Authors:
Wenjuan Li
1
;
Xuezeng Pan
2
;
Qifei Zhang
2
and
Lingdi Ping
2
Affiliations:
1
Zhejiang University and Hangzhou Normal University, China
;
2
Zhejiang University, China
Keyword(s):
Cloud computing, Job scheduling model, User fairness, System throughput.
Related
Ontology
Subjects/Areas/Topics:
Cloud Application Architectures
;
Cloud Computing
;
Cloud Computing Enabling Technology
;
Cloud Deployment Models: Public/Private/Hybrid Cloud
;
Fundamentals
;
Performance Development and Management
;
Platforms and Applications
;
Service Strategy
;
Services Science
Abstract:
Job scheduling and resource allocation are the key factors which affect the performance of parallel and distributed systems. This paper first analyzed several common used job scheduling models in large and distributed environment. Since the main purpose of cloud computing is to provide “low-cost” and “on-demand” services, this paper introduced a novel two-level based cloud scheduling model. In our model, job scheduling in the cloud is divided into User Scheduling and Task Scheduling which were designed to implement different scheduling strategies. In the User Scheduling level, it applies the distribution justice theory in the sociology area to achieve the highest average user fairness. While in Task Scheduling level, it applies adaptive strategies to achieve the binding of micro tasks and the virtual machines according to different objectives of tasks. The new model redefined the messaging mechanism between main cloud entities and a new advertising mode was introduced for cloud prov
iders to promote their services. The results of emulation experiments show that compared to traditional algorithms the proposed model can guarantee the user expectations and the relative fairness of the services on the basis of high overall efficiency of cloud systems.
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