Zobrazeno 1 - 10
of 281
pro vyhledávání: '"Casale, Giuliano"'
Intelligent Virtual Machine (VM) provisioning is central to cost and resource efficient computation in cloud computing environments. As bootstrapping VMs is time-consuming, a key challenge for latency-critical tasks is to predict future workload dema
Externí odkaz:
http://arxiv.org/abs/2302.05630
The emergence of latency-critical AI applications has been supported by the evolution of the edge computing paradigm. However, edge solutions are typically resource-constrained, posing reliability challenges due to heightened contention for compute a
Externí odkaz:
http://arxiv.org/abs/2212.01302
Autor:
Gias, Alim Ul, Gao, Yicheng, Sheldon, Matthew, Perusquía, José A., O'Brien, Owen, Casale, Giuliano
Since only a small number of traces generated from distributed tracing helps in troubleshooting, its storage requirement can be significantly reduced by biasing the selection towards anomalous traces. To aid in this scenario, we propose SampleHST, a
Externí odkaz:
http://arxiv.org/abs/2210.04595
Edge Federation is a new computing paradigm that seamlessly interconnects the resources of multiple edge service providers. A key challenge in such systems is the deployment of latency-critical and AI based resource-intensive applications in constrai
Externí odkaz:
http://arxiv.org/abs/2208.07658
Autor:
Tuli, Shreshth, Mirhakimi, Fatemeh, Pallewatta, Samodha, Zawad, Syed, Casale, Giuliano, Javadi, Bahman, Yan, Feng, Buyya, Rajkumar, Jennings, Nicholas R.
In recent years, the landscape of computing paradigms has witnessed a gradual yet remarkable shift from monolithic computing to distributed and decentralized paradigms such as Internet of Things (IoT), Edge, Fog, Cloud, and Serverless. The frontiers
Externí odkaz:
http://arxiv.org/abs/2208.00761
Task scheduling is a well-studied problem in the context of optimizing the Quality of Service (QoS) of cloud computing environments. In order to sustain the rapid growth of computational demands, one of the most important QoS metrics for cloud schedu
Externí odkaz:
http://arxiv.org/abs/2205.10642
The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedul
Externí odkaz:
http://arxiv.org/abs/2205.10640
In recent years, deep learning models have become ubiquitous in industry and academia alike. Deep neural networks can solve some of the most complex pattern-recognition problems today, but come with the price of massive compute and memory requirement
Externí odkaz:
http://arxiv.org/abs/2205.10635
Autor:
Gao, Yicheng, Casale, Giuliano
With constrained resources, what, where, and how to cache at the edge is one of the key challenges for edge computing systems. The cached items include not only the application data contents but also the local caching of edge services that handle inc
Externí odkaz:
http://arxiv.org/abs/2205.04575
In recent years, the deployment of large-scale Internet of Things (IoT) applications has given rise to edge federations that seamlessly interconnect and leverage resources from multiple edge service providers. The requirement of supporting both laten
Externí odkaz:
http://arxiv.org/abs/2203.07140