I träkonstruktioner används olika förbindare av stål för att sammanfoga olika virkesdelar. För att kunna välja en lämplig förbindare måste konstruktören därför veta vilka skillnader som finns mellan dem och ha kunskap om vilka regler som gäller vid beräkning av förbandets bärförmåga. Rapportens syfte är att synliggöra skillnaderna i bärförmåga mellan olika förbindare. Det görs genom attjämföra bärförmågan för en given knutpunkt med olika förbindare. Fokus har legat på att jämföra bärförmågan i brottgränstillstånd, men även andra aspekter som monteringstid har berörts. Resultatet visar att om man vill maximera bärförmågan för ett förband är det bäst att använda skruvar ismå dimensioner. Det är dock sällan skruvar med en diameter under 10 mm används. I praktiskt arbeteblir därför ofta spik det bästa alternativet om bärförmågan ska maximeras. Träskruv har lägre bärförmåga än både spik och skruv för en knutpunkt med virkestjocklekar upp till 45 mm. Som tumregelkan nämnas att i karakteristiska värden håller en 90 mm lång 3,3 mm trådspik för drygt 1 kN, en 6 mm träskruv för 1,5 kN och en M10 för 4 kN. I eurokod 5 finns det vissa avsnitt som kan vara svåra att tolka, exempelvis effektiv diameter och axiell bärförmåga för träskruv. Efter att ha försökt ta reda på hur reglerna ska tolkas är min slutsats att tolkningarna varierar även bland auktoriteter på området. Det kan därför ofta vara enklare att användatabellerade värden för bärförmågan som tillverkaren redovisar än att försöka räkna på förbandet själv. / In timber construction, different metal dowel-type fasteners are used to attach members in timber frame construction. Therefore, in order to be able to choose a suitable fastener, you must know the differences between them and have knowledge of which rules to apply when calculating the load-carrying capacity. The purpose of this report is to show the differences in load-carrying capacity between different fasteners. This is done by comparing the load-carrying capacity of a connection with different fasteners. The focus has been on comparing the load-carrying capacity in ultimate limit state, but also other aspects such as mounting has been mentioned. The results show that if you want to maximize the load-carrying capacity of a connection, the best option is to use bolts in small dimensions. However, bolts with a diameter less than 10 mm are rarely used. In practical work, nails are often the best option if you want to maximize the load-carrying capacity. Screws have a lower load-carrying capacity than both nails and bolts for a connection with wood thickness up to 45 mm. In Eurocode 5, there are some sections that can be difficult to interpret and understand, for example the effective diameter and axial load-carrying capacity for screws. I have analyzed how the rules should be interpreted and my conclusion is that the interpretations differ also among professors and screw manufacturers. It can therefore be easier to use tabulated values for the load-carrying capacity from the manufacturer than to try to calculate it yourself.

Enterprise Database Management Systems (DBMSs) have to contend with resource-intensive and time-varying workloads, making them well-suited candidates for migration to cloud plat-forms { specifically, they can dynamically leverage the resource elasticity while retaining affordability through the pay-as-you-go rental interface. The current design of database engine components lays emphasis on maximizing computing efficiency, but to fully capitalize on the cloud's benefits, the outlays of these computations also need to be factored into the planning exercise. In this thesis, we investigate this contemporary problem in the context of industrial-strength deployments of relational database systems on real-world cloud platforms. Specifically, we consider how the traditional metric used to compare query execution plans, namely response-time, can be augmented to incorporate monetary costs in the decision process. The challenge here is that execution-time and monetary costs are adversarial metrics, with a decrease in one entailing a rise in the other. For instance, a Virtual Machine (VM) with rich physical resources (RAM, cores, etc.) decreases the query response-time, but is expensive with regard to rental rates. In a nutshell, there is a tradeoff between money and time, and our goal therefore is to identify the VM that others the best tradeoff between these two competing considerations. In our study, we pro le the behavior of money versus time for a given query, and de ne the best tradeoff as the \knee" { that is, the location on the pro le with the minimum Euclidean distance from the origin. To study the performance of industrial-strength database engines on real-world cloud infrastructure, we have deployed a commercial DBMS on Google cloud services. On this platform, we have carried out extensive experimentation with the TPC-DS decision-support benchmark, an industry-wide standard for evaluating database system performance. Our experiments demonstrate that the choice of VM for hosting the database server is a crucial decision, because: (i) variation in time and money across VMs is significant for a given query, (ii) no one VM offers the best money-time tradeoff across all queries. To efficiently identify the VM with the best tradeoff from a large suite of available configurations, we propose a technique to characterize the money-time pro le for a given query. The core of this technique is a VM pruning mechanism that exploits the property of partially ordered set of the VMs on their resources. It processes the minimal and maximal VMs of this poset for estimated query response-time. If the response-times on these extreme VMs are similar, then all the VMs sandwiched between them are pruned from further consideration. Otherwise, the already processed VMs are set aside, and the minimal and maximal VMs of the remaining unprocessed VMs are evaluated for their response-times. Finally, the knee VM is identified from the processed VMs as the one with the minimum Euclidean distance from the origin on the money-time space. We theoretically prove that this technique always identifies the knee VM; further, if it is acceptable to and a \near-optimal" knee by providing a relaxation-factor on the response-time distance from the optimal knee, then it is also capable of finding more efficiently a satisfactory knee under these relaxed conditions. We propose two favors of this approach: the first one prunes the VMs using complete plan information received from database engine API, and named as Plan-based Identification of Knee (PIK). On the other hand, to further increase the efficiency of the identification of the knee VM, we propose a sub-plan based pruning algorithm called Sub-Plan-based Identification of Knee (SPIK), which requires modifications in the query optimizer. We have evaluated PIK on a commercial system and found that it often requires processing for only 20% of the total VMs. The efficiency of the algorithm is further increased significantly, by using 10-20% relaxation in response-time. For evaluating SPIK , we prototyped it on an open-source engine { Postgresql 9.3, and also implemented it as Java wrapper program with the commercial engine. Experimentally, the processing done by SPIK is found to be only 40% of the PIK approach. Therefore, from an overall perspective, this thesis facilitates the desired migration of enterprise databases to cloud platforms, by identifying the VM(s) that offer competitive tradeoffs between money and time for the given query.