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A survey of data partitioning and sampling methods to support big data analysis
Computer clusters with the shared-nothing architecture are the major computing platforms
for big data processing and analysis. In cluster computing, data partitioning and sampling …
for big data processing and analysis. In cluster computing, data partitioning and sampling …
QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment
Cloud computing is a computing model that fully utilizes the resources on the Internet to
maximize the utilization of resources. Due to a large number of users and tasks, it is …
maximize the utilization of resources. Due to a large number of users and tasks, it is …
Quantum computing-inspired network optimization for IoT applications
Internet of Things (IoT) is defined as the interconnection of millions of wireless devices to
acquire data in a ubiquitous manner. With multiple devices targeting to perceive data over a …
acquire data in a ubiquitous manner. With multiple devices targeting to perceive data over a …
Quantum-based predictive fog scheduler for IoT applications
Load scheduling across distributed fog computing nodes has been a major challenge to
meet the increased demand of real-time data analysis, and time-sensitive decision-making …
meet the increased demand of real-time data analysis, and time-sensitive decision-making …
Deep‐Q learning‐based heterogeneous earliest finish time scheduling algorithm for scientific workflows in cloud
The complex and large‐scale scientific workflow applications are effectively executes on the
cloud. The performance of cloud computing highly depends on the task scheduling. Optimal …
cloud. The performance of cloud computing highly depends on the task scheduling. Optimal …
Intermediate data placement and cache replacement strategy under Spark platform
Spark is widely used due to its high performance caching mechanism and high scalability,
which still causes uneven workloads and produces useless intermediate caching results …
which still causes uneven workloads and produces useless intermediate caching results …
DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment
Task scheduling has always been one of the crucial problem in cloud computing. With the
transition of task types from static batch processing to dynamic stream processing, the …
transition of task types from static batch processing to dynamic stream processing, the …
Distributed nearest neighbor classification for large-scale multi-label data on spark
Modern data is characterized by its ever-increasing volume and complexity, particularly
when data instances belong to many categories simultaneously. This learning paradigm is …
when data instances belong to many categories simultaneously. This learning paradigm is …
Quantumized approach of load scheduling in fog computing environment for IoT applications
Load scheduling has been a major challenge in distributed fog computing environments for
meeting the demands of decision-making in real-time. This research proposes an …
meeting the demands of decision-making in real-time. This research proposes an …
SLA based healthcare big data analysis and computing in cloud network
Large volume of multi-structured and low-latency patient data are generated in healthcare
services, which is achallenging task to process and analyze within the Service Level …
services, which is achallenging task to process and analyze within the Service Level …