Resource Management in Multimedia Networked Systems

Error-free multimedia data processing and communication includes providing guaranteed services such as the colloquial telephone. A set of problems have to be solved and handled in the control-management level of the host and underlying network architectures. We discuss in this paper \u27resource management\u27 at the host and network level, and their cooperation to achieve global guaranteed transmission and presentation services, which means end-to-end guarantees. The emphasize is on \u27network resources\u27 (e.g., bandwidth, buffer space) and \u27host resources\u27 (e.g., CPU processing time) which need to be controlled in order to satisfy the Quality of Service (QoS) requirements set by the users of the multimedia networked system. The control of the specified resources involves three actions: (1) properly allocate resources (end-to-end) during the multimedia call establishment, so that traffic can flow according to the QoS specification; (2) control resource allocation during the multimedia transmission; (3) adapt to changes when degradation of system components occurs. These actions imply the necessity of: (a) new services, such as admission services, at the hosts and intermediate network nodes; (b) new protocols for establishing connections which satisfy QoS requirements along the path from send to receiver(s), such as resource reservation protocol; (c) new control algorithms for delay, rate and error control; (d) new resource monitoring protocols for reporting system changes, such as resource administration protocol; (e) new adaptive schemes for dynamic resource allocation to respond to system changes; and (f) new architectures at the hosts and switches to accommodate the resource management entities. This article gives an overview of services, mechanisms and protocols for resource management as outlined above

Final report on the evaluation of RRM/CRRM algorithms

Deliverable public del projecte EVERESTThis deliverable provides a definition and a complete evaluation of the RRM/CRRM algorithms selected in D11 and D15, and evolved and refined on an iterative process. The evaluation will be carried out by means of simulations using the simulators provided at D07, and D14.Preprin

Multiple streaming at the network edge

Streaming video over the Internet, including cellular networks, has now become commonplace. Network operators typically use multicasting or variations of multiple unicasting to deliver streams to the user terminal in a controlled fashion. An emerging alternative is P2P streaming, which is theoretically more scalable but suffers from other issues arising from the dynamic nature of the system. User’s terminals become streaming nodes but these are not constantly connected. Another issue is that they are based on logical overlays, which are not optimized for the physical underlay infrastructure. An important proposition is that of finding effective ways to increase the resilience of the overlay whilst at the same time not conflicting with the network. In this article we look at the combination of two techniques, multi-streaming (redundancy) and locality (network efficiency) in the context of both live and video-on-demand streaming. We introduce a new technique and assess it via a comparative, simulation-based study. We find that redundancy affects network utilization only marginally if traffic is kept at the edges via localization technique

UpStream: storage-centric load management for streaming applications with update semantics

This paper addresses the problem of minimizing the staleness of query results for streaming applications with update semantics under overload conditions. Staleness is a measure of how out-of-date the results are compared with the latest data arriving on the input. Real-time streaming applications are subject to overload due to unpredictably increasing data rates, while in many of them, we observe that data streams and queries in fact exhibit "update semantics” (i.e., the latest input data are all that really matters when producing a query result). Under such semantics, overload will cause staleness to build up. The key to avoid this is to exploit the update semantics of applications as early as possible in the processing pipeline. In this paper, we propose UpStream, a storage-centric framework for load management over streaming applications with update semantics. We first describe how we model streams and queries that possess the update semantics, providing definitions for correctness and staleness for the query results. Then, we show how staleness can be minimized based on intelligent update key scheduling techniques applied at the queue level, while preserving the correctness of the results, even for complex queries that involve sliding windows. UpStream is based on the simple idea of applying the updates in place, yet with great returns in terms of lowering staleness and memory consumption, as we also experimentally verify on the Borealis syste

QoE-based mobility-aware collaborative video streaming on the edge of 5G

Today's Internet traffic is dominated by video streaming applications transmitted through wireless/cellular interfaces of mobile devices. Although ultrahigh-definition videos are now easily transmitted through mobile devices, video quality level that users perceive is generally lower than expected due to distance-based high latency between sources and end-users. Mobile edge computing (MEC) paradigm is expected to address this issue and provide users with higher perceived quality of experience (QoE) for latency-critical applications, deploying MEC servers at edges. However, due to capacity concerns on MEC servers, a more comprehensive approach is needed to meet users' expectations applying all possible operations over the resources such as caching, prefetching, and task offloading policies depending on the data repetition or memory/CPU utilization. To address these issues, this article proposes a novel collaborative QoE-based mobility-aware video streaming scheme deployed at MEC servers. Throughout the article, we demonstrate how the proposed scheme can be implemented so as to preserve the desired QoE level per user during entire video sessions. Performance of the proposed scheme has been investigated by extensive simulations. In comparison to existing schemes, the results illustrate that high efficiency is achieved through collaboration among MEC servers, utilizing explicit window size adaptation, collaborative prefetching, and handover among the edges

Elastic-PPQ: A two-level autonomic system for spatial preference query processing over dynamic data streams

Paradigms like Internet of Things and the most recent Internet of Everything are shifting the attention towards systems able to process unbounded sequences of items in the form of data streams. In the real world, data streams may be highly variable, exhibiting burstiness in the arrival rate and non-stationarities such as trends and cyclic behaviors. Furthermore, input items may be not ordered according to timestamps. This raises the complexity of stream processing systems, which must support elastic resource management and autonomic QoS control through sophisticated strategies and run-time mechanisms. In this paper we present Elastic-PPQ, a system for processing spatial preference queries over dynamic data streams. The key aspect of the system design is the existence of two adaptation levels handling workload variations at different time-scales. To address fast time-scale variations we design a fine regulatory mechanism of load balancing supported by a control-theoretic approach. The logic of the second adaptation level, targeting slower time-scale variations, is incorporated in a Fuzzy Logic Controller that makes scale in/out decisions of the system parallelism degree. The approach has been successfully evaluated under synthetic and real-world datasets

Towards video streaming in IoT environments: vehicular communication perspective

Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in an immediate surroundings. Today's vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, significance of video streaming in vehicular IoT environments is highlighted focusing on integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues