Eigenvalue estimates for non-normal matrices and the zeros of random orthogonal polynomials on the unit circle

We prove that for any $n\times n$ matrix, $A$, and $z$ with $|z|\geq \|A\|$, we have that \|(z-A)^{-1}\|\leq\cot (\frac{\pi}{4n}) \dist (z, \spec(A))^{-1}. We apply this result to the study of random orthogonal polynomials on the unit circle.Comment: 27 page

Contested sites, land claims and economic development in Poum, New Caledonia

Property relations are often ambiguous in postcolonial settings. Property is only considered as such if socially legitimate institutions sanction it. In indigenous communities, access to natural resources is frequently multidimensional and overlapping, subject to conflict and negotiation in a ‘social arena’. Settler arrivals and new economic possibilities challenge these norms and extend the arena. The article analyses conflicts and negotiations in the French overseas territory of New Caledonia in the light of its unique settler history and economic activity, focussing on the little-studied remote northern district of Poum on the Caledonian main island Grande Terre. In this region the descendants of British fishermen intermarried with the majority Kanak clans. We illustrate the interaction between customary conflicts, European settlement, struggles for independence, and a desire for economic development. Customary claims are in tension with the attractions of economic growth and service delivery, which has been slow in coming to Poum for reasons largely outside the control of local people

Improving Confidence in Evolutionary Mine Scheduling via Uncertainty Discounting

Mine planning is a complex task that involves many uncertainties. During early stage feasibility, available mineral resources can only be estimated based on limited sampling of ore grades from sparse drilling, leading to large uncertainty in under-sampled parts of the deposit. Planning the extraction schedule of ore over the life of a mine is crucial for its economic viability. We introduce a new approach for determining an "optimal schedule under uncertainty" that provides probabilistic bounds on the profits obtained in each period. This treatment of uncertainty within an economic framework reduces previously difficult-to-use models of variability into actionable insights. The new method discounts profits based on uncertainty within an evolutionary algorithm, sacrificing economic optimality of a single geological model for improving the downside risk over an ensemble of equally likely models. We provide experimental studies using Maptek's mine planning software Evolution. Our results show that our new approach is successful for effectively making use of uncertainty information in the mine planning process

Dynamic Service Level Agreement Management for Efficient Operation of Elastic Information Systems

The growing awareness that effective Information Systems (IS), which contribute to sustainable business processes, secure a long-lasting competitive advantage has increasingly focused corporate transformation efforts on the efficient usage of Information Technology (IT). In this context, we provide a new perspective on the management of enterprise information systems and introduce a novel framework that harmonizes economic and operational goals. Concretely, we target elastic n-tier applications with dynamic on-demand cloud resource provisioning. We design and implement a novel integrated management model for information systems that induces economic influence factors into the operation strategy to adapt the performance goals of an enterprise information system dynamically (i.e., online at runtime). Our framework forecasts future user behavior based on historic data, analyzes the impact of workload on system performance based on a non-linear performance model, analyzes the economic impact of different provisioning strategies, and derives an optimal operation strategy. The evaluation of our prototype, based on a real production system workload trace, is carried out in a custom test infrastructure (i.e., cloud testbed, n-tier benchmark application, distributed monitors, and control framework), which allows us to evaluate our approach in depth, in terms of efficiency along the entire SLA lifetime. Based on our thorough evaluation, we are able to make concise recommendations on how to use our framework effectively in further research and practice

TOWARDS AUTONOMIC COST-AWARE ALLOCATION OF CLOUD RESOURCES

While clouds conceptually facilitate very fine-grained resource provisioning, information systems that are able to fully leverage this potential remain an open research problem. This is due to factors such as significant reconfiguration lead-times and non-trivial dependencies between software and hardware resources. In this work we address these factors explicitly and introduce an accurate workload forecasting model, based on Fourier Transformation and stochastic processes, paired with an adaptive provisioning framework. By automatically identifying the key characteristics in the workload process and estimating the residual variation, our model forecasts the workload process in the near future with very high accuracy. Our preliminary experimental evaluation results show great promise. When evaluated empirically on a real Wikipedia trace our resource provisioning framework successfully utilizes the workload forecast module to achieve superior resource utilization efficiency under constant service level objective satisfaction. More generally, this work corroborates the potential of holistic cloud management approaches that fuse domain specific solutions from areas such as workload prediction, autonomic system management, and empirical analysis

EFFICIENT AND FLEXIBLE MANAGEMENT OF ENTERPRISE INFORMATION SYSTEMS

The growing awareness of the substantial environmental footprint of Information System has increasingly focused corporate transformation efforts on the efficient usage of Information Technology. In this context, we provide a new concept to enterprise IS operation and introduce a novel adaptation framework that harmonizes operational requirements with efficiency goals. We concretely target elastic n-tier applications with dynamic on-demand resource provisioning for component servers and implement an adaptation engine prototype. Our framework forecasts future user behavior, analyzes the impact of workload on system performance, evaluates the economic impact of different provisioning strategies, and derives an optimal operation strategy. More generally, our adaptation engine optimizes IT system operation based on a holistic evaluation of the key factors of influence. In the evaluation, we systematically investigate practicability, optimization potential, as well as effectiveness. Additionally, we show that our framework allows flexible IS operation with up to a 40 percent lower cost of operation

Taming Energy Costs of Large Enterprise Systems Through Adaptive Provisioning

One of the most pressing concerns in modern datacenter management is the rising cost of operation. Therefore, reducing variable expense, such as energy cost, has become a number one priority. However, reducing energy cost in large distributed enterprise system is an open research topic. These systems are commonly subjected to highly volatile workload processes and characterized by complex performance dependencies. This paper explicitly addresses this challenge and presents a novel approach to Taming Energy Costs of Larger Enterprise Systems (Tecless). Our adaptive provisioning methodology combines a low-level technical perspective on distributed systems with a high-level treatment of workload processes. More concretely, Tecless fuses an empirical bottleneck detection model with a statistical workload prediction model. Our methodology forecasts the system load online, which enables on-demand infrastructure adaption while continuously guaranteeing quality of service. In our analysis we show that the prediction of future workload allows adaptive provisioning with a power saving potential of up 25 percent of the total energy cost