Cloud Computing and Resource Management
332 papers on this topic
Multi-Cell Mobile Edge Computing: Joint Service Migration and Resource Allocation
Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the...
Bang for the Buck: Vector Search on Cloud CPUs
Vector databases have emerged as a new type of systems that support efficient querying of high-dimensional vectors. Many of these offer their database as a service in the cloud. However, the...
PowerTCP: Pushing the Performance Limits of Datacenter Networks
Increasingly stringent throughput and latency requirements in datacenter networks demand fast and accurate congestion control. We observe that the reaction time and accuracy of existing datacenter...
Large-scale Machine Learning Cluster Scheduling via Multi-agent Graph Reinforcement Learning
Efficient scheduling of distributed deep learning (DL) jobs in large GPU clusters is crucial for resource efficiency and job performance. While server sharing among jobs improves resource...
Enhancing Cloud-Native Resource Allocation with Probabilistic Forecasting Techniques in O-RAN
The need for intelligent and efficient resource provisioning for the productive management of resources in real-world scenarios is growing with the evolution of telecommunications towards the 6G...
Online Evolutionary Batch Size Orchestration for Scheduling Deep Learning Workloads in GPU Clusters
Efficient GPU resource scheduling is essential to maximize resource utilization and save training costs for the increasing amount of deep learning workloads in shared GPU clusters. Existing GPU...
An Online Orchestration Mechanism for General-Purpose Edge Computing
In recent years, the fast development of mobile communications and cloud systems has substantially promoted edge computing. By pushing server resources to the edge, mobile service providers can...
GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments
Recently, intelligent scheduling approaches using surrogate models have been proposed to efficiently allocate volatile tasks in heterogeneous fog environments. Advances like deterministic...
SQL and NoSQL Databases Software architectures performance analysis and assessments -- A Systematic Literature review
Context: The efficient processing of Big Data is a challenging task for SQL and NoSQL Databases, where competent software architecture plays a vital role. The SQL Databases are designed for...
Persia: An Open, Hybrid System Scaling Deep Learning-based Recommenders up to 100 Trillion Parameters
Deep learning based models have dominated the current landscape of production recommender systems. Furthermore, recent years have witnessed an exponential growth of the model scale--from Google's...
Understanding Training Efficiency of Deep Learning Recommendation Models at Scale
The use of GPUs has proliferated for machine learning workflows and is now considered mainstream for many deep learning models. Meanwhile, when training state-of-the-art personal recommendation...
Analyzing and Mitigating Data Stalls in DNN Training
Training Deep Neural Networks (DNNs) is resource-intensive and time-consuming. While prior research has explored many different ways of reducing DNN training time, the impact of input data...
The MIT Supercloud Dataset
Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers and commercial...
Forecasting SQL Query Cost at Twitter
With the advent of the Big Data era, it is usually computationally expensive to calculate the resource usages of a SQL query with traditional DBMS approaches. Can we estimate the cost of each...
Deep Reinforcement Agent for Scheduling in HPC
Cluster scheduler is crucial in high-performance computing (HPC). It determines when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics...
Accurate GPU Memory Prediction for Deep Learning Jobs through Dynamic Analysis
The benefits of Deep Learning (DL) impose significant pressure on GPU resources, particularly within GPU cluster, where Out-Of-Memory (OOM) errors present a primary impediment to model training...
Block size estimation for data partitioning in HPC applications using machine learning techniques
The extensive use of HPC infrastructures and frameworks for running dataintensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, application...
dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
Multi-access-Mobile Edge Computing (MEC) is a promising solution for computationally demanding rigorous applications, that can meet 6G network service requirements. However, edge servers incur...