• Corpus ID: 195346878

RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows

@article{Chen2017RIOTAN,
  title={RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows},
  author={Jianfeng Chen and Tim Menzies},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.08127}
}
Traditional tools for configuring cloud services can run much slower than the workflows they are trying to optimize. For example, in the case studies reported here, we find cases where (using traditional methods) it takes hours to find ways to make a workflow terminate in tens of seconds. Such slow optimizers are a poor choice of tools for reacting to changing operational environmental conditions. Hence, they are unsuited for cloud services that support rapidly changing workflows, e.g… 
“Sampling” as a Baseline Optimizer for Search-Based Software Engineering
TLDR
This paper compares Sway versus state-of-the-art search-based SE tools using seven models: five software product line models; and two other software process control models (concerned with project management, effort estimation, and selection of requirements) during incremental agile development.
Data-Driven Search-Based Software Engineering
TLDR
It is argued that combining these two fields is useful for situations which require learning from a large data source or when optimizers need to know the lay of the land to find better solutions, faster.
Is one hyperparameter optimizer enough?
TLDR
It is concluded that hyperparameter optimization is more nuanced than previously believed and, while such optimization can certainly lead to large improvements in the performance of classifiers used in software analytics, it remains to be seen which specific optimizers should be applied to a new dataset.

References

SHOWING 1-10 OF 61 REFERENCES
A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments
TLDR
This work identifies challenges and studies existing algorithms from the perspective of the scheduling models they adopt as well as the resource and application model they consider, and a detailed taxonomy that focuses on features particular to clouds is presented.
BTS: Resource capacity estimate for time-targeted science workflows
TLDR
An approximation algorithm named BTS (Balanced Time Scheduling), which estimates the minimum number of computing hosts required to execute workflows within a user-specified finish time, and can be easily integrated with any resource description languages and resource provisioning systems.
Deadline Based Resource Provisioningand Scheduling Algorithm for Scientific Workflows on Clouds
TLDR
An algorithm based on the meta-heuristic optimization technique, particle swarm optimization (PSO), which aims to minimize the overall workflow execution cost while meeting deadline constraints is presented.
Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds
TLDR
Two workflow scheduling algorithms are proposed which aim to minimize the workflow execution cost while meeting a deadline and have a polynomial time complexity which make them suitable options for scheduling large workflows in IaaS Clouds.
Multi-objective workflow scheduling in Amazon EC2
TLDR
MOHEFT, a Pareto-based list scheduling heuristic that provides the user with a set of tradeoff optimal solutions from which the one that better suits the user requirements can be manually selected, is analysed.
A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments
TLDR
This paper proposes a general framework and heuristic algorithm for multi-objective static scheduling of scientific workflows in heterogeneous computing environments and demonstrates that the solutions generated by the algorithm are superior to user-defined constraints most of the time.
Evolutionary Multi-Objective Workflow Scheduling in Cloud
TLDR
An evolutionary multi-objective optimization (EMO)-based algorithm is proposed to solve this workflow scheduling problem on an infrastructure as a service (IaaS) platform and can achieve significantly better solutions than existing state-of-the-art QoS optimization scheduling algorithms in most cases.
An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements
  • Wei-neng Chen, Jun Zhang
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)
  • 2009
TLDR
This paper proposes an ant colony optimization (ACO) algorithm to schedule large-scale workflows with various QoS parameters and designs seven new heuristics for the ACO approach and proposes an adaptive scheme that allows artificial ants to select heuristic based on pheromone values.
Characterization of scientific workflows
TLDR
This work provides a characterization of workflows from five diverse scientific applications, describing their composition and data and computational requirements, and describes a workflow generator that produces synthetic, parameterizable workflows that closely resemble these workflows.
Runtime measurements in the cloud
TLDR
A study of the performance variance of the most widely used Cloud infrastructure (Amazon EC2) from different perspectives using established microbenchmarks to measure performance variance in CPU, I/O, and network and a multi-node MapReduce application to quantify the impact on real dataintensive applications.
...
1
2
3
4
5
...