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

2021 ◽  
Vol 224 ◽  
pp. 107050
Author(s):  
Chunlin Li ◽  
Jun Liu ◽  
Weigang Li ◽  
Youlong Luo
Author(s):  
Kim Reichert ◽  
Alexander Pokahr ◽  
Till Hohenberger ◽  
Christopher Haubeck ◽  
Winfried Lamersdorf

Author(s):  
Sebastian Dippl ◽  
Michael C. Jaeger ◽  
Achim Luhn ◽  
Alexandra Shulman-Peleg ◽  
Gil Vernik

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.


Author(s):  
Ismail M. Ali ◽  
Karam M. Sallam ◽  
Nour Moustafa ◽  
Ripon Chakraborty ◽  
Michael J. Ryan ◽  
...  

Kybernetes ◽  
2015 ◽  
Vol 44 (10) ◽  
pp. 1455-1471 ◽  
Author(s):  
Mehran Ashouraie ◽  
Nima Jafari Navimipour

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.


2017 ◽  
Vol 21 (2) ◽  
pp. 241-259 ◽  
Author(s):  
Sanjaya K. Panda ◽  
Indrajeet Gupta ◽  
Prasanta K. Jana

Sign in / Sign up

Export Citation Format

Share Document