<p>In this thesis we investigate different algorithms for dynamic resource allocation in wireless networks. We introduce a general framework for modeling systems whichis applicable to many scenarios. We also analyze a specific scenario with adaptivebeamforming and show how it fits into the proposed framework. We then studytwo different resource allocation problems: Quality-of-Service (QoS) constraineduser scheduling and sum-rate maximization. For user scheduling, we select some“good” set of users that is allowed to use a specific resource. We investigatedifferent algorithms with varying complexities. For the sum-rate maximizationwe find the global optimum through an algorithm that takes advantage of thestructure of the problem by reformulating it as a D.C. program, i.e., a minimizationover a difference of convex functions. We validate this approach by showing that itis more efficient than an exhaustive search at exploring the space of solutions. Thealgorithm provides a good benchmark for more suboptimal algorithms to comparewith. The framework in which we construct the algorithm, apart from being verygeneral, is also very flexible and can be used to implement other low complexitybut suboptimal algorithms.</p>

In a data centre, server clusters are typically used to provide the required processing capacity to provide acceptable response time performance to interactive applications. The workload of each application may be time-varying. Static allocation to meet peak demand is not an efficient usage of resources. Dynamic resource allocation, on the other hand, can result in efficient resource utilization while meeting the performance goals of individual applications. In this thesis, we develop a new interactive system model where the number of logon users changes over time. Our objective is to obtain results that can be used to guide dynamic resource allocation decisions. We obtain approximate analytic results for the response time distribution at steady state for our model. Using numerical examples, we show that these results are acceptable in terms of estimating the steady state probabilities of the number of logon users. We also show by comparison with simulation that our results are acceptable in estimating the response time distribution under a variety of dynamic resource allocation scenarios. More importantly, we show that our results are accurate in terms of predicting the minimum number of processor nodes required to meet the performance goal of an interaction application. Such information is valuable to resource provisioning and we discuss how our results can be used to guide dynamic resource allocation decisions.

In this thesis we investigate different algorithms for dynamic resource allocation in wireless networks. We introduce a general framework for modeling systems whichis applicable to many scenarios. We also analyze a specific scenario with adaptivebeamforming and show how it fits into the proposed framework. We then studytwo different resource allocation problems: Quality-of-Service (QoS) constraineduser scheduling and sum-rate maximization. For user scheduling, we select some“good” set of users that is allowed to use a specific resource. We investigatedifferent algorithms with varying complexities. For the sum-rate maximizationwe find the global optimum through an algorithm that takes advantage of thestructure of the problem by reformulating it as a D.C. program, i.e., a minimizationover a difference of convex functions. We validate this approach by showing that itis more efficient than an exhaustive search at exploring the space of solutions. Thealgorithm provides a good benchmark for more suboptimal algorithms to comparewith. The framework in which we construct the algorithm, apart from being verygeneral, is also very flexible and can be used to implement other low complexitybut suboptimal algorithms.

In a data centre, server clusters are typically used to provide the required processing capacity to provide acceptable response time performance to interactive applications. The workload of each application may be time-varying. Static allocation to meet peak demand is not an efficient usage of resources. Dynamic resource allocation, on the other hand, can result in efficient resource utilization while meeting the performance goals of individual applications. In this thesis, we develop a new interactive system model where the number of logon users changes over time. Our objective is to obtain results that can be used to guide dynamic resource allocation decisions. We obtain approximate analytic results for the response time distribution at steady state for our model. Using numerical examples, we show that these results are acceptable in terms of estimating the steady state probabilities of the number of logon users. We also show by comparison with simulation that our results are acceptable in estimating the response time distribution under a variety of dynamic resource allocation scenarios. More importantly, we show that our results are accurate in terms of predicting the minimum number of processor nodes required to meet the performance goal of an interaction application. Such information is valuable to resource provisioning and we discuss how our results can be used to guide dynamic resource allocation decisions.

