Challenges for the comprehensive management of cloud services in a PaaS framework

The 4CaaSt project aims at developing a PaaS framework that enables flexible definition, marketing, deployment and management of Cloud-based services and applications. The major innovations proposed by 4CaaSt are the blueprint and its lifecycle management, a one stop shop for Cloud services and a PaaS level resource management featuring elasticity. 4CaaSt also provides a portfolio of ready to use Cloud native services and Cloud-aware immigrant technologies

Decision Support Tools for Cloud Migration in the Enterprise

This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective; this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud.Comment: To appear in IEEE CLOUD 201

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft

Is There a Market for Work Group Servers? Evaluating Market Level Demand Elasticities Using Micro and Macro Models

This paper contains an empirical analysis demand for "work-group" (or low-end) servers. Servers are at thecentre of many US and EU anti-trust debates, including the Hewlett-Packard/Compaq merger and investigationsinto the activities of Microsoft. One question in these policy decisions is whether a high share of work serversindicates anything about shortrun market power. To investigate price elasticities we use model-level panel dataon transaction prices, sales and characteristics of practically every server in the world. We contrast estimatesfrom the traditional "macro" approaches that aggregate across brands and modern "micro" approaches that usebrand-level information (including both "distance metric" and logit based approaches). We find that the macroapproaches lead to overestimates of consumer price sensitivity. Our preferred micro-based estimates of themarket level elasticity of demand for work group servers are around 0.3 to 0.6 (compared to 1 to 1.3 in themacro estimates). Even at the higher range of the estimates, however, we find that demand elasticities aresufficiently low to imply a distinct "anti-trust" market for work group servers and their operating systems. It isunsurprising that firms with large shares of work group servers have come under some antitrust scrutiny.demand elasticities, network servers, computers, anti-trust

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications