Pub Date : 2020-06-03DOI: 10.1109/JSAC.2020.2999685
Salma Matoussi, Ilhem Fajjari, Salvatore Costanzo, N. Aitsaadi, R. Langar
5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.
{"title":"5G RAN: Functional Split Orchestration Optimization","authors":"Salma Matoussi, Ilhem Fajjari, Salvatore Costanzo, N. Aitsaadi, R. Langar","doi":"10.1109/JSAC.2020.2999685","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2999685","url":null,"abstract":"5G RAN aims to evolve new technologies spanning the Cloud infrastructure, virtualization techniques and Software Defined Network capabilities. Advanced solutions are introduced to split the functions of the Radio Access Network (RAN) between centralized and distributed locations. Such paradigms improve RAN flexibility and reduce the infrastructure deployment cost without impacting the user quality of service. We propose a novel functional split orchestration scheme that aims at minimizing the RAN deployment cost, while considering the requirements of its processing network functions and the capabilities of the Cloud infrastructure. With a fine grained approach on user basis, we show that the proposed solution optimizes both processing and bandwidth resource usage, while minimizing the overall energy consumption compared to i) cell-centric, ii) distributed and iii) centralized Cloud-RAN approaches. Moreover, we evaluate the effectiveness of our proposal in a 5G experimental prototype, based on Open Air Interface (OAI). We show that our solution achieves good performance in terms of total deployment cost and resolution time.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1448-1463"},"PeriodicalIF":16.4,"publicationDate":"2020-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2999685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47327244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-17DOI: 10.1109/JSAC.2020.2986692
Mouhamad Dieye, Wael Jaafar, H. Elbiaze, R. Glitho
The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation.
{"title":"Market Driven Multidomain Network Service Orchestration in 5G Networks","authors":"Mouhamad Dieye, Wael Jaafar, H. Elbiaze, R. Glitho","doi":"10.1109/JSAC.2020.2986692","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986692","url":null,"abstract":"The advent of a new breed of enhanced multimedia services has put network operators into a position where they must support innovative services while ensuring both end-to-end Quality of Service requirements and profitability. Recently, Network Function Virtualization (NFV) has been touted as a cost-effective underlying technology in 5G networks to efficiently provision novel services. These NFV-based services have been increasingly associated with multi-domain networks. However, several orchestration issues, linked to cross-domain interactions and emphasized by the heterogeneity of underlying technologies and administrative authorities, present an important challenge. In this paper, we tackle the cross-domain interaction issue by proposing an intelligent and profitable auction-based approach to allow inter-domains resource allocation.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1417-1431"},"PeriodicalIF":16.4,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986692","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47969730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-16DOI: 10.1109/JSAC.2020.2986871
D. Wu, Junjie Yan, Honggang Wang, Ruyang Wang
The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.
{"title":"User-Centric Edge Sharing Mechanism in Software-Defined Ultra-Dense Networks","authors":"D. Wu, Junjie Yan, Honggang Wang, Ruyang Wang","doi":"10.1109/JSAC.2020.2986871","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986871","url":null,"abstract":"The emerging mobile edge computing (MEC) evolutionarily extends the cloud services to the network edge. In order to efficiently coordinate distributed edge resources, software defined networking (SDN) at the network edge has been explored to realize the integrated management of communication, computation, and cache (3C) resources. However, many research efforts, in software-defined edge networks, are mainly devoted to 1C or 2C resource sharing. Motivated by high service performance and user demands, we propose a user-centric edge resource sharing model for software-defined ultra-dense network (SD-UDN) where multiple MEC servers around small base stations (SBSs) can share their 3C resources through OpenFlow-enabled switches. In particular, the service models of MEC servers and users are formulated to optimize the service process by minimizing the service delay, which is NP-hard. To address this NP-hard issue, a service association model is constructed based on design structure matrix (DSM), and a simulated annealing algorithm is employed to further optimize the service association model for reducing time complexity and offering a nearoptimal solution. Compared with traditional 1C or 2C resource sharing, the proposed edge resource sharing model can guarantee lower service delay for users.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1531-1541"},"PeriodicalIF":16.4,"publicationDate":"2020-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986871","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44104257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-13DOI: 10.1109/JSAC.2020.2986867
Abdelhamid Alleg, T. Ahmed, M. Mosbah, R. Boutaba
Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of “N” active instances called replicas while redundancy provides “P” standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service Function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.
