Congestion-Aware Traffic Allocation for Geo-Distributed Data Centers

Tao, Xiaoyi; Ota, Kaoru; Dong, Mianxiong; Borjigin, Wuyunzhaola; Qi, Heng; Li, Keqiu

doi:10.1109/tcc.2020.3001192

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…The work in [34] considers WAN bandwidth limitations of a geo-distributed streaming cluster to schedule the streaming task in Apache Spark streaming using Amazon EC2. CONA [36] addresses the congestion problem in the inter-datacenter transfer methods that use the bandwidth allocation for high utilization. However, the contributions of these works consider some of the network-relevant parameters for the task execution in wide-area scenarios.…”

Section: Related Workmentioning

confidence: 99%

SNR: Network-aware Geo-Distributed Stream Analytics

Mostafaei

Afridi

Abawajy

2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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Emerging applications such as those running on the Internet of Things (IoT) devices produce constant data streams that need to be processed in real-time. Distributed stream processing systems (DSPs), with geographically distributed cluster networks interconnected via wide area network (WAN) links, have recently gained interest in handling these applications. However, these applications have stringent requirements such as lowlatency and high bandwidth that must be guaranteed to ensure the quality of service (QoS). These application requirements raise fundamental DSPs resource management and scheduling challenge. In this paper, we formulate the problem of placement of worker nodes on a geo-distributed DSPs cluster network as a multi-criteria decision-making problem and propose an additive weighting-based approach to solve it. The proposed solution finds the trade-off among different network parameters and allows executing the tasks according to the desired performance metrics. We evaluated the proposed approach using the Yahoo! streaming benchmark on a testbed and compare it against mechanisms deployed in Apache Spark, Apache Storm, and Apache Flink. The results of the evaluation show that our approach improves the performance of Spark up to 2.2x-7.2x, Storm up to 1.2x-3.4x, and Flink up to 1.4x-3.3x compared to other approaches, which makes our approach useful for use in practical environments.

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Section: Related Workmentioning

confidence: 99%

SNR: Network-aware Geo-Distributed Stream Analytics

Mostafaei

Afridi

Abawajy

2021

2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

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“…Further different types of networks and transport protocols are required in different aspects of the DC networks. Inter-DC [176] and intra-DC are the two main aspects of DC networks that have their specific characteristics and transport requirements. Jain et al in [177] utilized SDN networks to design, implement, and evaluate the B4 as a private WAN connecting Google's data centers across the planet.…”

Section: F Latency and Throughput Optimization (Landt) In Data Center...mentioning

confidence: 99%

Latency and Throughput Optimization in Modern Networks: A Comprehensive Survey

Mirzaeinnia¹,

Mirzaeinia²,

Rezgui³

2020

Preprint

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“…In the previous decades, different WAN technologies have been developed to provide better service quality in terms of cost and speed such as VPN (Virtual Private Network), ATM (Asynchronous Transfer Mode), and MPLS (Multi-Protocol Label Switching). Despite the improvements in the bandwidth and speed of WAN networks and internet services, they are still congested with high traffic loads that cause data loss and jitters [3] which impact the performance and quality of the provided services.…”

Section: Introductionmentioning

confidence: 99%

Data Backup Approach using Software-defined Wide Area Network

Attia¹,

Khalifa²,

Kotb³

2021

IJACSA

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Over the past several years, the traditional approaches of managing and utilizing hybrid Wide Area Network (WAN) connections, between sites across geographical regions, have posed many challenges to enterprises. Software-Defined Wide Area Network (SD-WAN) has emerged as a new paradigm that can overcome the traditional WAN challenges like the lack of visibility for WAN bandwidth utilization and the inefficient usage of expensive WAN resources. The flexibility and agility brought to WAN by applying the SD-WAN paradigm helped to improve the efficiency of bandwidth utilization and to address the surge of bandwidth demands. The SD-WAN capabilities become essential for meeting the heavy inter-data center's traffic exchange required for business continuity and disaster recovery operations. In this paper, a data backup approach is introduced using SD-WAN that makes the network centrally programmable. This will leverage the ability to make fine-grained traffic engineering for different data flows over WAN to optimize the bandwidth utilization of the expensive WAN resources by balancing the traffic load across network links between data centers and to minimize the time required to transfer backup data to disaster recovery sites. The proposed approach proved its efficiency according to the bandwidth utilization if it is compared to the other related works.

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Congestion-Aware Traffic Allocation for Geo-Distributed Data Centers

Cited by 8 publications

References 36 publications

SNR: Network-aware Geo-Distributed Stream Analytics

SNR: Network-aware Geo-Distributed Stream Analytics

Latency and Throughput Optimization in Modern Networks: A Comprehensive Survey

Data Backup Approach using Software-defined Wide Area Network

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