Rámec pro plánování problémy

Import 22/07/2015Scheduling problems form an important subclass of combinatorial optimisation problems with many applications in manufacturing and logistics. Predominately these problems are NP-complete (decision based) and NP-hard (optimisation based), hence the main course of research in solving them concentrates on the design of efficient heuristic algorithms. Two main categories of these algorithms exist: deterministic algorithms and evolutionary metaheuristics. The deterministic algorithms comprise local improvement techniques, such as k-opt algorithm, which try to improve existing feasible solution, and constructive heuristics, such as NEH, which build a solution starting from scratch, adding one job at a time. Evolutionary metaheuristics have prospered in the past decades, owing to their efficiency and flexibility. Drawing inspiration from the theory of natural evolution or swarm behavioural patterns, the most popular of these algorithms in practice include for instance Genetic Algorithms, Differential Evolution, Particle Swarm Optimisation, amongst others. However, even though these heuristics provide in most cases close to optimal solution at reasonable execution time, this time is still impractically long for many applications. Therefore much effort has been dedicated to accelerating these algorithms. Since the development of hardware turns away from increasing the clock speed towards the parallel processing units, owing to reaching the limits of technology due to the increased power consumption and heat dissipation, this effort goes into parallelisation of the existing algorithms, to enable exploitation of the computing power of multi-core or many-core platforms. This is the goal of the first part of the thesis, accelerating two of the deterministic algorithms, NEH and 2-opt, with interesting results. Another approach has been taken in the second part, with the core premise of exploring the influence of stochasticity on the performance of an evolutionary algorithm, selecting the relatively recent and promising Discrete Artificial Bee Colony algorithm. The pseudo-random number generator has been replaced with the different types of dissipative chaos maps, with some of them improving the algorithm significantly. It has been shown that the population based evolutionary algorithms often form complex networks, taken from the point of view of the information exchange between individual solutions during the course of population development. The final part of this thesis puts this observation into practice by embedding the complex network analysis based self-adaptive mechanism into the ABC algorithm, a continuous optimisation problems solving evolutionary algorithm, which is however the basis for the afore mentioned DABC algorithm, and proving the effectiveness for some of the developed versions, currently on the standard continuous optimisation test functions, with the possibility to extend this modification to the combinatorial optimisations problems in the future being discussed in the conclusion.Rozvrhovací problémy jsou důležitou podtřídou úloh kombinatorické optimalizace s řadou aplikací ve výrobě a logistice. Většina těchto problémů je NP-úplných (rozhodovací forma) a NP-těžkých (optimalizační forma), proto se výzkum zaměřuje na návrh efektivních heuristických algoritmů. Dvě hlavní kategorie těchto algoritmů jsou deterministické algoritmy a evoluční metaheuristiky. Deterministické algoritmy zahrnují techniky lokálního prohledávání, například algoritmus k-opt, jejichž cílem je zlepšení existujícího přípustného řešení problému, dále pak konstruktivní heuristiky, jejichž příkladem je algoritmus NEH, které hledané řešení vytvářejí inkrementálně, bez potřeby znalosti vstupního bodu v prohledávaném prostoru řešení. Evoluční metaheuristiky mají za sebou historii úspěšného vývoje v posledních desetiletích, zejména díky jejich efektivitě a flexibilitě. Jejich inspirací jsou poznatky převzaté z biologie, teorie evoluce a inteligence hejna. Mezi nejpopulárnějšími z těchto algoritmů jsou, mimo jiné, genetické algoritmy, diferenciální evoluce, rojení částic (Particle Swarm Optimisation). Ačkoli tyto heuristiky nalézají ve většině případů řešení blížící se globálnímu optimu v přípustném výpočetním čase, pro řadu aplikací mohou být stále ještě nepřijatelně pomalé. Velké úsilí bylo věnováno zrychlení těchto algoritmů. Protože se vývoj hardware díky dosažení technologických limitů, vzhledem ke zvyšující se spotřebě energie a tepelnému vyzařování, obrací od zvyšování frekvence jednojádrového procesoru k vícejádrovým procesorům a paralelnímu zpracování, je tato snaha většinou orientovaná na paralelizaci existujících algoritmů, aby bylo umožněno využití výpočetní síly vícejádrových platforem (multi-core a many-core). Prvním cílem této práce je tudíž akcelerace dvou deterministických algoritmů, NEH a 2-opt, přičemž bylo dosaženo zajímavých výsledků. Jiný přístup byl zvolen ve druhé části, s hlavní myšlenkou prozkoumání vlivu náhodnosti na výkon evolučního algoritmu. Za tímto účelem byl zvolen relativně nový a slibný algoritmus Discrete Artificial Bee Colony. Generátor pseudonáhodných čísel byl nahrazen několika různými chaotickými mapami, z nichž některé znatelně zlepšily výsledky algoritmu. Bylo ukázáno, že evoluční algoritmy založené na populaci často formují komplexní sítě, vzato z pohledu výměny informací mezi jednotlivými řešeními v populaci během jejího vývoje. Závěrečná část práce aplikuje toto pozorování vložením samo přizpůsobivého mechanismu založeném na analýze komplexní sítě do algoritmu ABC, který je evolučním algoritmem pro spojitou optimalizaci a zároveň základem dříve zmíněného DABC algoritmu. Efektivita několika verzí algoritmu založeném na této myšlence je dokázána na standardní sadě testovacích funkcí pro spojitou optimalizaci. Možnost rozšíření této modifikace na kombinatorické optimalizační problémy je diskutována v závěru práce.460 - Katedra informatikyvýborn

