The increasing carbon emissions from cloud computing requires new methods to reduce its environmental impact. We explore extending data center server lifetimes to reduce embodied carbon emissions (from hardware manufa...
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ISBN:
(纸本)9798400702426
The increasing carbon emissions from cloud computing requires new methods to reduce its environmental impact. We explore extending data center server lifetimes to reduce embodied carbon emissions (from hardware manufacturing), rather than operational (from running hardware). Our experiments are the first to analyze a data center application's end-to-end performance on different server generations, to reveal that older hardware can preserve performance in certain conditions (e.g., low load). Our observations show the need for a carbon-aware data center scheduler that schedules on older hardware when suitable. However, quantifying such a scheduler's carbon savings is challenging today due to the lack of practical carbon measurement metrics/tools. We identify gaps in current methods for measuring operational and embodied carbon and call upon the broader systems research community to take action and conduct research that can pave the way for future carbon footprint analysis in systems.
Microservice architecture is the computing paradigm of choice for large, service-oriented software catering to real-time requests. Individual programs in such a system perform Remote Procedure Calls (RPCs) to other mi...
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Microservice architecture is the computing paradigm of choice for large, service-oriented software catering to real-time requests. Individual programs in such a system perform Remote Procedure Calls (RPCs) to other microservices to accomplish sub-tasks. Microservices are designed to be robust;top-level requests can succeed despite errors returned from RPC sub-tasks, referred to as non-fatal errors. Because of this design, the top-level microservices tend to "live with" non-fatal errors. Hence, a natural question to ask is "how prevalent are non-fatal errors and what impact do they have on the exposed latency of top-level requests?" In this paper, we present a large-scale study of errors in microservices. We answer the aforementioned question by analyzing 11 Billion RPCs covering 1,900 user-facing endpoints at the Uber serving requests of hundreds of millions of active users. To assess the latency impact of non-fatal errors, we develop a methodology that projects potential latency savings for a given request as if the time spent on failing APIs were eliminated. This estimator allows ranking and bubbling up those APIs that are worthy of further investigations, where the non-fatal errors likely resulted in operational inefficiencies. Finally, we employ our error detection and impact estimation techniques to pinpoint operational inefficiencies, which a) result in a tail latency reduction of a critical endpoint by 30% and b) offer insights into common inefficiency-introducing patterns.
In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in these environments has become a pressi...
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ISBN:
(纸本)9798400704895
In the wake of the rapid deployment of large-scale low-Earth orbit satellite constellations, exploiting the full computing potential of Commercial Off-The-Shelf (COTS) devices in these environments has become a pressing issue. However, understanding this problem is far from straightforward due to the inherent differences between the terrestrial infrastructure and the satellite platform in space. In this paper, we take an important step towards closing this knowledge gap by presenting the first measurement study on the thermal control, power management, and performance of COTS computing devices on satellites. Our measurements reveal that the satellite platform and COTS computing devices significantly interplay in terms of the temperature and energy, forming the main constraints on satellite computing. Further, we analyze the critical factors that shape the characteristics of onboard COTS computing devices. We provide guidelines for future research on optimizing the use of such devices for computing purposes. Finally, we have released the datasets to facilitate further study in satellite computing.
In modern HPC systems, performance measurements are often disturbed by noise. Because repeating measurements to increase confidence in their results is costly, alternative noise-resilient techniques are desirable. The...
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This study presents the first global analysis of on-demand video streaming over Low Earth Orbit (LEO) satellite networks, using data from over one million households across 85 countries. We highlight Starlink's ro...
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This study presents the first global analysis of on-demand video streaming over Low Earth Orbit (LEO) satellite networks, using data from over one million households across 85 countries. We highlight Starlink's role as a major LEO provider, enhancing connectivity in underserved regions. Our findings reveal that while overall video quality on Starlink matches that of traditional networks, the inherent variability in LEO conditions-such as throughput fluctuations and packet loss-leads to an increase in bitrate switches and rebuffers. To further improve the quality of experience for the LEO community, we manipulate existing congestion control and adaptive bitrate streaming algorithms using simulation and real A/B tests deployed on over one million households. Our results underscore the need for video streaming and congestion control algorithms to adapt to rapidly evolving network landscapes, ensuring high-quality service across diverse and dynamic network types.
Gacha game is a special opaque selling approach, where the seller is selling gacha pulls to the buyer. Each gacha pull provides a certain probability for the buyer to win the gacha game reward. The gacha game has been...
