This project was done at Mälardalens University at the school of Innovation, Design & Engineering. The project is a thesis of 15hp for the program in innovation and product design, the project began in spring 2018 and was completed in the summer 2020, its focus on design and construction with regards to, among other things, the environment. The work covers a project development process where a self-sufficient accommodation inside of a shipping container is designed and constructed with a goal to end in an overall construction. The project is not for a company but comes from an individual idea where the result is presented in the form of a report and a entailed CAD-assembly of the accommodations different parts. The content and construction of the accommodation is produced with the first phases of the project development processes, information is collected, concepts are created, and solutions are designed. These phases are iterated so that the result generates a as solid base as possible. Through the work process, the environment and cost are of great importance. The result of the project was an accommodation, which for a week is self-sufficient in electricity and water usage for one person. The resulting accommodation includes; bathroom with shower and toilet, kitchen with sink and gas-hob, electricity and water central where electricity and water is stored and processed, and bed, table and storage. All these functions are located on a floor area of 9,3 m2. The accommodation is constructed in CAD, where its different systems have been developed and designed at a basic level for easy further work at a detail level. The accommodation is not fully designed/constructed but provides an overall plan on how this type of accommodation can be created and its approximate cost, 95´000:-. The solutions and costs may differ in the realization of the accommodation as certain delimitations were made so that this work could be carried out. The project har several development directions where the focus can be within; detail development, selfsufficient living, or temporary conventional accommodation. / Detta examensarbete gjordes på Mälardalens Högskola på akademin för Innovation, Design Teknik. Arbetet är ett examensarbete på 15hp för programmet i innovation och produktdesign, arbetet påbörjades våren 2018 och slutfördes sommaren 2020, det är inriktat på design och konstruktion med avseende på bland annat miljö. Arbetet består av en produktutvecklingsprocess där ett självförsörjande boende inuti en container designas och konstrueras med avseende att sluta i en övergriplig konstruktion. Arbetet sker inte mot ett företag utan kommer från en individuell idé där resultatet presenteras i form av en rapport och en medförande CAD-sammanställning som utgör boendets olika delar. Boendets innehåll och konstruktion har tagitsfram med hjälp av produktutvecklingsprocessens första faser, insamlad information leder till koncept som leder till lösningar. Dessa faser itereras för att resultatet ska generera en så ordentlig bas som möjligt. Genom arbetets gång har miljö och kostnad stor vikt. Resultatet blev ett boende vilket under en veckas period är självförsörjande av el och vatten för en person. Boendet inkluderar; badrum med dusch och toalett, kök med diskho och gasolplatta, el-och vatten-central där el och vatten förvaras och processas, och säng, bord och förvaring. Alla dessa funktioner ligger på en golvyta av 9,3 m2.Boendet är konstruerat i CAD där dess olika delsystem tagits fram och designats på grundnivå för enkelt vidarearbete av detaljnivå. Boendet är inte designat/konstruerat till fullo utan ger en övergriplig plan om hur ett boende av denna typ kan skapas och dess ungefärliga kostnad, 95 ́000:-. Boendets lösningaroch kostnader kan skilja sig vid förverkligandet av boendet då vissa avgränsningar gjordes för att detta arbete skulle kunna genomföras. Arbetet har flera potentiella utvecklingsriktningar där fokus kan ligga inom; detaljutveckling, självförsörjning, eller portabelt temporärt konventionell boende.

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In the era of artificial intelligence and machine learning, AI/ML inference tasks have become exceedingly popular. However, executing these workloads on dedicated hardware may not be feasible for many users due to high maintenance costs, varying load patterns, and time to production. Furthermore, ML inference workloads are stateless, and most of them are not extremely latency sensitive. For example, tasks such as fake review removal, abusive language detection, tweet classification, image tagging, and free-tier-chat-bots do not require real-time inference. All these characteristics make serverless platforms a good fit for deployment, and in this work, we identify the bottlenecks involved in hosting these inference jobs on serverless and optimize serverless for better performance and resource utilization. Specifically, we identify model loading and model memory duplication as major bottlenecks in Serverless Inference, and to address these problems, we propose a new approach that rethinks the way we serve FaaS requests. To support this design, we employ a hybrid scaling approach to implement the autoscale feature of serverless. / Master of Science / Most modern software applications leverage the power of machine learning to incorporate intelligent features. For instance, platforms like Yelp employ machine learning algorithms to detect fake reviews, while intelligent chatbots such as ChatGPT provide interactive conversations. Even Netflix relies on machine learning to recommend personalized content to its users. The process of creating these machine learning services involves several stages, including data collection, model training using the collected data, and serving the trained model to deploy the service. This final stage, known as inference, is crucial for delivering real-time predictions or responses to user queries. In our research, we focus on selecting serverless computing as the preferred infrastructure for deploying these popular inference workloads. Serverless, also referred to as Function as a Service (FaaS), is an execution paradigm in cloud computing that allows users to efficiently run their code by providing scalability, elasticity and fine-grained billing. In this work we identified, model loading and model memory duplication as major bottlenecks in Serverless Inference. To solve these problems we propose a new approach which rethinks the way we serve FaaS requests. To support this design we use a hybrid scaling approach to implement the autoscale feature of serverless.

