Results 51 to 60 of about 181,670 (124)
Discover SustainabilityCloud computing (CC) is a paradigm offering on-demand access to software, platforms, and infrastructure as a service (IaaS). IaaS enables cloud providers to supply resources like Virtual Machines (VMs) and storage, making resource management critical for
Satya Nagamani Pothu, Swathi Kailasam
doaj +1 more source
Enabling autoscaling for in-memory storage in cluster computing framework [PDF]
, 2019 2019 Spring.Includes bibliographical references.IoT enabled devices and observational instruments continuously generate voluminous data. A large portion of these datasets are delivered with the associated geospatial locations.
Shrestha, Bibek Raj, author +3 more
core
CoRRProcessing data at high speeds is becoming increasingly critical as digital economies generate enormous data. The current paradigms for timely data processing are edge computing and data stream processing (DSP). Edge computing places resources closer to where data is generated, while stream processing analyzes the unbounded high-speed data in motion ...
Eugene Armah, Linda Amoako Bannning
openaire +2 more sources
Autoscaling Method for Docker Swarm Towards Bursty Workload [PDF]
The autoscaling mechanism of cloud computing can automatically adjust computing resources according to user needs, improve quality of service (QoS) and avoid over-provision.
Huang, Qichen, Ding, Zhijun, Wang, Song
core
CoRRHybrid cloud-edge infrastructures now support latency-critical workloads ranging from autonomous vehicles and surgical robotics to immersive AR/VR. However, they continue to experience crippling long-tail latency spikes whenever bursty request streams exceed the capacity of heterogeneous edge and cloud tiers.
Eunil Seo +2 more
openaire +3 more sources
PAX: Partition-Aware Autoscaling for the Cassandra NoSQL Database
, 2018 <p>Apache Cassandra has emerged as one of the most widely adopted NoSQL databases. However, there is still a limited understanding on how to optimally operate Cassandra in the cloud using autoscaling methods, by which resources can be scaled up or ...
Rajkumar Buyya +5 more
core +1 more source
A trace-based performance study of autoscaling workloads of workflows in datacenters [PDF]
, 2018 To improve customer experience, datacenter operators offer support for simplifying application and resource management. For example, running workloads of workflows on behalf of customers is desirable, but requires increasingly more sophisticated ...
Laurens Versluis +5 more
core +1 more source
Preprint: MAS-H2: A Hierarchical Multi-Agent System for Holistic Cloud-Native Autoscaling
Autoscaling in cloud-native platforms like Kubernetes is reactive and metric-driven, leading to a strategic void problem. This comes from the decoupling of higher-level business policies from lower-level resource provisioning. The strategic void, coupled
Hamed Hamzeh, Parisa Vahdatian
core +1 more source
Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments
Dynamic workloads and latency-sensitive applications require efficient autoscaling in cloud computing environments. However, most existing approaches rely on reactive mechanisms based on static thresholds, resulting in delayed responses and scaling oscillations under workload uncertainty. To address these limitations, we propose a double deep Q-Network-
Kumar, Bablu +2 more
openaire +2 more sources
An intelligent model for supporting edge migration for virtual function chains in next generation internet of things. [PDF]
Sci Rep, 2023 Tsakanikas V +4 more
europepmc +1 more source