A Survey on Data Center Networking (DCN): Infrastructure and Operations

Wei, Xiaoying; Zhao, Peng; Wen, Yonggang; Xie, Haiyong

doi:10.1109/comst.2016.2626784

Cited by 193 publications

(66 citation statements)

References 108 publications

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“…Thus, the fundamental observations from our experiments still hold. Moreover, traffic flow control within the data center networks (at the edge) can be used to reduce the service latency between different components for a large number of users [48]. Indeed, issues related to network access would become more critical in a scenario involving a large number of users.…”

Section: B Experimental Resultsmentioning

confidence: 99%

Edge Computing for the Internet of Things: A Case Study

Premsankar

Francesco

Taleb

2018

IEEE Internet Things J.

535

238

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The amount of data generated by sensors, actuators and other devices in the Internet of Things (IoT) has substantially increased in the last few years. IoT data are currently processed in the cloud, mostly through computing resources located in distant data centers. As a consequence, network bandwidth and communication latency become serious bottlenecks. This article advocates edge computing for emerging IoT applications that leverage sensor streams to augment interactive applications. First, we classify and survey current edge computing architectures and platforms, then describe key IoT application scenarios that benefit from edge computing. Second, we carry out an experimental evaluation of edge computing and its enabling technologies in a selected use case represented by mobile gaming. To this end, we consider a resource-intensive 3D application as a paradigmatic example and evaluate the response delay in different deployment scenarios. Our experimental results show that edge computing is necessary to meet the latency requirements of applications involving virtual and augmented reality. We conclude by discussing what can be achieved with current edge computing platforms and how emerging technologies will impact on the deployment of future IoT applications.

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Section: B Experimental Resultsmentioning

confidence: 99%

Edge Computing for the Internet of Things: A Case Study

Premsankar

Francesco

Taleb

2018

IEEE Internet Things J.

535

238

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“…In PaaS, the supplier has the https://doi.org/10.31436/iiumej.v20i2.1130 instrumentation and programming framework [30][31][32][33]. Thus, PaaS allows its shoppers to put in in-house instrumentation and programming to form or run another applications [34]. Figure 2 demonstrates the various administration models of cloud computing.…”

Section: Cloud Computingmentioning

confidence: 99%

A Review on Software-Defined Networking Enabled Iot Cloud Computing

Badotra¹,

Panda²

2019

IIUMEJ

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Making use of Internet of Things (IoT) is becoming the necessity of today’s life. The data collected and stored through IoT devices comes from the cloud and therefore cloud computing is acting as a backbone for supporting IoT. But it is easy to forget that the cloud is not completely digital in some areas of the world and there is a need for a data centre where data storage can be achieved. Cloud data centres are facing many difficulties and issues because they are using traditional methods of networking. This is where Software Defined Networking (SDN) has come into view; where it has changed the way traditional networks are operated. For example, the separation of the intelligence of the network devices within the data path can be useful in making networks agile and manageable. This paper aims to provide problem areas in the networking used by the cloud data centres and the role of SDN to overcome these issues. Platforms for providing the experimental setup for collaboration between SDN and cloud will ultimately be beneficial in setting up the SDN- enabled IoT cloud, and this is also discussed with the open research problems. ABSTRAK: Penggunaan Internet Benda (IoT) menjadi keperluan kepada kehendak hari ini. Data yang dikumpul dan disimpan melalui peranti IoT berasal dari awan dan oleh itu pengkomputeran awan bertindak sebagai tulang belakang bagi menyokong IoT. Tetapi, satu perkara yang dilupakan adalah bahawa awan itu tidak sepenuhnya digital, di suatu tempat dalam dunia ini, terdapat keperluan pada pusat data di mana penyimpanan data boleh dicapai. Pusat data awan menghadapi banyak masalah dan isu kerana ianya menggunakan kaedah rangkaian tradisional. Di sinilah Perisian Definisi Perangkaian (SDN) dipandang; ia telah mengubah cara rangkaian tradisional dikendalikan. Sebagai contoh, pemisahan kecerdasan peranti rangkaian dengan laluan data berguna bagi membuat rangkaian pintar dan boleh diurus. Kajian ini bertujuan bagi menyediakan ruang masalah dalam rangkaian yang digunakan oleh pusat Data Awan dan peranan SDN dalam mengatasi masalah ini. Platform persediaan eksperimen bagi kerjasama SDN dan awan akhirnya bermanfaat dalam menubuhkan pengaktifan-SDN awan IOT, juga turut dibincangkan dalam kajian ini berkenaan masalah penyelidikan terbuka.

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“…where f ac (1,2) refers to map the node a and c to the nodes 1 and 2, respectively. The hard constraint in GraphMap is that different nodes in workload can not be mapped to the same substrate node, so f i j (k, k) = +∞.…”

Section: Definition 5 (Cop)mentioning

confidence: 99%

“…In cloud environment, data center networks (DCN) have been built around the world for providing various cloud computing services to enterprise class and individual users, which may connect a large number of servers. [1][2][3] The Service Level Agreement (SLA) of today's utility computing is centered around with multiple resources (denoted as multi-resource), such as computation, storage, Internet traffic, bandwidth, and the availability of these resources. However, few large cloud systems consider all the multi-resource as they map the workloads in their data center networks, most of them merely consider one or two kind of resources.…”

Section: Introductionmentioning

confidence: 99%

Multi‐resource workload mapping with minimum cost in cloud environment

Tan

et al. 2019

Concurrency and Computation

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Summary Workload mapping in cloud environment refers to map multiple workloads provided by the cloud users/tenants to the substrate network provided by the cloud providers, which is a NP‐hard problem. The workload is a service demand made to the cloud, which is modeled as a logical network consists of virtual nodes and virtual links. Substrate network is a physical network consists of physical nodes that are inter‐connected via communication links. Devising heuristic methods has become the mainstream of workload mapping problem, which can obtain a feasible solution, but the quality of the solution is not guaranteed. Pointing to this issue, this paper takes the mapping cost of the workloads as the solving objective, and models the workload mapping as a constraint optimization problem. Based on the constraint optimization model, we devise two algorithms to solve the problem. These algorithms can not only find the feasible solution, but also ensure the solution is optimal. Lastly, we have demonstrated the optimality of the proposed algorithms through theoretical proof and evaluated the performance of them through simulation experiment.

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A Survey on Data Center Networking (DCN): Infrastructure and Operations

Cited by 193 publications

References 108 publications

Edge Computing for the Internet of Things: A Case Study

Edge Computing for the Internet of Things: A Case Study

A Review on Software-Defined Networking Enabled Iot Cloud Computing

Multi‐resource workload mapping with minimum cost in cloud environment

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