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The aim of this Bachelor of Science thesis is to study the black housing market, its spread and expansion within the Stockholm region. This hidden sort of crime is compromised and organized more than ever before. According to Fastighetsägarna the turnover is over a billion Swedish krona in Stockholm City, Sundbyberg and Solna. The demands of tenancies are at an all-time high and queue is all-time long. There is a housing shortage in the region and very few tenancies are built. Besides the fact that the regulated rent may be a cause, it is also the fact that Sweden today has the highest building costs in the European Union, although the country is one of the most resource-rich. The politics regarding the possibility to be able to live in the inner-city is primitive when the geographical location of the tenancies is not reflected in the rent itself. This leads to a hidden economic value of tenancies that later on are resold in the black housing market. Instead of an increase in the housing market, a creation of a black trade has been formed. Black landlords that sell these tenancies illegally reach out to their buyers through contacts and accommodation adds on forums such as Björnsbytare and Blocket. This Bachelor of Science thesis intends to analyze what this criminality looks like and what alternative ways there are to solve these problems. A questionnaire with over 200 respondents has been made and interviews with representatives from interest organizations have been performed due to the fact that literature within this field is inadequate. Radical actions are needed if the housing market is going to recover. There are various ways of doing this which will be discussed further in this thesis. Most importantly are our results that indicate how a new generation has been raised to think that the black housing market is acceptable. It has become a natural feature in their everyday lives. We need to restore the general public attitude if we ever want to receive a functioning housing market. / Syftet med detta kandidatarbete är att studera den svarta bostadsmarknaden samt dess utbredning och attityder som utvecklats i Stockholmsregionen. Denna dolda brottslighet är mer omfattande och organiserad än någonsin. Enligt Fastighetsägarna omsätter den över en miljard kronor i Stockholm City, Sundbyberg och Solna. Efterfrågan på bostäder är rekordhög och kötiderna för hyresrätter i Stockholms innerstad är rekordlånga. Det råder bostadsbrist i regionen och det byggs för få hyresrätter. Det är även så att Sverige idag har de högsta byggkostnaderna i hela EU trots att landet är det ett av de mest resursrika. Politiken kring att alla ska ha möjlighet att bo i innerstaden är primitiv då en hyresrätts geografiska läge inte avspeglas i hyran. Detta leder till ett dolt ekonomiskt värde på hyreskontrakt som sedan säljs vidare svart. Istället för en sund tillväxt på bostadsmarknaden har det skapats en marknad av svarthandel. Svartmäklare når enkelt ut till sina köpare via kontakter och bostadsannonser på forum som Björnsbytare och Blocket. Denna uppsats avser att analysera hur denna brottslighet ser ut, hur den kan åtgärdas och vilka alternativa sätt det finns att lösa problematiken. En enkätundersökning med över 200 svarande har utförts och intervjuer med representanter från intresseorganisationer har genomförts då litteratur kring ämnet är bristfällig. Radikala åtgärder krävs för att återställa marknadens balans. Det finns olika sätt att göra detta på, vilket kommer att diskuteras vidare i denna uppsats. Det allra viktigaste är dock att våra resultat visar hur en ny generation har uppfostrats till att tycka att svarthandeln är ganska okej. Den har blivit ett naturligt inslag i deras vardag. Vi behöver förändra människors värderingar kring detta om vi någonsin ska få en fungerande bostadsmarknad. Den åtgärd som först och främst måste vidtas är att bygga fler hyresrätter. Från politiskt håll finns olika sätt att stimulera bostadsbyggandet.

