Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. For flexible and highly adaptive solutions, the capability for a quick response should be driven from early (real-time) and low-complexity content popularity detection schemes. In this paper, we focus on the early and low-complexity detection of video content popularity, which we address as a statistical change point (CP) detection problem. Our proposed methodology estimates in real-time the existence, the number, the magnitude and the direction of changes in the average number of video visits by combining: (i) offline and on-line CP schemes; (ii) an improved measurements window segmentation heuristic for the detection of multiple CPs; and (iii) a variation of the moving average convergence divergence (MACD) indicator to detect the direction of changes. We evaluated the proposed framework using a large database of real youtube video visits. The proposed algorithm is shown to accurately identify CPs and the direction of change in the offline phase. Finally, a few illustrative examples of two variations of the on-line algorithm are also included.
Video content is responsible for more than 70% of the global IP traffic. Consequently, it is important for content delivery infrastructures to rapidly detect and respond to changes in content popularity dynamics. In this paper, we propose the employment of on-line change point (CP) analysis to implement real-time, autonomous and low-complexity video content popularity detection. Our proposal, denoted as realtime change point detector (RCPD), estimates the existence, the number and the direction of changes on the average number of video visits by combining: (i) off-line and on-line CP detection algorithms; (ii) an improved time-series segmentation heuristic for the reliable detection of multiple CPs; and (iii) two algorithms for the identification of the direction of changes. The proposed detector is validated against synthetic data, as well as a large database of real YouTube video visits. It is demonstrated that the RCPD can accurately identify changes in the average content popularity and the direction of change. In particular, the success rate of the RCPD over synthetic data is shown to exceed 94% for medium and large changes in content popularity. Additionally, the dynamic time warping distance, between the actual and the estimated changes, has been found to range between 20 samples on average, over synthetic data, to 52 samples, in real data. The rapid responsiveness of the RCPD is instrumental in the deployment of real-time, lightweight load balancing solutions, as shown in a real example.Index Terms-Video content popularity detection, change point analysis, on-line change point detection, binary segmentation algorithm, load balancing.
The Novel Enablers for Cloud Slicing (NECOS) project addresses the limitations of current cloud computing infrastructures to respond to the demand for new services, as presented in two use-cases, that will drive the whole execution of the project. The first use-case is focused on Telco service provider and is oriented towards the adoption of cloud computing in their large networks. The second use-case is targeting the use of edge clouds to support devices with low computation and storage capacity. The envisaged solution is based on a new concept, the Lightweight Slice Defined Cloud (LSDC), as an approach that extends the virtualization to all the resources in the involved networks and data centers and provides uniform management with a high-level of orchestration. In this position paper, we discuss the motivation, objectives, architecture, research challenges (and how to overcome them) and initial efforts for the NECOS project.
This paper addresses the problem of provisioning management/monitoring nodes within highly dynamic network environments, particularly virtual networks. In a network where nodes and links may be spontaneously created and destroyed (perhaps rapidly) there is a need for stable and responsive management and monitoring which does not create a large load (in terms of traffic or processing) for the system. A subset of nodes has to be chosen for management/monitoring, each of which will manage a subset of the nodes in the network. A new, simple and locally optimal greedy algorithm called Pressure is provided for choice of node position to minimise traffic. This algorithm is combined with a system for predicting the lifespan of nodes, and a tunable parameter is also given so that a system operator could express a preference for elected nodes to be chosen to reduce traffic, to be "stable", or some compromise between these positions. The combined algorithm called PressureTime is lightweight and could be run in a distributed manner. The resulting algorithms are tested both in simulation and in a testbed environment of virtual routers. They perform well, both at reducing traffic and at choosing long lifespan nodes.
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