{"title":"Joint Diversity and Redundancy for Resilient Service Chain Provisioning","authors":"Abdelhamid Alleg, T. Ahmed, M. Mosbah, R. Boutaba","doi":"10.1109/JSAC.2020.2986867","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986867","url":null,"abstract":"Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of “N” active instances called replicas while redundancy provides “P” standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service Function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1490-1504"},"PeriodicalIF":16.4,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986867","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46500608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-13DOI: 10.1109/JSAC.2020.2986668
Shrinivas Petale, Jaisingh Thangaraj
In traditional networks, the pre-routed packets are dropped during the link failure leading to a huge data loss. Survivability techniques such as protection and restoration are available to provide the solution before and after the link failure. But, the new flow entries that are to be added in the flow table increase the initial network demand leading to an increase in memory demand per switch. The saved data not only reduces the network speed but also demands of repeated processing of entries. In this paper, we propose a new scheme of Group Table based Rerouting (GTR) technique to find the response against single link failure through Fast Fail-over (FF) group table feature provided by OpenFlow. This scheme provides equal roles for both controllers and forwarding OpenFlow enabled switches. Here, the controller maintains a look-up table which is updated periodically according to the change in network structure. Also, it has to update the FF group table simultaneously corresponding to every active port of the switches. The controller relabels the packets and updates the flow entries on respective switches.
{"title":"Link Failure Recovery Mechanism in Software Defined Networks","authors":"Shrinivas Petale, Jaisingh Thangaraj","doi":"10.1109/JSAC.2020.2986668","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986668","url":null,"abstract":"In traditional networks, the pre-routed packets are dropped during the link failure leading to a huge data loss. Survivability techniques such as protection and restoration are available to provide the solution before and after the link failure. But, the new flow entries that are to be added in the flow table increase the initial network demand leading to an increase in memory demand per switch. The saved data not only reduces the network speed but also demands of repeated processing of entries. In this paper, we propose a new scheme of Group Table based Rerouting (GTR) technique to find the response against single link failure through Fast Fail-over (FF) group table feature provided by OpenFlow. This scheme provides equal roles for both controllers and forwarding OpenFlow enabled switches. Here, the controller maintains a look-up table which is updated periodically according to the change in network structure. Also, it has to update the FF group table simultaneously corresponding to every active port of the switches. The controller relabels the packets and updates the flow entries on respective switches.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1285-1292"},"PeriodicalIF":16.4,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45088320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-13DOI: 10.1109/JSAC.2020.2986851
Guangchao Wang, Sheng Zhou, Shan Zhang, Z. Niu, Xuemin Shen
Space-air-ground integrated networks (SAGIN) extend the capability of wireless networks and will be the essential building block for many advanced applications, like autonomous driving, earth monitoring, and etc. However, coordinating heterogeneous physical resources is very challenging in such a large-scale dynamic network. In this paper, we propose a reconfigurable service provisioning framework based on service function chaining (SFC) for SAGIN. In SFC, the network functions are virtualized and the service data needs to flow through specific network functions in a predefined sequence. The inherent issue is how to plan the service function chains over large-scale heterogeneous networks, subject to the resource limitations of both communication and computation. Specifically, we must jointly consider the virtual network functions (VNFs) embedding and service data routing. We formulate the SFC planning problem as an integer non-linear programming problem, which is NP-hard. Then, a heuristic greedy algorithm is proposed, which concentrates on leveraging different features of aerial and ground nodes and balancing the resource consumptions. Furthermore, a new metric, aggregation ratio (AR) is proposed to elaborate the communication-computation tradeoff. Extensive simulations shows that our proposed algorithm achieves near-optimal performance. We also find that the SAGIN significantly reduces the service blockage probability and improves the efficiency of resource utilization. Finally, a case study on multiple intersection traffic scheduling is provided to demonstrate the effectiveness of our proposed SFC-based service provisioning framework.
