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

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

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

Strategic Decision Support for Smart-Leasing Infrastructure-as-a-Service

In this work we formulate strategic decision models describing when and how many reserved instances should be bought when outsourcing workload to an IaaS provider. Current IaaS providers offer various pricing options for leasing computing resources. When decision makers are faced with the choice and most importantly with uneven workloads, the decision at which time and with which type of computing resource to work is no longer trivial. We present case studies taken from the online services industry and present solution models to solve the various use case problems and compare them. Following a thorough numerical analysis using both real, as well as augmented workload traces in simulations, we found that it is cost efficient to (1) have a balanced portfolio of resource options and (2) avoiding commitments in the form of upfront payments when faced with uncertainty. Compared to a simple IaaS benchmark, this allows cutting costs by 20%

AUTONOMIC MANAGEMENT OF SOFTWARE AS A SERVICE SYSTEMS WITH MULTIPLE QUALITY OF SERVICE CLASSES

In recent years the emergence of Software as a Service (SaaS) provision and cloud computing in general had a tremendous impact on corporate information technology. While the implementation and successful operation of powerful information systems continues to be a cornerstone of success in modern enterprises, the ability to acquire IT infrastructure, software, or platforms on a pay-as-you-go basis has opened a new avenue for optimizing operational costs and processes. In this context we target elastic SaaS systems with on-demand cloud resource provisioning and implement an autonomic management artifact. Our framework forecasts future user behavior based on historic data, analyzes the impact of different workload levels on system performance based on a non-linear performance model, analyzes the economic impact of different provisioning strategies, derives an optimal operation strategy, and automatically assigns requests from users belonging to different Quality of Service (QoS) classes to the appropriate server instances. More generally, our artifact optimizes IT system operation based on a holistic evaluation of key aspects of service operation (e.g., system usage patterns, system performance, Service Level Agreements). The evaluation of our prototype, based on a real production system workload trace, indicates a cost-of-operation reduction by up to 60 percent without compromising QoS requirements

Recovery of scandium from acidic waste solutions by means of polymer inclusion membranes

Scandium is a raw material with properties that promise considerable potential for application in alloys to enable aviation fuel savings and as dopants for use in sustainable energy production using solid oxide fuel cells. Despite these attractive properties, scandium is rarely used due to its scarcity and unreliable supply. Therefore, new strategies for scandium recovery are of economic priority. In this study, polymer inclusion membranes (PIMs) consisting of PVDF-HFP, 2-NPOE and DEHPA, were optimised for selective scandium separation from real TiO2 production waste. With the optimised system, >60% of the scandium was recovered with high selectivity, resulting in scandium mole fraction at more than two orders of magnitude higher in the receiving phase than in the original waste. This suggests PIMs may be an effective way to recover scandium from bulk waste, thus easing the scarcity and insecurity that currently limit its bulk application

Effects of cold winters and roost site stability on population development of non-native Asian ring-necked parakeets (Alexandrinus manillensis) in temperate Central Europe – Results of a 16-year census

Asian ring-necked parakeets (Alexandrinus manillensis, formerly Psittacula krameri, hereafter RNP) first bred in Germany in 1969. Since then, RNP numbers increased in all three major German subpopulations (Rhineland, Rhine-Main, Rhine-Neckar) over the period 2003–2018. In the Rhine-Neckar region, the population increased to more than fivefold within only 15 years. Interestingly, there was no significant breeding range expansion of  RNP in the period 2010–2018. In 2018, the total number of RNP in Germany amounted to >16,200 birds. Differences in RNP censuses between years were evident. Surprisingly, cold winters (extreme value, −13.7 °C) and cold weather conditions in the breeding season (coldest month average, −1.36 °C) were not able to explain between-year variation. This finding suggests that in general winter mortality is low – with exceptions for winters 2008/2009 and 2009/2010, and a population-relevant loss of broods is low in our study population. Surprisingly, the social behaviour in terms of spatio-temporal stability of roost sites could well explain positive and negative population trends. Years of spatially stable and regularly used roost sites seem to correlate with increasing population sizes. In contrast, known shifts of RNP among different roost sites or the formations of new roost sites by split are related to population stagnation or a decrease in numbers. Climate change may lead to further range expansion as cities not suitable yet for RNP may become so in the near future.