Managing ServerHotel

This graduate study was assigned by Unisys Oy Ab. The purpose of this study was to find tools to monitor and manage servers and objects in a hosting environment and to remotely connect to the managed objects. Better solutions for promised services were also researched. Unisys provides a ServerHotel service to other businesses which do not have time or resources to manage their own network, servers or applications. Contracts are based on a Service Level Agreement where service level is agreed upon according to the customer's needs. These needs have created a demand for management tools. Unisys wanted to find the most appropriate tools for its hosting environment to fulfill the agreed service level with reasonable costs. The theory consists of literary research focusing on general agreements used in the Finnish IT business, different types of monitoring and management tools and the common protocols used inthem. The theory focuses mainly on the central elements of the above mentioned topics and on their positive and negative features. The second part of the study focuses on general hosting agreements and what management tools Unisys has selected for hosting and why. It also gives a more detailed account of the hosting environment and its features in more detail. Because of the results of the study Unisys decided to use Servers Alive to monitor network and MS applications’ services. Cacti was chosen to monitor disk spaces, which gives us an idea of future disk growth. For remote connections the Microsoft’s Remote Desktop tool was the mostappropriate when the connection was tunneled through Secure Shell (SSH). Finding proper tools for the intended purposes with cost-conscious financial resources proved challenging. This study showed that if required, it is possible to build a professional hosting environment.

Solving the Vehicle Routing Problem with Genetic ALgorithm and Simulated Annealing

This Thesis Work will concentrate on a very interesting problem, the Vehicle Routing Problem (VRP). In this problem, customers or cities have to be visited and packages have to be transported to each of them, starting from a basis point on the map. The goal is to solve the transportation problem, to be able to deliver the packages-on time for the customers,-enough package for each Customer,-using the available resources- and – of course - to be so effective as it is possible.Although this problem seems to be very easy to solve with a small number of cities or customers, it is not. In this problem the algorithm have to face with several constraints, for example opening hours, package delivery times, truck capacities, etc. This makes this problem a so called Multi Constraint Optimization Problem (MCOP). What’s more, this problem is intractable with current amount of computational power which is available for most of us. As the number of customers grow, the calculations to be done grows exponential fast, because all constraints have to be solved for each customers and it should not be forgotten that the goal is to find a solution, what is best enough, before the time for the calculation is up. This problem is introduced in the first chapter: form its basics, the Traveling Salesman Problem, using some theoretical and mathematical background it is shown, why is it so hard to optimize this problem, and although it is so hard, and there is no best algorithm known for huge number of customers, why is it a worth to deal with it. Just think about a huge transportation company with ten thousands of trucks, millions of customers: how much money could be saved if we would know the optimal path for all our packages.Although there is no best algorithm is known for this kind of optimization problems, we are trying to give an acceptable solution for it in the second and third chapter, where two algorithms are described: the Genetic Algorithm and the Simulated Annealing. Both of them are based on obtaining the processes of nature and material science. These algorithms will hardly ever be able to find the best solution for the problem, but they are able to give a very good solution in special cases within acceptable calculation time.In these chapters (2nd and 3rd) the Genetic Algorithm and Simulated Annealing is described in details, from their basis in the “real world” through their terminology and finally the basic implementation of them. The work will put a stress on the limits of these algorithms, their advantages and disadvantages, and also the comparison of them to each other.Finally, after all of these theories are shown, a simulation will be executed on an artificial environment of the VRP, with both Simulated Annealing and Genetic Algorithm. They will both solve the same problem in the same environment and are going to be compared to each other. The environment and the implementation are also described here, so as the test results obtained.Finally the possible improvements of these algorithms are discussed, and the work will try to answer the “big” question, “Which algorithm is better?”, if this question even exists.