{"title":"SFC-Based Service Provisioning for Reconfigurable Space-Air-Ground Integrated Networks","authors":"Guangchao Wang, Sheng Zhou, Shan Zhang, Z. Niu, Xuemin Shen","doi":"10.1109/JSAC.2020.2986851","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986851","url":null,"abstract":"Space-air-ground integrated networks (SAGIN) extend the capability of wireless networks and will be the essential building block for many advanced applications, like autonomous driving, earth monitoring, and etc. However, coordinating heterogeneous physical resources is very challenging in such a large-scale dynamic network. In this paper, we propose a reconfigurable service provisioning framework based on service function chaining (SFC) for SAGIN. In SFC, the network functions are virtualized and the service data needs to flow through specific network functions in a predefined sequence. The inherent issue is how to plan the service function chains over large-scale heterogeneous networks, subject to the resource limitations of both communication and computation. Specifically, we must jointly consider the virtual network functions (VNFs) embedding and service data routing. We formulate the SFC planning problem as an integer non-linear programming problem, which is NP-hard. Then, a heuristic greedy algorithm is proposed, which concentrates on leveraging different features of aerial and ground nodes and balancing the resource consumptions. Furthermore, a new metric, aggregation ratio (AR) is proposed to elaborate the communication-computation tradeoff. Extensive simulations shows that our proposed algorithm achieves near-optimal performance. We also find that the SAGIN significantly reduces the service blockage probability and improves the efficiency of resource utilization. Finally, a case study on multiple intersection traffic scheduling is provided to demonstrate the effectiveness of our proposed SFC-based service provisioning framework.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1478-1489"},"PeriodicalIF":16.4,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986851","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43763025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-13DOI: 10.1109/JSAC.2020.2986898
Matteo Pozza, Patrick K. Nicholson, D. Lugones, Ashwin Rao, H. Flinck, S. Tarkoma
The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches.
{"title":"On Reconfiguring 5G Network Slices","authors":"Matteo Pozza, Patrick K. Nicholson, D. Lugones, Ashwin Rao, H. Flinck, S. Tarkoma","doi":"10.1109/JSAC.2020.2986898","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986898","url":null,"abstract":"The virtual resources of 5G networks are expected to scale and support migration to other locations within the substrate. In this context, a configuration for 5G network slices details the instantaneous mapping of the virtual resources across all slices on the substrate, and a feasible configuration satisfies the Service-Level Objectives (SLOs) without overloading the substrate. Reconfiguring a network from a given source configuration to the desired target configuration involves identifying an ordered sequence of feasible configurations from the source to the target. The proposed solutions for finding such a sequence are optimized for data centers and cannot be used as-is for reconfiguring 5G network slices. We present Matryoshka, our divide-and-conquer approach for finding a sequence of feasible configurations that can be used to reconfigure 5G network slices. Unlike previous approaches, Matryoshka also considers the bandwidth and latency constraints between the network functions of network slices. Evaluating Matryoshka required a dataset of pairs of source and target configurations. Because such a dataset is currently unavailable, we analyze proof of concept roll-outs, trends in standardization bodies, and research sources to compile an input dataset. On using Matryoshka on our dataset, we observe that it yields close-to-optimal reconfiguration sequences 10X faster than existing approaches.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1542-1554"},"PeriodicalIF":16.4,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986898","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48580963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-13DOI: 10.1109/JSAC.2020.2986959
James Lembke, Srivatsan Ravi, P. Eugster, S. Schmid
In many Software-Defined Networking (SDN) deployments the control plane ends up being actually centralized, yielding a single point of failure and attack. This paper models the interaction between the data plane and a distributed control plane consisting of a set of failure-prone and potentially malicious (compromised) control devices, and implements a secure and robust controller platform that allows network administrators to integrate new network functionality as with a centralized approach. Concretely, the network administrator may program the data plane from the perspective of a centralized controller without worrying about distribution, asynchrony, failures, attacks, or coordination problems that any of these could cause. We introduce a formal SDN computation model for applying network policies and show that it is impossible to implement asynchronous non-blocking and strongly consistent SDN controller platforms in that model. We then present a robust SDN controller protocol (RoSCo) which implements (i) a protocol with provably linearizable semantics for applying network policies that is resilient against faulty/malicious control devices as long as a correct majority exists, and (ii) a modification to the protocol that improves performance by relaxing the guarantees of linearizability to exploit commutativity among updates. Extensive experiments conducted with a functional prototype of RoSCo over a large networked infrastructure supporting Open vSwitch (OVS)-compatible Agilio CX™ SmartNIC hardware show that RoSCo induces bearable overhead. In fact, RoSCo achieves higher throughput in most cases investigated than the seminal Ravana platform which addresses only benign (crash) failures.
