PTCP: A priority-based transport control protocol for timeout mitigation in commodity data center

Ruan, Chang; Wang, Jianxin; Jiang, Wanchun; Min, Geyong; Pan, Yi

doi:10.1016/j.future.2019.08.036

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…The method utilizes a simple single hop to split multicast, which leads to a reduction in flow completion time. Ruan et al [29] propose a priority-based transport control protocol which has a recovery approach through packet priority while also tackling congestion. In addition, the protocol contemplates queue length at the bottleneck switch.…”

Section: B Traffic Engineering Without Considering Flow Sizementioning

confidence: 99%

Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution

Abdollahi

Deldari

Asadi

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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With the rapid growth of different massive applications and parallel flow requests in Data Center Networks (DCNs), today's providers are confronting challenges in flow forwarding decisions. Since Software Defined Networking (SDN) provides fine granular control, it can be intelligently programmed to distinguish between flow requirements. The present article proposes a knapsack model in which the link bandwidth and incoming flows are modeled as a knapsack capacity and items, respectively. Furthermore, each flow consists of two size and value aspects, acquired through flow size extraction and the type of service value assigned by the SDN controller decision. Indeed, the current work splits the incoming flow size range into Type of Service (ToS) decimal value numbers. The lower the flow size category, the higher the value dedicated to the flow. Particle Swarm Optimization (PSO) optimizes the knapsack problem and first forwards the selected-flows by KP-PSO, and the non-selectedflows second. To address the shortcomings of these methods in the event of dense parallel flow detection, the present study puts the link under the threshold of a 70% load by simultaneous requests. Experimental results indicate that the proposed method outperforms Sonum, Hedera, and ECMP in terms of flow completion time, packet loss rate, and goodput regarding flow size requirements.

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Section: B Traffic Engineering Without Considering Flow Sizementioning

confidence: 99%

Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution

Abdollahi

Deldari

Asadi

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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“…More recent approaches have also identified the importance of the timeout problem in data-center environments [51,61,67]. For instance, the authors in [51] proposed injects high-priority packet after each window worth of packets. They infer the network congestion by checking the sequences of the received ACKs of high/low priorities.…”

Section: Related Workmentioning

confidence: 99%

T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks

Abdelmoniem

Bensaou

2021

IEEE/ACM Trans. Networking

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Cloud interactive data-driven applications generate swarms of small TCP flows that compete for the small buffer space in data-center switches. Such applications require a short flow completion time (FCT) to perform their jobs effectively. However, TCP is oblivious to the composite nature of application data and artificially inflates the FCT of such flows by several orders of magnitude. This is due to TCP's Internet-centric design that fixes the retransmission timeout (RTO) to be at least hundreds of milliseconds. To better understand this problem, in this paper, we use empirical measurements in a small testbed to study, at a microscopic level, the effects of various types of packet losses on TCP's performance. In particular, we single out packet losses that impact the tail end of small flows, as well as bursty losses, that span a significant fraction of the small congestion window of TCP flows in data-centers, to show a non-negligible effect on the FCT. Based on this, we propose the so-called, timely-retransmitted ACKs (or T-RACKs), a simple loss recovery mechanism to conceal the drawbacks of the long RTO even in the presence of heavy packet losses. Interestingly enough, T-RACKS achieves this transparently to TCP itself as it does not require any change to TCP in the tenant's virtual machine (VM). T-RACKs can be implemented as a software shim layer in the hypervisor between the VMs and server's NIC or in hardware as a networking function in a SmartNIC. Simulation and real testbed results show that T-RACKs achieves remarkable performance improvements.

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“…We normalized the data in the range [1], [2] before conducting the training and testing process by the prediction model. By using the linear transformation formula, the host resource utilization normalization is performed as follow:…”

Section: ) Dataset Pre-processingmentioning

confidence: 99%

“…This metric is produced as a value in [0,1]. It also determines how the regression techniques (the goodness-of-fit) fit the observed host utilization values and how the predicted values are close to the actual ones.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

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Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

Abdullah

Al-Jamali

et al. 2020

IEEE Access

Self Cite

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Studies on the resource workload demand in cloud computing environment aim at reducing resource wastage by optimizing the resource utilization in a cloud data center. Based on this goal, most of the existing approaches rely on resource management mechanisms such as resource allocation and Virtual Machine (VM) consolidation to reach an ideal solution for reducing resource wastage. Because of instability and high variability of the cloud resource usage and workloads, there is a demand for cloud providers to apply the prediction methods for forecasting the future cloud resource utilization. This paper employs a supervised statistical learning method, i.e., Support Vector Regression Technique (SVRT), to forecast the future usage of multi-attribute host resource. The method is particularly suitable to handle a non-linear cloud resource workload. To improve the prediction accuracy of SVRT, we decide Radial Basis Function as the kernel function of SVRT and apply Sequential Minimal Optimization Algorithm (SMOA) for the training and regression estimation of the prediction method. Besides, compared with the existing work, we consider the multi-attribute cloud resources other than the single resource. The method is employed under eight sets of real-world workloads, which are collected from BitBrain (BB), PlanetLab (PL) and Google Cluster Workload Traces (GCWT). Series of experiments conducted on the workload dataset show the effectiveness of our approach. Based on evaluation metrics, the final results show that the accuracy was enhanced by approximately 4%-16% and the error percentage was reduced by approximately 8%-60% compared with the state-of-the-art methods.

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PTCP: A priority-based transport control protocol for timeout mitigation in commodity data center

Cited by 5 publications

References 14 publications

Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution

Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution

T-RACKs: A Faster Recovery Mechanism for TCP in Data Center Networks

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

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