BACKGROUND: Biomedical research projects deal with data management requirements from multiple sources like funding agencies' guidelines, publisher policies, discipline best practices, and their own users' needs. We describe functional and quality requirements based on many years of experience implementing data management for the CRC 1002 and CRC 1190. A fully equipped data management software should improve documentation of experiments and materials, enable data storage and sharing according to the FAIR Guiding Principles while maximizing usability, information security, as well as software sustainability and reusability. RESULTS: We introduce the modular web portal software menoci for data collection, experiment documentation, data publication, sharing, and preservation in biomedical research projects. Menoci modules are based on the Drupal content management system which enables lightweight deployment and setup, and creates the possibility to combine research data management with a customisable project home page or collaboration platform. CONCLUSIONS: Management of research data and digital research artefacts is transforming from individual researcher or groups best practices towards project- or organisation-wide service infrastructures. To enable and support this structural transformation process, a vital ecosystem of open source software tools is needed. Menoci is a contribution to this ecosystem of research data management tools that is specifically designed to support biomedical research projects.

One of the most challenging obstacles for the advancement of clouds is the lack of assurance and transparency, along with the current paucity of techniques to quantify security. A fundamental requirement for solving this problem is to provide proper levels of security based on the requirements of cloud customers and sensitivity of data. However, generating these security levels only partially serves the requirements of cloud customers especially if it's not linked to the management of SLC commitments. Accordingly, a novel approach has been proposed in this paper to define and manage security indicators in the generated security levels. These indicators are used to react to eventualities that may threaten the established mechanisms of the generated security rings, to ensure the fulfillment of agreed assurance levels, and to minimize the damages in case of attacks, unpredictable events or unavoidable changes. The proposed schema uses simultaneous monitoring with two different stand-alone agents per security level. These agents are initialized based on all security policies in the SLA to enhance the process of monitoring and rectification and to increase the rate of satisfaction and reliability in clouds.

For many application scenarios, interconnected data centers provide high service flexibility, reduce response time, and facilitate timely data backup. Many data center system parameters might have variant impact on the interconnection performance. Despite many studies on data center network performance, there exist few analytical work that reveal insightful knowledge with wide range of system parameters as input, especially focusing on data center interconnects (DCI). This paper creates analytical models for representative data center network architectures and provides the performance calculus aiming to apply for data center interconnects. By parameterising the number of devices, the arriving traffics, the switch link capacities, and the traffic locality, we derive the relationship among the DCI bandwidth, inter-DC latency, and these parameters. Based on this, further discussion and numerical examples investigate and evaluate the modelling and calculus from multiple angles and show the possibility how this calculus assists DC/DCI design and operation.

Customers often suffer from the variability of data access time in cloud storage service, caused by network congestion, load dynamics, etc. One solution to guarantee a reliable latency-sensitive service is to issue requests with multiple download/upload sessions, accessing the required data (replicas) stored in one or more servers. In order to minimize storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to tackle. In this paper, we study the latency-sensitive data allocation problem for cloud storage. We model the data access time as a given distribution whose Cumulative Density Function (CDF) is known, and prove that this problem is NP-hard. To solve it, we propose both exact Integer Nonlinear Program (INLP) and Tabu Search-based heuristic. The proposed algorithms are evaluated in terms of the number of used servers, storage utilization and throughput utilization.

In current cloud computing systems, when leveraging virtualization technology, the customer’s requested data computing or storing service is accommodated by a set of communicated virtual machines (VM) in a scalable and elastic manner. These VMs are placed in one or more server nodes according to the node capacities or failure probabilities. The VM placement availability refers to the probability that at least one set of all customer’s requested VMs operates during the requested lifetime. In this paper, we first study the problem of placing at most$H$groups of$k$requested VMs on a minimum number of nodes, such that the VM placement availability is no less than$\delta$, and that the specified communication delay and connection availability for each VM pair under the same placement group are not violated. We consider this problem with and without Shared-Risk Node Group (SRNG) failures, and prove this problem is NP-hard in both cases. We subsequently propose an exact Integer Nonlinear Program (INLP) and an efficient heuristic to solve this problem. We conduct simulations to compare the proposed algorithms with two existing heuristics in terms of performance. Finally, we study the related reliable routing problem of establishing a connection over at most$w$link-disjoint paths from a source to a destination, such that the connection availability requirement is satisfied and each path delay is no more than a given value. We devise an exact algorithm and two heuristics to solve this NP-hard problem, and evaluate them via simulations.

The immense research dataset growth requires new strategies and concepts for an appropriate handling at the corresponding research data repositories. This is especially true for the concept of Persistent Identifiers (PIDs), which is designed to provide a persistent identification layer on top of the address-based resource retrieval methodology of the current Internet. For research datasets, which are referenced, further processed and transferred, such a persistent identification concept is highly crucial for ensuring a long-term scientific exchange. Often, each individual research dataset stored in a research data repository, is registered at a particular PID registration agency in order to be assigned a globally unique PID. However, for the explosive growth of research datasets this concept of registering each individual research dataset is in terms of the performance highly inappropriate. Therefore, the focus of this work is on a concept for enabling a high-performance persistent identification of research datasets. Recent research data repositories often are equipped with a built-in naming component for assigning immutable and internally unique identifiers for their incoming research datasets. Thus, the core idea in this work is to enable these internal identifiers to be directly resolvable at the well-known global PID resolution systems. This work will therefore provide insight into the implementation of this idea into the well-known Handle System. Finally, in this work, we will provide an experimental evaluation of our proposed concept.

