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A software agents based manufacturing scheduling module for Advanced Planning and Scheduling (APS) is presented. The problem considered is scheduling of jobs with multiple operations, distinct operation processing times, arrival times, and due dates in a job shop environment. Sequence dependent setups are also considered. The additional constraints of material and resource availability are also taken into consideration. The scheduling is to be considered in integration with production planning. The production plans can be changed dynamically and the schedule is to be generated to reflect the appropriate changes. The design of a generic multi-agent framework which is domain independent along with algorithms that are used by the agents is also discussed. / Master of Science

Cloud computing has become a powerful enabler for many IT services and new technolo-gies. It provides access to an unprecedented amount of resources in a fine-grained andon-demand manner. To deliver such a service, cloud providers should be able to efficientlyand reliably manage their available resources. This becomes a challenge for the manage-ment system as it should handle a large number of heterogeneous resources under diverseworkloads with fluctuations. In addition, it should also satisfy multiple operational require-ments and management objectives in large scale data centers.Autonomic computing techniques can be used to tackle cloud resource managementproblems. An autonomic system comprises of a number of autonomic elements, which arecapable of automatically organizing and managing themselves rather than being managedby external controllers. Therefore, they are well suited for decentralized control, as theydo not rely on a centrally managed state. A decentralized autonomic system benefits fromparallelization of control, faster decisions and better scalability. They are also more reliableas a failure of one will not affect the operation of the others, while there is also a lower riskof having faulty behaviors on all the elements, all at once. All these features are essentialrequirements of an effective cloud resource management.This thesis investigates algorithms, models, and techniques to autonomously managejobs, services, and virtual resources in a cloud data center. We introduce a decentralizedresource management framework, that automates resource allocation optimization and ser-vice consolidation, reliably schedules jobs considering probabilistic failures, and dynam-icly scales and repacks services to achieve cost efficiency.As part of the framework, we introduce a decentralized scheduler that provides andmaintains durable allocations with low maintenance costs for data centers with dynamicworkloads. The scheduler assigns resources in response to virtual machine requests andmaintains the packing efficiency while taking into account migration costs, topologicalconstraints, and the risk of resource contention, as well as fluctuations of the backgroundload.We also introduce a scheduling algorithm that considers probabilistic failures as part ofthe planning for scheduling. The aim of the algorithm is to achieve an overall job reliabil-ity, in presence of correlated failures in a data center. To do so, we study the impacts ofstochastic and correlated failures on job reliability in a virtual data center. We specificallyfocus on correlated failures caused by power outages or failure of network components onjobs running large number of replicas of identical tasks.Additionally, we investigate the trade-offs between vertical and horizontal scaling. Theresult of the investigations is used to introduce a repacking technique to automatically man-age the capacity required by an elastic service. The repacking technique combines thebenefits of both scaling strategies to improve its cost-efficiency. / Datormoln har kommit att bli kraftfulla möjliggörare för många nya IT-tjänster. De ger tillgång till mycket storskaliga datorresurser på ett finkornigt och omedelbart sätt. För att tillhandahålla sådana resurser krävs att de underliggande datorcentren kan hantera sina resurser på ett tillförlitligt och effektivt sätt. Frågan hur man ska designa deras resurshanteringssystem är en stor utmaning då de ska kunna hantera mycket stora mängder heterogena resurser som i sin tur ska klara av vitt skilda typer av belastning, ofta med väldigt stora variationer över tid. Därtill ska de typiskt kunna möta en mängd olika krav och målsättningar för hur resurserna ska nyttjas. Autonomiska system kan med fördel användas för att realisera sådana system. Ett autonomt system innehåller ett antal autonoma element som automatiskt kan organisera och hantera sig själva utan stöd av externa regulatorer. Förmågan att hantera sig själva gör dem mycket lämpliga som komponenter i distribuerade system, vilka i sin tur kan bidra till snabbare beslutsprocesser, bättre skalbarhet och högre feltolerans. Denna avhandling fokuserar på algoritmer, modeller och tekniker för autonom hantering av jobb och virtuella resurser i datacenter. Vi introducerar ett decentraliserat resurshanteringssystem som automatiserar resursallokering och konsolidering, schedulerar jobb tillförlitligt med hänsyn till korrelerade fel, samt skalar resurser dynamiskt för att uppnå kostnadseffektivitet. Som en del av detta ramverk introducerar vi en decentraliserad schedulerare som allokerar resurser med hänsyn till att tagna beslut ska vara bra för lång tid och ge låga resurshanteringskostnader för datacenter med dynamisk belastning. Scheduleraren allokerar virtuella maskiner utifrån aktuell belastning och upprätthåller ett effektivt nyttjande av underliggande servrar genom att ta hänsyn till migrationskostnader, topologiska bivillkor och risk för överutnyttjande. Vi introducerar också en resursallokeringsalgoritm som tar hänsyn till korrelerade fel som ett led i planeringen. Avsikten är att kunna uppnå specificerade tillgänglighetskrav för enskilda tjänster trots uppkomst av korrelerade fel. Vi fokuserar främst på korrelerade fel som härrör från problem med elförsörjning och från felande nätverkskomponenter samt deras påverkan på jobb bestående av många identiska del-jobb. Slutligen studerar vi även hur man bäst ska kombinera horisontell och vertikal skalning av resurser. Resultatet är en process som ökar kostnadseffektivitet genom att kombinera de två metoderna och därtill emellanåt förändra fördelning av storlekar på virtuella maskiner.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Computational Grids are large infrastructures composed of several components such as clusters, or massively parallel machines, generally spread across a country or the world, linked together through some network such as Internet, and allowing a transparent access to any resource. Grids have become unavoidable for a large part of the scientific community requiring computational power such as high-energy physics, bioinformatics or earth observation. Large projects are emerging, often at an international level, but even if Grids are on the way of being efficient and user-friendly systems, computer scientists and engineers still have a huge amount of work to do in order to improve their efficiency. Amongst a large number of problems to solve or to improve upon, the problem of scheduling the work and balancing the load is of first importance. <p> This work concentrates on the way the work is dispatched on such systems, and mainly on how the first level of scheduling – generally name brokering, or meta-sheduling – is performed. We deeply analyze the behavior of popular strategies, compare their efficiency, and propose a new very efficient brokering policy providing notable performances, attested by the large number of simulations we performed and provided in the document. <p> The work is mainly split in two parts. After introducing the mathematical framework on which the following of the manuscript is based, we study systems where the grid brokering is done without any feed-back information, i.e. without knowing the current state of the clusters when the resource broker – the grid component receiving jobs from clients and performing the brokering – makes its decision. We show here how a computational grid behaves if the brokering is done is such a way that each cluster receives a quantity of work proportional to its computational capacity. <p> The second part of this work is rather independent from the first one, and consists in the presentation of a brokering strategy, based on Whittle's indices, trying to minimize as much as possible the average sojourn time of jobs. We show how efficient the proposed strategy is for computational grids, compared to the ones popular in production systems. We also show its robustness to several parameter changes, and provide several very efficient algorithms allowing to make the required computations for this index policy. We finally extend our model in several directions.

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