A challenge in today's society is to handle a large amount of vehicles traversing an intersection. Traffic lights are often used to control the traffic flow in these intersections. However, there are inefficiencies since the algorithms used to control the traffic lights do not perfectly adapt to the traffic situation. The purpose of this paper is to compare three different types of algorithms used in traffic control systems to find out how to minimize vehicle waiting times. A pretimed, a deterministic and a reinforcement learning algorithm were compared with each other. Test were conducted on a four-way intersection with various traffic demands using the program Simulation of Urban MObility (SUMO). The results showed that the deterministic algorithm performed best for all demands tested. The reinforcement learning algorithm performed better than the pretimed for low demands, but worse for varied and higher demands. The reasons behind these results are the deterministic algorithm's knowledge about vehicular movement and the negative effects the curse of dimensionality has on the training of the reinforcement learning algorithm. However, more research must be conducted to ensure that the results obtained are trustworthy in similar and different traffic situations. / En utmaning i dagens samhälle är att hantera en stor mängd fordon som kör igenom en korsning. Trafikljus används ofta för att kontrollera trafikflödena genom dessa korsningar. Det finns däremot ineffektiviteter eftersom algoritmerna som används för att kontrollera trafikljusen inte är perfekt anpassade till trafiksituationen. Syftet med denna rapport är att jämföra tre typer av algoritmer som används i trafiksystem för att undersöka hur väntetid för fordon kan minimeras. En tidsbaserad, en deterministisk och en förstärkande inlärning-algoritm jämfördes med varandra. Testerna utfördes på en fyrvägskorsning med olika trafikintensiteter med hjälp av programmet Simulation of Urban MObility (SUMO). Resultaten visade att den deterministiska algoritmen presterade bäst för alla olika trafikintensiteter. Inlärningsalgoritmen presterade bättre än den tidsbaserade på låga intensiteter, men sämre på varierande och högre intensiteter. Anledningarna bakom resultaten är att den deterministiska algoritmen har kunskap om hur fordon rör sig samt att dimensionalitetsproblem påverkar träningen av inlärningsalgoritmen negativt. Det krävs däremot mer forskning för att säkerställa att resultaten är pålitliga i liknande och annorlunda trafiksituationer.

This study investigates whether Gaussian Process Regression can be applied to evaluate buffer queue times in large scale data processing systems. It is additionally considered whether high-frequency data stream rates can be generalized into a small subset of the sample space. With the aim of providing basis for dynamic software optimization, a promising foundation for continued research is introduced. The study is intended to contribute to Direct Market Access financial trading systems which processes immense amounts of market data daily. Due to certain limitations, we shoulder a naïve approach and model latencies as a function of only data throughput in eight small historical intervals. The training and test sets are represented from raw market data, and we resort to pruning operations to shrink the datasets by a factor of approximately 0.0005 in order to achieve computational feasibility. We further consider four different implementations of Gaussian Process Regression. The resulting algorithms perform well on pruned datasets, with an average R2 statistic of 0.8399 over six test sets of approximately equal size as the training set. Testing on non-pruned datasets indicate shortcomings from the generalization procedure, where input vectors corresponding to low-latency target values are associated with less accuracy. We conclude that depending on application, the shortcomings may be make the model intractable. However for the purposes of this study it is found that buffer queue times can indeed be modelled by regression algorithms. We discuss several methods for improvements, both in regards to pruning procedures and Gaussian Processes, and open up for promising continued research. / Denna studie undersöker huruvida Gaussian Process Regression kan appliceras för att utvärdera buffer-kötider i storskaliga dataprocesseringssystem. Dessutom utforskas ifall dataströmsfrekvenser kan generaliseras till en liten delmängd av utfallsrymden. Medmålet att erhålla en grund för dynamisk mjukvaruoptimering introduceras en lovandestartpunkt för fortsatt forskning. Studien riktas mot Direct Market Access system för handel på finansiella marknader, somprocesserar enorma mängder marknadsdata dagligen. På grund av vissa begränsningar axlas ett naivt tillvägagångssätt och väntetider modelleras som en funktion av enbartdatagenomströmning i åtta små historiska tidsinterval. Tränings- och testdataset representeras från ren marknadsdata och pruning-tekniker används för att krympa dataseten med en ungefärlig faktor om 0.0005, för att uppnå beräkningsmässig genomförbarhet. Vidare tas fyra olika implementationer av Gaussian Process Regression i beaktning. De resulterande algorithmerna presterar bra på krympta dataset, med en medel R2 statisticpå 0.8399 över sex testdataset, alla av ungefär samma storlek som träningsdatasetet. Tester på icke krympta dataset indikerar vissa brister från pruning, där input vektorermotsvararande låga latenstider är associerade med mindre exakthet. Slutsatsen dras att beroende på applikation kan dessa brister göra modellen obrukbar. För studiens syftefinnes emellertid att latenstider kan sannerligen modelleras av regressionsalgoritmer. Slutligen diskuteras metoder för förbättrning med hänsyn till både pruning och GaussianProcess Regression, och det öppnas upp för lovande vidare forskning.

