Service Ratio-Optimal, Content Coherence-Aware Data Push Systems

Abstract

Advertising new information to users via push is the trigger of operation for many contemporary information systems. Furthermore, passive optical networks are expected to extend the reachability of high-quality push services to thousands of clients. The efficiency of a push service is the ratio of successfully informed users. However, pushing only data of high popularity can degrade the thematic coherency of the content. The present work offers a novel, analysis-derived, tunable way for selecting data for push services. The proposed scheme can maximize the service ratio of a push system with regard to data coherence constraints. Extensive simulations demonstrate the efficiency of the scheme compared to alternative solutions. The proposed scheme is the first to tackle the problem of data coherence-aware, service ratio optimization of push services.

CDLB: a cross-domain load balancing mechanism for software defined networks in cloud data centre

“…The architecture enables physical distributed data centres connect rack-to-rack on demand while Casellas et al (2015) shows a distributed SDN control plane which makes use of multi-protocol label switching protocols (GMPLS) as their east/west interfaces. In addition to the related technology of XMPP, ISMF (Si et al, 2013) shows an example of how to simplify cloud server management and enhance scalability and flexibility in large-scale data centre through the push mechanism (Liaskos and Tsioliaridou, 2016) of XMPP. The active XML rules enable servers to send relevant information to interested remote users in response to new events occurring in the XML-based repositories.…”

On efficient downlink channel aggregation in adaptive wireless push systems

“…The different applications are created by the fact that each client group accesses different continuous data item sets, with all sets having a size of Num . The demand probability d i for each item in place in a set is derived via the Zipf pdf: d ( i ) = q (1/ i ) θ , Parameter θ is known as the data access skew. As θ increases, the demand pattern is a more skewed one.…”

“…The different applications are created by the fact that each client group accesses different continuous data item sets, with all sets having a size of Num. The demand probability d i for each item in place in a set is derived via the Zipf pdf 1,2,4 :…”

“…Adaptive broadcasting of data (e.g.,) is a cost‐efficient way for disseminating data to clients, in environments where the client demands exhibit significant commonality and is a priori unknown to the system. Adaptivity in this setting is the capability of the system to learn the actual demand of clients for the data item broadcast and thus schedule the broadcast program accordingly.…”