ترغب بنشر مسار تعليمي؟ اضغط هنا

A Benchmarking Framework for Interactive 3D Applications in the Cloud

132   0   0.0 ( 0 )
 نشر من قبل Tianyi Liu
 تاريخ النشر 2020
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

With the growing popularity of cloud gaming and cloud virtual reality (VR), interactive 3D applications have become a major type of workloads for the cloud. However, despite their growing importance, there is limited public research on how to design cloud systems to efficiently support these applications, due to the lack of an open and reliable research infrastructure, including benchmarks and performance analysis tools. The challenges of generating human-like inputs under various system/application randomness and dissecting the performance of complex graphics systems make it very difficult to design such an infrastructure. In this paper, we present the design of a novel cloud graphics rendering research infrastructure, Pictor. Pictor employs AI to mimic human interactions with complex 3D applications. It can also provide in-depth performance measurements for the complex software and hardware stack used for cloud 3D graphics rendering. With Pictor, we designed a benchmark suite with six interactive 3D applications. Performance analyses were conducted with these benchmarks to characterize 3D applications in the cloud and reveal new performance bottlenecks. To demonstrate the effectiveness of Pictor, we also implemented two optimizations to address two performance bottlenecks discovered in a state-of-the-art cloud 3D-graphics rendering system, which improved the frame rate by 57.7% on average.

قيم البحث

اقرأ أيضاً

To harness the potential of advanced computing technologies, efficient (real time) analysis of large amounts of data is as essential as are front-line simulations. In order to optimise this process, experts need to be supported by appropriate tools t hat allow to interactively guide both the computation and data exploration of the underlying simulation code. The main challenge is to seamlessly feed the user requirements back into the simulation. State-of-the-art attempts to achieve this, have resulted in the insertion of so-called check- and break-points at fixed places in the code. Depending on the size of the problem, this can still compromise the benefits of such an attempt, thus, preventing the experience of real interactive computing. To leverage the concept for a broader scope of applications, it is essential that a user receives an immediate response from the simulation to his or her changes. Our generic integration framework, targeted to the needs of the computational engineering domain, supports distributed computations as well as on-the-fly visualisation in order to reduce latency and enable a high degree of interactivity with only minor code modifications. Namely, the regular course of the simulation coupled to our framework is interrupted in small, cyclic intervals followed by a check for updates. When new data is received, the simulation restarts automatically with the updated settings (boundary conditions, simulation parameters, etc.). To obtain rapid, albeit approximate feedback from the simulation in case of perpetual user interaction, a multi-hierarchical approach is advantageous. Within several different engineering test cases, we will demonstrate the flexibility and the effectiveness of our approach.
Container technologies have been evolving rapidly in the cloud-native era. Kubernetes, as a production-grade container orchestration platform, has been proven to be successful at managing containerized applications in on-premises datacenters. However , Kubernetes lacks sufficient multi-tenant supports by design, meaning in cloud environments, dedicated clusters are required to serve multiple users, i.e., tenants. This limitation significantly diminishes the benefits of cloud computing, and makes it difficult to build multi-tenant software as a service (SaaS) products using Kubernetes. In this paper, we propose Virtual-Cluster, a new multi-tenant framework that extends Kubernetes with adequate multi-tenant supports. Basically, VirtualCluster provides both control plane and data plane isolations while sharing the underlying compute resources among tenants. The new framework preserves the API compatibility by avoiding modifying the Kubernetes core components. Hence, it can be easily integrated with existing Kubernetes use cases. Our experimental results show that the overheads introduced by VirtualCluster, in terms of latency and throughput, is moderate.
As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prior studies have proposed techniques to enable efficient error-resilienc e techniques (e.g., selective instruction duplication), a fundamental requirement for realizing these techniques is a detailed understanding of the applications resilience. In this work, we present TensorFI, a high-level fault injection (FI) framework for TensorFlow-based applications. TensorFI is able to inject both hardware and software faults in general TensorFlow programs. TensorFI is a configurable FI tool that is flexible, easy to use, and portable. It can be integrated into existing TensorFlow programs to assess their resilience for different fault types (e.g., faults in particular operators). We use TensorFI to evaluate the resilience of 12 ML programs, including DNNs used in the autonomous vehicle domain. Our tool is publicly available at https://github.com/DependableSystemsLab/TensorFI.
In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massive ly parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.
Big data applications and analytics are employed in many sectors for a variety of goals: improving customers satisfaction, predicting market behavior or improving processes in public health. These applications consist of complex software stacks that are often run on cloud systems. Predicting execution times is important for estimating the cost of cloud services and for effectively managing the underlying resources at runtime. Machine Learning (ML), providing black box solutions to model the relationship between application performance and system configuration without requiring in-detail knowledge of the system, has become a popular way of predicting the performance of big data applications. We investigate the cost-benefits of using supervised ML models for predicting the performance of applications on Spark, one of todays most widely used frameworks for big data analysis. We compare our approach with textit{Ernest} (an ML-based technique proposed in the literature by the Spark inventors) on a range of scenarios, application workloads, and cloud system configurations. Our experiments show that Ernest can accurately estimate the performance of very regular applications, but it fails when applications exhibit more irregular patterns and/or when extrapolating on bigger data set sizes. Results show that our models match or exceed Ernests performance, sometimes enabling us to reduce the prediction error from 126-187% to only 5-19%.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا