Multiple workflows scheduling in multi-tenant distributed systems: A taxonomy and future directions
Workflows are an application model that enables the automated execution of multiple
interdependent and interconnected tasks. They are widely used by the scientific community …
interdependent and interconnected tasks. They are widely used by the scientific community …
Multi-objective decision-making for mobile cloud offloading: A survey
Running very complex applications on mobile devices is still challenging since they are
constrained by limited resources, such as memory capacity, network bandwidth, processor …
constrained by limited resources, such as memory capacity, network bandwidth, processor …
Resource allocation of industry 4.0 micro-service applications across serverless fog federation
The Industry 4.0 revolution has been made possible via AI-based applications (eg, for
automation and maintenance) deployed on the serverless edge (aka fog) computing …
automation and maintenance) deployed on the serverless edge (aka fog) computing …
dispel4py: A Python framework for data-intensive scientific computing
This paper presents dispel4py, a new Python framework for describing abstract stream-
based workflows for distributed data-intensive applications. These combine the familiarity of …
based workflows for distributed data-intensive applications. These combine the familiarity of …
Rethinking elastic online scheduling of big data streaming applications over high-velocity continuous data streams
Online scheduling plays a key role for big data streaming applications in a big data stream
computing environment, as the arrival rate of high-velocity continuous data stream might …
computing environment, as the arrival rate of high-velocity continuous data stream might …
Cost-aware streaming workflow allocation on geo-distributed data centers
The virtual machine (VM) allocation problem in cloud computing has been widely studied in
recent years, and many algorithms have been proposed in the literature. Most of them have …
recent years, and many algorithms have been proposed in the literature. Most of them have …
Throughput optimized scheduler for dispersed computing systems
Dispersed computing is promising paradigm to supplement the conventional cloud
computing. Performing computation on the edge leads to significant reduction in …
computing. Performing computation on the edge leads to significant reduction in …
Optimization of data-intensive workflows in stream-based data processing models
Stream computing applications require minimum latency and high throughput for efficiently
processing real-time data. Typically, data-intensive applications where large datasets are …
processing real-time data. Typically, data-intensive applications where large datasets are …
Hadoop-MapReduce job scheduling algorithms survey
The big data computing era is coming to be a fact in all daily life. As data-intensive become a
reality in many of scientific branches, finding an efficient strategy for massive data computing …
reality in many of scientific branches, finding an efficient strategy for massive data computing …
[HTML][HTML] Enhancing Generalization in Genetic Programming Hyper-heuristics through Mini-batch Sampling Strategies for Dynamic Workflow Scheduling
Abstract Genetic Programming Hyper-heuristics (GPHH) have been successfully used to
evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other …
evolve scheduling rules for Dynamic Workflow Scheduling (DWS) as well as other …