Recent Research Trends in Genetic Algorithm Based Flexible Job Shop Scheduling Problems

Flexible Job Shop Scheduling Problem (FJSSP) is an extension of the classical Job Shop Scheduling Problem (JSSP). The FJSSP is known to be NP-hard problem with regard to optimization and it is very difficult to find reasonably accurate solutions of the problem instances in a rational time. Extensive research has been carried out in this area especially over the span of the last 20 years in which the hybrid approaches involving Genetic Algorithm (GA) have gained the most popularity. Keeping in view this aspect, this article presents a comprehensive literature review of the FJSSPs solved using the GA. The survey is further extended by the inclusion of the hybrid GA (hGA) techniques used in the solution of the problem. This review will give readers an insight into use of certain parameters in their future research along with future research directions

Mapreduce and Heterogeneity: Power-Aware Bag-of-Tasks, Framework Parameter Sensitivity, and Dynamic Cluster Aware Framework Configuration

This dissertation presents the techniques for adaptation of MapReduce frameworks to incorporate heterogeneity-aware scheduling algorithms, an inspection of cluster configurations and how they impact these scheduling algorithms, an analysis regarding how the cluster configuration and the heterogeneity-aware scheduling can work together to minimize turnaround time and/or power consumption of the cluster when executing MapReduce applications, and how these lessons can be applied more broadly to Big Data infrastructure outside of MapReduce that supports multiple Big Data frameworks simultaneously. Heterogeneity exists in various capacities in any given cluster, from static (Physical and Platform) heterogeneity to dynamic heterogeneity (Transient Data, Transient Applications, and Irregular Hardware Behavior). Within the cluster there are historically several types of mitigation strategies for each of these types of heterogeneity, and each has their pros and cons. We discuss these mitigation strategies and the types of heterogeneity each of these strategies is able to address, and the history of the related work in the field. After this, we consider taking host-level metrics and using them to schedule tasks in real time, with a desire to address cluster-wide energy usage. To do this, we consider estimators for power consumption that are available on-chip, namely temperature. We establish a correlation between CPU temperature and power consumption, then derive a scheduling algorithm that eliminates nodes that are consuming too much power from the pool of schedule-able resources. In order to do this we focus on the ability of MapReduce frameworks, constructed as we have constructed the frameworks described in this thesis, to delay binding of tasks to specific workers. We analyze the impacts this has on turnaround time of a MapReduce application, with analysis around setting this threshold properly to reduce impact on turnaround time while shifting power consumption around in the cluster, away from nodes that are over-consuming. We also address concerns with respect to upgrading a cluster in stages, introducing more Physical Heterogeneity at various levels and the types of adjustments that need to be made to MapReduce configurations in order to combat the increased Heterogeneity. In particular, we look at the concerns for MapReduce platform mis-configuration and its impacts on turnaround time, analyzing the ways in which these types of errors can be mitigated between incremental platform upgrades. In an effort to address this, we introduce a Dynamic Heterogeneity Awareness (DHA) module to our MapReduce framework in order to address these upgrades, and allow better spreading of tasks by the framework, in order to further improve turnaround time and resource utilization. Finally we consider the implications for framework and application co-tenancy, and we describe the state of art in these areas. We focus on describing what co-tenancy is, why it\u27s important, and how the state of the art can be expanded to in order to leverage findings from this thesis to make these co-tenant clusters increase application and framework performance as well as improving these clusters with considerations for energy efficiency

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015) Krakow, Poland

Proceedings of: Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015). Krakow (Poland), September 10-11, 2015

Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes

The book documents 25 papers collected from the Special Issue “Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes”, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors

Proceedings of the 5th International Workshop on Reconfigurable Communication-centric Systems on Chip 2010 - ReCoSoC\u2710 - May 17-19, 2010 Karlsruhe, Germany. (KIT Scientific Reports ; 7551)

ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state of the art research around SoC related topics through plenary invited papers and posters. The workshop aims to provide a prospective view of tomorrow\u27s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability

MULTI-OBJECTIVE DESIGN AUTOMATION FOR RECONFIGURABLE MULTI-PROCESSOR SYSTEMS

Ph.DDOCTOR OF PHILOSOPH