Since the field of computer networks has rapidly grown in the last two decades, congestion control of traffic loads within networks has become a high priority. Congestion occurs in network routers when the number of incoming packets exceeds the available network resources, such as buffer space and bandwidth allocation. This may result in a poor network performance with reference to average packet queueing delay, packet loss rate and throughput. To enhance the performance when the network becomes congested, several different active queue management (AQM) methods have been proposed and some of these are discussed in this thesis. Specifically, these AQM methods are surveyed in detail and their strengths and limitations are highlighted. A comparison is conducted between five known AQM methods, Random Early Detection (RED), Gentle Random Early Detection (GRED), Adaptive Random Early Detection (ARED), Dynamic Random Early Drop (DRED) and BLUE, based on several performance measures, including mean queue length, throughput, average queueing delay, overflow packet loss probability, packet dropping probability and the total of overflow loss and dropping probabilities for packets, with the aim of identifying which AQM method gives the most satisfactory results of the performance measures. This thesis presents a new AQM approach based on the RED algorithm that determines and controls the congested router buffers in an early stage. This approach is called Dynamic RED (REDD), which stabilises the average queue length between minimum and maximum threshold positions at a certain level called the target level to prevent building up the queues in the router buffers. A comparison is made between the proposed REDD, RED and ARED approaches regarding the above performance measures. Moreover, three methods based on RED and fuzzy logic are proposed to control the congested router buffers incipiently. These methods are named REDD1, REDD2, and REDD3 and their performances are also compared with RED using the above performance measures to identify which method achieves the most satisfactory results. Furthermore, a set of discrete-time queue analytical models are developed based on the following approaches: RED, GRED, DRED and BLUE, to detect the congestion at router buffers in an early stage. The proposed analytical models use the instantaneous queue length as a congestion measure to capture short term changes in the input and prevent packet loss due to overflow. The proposed analytical models are experimentally compared with their corresponding AQM simulations with reference to the above performance measures to identify which approach gives the most satisfactory results. The simulations for RED, GRED, ARED, DRED, BLUE, REDD, REDD1, REDD2 and REDD3 are run ten times, each time with a change of seed and the results of each run are used to obtain mean values, variance, standard deviation and 95% confidence intervals. The performance measures are calculated based on data collected only after the system has reached a steady state. After extensive experimentation, the results show that the proposed REDD, REDD1, REDD2 and REDD3 algorithms and some of the proposed analytical models such as DRED-Alpha, RED and GRED models offer somewhat better results of mean queue length and average queueing delay than these achieved by RED and its variants when the values of packet arrival probability are greater than the value of packet departure probability, i.e. in a congestion situation. This suggests that when traffic is largely of a non bursty nature, instantaneous queue length might be a better congestion measure to use rather than the average queue length as in the more traditional models.

Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have / Ministry of Higher Education in Egypt and the Egyptian Cultural Centre and Educational Bureau in London

As the electricity demand increases dramatically in Sweden, the need of using the existing electricity grid as efficiently as possible gains more importance. Simultaneously as needs expand, so does production in the form of wind parks and solar parks. This has led to an increase in connection requests at Svenska Kraftnät, the Swedish transmission system operator. The current process for accepting or rejecting these requests is based on the first-come-first-serve principle, where each request is investigated separately. This thesis investigates an alternative way of processing the requests in clusters and optimizing which combination is the best to accept from a technical point of view. To handle this multiobjective combinatorial optimization problem, a multiobjective Genetic algorithm with a Pareto filter is developed. The Genetic Algorithm finds a refined Pareto front containing optimal solutions that are plotted with objective function values. The user can then easily analyze the optimal solutions and decide upon which the final optimal request combination is. The developed Genetic Algorithm reaches a close-optimal Pareto front estimation after exploring between 15-40% of the solution space.

A stochastic model for the dynamics of a limit order book is evaluated and tested on empirical data. Arrival of limit, market and cancellation orders are described in terms of a Markovian queuing system with exponentially distributed occurrences. In this model, several key quantities can be analytically calculated, such as the distribution of times between price moves, price volatility and the probability of an upward price move, all conditional on the state of the order book. We show that the exponential distribution poorly fits the occurrences of order book events and further show that little resemblance exists between the analytical formulas in this model and the empirical data. The log-normal and Weibull distribution are suggested as replacements as they appear to fit the empirical data better.

