We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.

This thesis studies the area of virtualization. The focus is on the sub-area live migration, a technique that allows a seamless migration of a virtual machine from one physical machine to another physical machine. Virtualization is an attractive technique, utilized in large computer systems, for example data centers. By using live migration, data center administrators can migrate virtual machines, seamlessly, without the users of the virtual machines taking notice about the migrations. Manually initiated migrations can become cumbersome, with an ever-increasing number of physical machines. The number of physical and virtual machines is not the only problem, deciding when to migrate and where to migrate are other problems that needs to be solved. Manually initiated migrations can also be inaccurate and untimely. Two different strategies for automated live migration have been developed in this thesis. The Push and the Pull strategies. The Push strategy tries to get rid of virtual machines and the Pull strategy tries to steal virtual machines. Both of these strategies, their design and implementation, are presented in the thesis. The strategies utilizes Shannon's Information Entropy to measure the balance in the system. The strategies further utilizes a cost model to predict the time a migration would require. This is used together with the Information Entropy to decide which virtual machine to migrate if and when a hotspot occurs. The implementation was done with the help of OMNeT++, an open-source simulation tool. The strategies are evaluated with the help of a set of simulations. These simulations include a variety of scenarios with different workloads. Our results shows that the developed strategies can re-balance a system of computers, after a large amount of virtual machines has been added or removed, in only 4-5 minutes. The results further shows that our strategies are able to keep the system balanced when the system load is at medium. This while virtual machines are continuously added or removed from the system. The contribution this thesis brings to the field is a model for how automated live migration of virtual machines can be done to improve the performance of a computer system, for example a data center.

We developed a diffusive load balancing scheme that equalizes the available capacities of nodes in a peer-to-peer (P2P) system. These nodes may have different resource capacities, geographic locations, or availabilities (i.e., length of time being part of the peer-to-peer system). The services on these nodes may have different service times and arrival rates of requests. Using the diffusive scheme, the system is able to maintain similar response times for its services. Our scheme is a modification of the diffusive load balancing algorithms proposed for parallel computing systems. This scheme is able to handle services with heterogeneous resource requirements and P2P nodes with heterogeneous capacities. We also adapted the diffusive scheme to clustered peer-to-peer system, where a load balancing operation may move services or nodes between clusters. After a literature survey of this field, this thesis investigates the following issues using analytical reasoning and extensive simulation studies. The load balancing operations equalize the available capacities of the nodes in a neighborhood to their averages. As a result, the available capacities of all nodes in the P2P system converge to a global average. We found that this convergence is faster when the scheme uses neighborhoods defined by the structure of the structured P2P overlay network rather than using randomly selected neighbors. For a system with churn (i.e. nodes joining and leaving), the load balancing operations maintain the standard deviation of the available capacities of nodes within a bound. This bound depends on the amount of churn and the frequency of load balancing operations, as well as on the capacities of the nodes. However, the sizes of the services have little impact on this bound. In a clustered peer-to-peer system, the size of the bound largely depends on the average cluster size. When nodes are moved among clusters for load balancing, the numbers of cluster splits and merges are reduced. This may reduce the maintenance cost of the overlay network.

The diploma thesis deals with a design of a crankshaft for a three-cylinder diesel engine for commercial vehicles. The engine is a member of a unified line of engines with the number of cylinders from two to six. The main points of the work are a draft of the crankshaft configuration, the balance of inertial forces and the moments in the crank mechanism, creation of drawings of the crankshaft and finally carrying out a strength analysis of the part, taking into account torsional vibration. The aim is formation of a part – crankshaft for potential usage in the automotive industry.

To make a check of an contemporary balancing verification of diesel engine Zetor UŘ III with type identification 1505, the engine power 90 kW and especially friction bearing design, which could replace existing needle bearing HK 4520 type, is the aim of this diploma thesis. Bearing type modification requires constructional adjustment of engine block and balancing shaft and therefore this proposal for construction another aim in my diploma thesis. The last part of my diploma thesis is an economic balance original imposition against newly designed. Calculations are made using mathematical software MathCad, the balancing shaft is made using ProEngineer software and control calculations of bearing stresses are made with ADAMS and FEM of ANSYS system. Motivation to write this work is to prove the possibility how to decrease manufacturing costs of ZETOR TRACTORS a.s. company.

The purpose of this thesis is two-cylinder in-line four stroke petrol engine design based on input parameters. The work consists of different variations of designed crankshaft balancing and balancing unit. Calculation method of torsion vibration and crankshaft fatigue failure solution based on stress analysis are included.

