Modern datacenter operators aim to maximize the utilization of limited and expensive resources, especially CPU cores. Achieving such an objective requires fast and accurate core scheduling policies. Meanwhile, operating at high utilization requires the employment of overload controllers that shed excess load beyond the allocated capacity. Currently, no analytical techniques exist to study the interactions between these controllers. In this paper, we use performance verification to establish bounds on the throughput and latency achieved by a server that employs state-of-the-art fine-grained core allocation and overload control mechanisms. Our model enables system performance analysis under a wide range of workload and system configurations (e.g., RTT, load, and burstiness). We show that worst-case throughput and latency degrade by $1.8 \times$ and $2.3 \times$, respectively, under high load and burstiness due to the interactions between overload control and fast core allocation. We validate our findings using simulations that demonstrate the plausibility of the identified worst-case behavior under realistic conditions.
The Internet of Things (IoT) is used for various domains such as monitoring the environment, health care, and smart cities. Monitoring and measuring energy consumption of these systems is a crucial step in making them energy efficient. External Hardware-based power monitoring is not always available for IoT edge nodes. An alternative is to create an accurate power model that relates easy-to-monitor parameters (e.g., instructions count, cache misses, node temperature, etc) to externally monitored power. This relationship helps to estimate the power drawn by the nodes. IoT edge nodes have several power optimization leverages like Dynamic Voltage and Frequency Scaling (DVFS). When models calibration does not consider these leverages, the gap between power estimation and actual power usage increases. In related works, several power models and corresponding Software-defined power meters do not consider CPU frequency on IoT edge nodes. These Software-defined power meters provide regression-based power models for IoT edge nodes. This work compares predictions made by these state of the art power models to accurate external power monitoring. We show that not considering CPU frequency can result in incorrect estimations. We investigate and compare several methodologies for building power models, considering the CPU frequency, power, and energy leverage. Different performance metrics and regression methods are explored to estimate power usage. We demonstrate that linear and polynomial regression-based models are able to account for various CPU frequencies on IoT edge nodes. Using these models, we can predict the power consumed by IoT edge nodes running a specific workload, with a MAPE of 2% compared to accurate Hardware-based power meters.
As enterprises adopt multi-cloud strategies understanding network behavior across heterogeneous cloud infrastructures becomes critical for performance and cost optimization. However, existing network simulators lack support for cloudnative hierarchies (providers, regions, zones) and cannot incorporate empirical inter-cloud measurements or egress cost modeling. We present MCSim, a discrete-event simulator designed specifically for empirical trace-driven multi-cloud network analysis. MCSim models hierarchical cloud topologies with time-varying metrics derived from real measurements, supports cost-aware routing policies, and also supports hybrid emulation by applying simulated network conditions to real hosts using Linux traffic control (tc), bridging simulation with real-world validation. We validate MCSim using 50+ million measurements across $\mathbf{1 0 5}$ VMs in AWS, GCP, and Azure regions. Statistical analysis shows MCSim achieves ${\lt }1 \%$ error in average latency reproduction, with $\mathbf{8 7 \%}$ of inter-cloud pairs showing $\lt \mathbf{5 m s}$ RMSE. Case studies demonstrate MCSim’s ability to analyze cost-performance tradeoffs (27% latency reduction with 57% cost increase), simulate failure scenarios (i.e., impact from regional outages), and enable hybrid testing with ${\lt }2 \%$ deviation from real deployments. MCSim scales to 1000+ nodes with spatial parallelization, enabling reproducible multi-cloud experimentation at unprecedented scale and fidelity.
The recent proliferation of cloud gaming (also referred to as game streaming), enabling high-fidelity gaming quality on edge devices without high-end hardware, promises a transformative and democratized gaming experience across diverse populations. Yet, streaming high definition (4K UHD) game frames to thin-client devices, especially mobile, requires substantially higher bandwidth than traditional video streaming and often results in frame drops, degrading user experience. We identified that this high bandwidth demand arises from inefficient compression of game frames with real-time compression requirement (60 frames per second (FPS)) because the rapid, irregular motion in game frames violates the predictability assumptions baked into standard video motion estimation algorithms.To address this issue, we propose and evaluate GSCoder, a realtime efficient game frame compression framework that utilizes motion cues from the game’s rendering pipeline to directly acquire and optimize encoding-compliant motion vectors precisely, bypassing the costly motion estimation step inherent in standard encoders. Our evaluation, conducted in five open-source games with varying motion complexities, demonstrates that GSCoder achieves on average 49% and 19% higher compression efficiency than state-of-the-art (SOTA) game and video encoders, respectively, while maintaining real-time performance and delivering high-quality streams. Moreover, GSCoder delivers at least $3.5 \times$ encoding speedup compared to SOTA video encoders.
In a system containing several distributed servers, messages of random sizes generated at different locations must be disseminated and processed in the same order by all hosts. A ring protocol is defined, where a number of folders carrying messages circulate in one direction without overtaking each other. A model involving parallel queues is analysed in the steady state and is solved approximately, allowing the computation of performance measures. A number of example systems are evaluated numerically and by simulations, leading to a heuristic for choosing the optimal number of folders.
