Fig 4 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
Example of multilateral complementarity relationship between resource scheduling results when m = 6, d = 4
Source publication
The diversification of computing resources and the increasing complexity of resource demand from applications in terms of type, granularity and quantity, call for more efficiency in resource scheduling. To meet this challenge, this paper proposes a resource description model based on a quantized polygon. It explores the theoretical basis for the mu...
Context in source publication
Context 1
... shown in Figure 4, (a) is the quantized polygon of the resource requests of no. 1, 3, and 5 granular application services in the example of Figure 3; (b) is the quantized polygon of the allocated resources in g 1 after the deployment of the above three granular application services in g 1 (the description in the corresponding traditional research process can be seen in Figure 1; (c) is an instance of a quantized polygon about g 1 |c = 100, which is in a multilateral complementarity relationship with the quantized polygon in the graph (b). It can be seen from a comparison of (b) and (c) that compared with the corresponding regular d-edged polygon, the two polygons in the multilateral complementary relationship have opposite values on each resource axis. ...
Citations
... In this study, a scheduling model that takes into account cost savings and load balancing was proposed for large-scale server clusters in heterogeneous cloud data centers. In practice, our scheduling method can be used general for cloud computing datacenter and also be applied to fine-granularity applications, such as traffic flow scheduling [32][33][34][35] and resource allocation service computing [36,37]. Future work should focus on how to dynamically build the most suitable server cluster to obtain the optimal cost and highest QoS corresponding to the task sequence intensity. ...
Cloud-based scientific workflow systems can play an important role in the development of cost-effective bioinformatics analysis applications. There are differences in the cost control and performance of many kinds of servers in heterogeneous cloud data centers for bioinformatics workflows running, which can lead to imbalance between operational/maintenance management costs and quality of service of server clusters. A task scheduling model that responds to the peaks and valleys of task sequencing—the number of tasks that arrive in a given unit of time—is related to indicators such as cost saving, load balancing and system performance (average task wait time, average response time and throughput). This study proposes a large-scale cost-saving and load-balancing scheduling model, called HDCBS, for the optimization of system throughput. First, queuing theory is used to model each computing node as an independent queuing system and to obtain the average system wait time and average task response time. Then, using convex optimization theory, a task assignment solution is proposed with a load-balancing mechanism. The validity of the task scheduling model is verified by simulation experiments, and the model performance is further validated through a comparison with other frequently used scheduling methods. The simulation results show that the credibility of HDCBS is greater than 95% in task scheduling.