The aim of this master’s thesis was to define force progression in crank-type mechanism from the set parameters of the cylindrical unit. Furthermore, the design of the possible balance of this crank-type mechanism with one balancing shaft was realized. The calculations of torsional vibration and stress analyses of the designed crank-type mechanism were realized too.

This thesis deals with possibilities of 2-cylinder engine balancing for tractor usage. The purpose of this thesis is to find a technical solution for the creation of the concept of balancing using balancing shaft. Check the proposed solution by FEM analysis and evaluate the fatigue safety. The content of the thesis is the creation of computational and graphical documentation too.

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Most services today are developed with a microservice architecture where each component is deployed with multiple replicas on servers all over the world. When requests go between service components, the role of a load balancer is to route each request to the least loaded instance of the target component. There are many algorithms that evaluate different parameters and select an instance from those. One approach is to optimize for latency, i.e., choose the instance that will result in the lowest latency. However, this approach does not take into consideration the geographical distribution of servers, or when requests have to cross networking boundaries, i.e., go from one physical data center to another. Crossing networking boundaries comes with an increased cost as connecting two data centers far apart is an expensive task. Therefore, the cloud computing provider will charge this traffic more than when just sending traffic within a single data center. This study set out to use Google Traffic Director, a service mesh that has information about the whole system and can, therefore, offer locality-aware load-balancing that tries to minimize the amount of traffic that crosses networking boundaries. This is compared to a latency-based algorithm without a service mesh architecture, namely Expected Latency Selector. The study was set up to evaluate how the different approaches performed in terms of cost, latency, and resilience. This evaluation was performed by setting up two testing environments where both load-balancing algorithms could run and relevant metrics were collected. This was then tested in three different scenarios: no disturbance, random delay in a zone, and the final being a zone failing all requests. Results show that in a perfect environment, a locality-aware approach with Traffic Director can reduce the networking cost to an optimal level by only sending a negligible amount of requests cross-zone, while still performing equally well as the latency-based approach in terms of latency. However, when a delay or failure is introduced, Traffic Director, in our setup, keeps the same behavior of prioritizing the locality instead of distributing requests to other zones to even out the latency and circumvent the faulty servers. / De flesta online tjänsterna idag är utvecklade med en mikrotjänst arkitektur där varje komponent är distribuerad med många kopior på servrar över hela världen. När en förfrågan går mellan en tjänsts komponenter, är en lastbalanserares roll att dirigera en förfrågan till den minst belastade instansen av målkomonenten. Det existerar många algoritmer som evaluerar olika parametrar och väljer en instanser på det sättet. Ett tillvägagångssätt är att optimera för latens d.v.s. välja den instansen som kommer att ge lägst latens. Detta tillvägagångssätt kommer däremot inte ta den geografiska distributionen av servrar eller när en förfrågan behöver korsa nätverksgränser i åtanke. Att korsa nätverksgränser kommer med en öka kostnad eftersom att förbinda två datacenter är omständigt och dyrt. Därav kommer molntjänstleverantören att ta mer betalt för denna typ av nätverkstrafik än trafik som håller sig inom ett datacenter. Denna studie använde sig därav av Googles Traffic Director, en service mesh som erbjuder lokalitets-medveten lastbalansering som försöker minimera mängden trafik som korsar nätverksgränser, och jämför det med en latens-baserad algorithm kallad Expected Latency Selector. Studie evaluerar hur de två olika tillvägagångsätten presterar sett till kostnad, latens och resiliens. Evalueringen genomfördes genom att sätta upp två testmiljöer där båda algoritmerna kunde köras och relevant data samlades. Detta kördes sedan under tre olika scenarion: ingen störning, slumpmässig fördröjning och en zon där varje förfrågan misslyckas. Resultaten indikerar att in en perfekt miljö kan ett lokalitets-medvetet tillvägagångssätt med Traffic Director reducera nätverkskostnaden till en optimal nivå genom att endast skicka en försumbar mängd förfrågan till andra zoner, och samtidigt prestera ekvivalent med latens-baserade tillvägagångssättet sett till latens. Däremot, när en fördröjning eller misslyckande av förfrågan introduceras kommer Traffic Director att behålla samma beteende av att prioritera lokalitet istället för att distribuera förfrågningar till andra zoner för att jämna ut latensen och kringgå felaktiga servrar.