Adaptive priority-based data placement and multi-task scheduling in geo-distributed cloud systems

A Taxonomy of Anomalies in Distributed Cloud Systems: The CRI-Model

Intelligent Distributed Computing XI - Studies in Computational Intelligence ◽

10.1007/978-3-319-66379-1_22 ◽

2017 ◽

pp. 247-261

Author(s):

Kim Reichert ◽

Alexander Pokahr ◽

Till Hohenberger ◽

Christopher Haubeck ◽

Winfried Lamersdorf

Keyword(s):

Cloud Systems ◽

Distributed Cloud

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Towards Federation and Interoperability of Cloud Storage Systems

Data Intensive Storage Services for Cloud Environments ◽

10.4018/978-1-4666-3934-8.ch005 ◽

2013 ◽

pp. 60-71 ◽

Cited By ~ 1

Author(s):

Sebastian Dippl ◽

Michael C. Jaeger ◽

Achim Luhn ◽

Alexandra Shulman-Peleg ◽

Gil Vernik

Keyword(s):

Big Data ◽

Cloud Storage ◽

Storage Systems ◽

Network Throughput ◽

Use Cases ◽

Single System ◽

Cloud Systems ◽

Logical Conclusion ◽

Big Data Applications ◽

Distributed Cloud

While it is common to use storage in a cloud-based manner, the question of true interoperability is rarely fully addressed. This question becomes even more relevant since the steadily growing amount of data that needs to be stored will supersede the capacity of a single system in terms of resources, availability, and network throughput quite soon. The logical conclusion is that a network of systems needs to be created that is able to cope with the requirements of big data applications and data deluge scenarios. This chapter shows how federation and interoperability will fit into a cloud storage scenario. The authors take a look at the challenges that federation imposes on autonomous, heterogeneous, and distributed cloud systems, and present approaches that help deal with the special requirements introduced by the VISION Cloud use cases from healthcare, media, telecommunications, and enterprise domains. Finally, the authors give an overview on how VISION Cloud addresses these requirements in its research scenarios and architecture.

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An Automated Task Scheduling Model using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems

IEEE Transactions on Cloud Computing ◽

10.1109/tcc.2020.3032386 ◽

2020 ◽

pp. 1-1

Author(s):

Ismail M. Ali ◽

Karam M. Sallam ◽

Nour Moustafa ◽

Ripon Chakraborty ◽

Michael J. Ryan ◽

...

Keyword(s):

Genetic Algorithm ◽

Task Scheduling ◽

Cloud Systems ◽

Scheduling Model

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Evaluating task scheduling in hadoop-based cloud systems

2013 IEEE International Conference on Big Data ◽

10.1109/bigdata.2013.6691697 ◽

2013 ◽

Cited By ~ 5

Author(s):

Shengyuan Liu ◽

Jungang Xu ◽

Zongzhen Liu ◽

Xu Liu

Keyword(s):

Task Scheduling ◽

Cloud Systems

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Temporal Task Scheduling for Delay-Constrained Applications in Geo-Distributed Cloud Data Centers

2018 IEEE 11th International Conference on Cloud Computing (CLOUD) ◽

10.1109/cloud.2018.00025 ◽

2018 ◽

Author(s):

Jing Bi ◽

Haitao Yuan ◽

Jia Zhang ◽

MengChu Zhou

Keyword(s):

Task Scheduling ◽

Data Centers ◽

Cloud Data ◽

Cloud Data Centers ◽

Distributed Cloud

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An efficient differential evolution algorithm for task scheduling in heterogeneous cloud systems

2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) ◽

10.1109/imcec46724.2019.8984085 ◽

2019 ◽

Author(s):

Pengcheng Han ◽

Chenglie Du ◽

Yifan Liu ◽

Jinchao Chen ◽

Xiaoyan Du

Keyword(s):

Differential Evolution ◽

Task Scheduling ◽

Differential Evolution Algorithm ◽

Cloud Systems ◽

Evolution Algorithm ◽

Heterogeneous Cloud

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Priority-based task scheduling on heterogeneous resources in the Expert Cloud

Kybernetes ◽

10.1108/k-12-2014-0293 ◽

2015 ◽

Vol 44 (10) ◽

pp. 1455-1471 ◽

Cited By ~ 52

Author(s):

Mehran Ashouraie ◽

Nima Jafari Navimipour

Keyword(s):

Genetic Algorithm ◽

Task Scheduling ◽

Design Methodology ◽

Fitness Function ◽

Content Type ◽

Cloud Systems ◽

New Class ◽

Complete Problems ◽

Np Complete ◽

Real World Situation

Purpose – Expert Cloud as a new class of Cloud systems provides the knowledge and skills of human resources (HRs) as a service using Cloud concepts. Task scheduling in the Expert Cloud is a vital part that assigns tasks to suitable resources for execution. The purpose of this paper is to propose a method based on genetic algorithm to consider the priority of arriving tasks and the heterogeneity of HRs. Also, to simulate a real world situation, the authors consider the human-based features of resources like trust, reputation and etc. Design/methodology/approach – As it is NP-Complete to schedule tasks to obtain the minimum makespan and the success of genetic algorithm in optimization and NP-Complete problems, the authors used a genetic algorithm to schedule the tasks on HRs in the Expert Cloud. In this method, chromosome or candidate solutions are represented by a vector; fitness function is calculated based on several factors; one point cross-over and swap mutation are also used. Findings – The obtained results demonstrated the efficiency of the proposed algorithm in terms of time complexity, task fail rate and HRs utilization. Originality/value – In this paper the task scheduling issue in the Expert Cloud and improving pervious algorithm are pointed out and the approach to resolve the problem is applied into a practical example.

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