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

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

CLOUD REQUIREMENT FRAMEWORK: REQUIREMENTS AND EVALUATION CRITERIA TO ADOPT CLOUD SOLUTIONS

Due to the fast growth, Cloud Computing has become a non-transparent market with providers and customers willing to adopt it. Furthermore, many offers only partially meet customers? requirements and it is not clear how exactly Cloud Computing influences the IT. That makes it difficult for customers to plan migration projects and implement sustainable Cloud solutions. There are important factors and considerations for the decision to adopt Cloud Computing. The current studies and research in this field can be summarized to focus around the questions why adoption of Cloud Computing would occur, how much adoption would take place or how it would be adopted. But the adoption requirements covering all three service models (SaaS, PaaS, IaaS) have barely been discussed in literature so far. A detailed understanding of Cloud requirements enables customers to adopt Cloud solutions efficiently. Therefore this paper aims to contribute a framework addressing the adoption and selection of Cloud services. A Cloud Requirement Framework (CRF) was developed, concentrating on relevant requirements for adopting Cloud services targeting all three service models. To develop this framework we followed a design science approach and conducted a systematic literature review, an extensive market analysis and an evaluation based on expert interviews

Green Information Systems: Directives for the IS Discipline

Green IS offers the promise for IS scholars to make a significant contribution to reducing greenhouse gas emissions and mitigating the effects of global climate change and other environmental problems. While significant achievements have been made in shaping Green IS as a subfield in the IS discipline, the emergence of Green IS is still by far too slow, given the magnitude of the problem. Against this background a panel was organized at ICIS 2012 in order to discuss future directives for the IS discipline. This article, co-authored by the panelists, reports on the major issues raised by this panel. First, the article gives an account of major achievements in the field of Green IS. Second, it presents five specific directives which we agree are important for the future of our discipline

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%

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