Olivier Nano

JetStream: enabling high performance event streaming across cloud data-centers

The easily-accessible computation power offered by cloud infrastructures coupled with the revolution of Big Data are expanding the scale and speed at which data analysis is performed. In their quest for finding the Value in the 3 Vs of Big Data, applications process larger data sets, within and across clouds. Enabling fast data transfers across geographically distributed sites becomes particularly important for applications which manage continuous streams of events in real time. Scientific applications (e.g. the Ocean Observatory Initiative or the ATLAS experiment) as well as commercial ones (e.g. Microsofts Bing and Office 365 large-scale services) operate on tens of data-centers around the globe and follow similar patterns: they aggregate monitoring data, assess the QoS or run global data mining queries based on inter site event stream processing. In this paper, we propose a set of strategies for efficient transfers of events between cloud data-centers and we introduce JetStream: a prototype implementing these strategies as a high performance batch-based streaming middleware. JetStream is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites. The prototype was validated on tens of nodes from US and Europe data-centers of the Windows Azure cloud using synthetic benchmarks and with application code from the context of the Alice experiment at CERN. The results show an increase in transfer rate of 250 times over individual event streaming. Besides, introducing an adaptive transfer strategy brings an additional 25% gain. Finally, the transfer rate can further be tripled thanks to the use of multi-route streaming.

Guest Editors' Introduction: Realizing Service-Centric Software Systems

Service-centric software system is a multidisciplinary paradigm concerned with software systems that are constructed as compositions of autonomous services. These systems extend the service-oriented architecture paradigm by focusing on the design, development, and maintenance of software built under SOAs. In this special issue, we present five articles that tackle service-centric software systems.

JetStream: Enabling high throughput live event streaming on multi-site clouds

Scientific and commercial applications operate nowadays on tens of cloud datacenters around the globe, following similar patterns: they aggregate monitoring or sensor data, assess the QoS or run global data mining queries based on inter-site event stream processing. Enabling fast data transfers across geographically distributed sites allows such applications to manage the continuous streams of events in real time and quickly react to changes. However, traditional event processing engines often consider data resources as second-class citizens and support access to data only as a side-effect of computation (i.e. they are not concerned by the transfer of events from their source to the processing site). This is an efficient approach as long as the processing is executed in a single cluster where nodes are interconnected by low latency networks. In a distributed environment, consisting of multiple datacenters, with orders of magnitude differences in capabilities and connected by a WAN, this will undoubtedly lead to significant latency and performance variations. This is namely the challenge we address in this paper, by proposing JetStream, a high performance batch-based streaming middleware for efficient transfers of events between cloud datacenters. JetStream is able to self-adapt to the streaming conditions by modeling and monitoring a set of context parameters. It further aggregates the available bandwidth by enabling multi-route streaming across cloud sites, while at the same time optimizing resource utilization and increasing cost efficiency. The prototype was validated on tens of nodes from US and Europe datacenters of the Windows Azure cloud with synthetic benchmarks and a real-life application monitoring the ALICE experiment at CERN. The results show a 3x increase of the transfer rate using the adaptive multi-route streaming, compared to state of the art solutions.

An Approach to Identity Management for Service Centric Systems

Today users consume applications composed by services from different providers across trust domains. By experience we know that security requirements and user identity management make services composition difficult. We believe that delegation of access rights across trust domains will become an essential mechanism in services composition scenarios. Users care about security but cannot deal with the variety of existing solutions for access control. A unified interface of access control and delegation is essential for multi-domain composite services. This paper addresses the problem of identity management for service-centric systems and proposes a novel approach based on an abstract delegation framework supporting different access control mechanisms. We show how the abstract delegation framework is designed to give control and clarity to the user consuming applications based on service composition. Besides the theoretical aspects, the paper shares experiences based on scenarios from the automotive industry.

Achieving high throughput for large scale event streaming across geographically distributed data-centers with JetStream

The increasing scale at which data processing is being performed nowadays calls for data management systems that enable high-performance data exchanges among geographically remote instances of large web services. In this demonstration we show how JetStream can increase the transfer rate of events which are streamed between geographically remote cloud data centers. The demonstration setup focuses on presenting how the binding can be done between JetStream and the event source on one hand and with the StreamInsight processing engine on the other hand. By considering a data source with an event generation rate that is variable in time, we demonstrate the importance of adapting the transfer scheme to the streaming context.