Virtualized Cloud platforms have become increasingly common and the number of online services hosted on these platforms is also increasing rapidly. A key problem faced by providers in managing these services is detecting the performance anomalies and adjusting resources accordingly. As online services generate a very large amount of monitored data in the form of time series, it becomes very difficult to process this complex data by traditional approaches. In this work, we present a novel distributed parallel approach for performance anomaly detection. We build upon Holt-Winters forecasting for automatic aberrant behavior detection in time series. First, we extend the technique to work with MapReduce paradigm. Next, we correlate the anomalous metrics with the target Service Level Objective (SLO) in order to locate the suspicious metrics. We implemented and evaluated our approach on a production Cloud encompassing IaaS and PaaS service models. Experimental results confirm that our approach is efficient and effective in capturing the metrics causing performance anomalies in large time series datasets.

Virtualization technologies of Infrastructure-as-a- Service enable the live migration of running Virtual Machines (VMs) to achieve load balancing, fault-tolerance and hardware consolidation in data centers. However, the downtime/service unavailability due to live migration may be substantial with relevance to the customers' expectations on responsiveness, as the latter are declared in established Service Level Agreements (SLAs). Moreover, it may cause significant (potentially exponential) SLA violation penalties to its associated higher- level domains (Platform-as-a-Service and Software-as-a-Service). Therefore, VM live migration should be managed carefully. In this paper, we present the OpenStack version of the Generic SLA Manager, alongside its strategies for VM selection and allocation during live migration of VMs. We simulate a use case where IaaS (OpenStack-SLAM) and PaaS (OpenShift) are combined, and assess performance and efficiency of the aforementioned VM placement strategies, when a multi-domain SLA pricing & penalty model is involved. We find that our proposal is efficient in managing trade-offs between the operational objectives of service providers (including financial considerations) and the customers' expected QoS requirements.

Software defined networking (SDN) and OpenFlow as the outcome of recent research and development efforts provided unprecedented access into the forwarding plane of networking elements. This is achieved by decoupling the network control out of the forwarding devices. This separation paves the way for a more flexible and innovative networking. While SDN concept and OpenFlow find their ways into commercial deployments, performance evaluation of the SDN concept and its scalability, delay bounds, buffer sizing and similar performance metrics are not investigated in recent researches. In spite of usage of benchmark tools (like OFlops and Cbench), simulation studies and very few analytical models, there is a lack of analytical models to express the boundary condition of SDN deployment. In this work we present a model based on network calculus theory to describe the functionality of an SDN switch and controller. To the best of our knowledge, this is for the first time that network calculus framework is utilized to model the behavior of an SDN switch in terms of delay and queue length boundaries and the analysis of the buffer length of SDN controller and SDN switch. The presented model can be used for network designers and architects to get a quick view of the overall SDN network deployment performance and buffer sizing of SDN switches and controllers.

The provision and processing of information in an e- Science environment are essential tasks. For this purpose, most environments provide information services which aggregate data from different information sources and make it available to users and other services. In this paper we present CIS, an extensible information service with an underlying unified information model. Designed according to service-oriented architectural principles, CIS consumes data from sources like Ganglia, formats it according to the Common Information Model, and delivers it against XQuery requests. We realised the information service in a Web Services environment and integrated it into an implied volatility application within the NextGRID project and the UNICORE middleware.

In Grid and Service Oriented Architecture-based environments the use of technologies supporting Service Level Agreements, advance reservation, and Virtual Organisations mark the start of a new way accessing distributed resources or services. Experience made so far indicate the potential for overcoming the limitations of the hitherto best effort approaches. In this paper we propose an approach that integrates remote laboratories as another resource into the Grid. We show how techniques developed for compute, storage, or network resources in Grids may be used for managing access to Collaborative Remote Laboratories. In particular we discuss how existing technologies for advance reservation, Service Level Agreements, and Virtual Organisations can help to set up, configure, and manage remote laboratories and provide support in the preparation of collaborative control of and mutual access to laboratory equipment.

Summary form only given. This work describes a software prototype developed at Research Center Julich to demonstrate the interoperability between UNICORE (Uniform Interface to Computer Resources) and Globus without changes to any of the systems. By combining UNICORE's workflow oriented approach to job submission with Globus, grid users can gain seamless access to a wide number of Globus enabled systems. We define the following scenario of a job submission from UNICORE to Globus: the user prepares the job via UNICORE's graphical user interface, chooses a Globus site where the job is to be computed and submits it to UNICORE's target system interface (TSI). This is the entity normally interfacing with the local batch system. It is enhanced to communicate with Globus. The TSI translates the job description from the UNICORE specific abstract job object (AJO) into the GRAM Resource Specification Language (RSL) and submits it to the GRAM Gatekeeper. Standard Globus mechanisms are used to monitor the status of the job and transfer the output back to the TSI.