This paper proposes a model to study the interaction of price competition and congestion in the cloud computing marketplace. Specifically, we propose a three-tier market model that captures a marketplace with users purchasing services from Software-as-a-Service (SaaS) providers, which in turn purchase computing resources from either Provider-as-a-Service (PaaS) or Infrastructure-as-a-Service (IaaS) providers. Within each level, we define and characterize market equilibria. Further, we use these characterizations to understand the relative profitability of SaaSs and PaaSs/IaaSs, and to understand the impact of price competition on the user experienced performance, i.e., the "price of anarchy" of the cloud marketplace. Our results highlight that both of these depend fundamentally on the degree to which congestion results from shared or dedicated resources in the cloud.
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.
OpenFOAM is a widely used open-source framework for the simulation in several areas of computational fluid dynamics (CFD) and engineering. As a partial differential equation (PDE) based framework, OpenFOAM suffers from a performance bottleneck in solving large-scale sparse linear systems of equations. In order to address the problem, this paper proposes a novel OpenFOAM-PETSc framework by inserting PETSc, a dedicated numerical solving package, into the OpenFOAM to speedup the process of solving linear equation systems. The design of the OpenFOAM-PETSc framework is described, and the implementation of an efficient matrix conversion algorithm is given as a case study. Validation tests on a 64-node cluster show that the OpenFOAM-PETSc reduces the solving time of PDEs by about 27% in lid-driven cavity flow case and by more than 50% in flow around cylinder case in comparison with OpenFOAM, without compromising the scalability. In addition, this paper also gives a preliminary performance analysis of different numerical solution methods, which provides guidelines for further optimizations.
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.