Live virtual machine migration: A survey, research challenges, and future directions
In recent years, cloud computing has emerged as a promising paradigm providing various
resources in the Cloud Data Center (CDC), including computations, storage, and even …
resources in the Cloud Data Center (CDC), including computations, storage, and even …
Efficient VM migrations using forecasting techniques in cloud computing: a comprehensive review
M Masdari, H Khezri - Cluster Computing, 2020 - Springer
High cost of data centers' energy consumption and its environmental effects such as CO 2
emissions have inspired numerous researches to provide more efficient VM management …
emissions have inspired numerous researches to provide more efficient VM management …
Autopilot: workload autoscaling at google
K Rzadca, P Findeisen, J Swiderski, P Zych… - Proceedings of the …, 2020 - dl.acm.org
In many public and private Cloud systems, users need to specify a limit for the amount of
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …
resources (CPU cores and RAM) to provision for their workloads. A job that exceeds its limits …
Applying reinforcement learning towards automating energy efficient virtual machine consolidation in cloud data centers
Energy awareness presents an immense challenge for cloud computing infrastructure and
the development of next generation data centers. Virtual Machine (VM) consolidation is one …
the development of next generation data centers. Virtual Machine (VM) consolidation is one …
Machine learning for energy-resource allocation, workflow scheduling and live migration in cloud computing: State-of-the-art survey
Abstract Machine learning and artificial intelligence techniques have been proven helpful
when pragmatic to a wide range of complex problems and areas such as energy …
when pragmatic to a wide range of complex problems and areas such as energy …
An imbalanced big data mining framework for improving optimization algorithms performance
EM Hassib, AI El-Desouky, ESM El-Kenawy… - IEEE …, 2019 - ieeexplore.ieee.org
Big data is an important factor almost in all nowadays technologies, such as, social media,
smart cities, and internet of things. Most of standard classifiers tends to be trapped in local …
smart cities, and internet of things. Most of standard classifiers tends to be trapped in local …
[HTML][HTML] Workflow performance prediction based on graph structure aware deep attention neural network
With the rapid growth of cloud computing, efficient operational optimization and resource
scheduling of complex cloud business processes rely on real-time and accurate …
scheduling of complex cloud business processes rely on real-time and accurate …
Workload time series prediction in storage systems: a deep learning based approach
Storage workload prediction is a critical step for fine-grained load balancing and job
scheduling in realtime and adaptive cluster systems. However, how to perform workload …
scheduling in realtime and adaptive cluster systems. However, how to perform workload …
Auto-scaling techniques for IoT-based cloud applications: a review
S Verma, A Bala - Cluster Computing, 2021 - Springer
Cloud and IoT applications have inquiring effects that can strongly influence today's ever-
growing internet life along with necessity to resolve numerous challenges for each …
growing internet life along with necessity to resolve numerous challenges for each …
WBATimeNet: A deep neural network approach for VM Live Migration in the cloud
A Mangalampalli, A Kumar - Future Generation Computer Systems, 2022 - Elsevier
Abstract Live Migration (LM) of Virtual Machines (VMs) is an important activity for most cloud
platforms, including Azure. LM impacts the availability of VMs, due to which workloads …
platforms, including Azure. LM impacts the availability of VMs, due to which workloads …