{"title":"RoSCo: Robust Updates for Software-Defined Networks","authors":"James Lembke, Srivatsan Ravi, P. Eugster, S. Schmid","doi":"10.1109/JSAC.2020.2986959","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2986959","url":null,"abstract":"In many Software-Defined Networking (SDN) deployments the control plane ends up being actually centralized, yielding a single point of failure and attack. This paper models the interaction between the data plane and a distributed control plane consisting of a set of failure-prone and potentially malicious (compromised) control devices, and implements a secure and robust controller platform that allows network administrators to integrate new network functionality as with a centralized approach. Concretely, the network administrator may program the data plane from the perspective of a centralized controller without worrying about distribution, asynchrony, failures, attacks, or coordination problems that any of these could cause. We introduce a formal SDN computation model for applying network policies and show that it is impossible to implement asynchronous non-blocking and strongly consistent SDN controller platforms in that model. We then present a robust SDN controller protocol (RoSCo) which implements (i) a protocol with provably linearizable semantics for applying network policies that is resilient against faulty/malicious control devices as long as a correct majority exists, and (ii) a modification to the protocol that improves performance by relaxing the guarantees of linearizability to exploit commutativity among updates. Extensive experiments conducted with a functional prototype of RoSCo over a large networked infrastructure supporting Open vSwitch (OVS)-compatible Agilio CX™ SmartNIC hardware show that RoSCo induces bearable overhead. In fact, RoSCo achieves higher throughput in most cases investigated than the seminal Ravana platform which addresses only benign (crash) failures.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1352-1365"},"PeriodicalIF":16.4,"publicationDate":"2020-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2986959","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46461784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user’s personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user’s privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users’ personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users’ QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes.
{"title":"DeepCP: Deep Learning Driven Cascade Prediction-Based Autonomous Content Placement in Closed Social Network","authors":"Qiong Wu, Muhong Wu, Xu Chen, Zhi Zhou, Kaiwen He, Liang Chen","doi":"10.1109/JSAC.2020.2999687","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2999687","url":null,"abstract":"Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user’s personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user’s privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users’ personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users’ QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"1570-1583"},"PeriodicalIF":16.4,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/JSAC.2020.2999687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46405333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-02-05DOI: 10.1109/JSAC.2020.2971898 10.1109/JSAC.2020.2971898
Mohammad Abu Alsheikh, D. Hoang, D. Niyato, Derek Leong, Ping Wang, Zhu Han
Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.
{"title":"Optimal Pricing of Internet of Things: A Machine Learning Approach","authors":"Mohammad Abu Alsheikh, D. Hoang, D. Niyato, Derek Leong, Ping Wang, Zhu Han","doi":"10.1109/JSAC.2020.2971898 10.1109/JSAC.2020.2971898","DOIUrl":"https://doi.org/10.1109/JSAC.2020.2971898 10.1109/JSAC.2020.2971898","url":null,"abstract":"Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.","PeriodicalId":13243,"journal":{"name":"IEEE Journal on Selected Areas in Communications","volume":"38 1","pages":"669-684"},"PeriodicalIF":16.4,"publicationDate":"2020-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45978675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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