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Gacha game is a special opaque selling approach, where the seller is selling gacha pulls to the buyer. Each gacha pull provides a certain probability for the buyer to win the gacha game reward. The gacha game has been enthusiastically embraced in numerous online video games and has a wide range of potential applications. In this work, we model the complex interaction between the seller and the buyer as a Stackelberg game, where the sequential decision of the buyer is modeled as a Markov Decision Process (MDP). We define the whale property in the context of gacha games. Then, we show that this is the necessary condition to achieve optimal revenue. Moreover, we provide the revenue-optimal gacha game design and show that it is equivalent to the single-item single-bidder Myerson auction. We further explore two popular multi-item gacha games, namely, the sequential multi-item gacha game and the banner-based multi-item gacha game. We also discuss the subsidies in the gacha game and demonstrate how subsidies may encourage the buyer to engage in grinding behavior. Finally, we provide a case study on blockchain systems as gacha games.
Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, grap...
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ISBN:
(纸本)9798400704369
Graph-theoretic algorithms and graph machine learning models are essential tools for addressing many real-life problems, such as social network analysis and bioinformatics. To support large-scale graph analytics, graph-parallel systems have been actively developed for over one decade, such as Google's Pregel and Spark's GraphX, which (i) promote a think-like-a-vertex computing model and target (ii) iterative algorithms and (iii) those problems that output a value for each vertex. However, this model is too restricted for supporting the rich set of heterogeneous operations for graph analytics and machine learning that many real applications demand. In recent years, two new trends emerge in graph-parallel systems research: (1) a novel think-like-a-task computing model that can efficiently support the various computationally expensive problems of subgraph search;and (2) scalable systems for learning graph neural networks. These systems effectively complement the diversity needs of graph-parallel tools that can flexibly work together in a comprehensive graph processing pipeline for real applications, with the capability of capturing structural features. This tutorial will provide an effective categorization of the recent systems in these two directions based on their computing models and adopted techniques, and will review the key design ideas of these systems.
Human activity recognition (HAR) using ambient sensors in smart homes has numerous applications for human healthcare and wellness. However, building general-purpose HAR models that can be deployed to new smart home en...
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Human activity recognition (HAR) using ambient sensors in smart homes has numerous applications for human healthcare and wellness. However, building general-purpose HAR models that can be deployed to new smart home environments requires a significant amount of annotated sensor data and training overhead. Most smart homes vary significantly in their layouts, i.e., floor plans and the specifics of sensors embedded, resulting in low generalizability of HAR models trained for specific homes. We address this limitation by introducing a novel, layout-agnostic modeling approach for HAR systems in smart homes that utilizes the transferrable representational capacity of natural language descriptions of raw sensor data. To this end, we generate Textual Descriptions Of Sensor Triggers (TDOST) that encapsulate the surrounding trigger conditions and provide cues for underlying activities to the activity recognition models. Leveraging textual embeddings, rather than raw sensor data, we create activity recognition systems that predict standard activities across homes without (re-)training or adaptation to target homes. Through an extensive evaluation, we demonstrate the effectiveness of TDOST-based models in unseen smart homes through experiments on benchmark Orange4Home and CASAS datasets. Furthermore, we conduct a detailed analysis of how the individual components of our approach affect downstream activity recognition performance.
Modern computer processors improve their computing power by having multiple cores. Traditionally these cores were homogeneous: many identical cores with the same capabilities. Instead it is possible to create processo...
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Adaptive schemes in physical layer security, designed to dynamically respond to the evolving conditions of wireless channels, play a crucial role in fortifying the security of wireless communication systems. We offer ...
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Adaptive schemes in physical layer security, designed to dynamically respond to the evolving conditions of wireless channels, play a crucial role in fortifying the security of wireless communication systems. We offer a thorough analysis of the current state of research on adaptive schemes in physical layer security, introducing a novel taxonomy to categorize and understand these schemes more effectively. A detailed comparison is drawn between the insights provided in this survey and those from the literature, highlighting the unique contributions of our work. We delve into the contributions and challenges associated with various adaptive schemes, providing valuable lessons and summaries to guide further research. The future research directions of the adaptive scheme are discussed in Part 2 of the Appendix, aiming to address the current and emerging demands of wireless communication systems. Through this survey, we aim to enrich the discourse on adaptive schemes in physical layer security, paving the way for advanced research and development in enhancing the security of wireless networks.
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