Microservice architecture, which involves breaking down applications into smaller and loosely coupled components, is becoming increasingly common in the development of modern systems. Connections between these components can be established in various ways. One of these approaches is event-driven architecture, where components in the system communicate asynchronously with each other through message queues.  AWS, Amazon Web Services, the largest provider of cloud-based services, offers such a messaging queue: SQS, Amazon Simple Queue Service, which can be integrated with SNS, Amazon Simple Notification Service, to enable "one-to-many" asynchronous communication.  An alternative tool is Apache Kafka, created by LinkedIn and later open-sourced under the Apache Software Foundation. Apache Kafka is an event logging and streaming platform that can also function as a message queue in an event-driven architecture.  The authors of this thesis have been commissioned by Scania to compare and evaluate the performance of these two tools and investigate whether there are use cases where one might be more suitable than the other. To achieve this, two prototypes were developed, each prototype consisting of a producer microservice and a consumer microservice. These prototypes were then used to conduct latency and load tests by producing messages and measuring the time interval until they were consumed.  The results of the tests show that Apache Kafka has a lower average latency than SNS/SQS and scales more efficiently with increasing data volumes, making it more suitable for use cases involving real-time data streaming. Its potential as a message bus for loosely coupled components in the system is also evident. In this context, SNS/SQS is equally valuable, as it operates as a dedicated message bus with good latency and offers a user-friendly and straightforward setup process. / Mikrotjänstarkitektur, som innebär att applikationer bryts ned till mindre och löst kopplade komponenter, är något som blir allt vanligare vid utvecklingen av moderna system. Kopplingar mellan dessa komponenter kan etableras på olika sätt. Ett av dessa tillvägagångssätt är händelsedriven arkitektur, där komponenterna i systemet kommunicerar asynkront med varandra genom meddelandeköer.  AWS, Amazon Web Services, som är den största leverantören av molnbaserade tjänster, tillhandahåller en sådan meddelandekö: SQS, Amazon Simple Queue Service, som kan integreras med SNS, Amazon Simple Notification Service för att möjliggöra ”en-till-många” asynkron kommunikation.  Ett alternativt verktyg är Apache Kafka, skapat av Linkedin och senare öppen källkodspublicerad under Apache Software Foundation. Apache Kafka är en händelselogg och strömningsplattform som även kan fungera som en meddelandekö i en händelsedriven arkitektur.  Författarna av detta arbete har på uppdrag av Scania blivit ombedda att jämföra och utvärdera prestandan hos de två verktygen samt undersöka om det finns användningsfall där det ena kan vara mer lämpligt än det andra. För att uppnå detta utvecklades två prototyper, där varje prototyp består av en producent- och en konsumentmikrotjänst. Dessa prototyper användes sedan för att genomföra latens- och lasttester genom att producera meddelanden och mäta tidsintervallet till dess att de konsumerades.  Resultatet från testerna visar att Apache Kafka har lägre genomsnittlig latens än SNS/SQS och skalar mer effektivt vid ökande datamängder, vilket gör det mer lämpat för användningsfall med realtidsströmning av data. Dess potential som meddelandebuss för löst kopplade komponenter i systemet är också tydlig. I detta sammanhang är SNS/SQS lika användbart, då det fungerar som en dedikerad meddelandebuss med god latens och en användarvänlig och enkel startprocess.

Along with playing an ever-increasing role in the integration of other communication networks and expanding in application diversities, the current Internet suffers from serious overuse and congestion bottlenecks. Efficient congestion control is fundamental to ensure the Internet reliability, satisfy the specified Quality-of-Service (QoS) constraints and achieve desirable performance in response to varying application scenarios. Active Queue Management (AQM) is a promising scheme to support end-to-end Transmission Control Protocol (TCP) congestion control because it enables the sender to react appropriately to the real network situation. Analytical performance models are powerful tools which can be adopted to investigate optimal setting of AQM parameters. Among the existing research efforts in this field, however, there is a current lack of analytical models that can be viewed as a cost-effective performance evaluation tool for AQM in the presence of heterogeneous traffic, generated by various network applications. This thesis aims to provide a generic and extensible analytical framework for analyzing AQM congestion control for various traffic types, such as non-bursty Poisson and bursty Markov-Modulated Poisson Process (MMPP) traffic. Specifically, the Markov analytical models are developed for AQM congestion control scheme coupled with queue thresholds and then are adopted to derive expressions for important QoS metrics. The main contributions of this thesis are listed as follows: • Study the queueing systems for modeling AQM scheme subject to single-class and multiple-classes Poisson traffic, respectively. Analyze the effects of the varying threshold, mean traffic arrival rate, service rate and buffer capacity on the key performance metrics. • Propose an analytical model for AQM scheme with single class bursty traffic and investigate how burstiness and correlations affect the performance metrics. The analytical results reveal that high burstiness and correlation can result in significant degradation of AQM performance, such as increased queueing delay and packet loss probability, and reduced throughput and utlization. • Develop an analytical model for a single server queueing system with AQM in the presence of heterogeneous traffic and evaluate the aggregate and marginal performance subject to different threshold values, burstiness degree and correlation. • Conduct stochastic analysis of a single-server system with single-queue and multiple-queues, respectively, for AQM scheme in the presence of multiple priority traffic classes scheduled by the Priority Resume (PR) policy. • Carry out the performance comparison of AQM with PR and First-In First-Out (FIFO) scheme and compare the performance of AQM with single PR priority queue and multiple priority queues, respectively.