The IEEE 802.15.4 standard is widely employed on power constrained scenarios, such as Wireless Sensor Networks deployments. Therefore, modeling this standard is useful to predict network performance and fine tune parameter settings. Previous work rely on determining all reachable network states, usually by the means of a Markov chain, which is often complex and error prone. In contrast, we provide a novel behavioral approach to IEEE 802.15.4 modeling, which covers the literature gap in assessing all metrics of interest, modeling asymmetric traffic condition and fully comprising the beacon-enabled mode. In addition, it is possible to test different values for the parameters of the standard, such as aMaxFrameRetries, macMaxCSMABackoffs, initialCW and UnitBackoffPeriod. The model was validated by NS2 simulations and a testbed composed of telosB motes.
Many video-on-demand (VoD) providers are relying on public cloud providers for video storage, access and streaming services. In this paper, we investigate how a VoD provider may make optimal bandwidth reservations from a cloud service provider to guarantee the streaming performance while paying for the bandwidth, storage and transfer cost. We propose a predictive resource auto-scaling system that dynamically books the minimum amount of bandwidth resources from multiple servers in a cloud storage system, in order to allow the VoD provider to match its short-term demand projections. We exploit the anti-correlation between the demands of different videos for statistical multiplexing to hedge the risk of under-provisioning. The optimal load direction from video channels to cloud servers without replication constraints is derived with provable performance. We further study the joint load direction and sparse content placement problem that aims to reduce bandwidth reservation cost under sparse content replication requirements. We propose several algorithms, and especially an iterative L1-norm penalized optimization procedure to efficiently solve the problem while effectively limiting the video migration overhead. The proposed system is backed up by a demand predictor that forecasts the expectation, volatility and correlation of the streaming traffic associated with different videos based on statistical learning. Extensive simulations are conducted to evaluate our proposed algorithms, driven by the real-world workload traces collected from a commercial VoD system.