Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Dong, Haiwei; Munir, Ali; Tout, Hanine; Ganjali, Yashar

doi:10.1109/access.2021.3117763

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…Network topologies are selected in light of their use in engineering practices. In particular, switch-centric network topologies mainly include Fat-tree [8], Spine-leaf [9], etc., while servercentric network topologies mainly include Bcube [10], Dcell [12], etc [13][14][15][16][17][18][19][20]. Consequently, in our analysis, we concentrate on these three categories of topologies.…”

Section: Related Workmentioning

confidence: 99%

Study of data center communication network topologies using complex network propagation model

et al. 2023

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Data center, as the core infrastructure of data storage and processing, calls for network security protection. Information security has been addressed in a number of specific ways. However, there are few studies that employ network topology features to prevent the transmission of viruses. When a virus spreads, different topologies display various properties. In this paper, we study three types of data center network topologies, i.e., Fat-tree, Leaf-spine, and Bcube, and quantify the propagation characteristics in every topology through the IC propagation model. The probability of the device being infected, the count of propagation sources, the access of propagation sources, and the topological parameters are all considered. Given that network security defenders can only change the topology and topological parameters, we propose a computational framework that combines factor analysis, which provides us with the selection of network topological parameters with a low virus propagation rate in the candidate parameter set. Through experiments, we find that Leaf-spine has a good inhibitory effect on viruses with high propagation probability. Meanwhile, each offers unique advantages. We hope that more data center network topologies will be studied to improve the security of all data centers using these network topologies.

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Section: Related Workmentioning

confidence: 99%

Study of data center communication network topologies using complex network propagation model

et al. 2023

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“…The core solution is based on software define network (SDN) orchestration domain. Also, authors in [21] highlighted new perspectives on DCN machine learning automation. Their work addressed concerns in the areas of workload forecasting, traffic flow control, traffic classification and scheduling, topology management, network state prediction, root cause analysis, and network security.…”

Section: Related Research Effortsmentioning

confidence: 99%

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

2022

KSII TIIS

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Cyber-physical systems (CPS) have been growing exponentially due to improved clouddatacenter infrastructure-as-a-service (CDIaaS). Incremental expandability (scalability), Quality of Service (QoS) performance, and reliability are currently the automation focus on healthy Tier 4 CDIaaS. However, stable QoS is yet to be fully addressed in Cyber-physical data centers (CP-DCS). Also, balanced agility and flexibility for the application workloads need urgent attention. There is a need for a resilient and fault-tolerance scheme in terms of CPS routing service including Pod cluster reliability analytics that meets QoS requirements. Motivated by these concerns, our contributions are fourfold. First, a Distributed Non-Recursive Cloud Model (DNRCM) is proposed to support cyber-physical workloads for remote lab activities. Second, an efficient QoS stability model with Routh-Hurwitz criteria is established. Third, an evaluation of the CDIaaS DCN topology is validated for handling largescale, traffic workloads. Network Function Virtualization (NFV) with Floodlight SDN controllers was adopted for the implementation of DNRCM with embedded rule-base in Open vSwitch engines. Fourth, QoS evaluation is carried out experimentally. Considering the nonrecursive queuing delays with SDN isolation (logical), a lower queuing delay (19.65%) is observed. Without logical isolation, the average queuing delay is 80.34%. Without logical resource isolation, the fault tolerance yields 33.55%, while with logical isolation, it yields 66.44%. In terms of throughput, DNRCM, recursive BCube, and DCell offered 38.30%, 36.37%, and 25.53% respectively. Similarly, the DNRCM had an improved incremental scalability profile of 40.00%, while BCube and Recursive DCell had 33.33%, and 26.67% respectively. In terms of service availability, the DNRCM offered 52.10% compared with recursive BCube and DCell which yielded 34.72% and 13.18% respectively. The average delays obtained for DNRCM, recursive BCube, and DCell are 32.81%, 33.44%, and 33.75% respectively. Finally, workload utilization for DNRCM, recursive BCube, and DCell yielded 50.28%, 27.93%, and 21.79% respectively.

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“…The problem is very relevant today and consists of forecasting the demand for resources, e.g. CPU, memory, network and storage, and their power consumption in data centers offering cloud computing services [ 42 , 43 ].…”

Section: Introduction Motivation and Contributionsmentioning

confidence: 99%

“…The objective is to manage in advance the virtual machines (VMs) and/or physical resources needed to elastically adapt supply to demand, complying with the quality of service (QoS) parameters specified in the service level agreements (SLAs) made with customers [ 42 , 43 ]. It is usually assumed that the forecasting procedure receives as input the time series of past events and its outcome will feed a management system capable of automatically commanding resource management in advance.…”

Section: Introduction Motivation and Contributionsmentioning

confidence: 99%

L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime

et al. 2023

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Several emerging non-volatile (NV) memory technologies are rising as interesting alternatives to build the Last-Level Cache (LLC). Their advantages, compared to SRAM memory, are higher density and lower static power, but write operations wear out the bitcells to the point of eventually losing their storage capacity. In this context, this paper presents a novel LLC organization designed to extend the lifetime of the NV data array and a procedure to forecast in detail the capacity and performance of such an NV-LLC over its lifetime. From a methodological point of view, although different approaches are used in the literature to analyze the degradation of an NV-LLC, none of them allows to study in detail its temporal evolution. In this sense, this work proposes a forecasting procedure that combines detailed simulation and prediction, allowing an accurate analysis of the impact of different cache control policies and mechanisms (replacement, wear-leveling, compression, etc.) on the temporal evolution of the indices of interest, such as the effective capacity of the NV-LLC or the system IPC. We also introduce L2C2, a LLC design intended for implementation in NV memory technology that combines fault tolerance, compression, and internal write wear leveling for the first time. Compression is not used to store more blocks and increase the hit rate, but to reduce the write rate and increase the lifetime during which the cache supports near-peak performance. In addition, to support byte loss without performance drop, L2C2 inherently allows N redundant bytes to be added to each cache entry. Thus, L2C2+N, the endurance-scaled version of L2C2, allows balancing the cost of redundant capacity with the benefit of longer lifetime. For instance, as a use case, we have implemented the L2C2 cache with STT-RAM technology. It has affordable hardware overheads compared to that of a baseline NV-LLC without compression in terms of area, latency and energy consumption, and increases up to 6-37 times the time in which 50% of the effective capacity is degraded, depending on the variability in the manufacturing process. Compared to L2C2, L2C2+6 which adds 6 bytes of redundant capacity per entry, that means 9.1% of storage overhead, can increase up to 1.4-4.3 times the time in which the system gets its initial peak performance degraded.

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Next-Generation Data Center Network Enabled by Machine Learning: Review, Challenges, and Opportunities

Cited by 15 publications

References 23 publications

Study of data center communication network topologies using complex network propagation model

Study of data center communication network topologies using complex network propagation model

Integrating Resilient Tier N+1 Networks with Distributed Non-Recursive Cloud Model for Cyber-Physical Applications

L2C2: Last-level compressed-contents non-volatile cache and a procedure to forecast performance and lifetime

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