissa ongelmissa stokastisilla optimointimenetelmillä on kuitenkin myös joitain ongelmia, kuten korkeat suoritusajat, päätyminen paikallisiin optimipisteisiin, yhteensopimattomuus hajautetun toteutuksen kanssa ja riippuvuus hakuavaruuden tyypistä. Tämä opinnäytetyö esittelee hajautetun ja kevyen metaheuristisen optimointimenetelmän (MICGA) monimutkaisille ongelmille keskittyen neljään päätavoitteeseen: 1) Ensisijaisena tavoitteena on pienentää suoritusaikaa MICGA:n avulla. 2) Lisäksi ehdotettu menetelmä lisää tulosten vakautta ja luotettavuutta käyttämällä monipopulaatiostrategiaa. 3) MICGA tukee hajautettua toteutusta. 4) Lopuksi MICGA-menetelmää sovelletaan erilaisiin optimointiongelmiin, jotka edustavat erityyppisiä hakuavaruuksia (jatkuvat, diskreetit ja järjestykseen perustuvat optimointiongelmat). Työssä MICGA-menetelmää verrataan muihin tehokkaisiin optimointimenetelmiin. Tulokset osoittavat, että ehdotetulla menetelmällä saavutetaan selkeitä parannuksia yllä mainittuihin stokastisten menetelmien pääongelmiin liittyen

Contention energy-aware real-time task mapping on NoC based heterogeneous MPSoCs

© 2018 IEEE. Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average 30% total energy improvement. Additionally, it maintains high QoS and robustness for real-time applications

Contention & Energy-aware Real-time Task Mapping on NoC based Heterogeneous MPSoCs

Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average ~30%..

Multiprocessor System-on-Chips based Wireless Sensor Network Energy Optimization

Wireless Sensor Network (WSN) is an integrated part of the Internet-of-Things (IoT) used to monitor the physical or environmental conditions without human intervention. In WSN one of the major challenges is energy consumption reduction both at the sensor nodes and network levels. High energy consumption not only causes an increased carbon footprint but also limits the lifetime (LT) of the network. Network-on-Chip (NoC) based Multiprocessor System-on-Chips (MPSoCs) are becoming the de-facto computing platform for computationally extensive real-time applications in IoT due to their high performance and exceptional quality-of-service. In this thesis a task scheduling problem is investigated using MPSoCs architecture for tasks with precedence and deadline constraints in order to minimize the processing energy consumption while guaranteeing the timing constraints. Moreover, energy-aware nodes clustering is also performed to reduce the transmission energy consumption of the sensor nodes. Three distinct problems for energy optimization are investigated given as follows: First, a contention-aware energy-efficient static scheduling using NoC based heterogeneous MPSoC is performed for real-time tasks with an individual deadline and precedence constraints. An offline meta-heuristic based contention-aware energy-efficient task scheduling is developed that performs task ordering, mapping, and voltage assignment in an integrated manner. Compared to state-of-the-art scheduling our proposed algorithm significantly improves the energy-efficiency. Second, an energy-aware scheduling is investigated for a set of tasks with precedence constraints deploying Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs. A novel population based algorithm called ARSH-FATI is developed that can dynamically switch between explorative and exploitative search modes at run-time. ARSH-FATI performance is superior to the existing task schedulers developed for homogeneous VFI-NoC-MPSoCs. Third, the transmission energy consumption of the sensor nodes in WSN is reduced by developing ARSH-FATI based Cluster Head Selection (ARSH-FATI-CHS) algorithm integrated with a heuristic called Novel Ranked Based Clustering (NRC). In cluster formation parameters such as residual energy, distance parameters, and workload on CHs are considered to improve LT of the network. The results prove that ARSH-FATI-CHS outperforms other state-of-the-art clustering algorithms in terms of LT.University of Derby, Derby, U