Adaptive online deployment for resource constrained mobile smart clients

Nowadays mobile devices are more and more used as a platform for applications. Contrary to prior generation handheld devices configured with a predefined set of applications, today leading edge devices provide a platform for flexible and customized application deployment. However, these applications have to deal with the limitations (e.g. CPU speed, memory) of these mobile devices and thus cannot handle complex tasks. In order to cope with the handheld limitations and the ever changing device context (e.g. network connections, remaining battery time, etc.) we present a middleware solution that dynamically offloads parts of the software to the most appropriate server. Without a priori knowledge of the application, the optimal deployment is calculated, that lowers the cpu usage at the mobile client, whilst keeping the used bandwidth minimal. The information needed to calculate this optimum is gathered on the fly from runtime information. Experimental results show that the proposed solution enables effective execution of complex applications in a constrained environment. Moreover, we demonstrate that the overhead from the middleware components is below 2%

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Increasing Memory Density through Dynamic Memory Extension with Memory1 through Flash

Distributed System

Virtualizing Stream Processing

Part 5: Notification and StreamingInternational audienceStream processing systems have evolved into established solutions as standalone engines but they still lack flexibility in terms of large-scale deployment, integration, extensibility, and interoperability. In the last years, a substantial ecosystem of new applications has emerged that can potentially benefit from stream processing but introduces different requirements on how stream processing solutions can be integrated, deployed, extended, and federated. To address these needs, we present an exoengine architecture and the associated ExoP platform. Together, they provide the means for encapsulating components of stream processing systems as well as automating the data exchange between components and their distributed deployment. The proposed solution can be used, e.g., to connect heterogeneous streaming engines, replace operators at runtime, and migrate operators across machines with a negligible overhead

DR-OSGi: Hardening Distributed Components with Network Volatility Resiliency

Abstract. Because middleware abstractions remove the need for lowlevel network programming, modern distributed component systems expose network volatility (i.e., frequent but intermittent outages) as application-level exceptions, requiring custom manual handling. Unfortunately, handling network volatility effectively is nontrivial—the programmer must consider not only the specifics of the application, but also of its target deployment environment. As a result, to make a distributed component application resilient against network volatility, programmers commonly create custom solutions that are ad-hoc, tedious, and error-prone. In addition, these solutions are difficult to customize for different networks and to reuse across different applications. To address these challenges, this paper presents a systematic approach to hardening distributed components to become resilient against network volatility. Specifically, we present an extensible framework for enhancing a distributed component application with the ability to continue executing in the presence of network volatility. To accommodate the diverse hardening needs of various combinations of networks and applications, our framework not only provides a collection of hardening strategies, but also simplifies the creation of new strategies. Our reference implementation, built on top of the R-OSGi infrastructure, is called DR-OSGi 1. DR-OSGi imposes a very low overhead on the hardened applications, requires no changes to their source code, and is plug-in extensible. Applying DR-OSGi to several realistic distributed applications has hardened them with resiliency to effectively withstand network volatility