Agent technology enables the designers of computer based systems to construct software agents that are able to model attitudes. We present a frame-work in which these artifacts aim to express the preferences of both their designers and their owners. Just like human societies need rules, regula-tions, norms and social laws, in order to function, societies of agents need coordination mechanisms in order to work efficiently. We show why some higher level goals of agents are incompatible, e.g. the automatic creation of coalitions among agents, and at the same time being self-interested and boundedly rational. One way to model the outcome of planned interactions between agents is to apply game theory. We use game theory for proving some results, e.g. a \No free lunch" theorem. For more practical applications, however, other approaches are often needed. One such domain is dynamic resource allocation, where agents through auction mechanisms or different kinds of mobile broker techniques solve the problem of coordinating the allocation. We present comparisons of the results of simulations of several of these approaches in a telecommunication networks application. Another interesting domain concerns mobile robots for playing soccer. To model this problem, a novel approach called artificial electrical fields, is used for both navigation and manipulation of objects. / Agentteknologin möjliggör design av mjukvaruagenter som kan representera åsikter. Vi presenterar ett ramverk i vilket både agenternas designrar, såväl som ägare, kan uttrycka sina preferenser. Precis som i verkligheten, där mänskliga samhällen behöver regler och lagar för att fungera, så behöver agenterna normer och koordineringsmekanismer för att fungera effektivt. Vi visar varför några av högnivåmålen i multi-agentsystem är motstridiga, tex rationalitet och förmåga att bygga koalitioner. Ett sätt att modellera interaktioner mellan agenter är att använda spelteori. Vi använder spelteori bland annat för att visa ett "No free lunch"-teorem för agentsystem, men i praktiska tillämpningar, så behöver vi ofta använda andra angreppssätt. En sådan problemdomän är dynamisk resursallokering i telekommunikationssystem, i vilken vi simulerat koordineringar mellan agenter för att lösa problemet. Vi presenterar resultaten av simuleringar av ett flertal olika arkitekturer, bland annat mobila mäklar-agenter och auktionsagenter. En ytterligare domän är robotfotboll till vilken vi utvecklat en heuristik för val av handlingar baserad på artificiella elektroniska fält.

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A fair and optimal mechanism is required for allocating bandwidth to virtual machine migration in a WAN environment. In this thesis, we propose a dynamic resource allocation algorithm running in either centralized or distributed environments. The centralized version of our algorithm collects information from individual users and dynamically allocates bandwidth according to their demands. The distributed version of our algorithm is running on the internal nodes (e.g. routers) in the network. In the distributed case, we show that even when the routers and the users do not exchange allocation information, the allocation is still stable and optimal if the users are elastic users. Another interesting problem we solved is emergency handling, which is also critical in virtual machine live migration.

This dissertation explores reifying the management science concept of organizations as a collection of decisions. Organizational management entails resource allocation activities that can be formulated in terms of elementary relational functions. All elasticity-type formulations, most generic "production" functions, and various projection models that organizations might require (such as sales forecasts) can all be represented by elementary relational functions. Therefore, information systems in organizations can be representative of relationships between decision requirements, as theorized in relational model bases. A relational model-base structure acts as an integrative device by relating an organization's elementary relational functions to each other, with all that is kept for any model being the current values for coefficients and the now prevailing parametric values for the state variables of the model.Anchoring management information systems around relational model bases is particularly appropriate for organizations that have some reliance on real-time management decision making by providing the answer to two requirements for such organizations: one being the requirement for more accurate and current real-time, operational decision making within the organization; the other being the integration of functions for decision-making purposes within an organization. Relational model bases thus enable more dynamic management and become a central information system type for organizations that have dynamic resource allocation requirements that can employ technical tactics around such relational model bases. The relational model base would reflect revealed needs in an organization as opposed to projected needs, easing an organization's reliance on forecasting and moving it toward real-time decision making. The case for the introduction of these information systems is further strengthened by the fact that relational model base-type structures are already operating in production environments within organizations. The methodology used in this dissertation involved modeling organizational decision requirements in particular organizational cases to determine the behavior of relational model bases within those prototypical organizations and the application of relational model bases to real-time decision making. The first organizational scenario is a recursive agribusiness e-commerce case, with the target application being precision agriculture. The second scenario is a non-recursive ecological economics case, with the target application being preservation of biodiversity through land (habitat) protection.

Network Processors are multicore processors capable of processing network packets at wire speeds of multi-Gbps. Due to their high performance and programmability, these processors have become the main computing elements in many demanding network processing equipments like enterprise, edge and core routers. With the ever increasing use of the internet, the processing demands of these routers have also increased. As a result, the number and complexity of the cores in network processors have also increased. Hence, efficiently managing these cores has become very challenging. This dissertation discusses two main issues related to efficient usage of large number of parallel cores in network processors: (1) How to allocate work to the processing cores to optimize performance? (2) How to meet the desired performance requirement power efficiently? This dissertation presents the design of a hash based scheduler to distribute packets to cores. The scheduler exploits multiple dimensions of locality to improve performance while minimizing out of order delivery of packets. This scheduler is designed to work seamlessly when the number of cores allocated to a service is changed. The design of a resource allocator is also presented which allocates different number of cores to services with changing traffic behavior. To improve the power efficiency, a traffic aware power management scheme is presented which exploits variations in traffic rates to save power. The results of simulation studies are presented to evaluate the proposals using real and synthetic network traces. These experiments show that the proposed packet scheduler can improve performance by as much as 40% by improving locality. It is also observed that traffic variations can be exploited to save significant power by turning off the unused cores or by running them at lower frequencies. Improving performance of the individual cores by careful scheduling also helps to reduce the power consumption because the same amount of work can now be done with fewer cores with improved performance. The proposals made in this dissertation show promising improvements over the previous work. Hashing based schedulers have very low overhead and are very suitable for data rates of 100 Gbps and even beyond. / text