Improving water efficiency is and will continue to be a top concern to meet the world food production demands for a growing population. By having a clear understanding of water efficiencies, communities will be able to address these concerns from an economic standpoint and use more productive methods to grow food and limit water consumption. This study examines the water efficiencies of three irrigation methods over a single growing season in southeastern Oklahoma. Two crops, tomatoes and cucumbers, were grown using drip irrigation, a self-wicking container, and a non-circulating hydroponics barrel. Results at the end of the season showed the drip irrigation method had the highest water efficiency in terms of yield of product over water applied for both crops. The drip irrigation method also had the lowest associated set up costs and second lowest time requirements after the hydroponics method. These results were found to be consistent with other studies that compared drip irrigation to other irrigation methods and showed drip to have the highest water efficiencies.

Reproducing distributed experiments is a challenging task for many researchers. There are many factors which make this problem harder to solve. In order to reproduce distributed experiments, researchers need to perform complex deployments which involve many dependent software stacks with many configurations and manual orchestrations. Further, researchers need to allocate a larger amount of money for clusters of machines and then spend their valuable time to perform those experiments. Also, some of the researchers spend a lot of time to validate a distributed scenario in a real environment as most of the pseudo distributed systems do not provide the characteristics of a real distributed system. Karamel provides solutions for the inconvenience caused by the manual orchestration by providing a comprehensive orchestration platform to deploy and run distributed experiments. But still, this solution may incur a similar amount of expenses as of a manual distributed setup since it uses virtual machines underneath. Further, it does not provide quick validations of a distributed setup with a quick feedback loop, as it takes considerable time to terminate and provision new virtual machines. Therefore, we provide a solution by integrating Docker that can co-exists with virtual machine based deployment model seamlessly. Our solution encapsulates the container-based deployment model for users to reproduce distributed experiment in a cost-effective and efficient manner. In this project, we introduce novel deployment model with containers that is not possible with the conventional virtual machine based deployment model. Further, we evaluate our solution with a real deployment of Apache Hadoop Terasort experiment which is a benchmark for Apache Hadoop map-reduce platform in order to explain how this model can be used to save the cost and improve the efficiency.

Three species of woody plants, Ilex crenata 'Helleri', Juniperus chinensis procumbens 'Nana', and Ligustrum lucidum, were grown in one-liter containers filled with pine bark to determine Fe and Mn requirements with regard to rate and ratio. FeEDTA and MnEDTA were applied at either a 2:1 or 1:2 ratio of Fe:Mn at 5 concentrations each, 3 times per week with each irrigation. Medium solutions were collected every 21 days on one species and analyzed for Fe and Mn levels. Dry weight and tissue Fe and Mn levels were determined for all three species. Neither rate nor ratio of applied Fe and Mn had an effect on shoot dry weights. Control treatments, in general, had the lowest medium solution and tissue levels of Fe and Mn, however, there was no difference in dry weights between control and treatment plants. These results suggest that pine bark supplies adequate levels of Fe and Mn for growth under the conditions of this study. In a second study, three sources of Fe and Mn were applied to Tagetes erecta 'Inca' growing in 500 cc plastic pots containing sieved pine bark at 3 lime rates: 0, 3, and 6 kg m⁻³. Sources of Fe and Mn were pre-plant Micromax, liquid sulfate salts, and liquid chelates applied in the irrigation water. No difference in growth between micronutrient sources was detected, however, growth was greater at the 3 and 6 kg m⁻³ lime rates. Levels of Fe and Mn in medium solution and tissue decreased with increasing lime rate, with availability of Fe and Mn greatest with chelate as the source, regardless of lime rate. A similar study was conducted with a control and liquid sulfate treatment. There was no difference in dry weight between the sulfate treatment and the control, except at 0 kg m⁻³ lime where the control plants were larger. Again, lime additions increased growth, and Fe and Mn availability in medium solution and tissue levels decreased. These results suggest that if Fe and Mn additions are needed, all sources provide adequate Fe and Mn for growth. / M.S.

