Many query languages for graph-structured data are based on regular path expressions, which describe relations among pairs of nodes. We propose an extension that allows to retrieve groups of nodes based on group structural characteristics and relations to other nodes or groups. It allows to express group selection queries in a concise and natural style, and can be integrated into any query language based on regular path queries. We present an efficient algorithm for evaluating group queries in polynomial time from an input data graph. Evaluations using real-world social networks demonstrate the practical feasibility of our approach.

With the recent advances of cloud computing, effec-tive resource usage (e.g., CPU, memory or network) becomes an important question as application developers have to continuously pay for rented resources, even if they are not used effectively. In order to maintain required performance levels, it is currently common to reserve resources for peak resource usage or possible resource usage overlaps, if more than one task is executed on a host. While this is a reasonable approach for long-running applications or web servers, for some applications with disperse resource usage over time, this strategy causes significant over-provisioning and thus resource wastage and financial loss. In this paper we present a profiling-based task scheduling approach for factory-worker applications that schedules tasks within the defined resource limitations (e.g., existing machine memory size or network quota) and distributes the tasks in the cloud environment in order to use resources effectively. The evaluation of our approach approved the efficiency of the proposed algorithm and minimal performance overhead. In case of evaluated application, presented scheduling process leads up to 33% resource saving with only 1% of performance loss. Abstract-With the recent advances of cloud computing, effective resource usage (e.g., CPU, memory or network) becomes an important question as application developers have to continuously pay for rented resources, even if they are not used effectively. In order to maintain required performance levels, it is currently common to reserve resources for peak resource usage or possible resource usage overlaps, if more than one task is executed on a host. While this is a reasonable approach for long-running applications or web servers, for some applications with disperse resource usage over time, this strategy causes significant overprovisioning and thus resource wastage and financial loss. In this paper we present a profiling-based task scheduling approach for factory-worker applications that schedules tasks within the defined resource limitations (e.g., existing machine memory size or network quota) and distributes the tasks in the cloud environment in order to use resources effectively. The evaluation of our approach approved the efficiency of the proposed algorithm and minimal performance overhead. In case of the evaluated application, the presented scheduling process leads up to 33% resource savings with only 1% of performance loss.

A core idea of cloud computing is elasticity, i.e., enabling applications to adapt to varying load by dynamically acquiring and releasing cloud resources. One concrete realization is cloud bursting, which is the migration of applications or parts of applications running in a private cloud to a public cloud to cover load spikes. Actually building a cloud bursting enabled application is not trivial. In this paper, we introduce a reference model and middleware realization for Cloud bursting, thus enabling elastic applications to run across the boundaries of different Cloud infrastructures. In particular, we extend our previous work on application-level elasticity in single clouds to multiple clouds, and apply it to implement an hybrid cloud model that combines good utilization of a private cloud with the unlimited scalability of a public cloud. By means of an experimental evaluation we show the feasibility of the approach and the benefits of adopting Cloud bursting in hybrid cloud models.

The Infrastructure-as-a-Service (IaaS) model of cloud computing is a promising approach towards building elastically scaling systems. Unfortunately, building such applications today is a complex, repetitive and error-prone endeavor, as IaaS does not provide any abstraction on top of naked virtual machines. Hence, all functionality related to elasticity needs to be implemented anew for each application. In this paper, we present JCLOUDSCALE, a Java-based middleware that supports building elastic applications on top of a public or private IaaS cloud. JCLOUDSCALE allows to easily bring applications to the cloud, with minimal changes to the application code. We discuss the general architecture of the middleware as well as its technical features, and evaluate our system with regard to both, user acceptance (based on a user study) and performance overhead. Our results indicate that JCLOUDSCALE indeed allowed many participants to build IaaS applications more efficiently, comparable to the convenience features provided by industrial Platformas-a-Service (PaaS) solutions. However, unlike PaaS, using JCLOUDSCALE does not lead to a loss of control and vendor lock-in for the developer.

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