Browsing by Author "Thai, Minh-Tuan"

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Hash-based Load Balanced Traffic Steering on Softswitches for Chaining Virtualized Network Functions
(2017-01-01)
Thai, Minh-Tuan
;
Lin, Ying-Dar
;
Linz, Po-Ching
;
Lai, Yuan-Cheng
;
交大名義發表
Prior load balancing solutions for chaining virtualized network functions cause significant control and data plane overheads and demand special requirements on network hardware. In this study, we present the design, implementation, and evaluation of Hash-based Traffic Steering on Softswitches (HATS), a load balancing mechanism that aims at mitigating such drawbacks. The method exploits flow hashing technique implemented on softswitches to perform server and network load balancing without triggering the control plane. We have implemented this design using OpenDayLight controller and Open vSwitch platform. The implementation demonstrates that HATS can be readily implemented with commodity network hardware. Furthermore, the experiment results confirm that HATS can reduce the number of flow entries and service chaining time up to 85% and 93%, respectively, when compared with Least Load First (LLF), a controller-based service chaining algorithm.
Towards load-balanced service chaining by Hash-based Traffic Steering on Softswitches
(2018-05-01)
Thai, Minh-Tuan
;
Lin, Ying-Dar
;
Lin, Po-Ching
;
Lai, Yuan-Cheng
;
資訊工程學系
In this paper we study Hash-based Traffic Steering on Softswitches (HATS) which is a load balancing scheme for chaining virtualized network functions (VNFs), with the aim of mitigating the control and data plane overheads of existing methods. Our method uses a flow-hashing technique applied to softswitches to carry out server and network load balancing without triggering the controller. By exploiting the advantages of HATS, we then derive two algorithms, HATS with Flowcell-based Multipathing (HATS-Flowcell) and Dynamic Weight Adjustment for HATS (D-HATS), to address hash collision problems which downgrade system performance. The first algorithm divides an elephant flow into various equal-size flowcells, which are distributed over network paths as individual flows. The second algorithm periodically updates the hashing weights of VNFs and network paths according to their current load status. Our implementation demonstrates that HATS can be readily deployed on commodity network hardware. Furthermore, our experimental results show that D-HATS has roughly the same load balancing performance with Least Load First (LLF), a controller-based service chaining algorithm; while significantly reducing the number of flow entries and service chaining time by 54% and 93%.
Two-tier project and job scheduling for SaaS cloud service providers
(2015-06-01)
Lin, Ying-Dar
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Thai, Minh-Tuan
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Wang, Chih-Chiang
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Lai, Yuan-Cheng
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資訊工程學系
This study addresses a two-tier job scheduling problem for SaaS cloud Service providers which rely on resources leased from laaS cloud providers to achieve elasticity to computational power. In our model, a project represents a user request which consists of multiple jobs; the SaaS is obligated to complete projects using multiple resources leased from IaaS or PaaS providers. The goals are to reduce the project turn-around time and to support priority scheduling by employing suitable scheduling algorithms. We propose a set of two-tier backfilling algorithms which extend the well-known conservative backfilling algorithm with project slack-time and priority concepts. Among the proposed algorithms, Two-Tier Strict Backfilling (2TSB) does not allow preemption in job waiting queues. On the other hand, preemption is allowable in Two-tier Flexible Backfilling which has two versions: 2TFB and 2TFB-SF (slack factor). In 2TFB, a new incoming project can preempt waiting jobs but not waiting projects, while 2TFB-SF permits preemption in both job and project waiting queues. Two-Tier Priority Backfilling (2TPB) algorithm takes priority into account such that only high-priority projects can preempt the low-priority ones. The experimental results indicate that, compared with 2TSB, 2TPB could reduce the mean turn-around time of high-priority projects by more than 25%. (C) 2015 Elsevier Ltd. All rights reserved.
Workload and Capacity Optimization for Cloud-Edge Computing Systems with Vertical and Horizontal Offloading
(2020-03-01)
Thai, Minh-Tuan
;
Lin, Ying-Dar
;
Lai, Yuan-Cheng
;
Chien, Hsu-Tung
;
交大名義發表
A collaborative integration between cloud and edge computing is proposed to be able to exploit the advantages of both technologies. However, most of the existing studies have only considered two-tier cloud-edge computing systems which merely support vertical offloading between local edge nodes and remote cloud servers. This paper thus proposes a generic architecture of cloud-edge computing with the aim of providing both vertical and horizontal offloading between service nodes. To investigate the effectiveness of the design for different operational scenarios, we formulate it as a workload and capacity optimization problem with the objective of minimizing the system computation and communication costs. Because such a mixed-integer nonlinear programming (MINLP) problem is NP-hard, we further develop an approximation algorithm which applies a branch-and-bound method to obtain optimal solutions iteratively. Experimental results show that such a cloud-edge computing architecture can significantly reduce total system costs by about 34%, compared to traditional designs which only support vertical offloading. Our results also indicate that, to accommodate the same number of input workloads, a heterogeneous service allocation scenario requires about a 23% higher system costs than a homogeneous scenario.