Over the last decade, container technology has fundamentally changed the landscape of commercial cloud computing services. In contrast to traditional VM technologies, containers theoretically provide the same process isolation guarantees with less overhead and additionally introduce finer grained options for resource allocation. Cloud providers have widely adopted container based architectures as the standard for multi-tenant hosting services and rely on underlying security guarantees to ensure that adversarial workloads cannot disrupt the activities of coresident containers on a given host. Unfortunately, recent work has shown that the isolation guarantees provided by containers are not absolute. Due to inconsistencies in the way cgroups have been added to the Linux kernel, there exist vulnerabilities that allow containerized processes to generate "out of band" workloads and negatively impact the performance of the entire host without being appropriately charged. Because of the relative complexity of the kernel, discovering these vulnerabilities through traditional static analysis tools may be very challenging. In this work, we present TORPEDO, a set of modifications to the SYZKALLER fuzzing framework that creates containerized workloads and searches for sequences of system calls that break process isolation boundaries. TORPEDO combines traditional code coverage feedback with resource utilization measurements to motivate the generation of "adversarial" programs based on user-defined criteria. Experiments conducted on the default docker runtime runC as well as the virtualized runtime gVisor independently reconfirm several known vulnerabilities and discover interesting new results and bugs, giving us a promising framework to conduct more research. / Master of Science / Over the last decade, container technology has fundamentally changed the landscape of commercial cloud computing services. By abstracting away many of the system details required to deploy software, developers can rapidly prototype, deploy, and take advantage of massive distributed frameworks when deploying new software products. These paradigms are supported with corresponding business models offered by cloud providers, who allocate space on powerful physical hardware among many potentially competing services. Unfortunately, recent work has shown that the isolation guarantees provided by containers are not absolute. Due to inconsistencies in the way containers have been implemented by the Linux kernel, there exist vulnerabilities that allow containerized programs to generate "out of band" workloads and negatively impact the performance of other containers. In general, these vulnerabilities are difficult to identify, but can be very severe. In this work, we present TORPEDO, a set of modifications to the SYZKALLER fuzzing framework that creates containerized workloads and searches for programs that negatively impact other containers. TORPEDO uses a novel technique that combines resource monitoring with code coverage approximations, and initial testing on common container software has revealed new interesting vulnerabilities and bugs.

Cranes are increasingly used in transportation and construction. increasing demand and faster requirements necessitate better and more efficient controllers to guarantee fast turn-around time and to meet safety requirements. Container cranes are used extensively in ship-to-port and port-to-ship transfer operations. In this work, we will extend the recently developed delayed position feedback controller to container cranes. In contrast with traditional work, which models a crane as a simple pendulum consisting of a hoisting cable and a lumped mass at its end, we have modeled the crane as a four-bar mechanism. The actual configuration of the hoisting mechanism is significantly different from a simple pendulum. It consists typically of a set of four hoisting cables attached to four different points on the trolley and to four points on a spreader bar. The spreader bar is used to lift the containers. Therefore, the dynamics of hoisting assemblies of large container cranes are different from that of a simple pendulum. We found that a controller which treats the system as a four-bar mechanism has an improved response. We developed a controller to meet the following requirements: traverse an 80-ton payload 50 m in 21.5 s, including raising the payload 15 m at the beginning and lowering the payload 15 m at the end of motion, while reducing the sway to 50 mm within 5.0 s at the end of the transfer maneuver. The performance of the controller has been demonstrated theoretically using numerical simulation. Moreover, the performance of the controller has been demonstrated experimentally using a 1/10th scale model. For the 1/10th scale model, the requirements translate into: traverse an 80 kg payload 5 m in 6.8 s, including raising 1.5 m at the beginning and lowering 1.5 m at the end of motion, while reducing the sway to 5 mm in under 1.6 s. The experiments validated the controller. / Master of Science

The objectives for this research were to determine: 1) the rate of Micromax which will produce maximum growth of pin oak (Quercus palustris Munchh.), a landscape tree which has shown a previous growth response to the addition of Micromax at the manufacturer's recommended rate, 2) which micronutrient(s) is most associated with maximum growth, and 3) the rate of Cu, Fe, Mn, and Zn required to produce maximum growth of Quercus palustris. Q. palustris seedlings were container-grown in pine bark amended with the following rates of Micromax: 0, 0.15, 0.3, 0.6, 0.9, 1.8, or 2.7 kg(m-3 in 2000, 2001, and 2002. For all three years, the maximum growth was obtained at rates near the manufacturer's recommended rate of 0.9 kg(m-3. A micronutrient mix was formulated by increasing the levels of Zn, Mn, Fe, and Cu individually while holding the other micronutrients constant based on the grams of each micronutrient contained in Micromax at 0, 0.15, 0.3, 0.6, 0.9, 1.8, or 2.7 kg(m-3 for 2001 and 0, 0.45, 0.9, or 1.8 kg(m-3 for 2002. In addition, Cu, Fe, Mn, and Zn were also applied alone to pine bark at rates of 0, 0.45, 0.9, or 1.8 kg(m-3 without the addition of any other micronutrients. Holding all other micronutrients constant and increasing the rate of one micronutrient did not increase growth. However, when Cu, Fe, Mn, or Zn was added to pine bark alone at increasing rates, growth increased. For Cu and Zn, the growth increased was linear suggesting that a higher rate of Cu and Zn than that provided by Micromax at the manufacturer's recommended rate might be advantageous. / Master of Science