Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Baccarelli, Enzo; Scarpiniti, Michele; Momenzadeh, Alireza

doi:10.1109/access.2018.2860249

Cited by 22 publications

(34 citation statements)

References 41 publications

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“…Second, reducing the energy consumption for IoT devices is a significant issue [23]. Our proposal reduces the transmission time of the IoT communication interfaces, thus increasing the sleep time of IoT devices to save energy.…”

Section: Discussionmentioning

confidence: 99%

“…Energy Efficiency of MPTCP: Although MPTCP renders enhanced throughput to the client, it may still use more energy consumption. There has been some work that considers energy in MPTCP [22,23]. eMPTCP [22] uses power-aware subflow management and delayed subflow establishment to reduce the energy consumption of MPTCP.…”

Section: Related Workmentioning

confidence: 99%

“…eMPTCP [22] uses power-aware subflow management and delayed subflow establishment to reduce the energy consumption of MPTCP. The work in [23] studies the energy performances of MPTCP under hard delay-constraints via numerical simulations by using the Settable-Complexity Bandwidth Manager (SCBM).…”

Section: Related Workmentioning

confidence: 99%

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A Memory-Efficient Transmission Scheme for Multi-Homed Internet-of-Things (IoT) Devices

Hwang

Yoo

2020

Sensors

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Internet-of-things (IoT) is a wide spreading technique that enables intelligence to the everyday objects, however, IoT devices are limited in computation and memory space due to their small physical sizes. As a result, IoT applications generally connect to the remote cloud that provides high computation and large storage. To enhance this communication, some IoT devices are equipped with multiple networks, e.g., cellular or Wi-Fi, by using Multipath TCP (MPTCP). However, MPTCP requires large buffer memory space compared to the legacy TCP, which is problematic for low-memory IoT devices. This paper proposes a new MPTCP scheme that leverages the multi-homed feature of low-memory IoT devices. Our design utilizes an application-level distributor that transmits packets to each MPTCP socket at each endpoint of the data. This scheme cleverly avoids the buffer blocking problem, while still maintaining the benefits of multi-homing. The main contribution of our paper is three-fold. First, our proposal achieves the benefits of multipath while avoiding buffer blocking due to out-of-order packets. Second, since our scheme utilizes the original MPTCP and modifies only the application level, it can be deployed more easily to the legacy systems. Finally, our experimental results, conducted on a Linux testbed and real-world cellular/Wi-Fi, show that the proposed scheme requires only half or less memory to achieve the performance of MPTCP.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Memory-Efficient Transmission Scheme for Multi-Homed Internet-of-Things (IoT) Devices

Hwang

Yoo

2020

Sensors

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“…To formally characterize the LOP solution, let us assume that the LOP in (41) is convex and feasible, and, then, let: [43] guarantee that the Karush-Kuhn-Tucker (KKT) conditions are necessary and sufficient for the evaluation of the LOP solution in (46) when the LOP also meets the Slater's qualification condition. In this regard, the following formal result can be proved.…”

Section: The Lifo Optimization Problem (Lop) and Its Structural Pmentioning

confidence: 99%

Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

et al. 2021

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Fog Computing (FC) and Conditional Deep Neural Networks (CDDNs) with early exits are two emerging paradigms which, up to now, are evolving in a standing-alone fashion. However, their integration is expected to be valuable in IoT applications in which resource-poor devices must mine large volume of sensed data in real-time. Motivated by this consideration, this paper focuses on the optimized design and performance validation of Learning-in-the-Fog (LiFo), a novel virtualized technological platform for the minimum-energy and delay-constrained execution of the inference-phase of CDDNs with early exits atop multi-tier networked computing infrastructures composed by multiple hierarchically-organized wireless Fog nodes. The main research contributions of this paper are threefold, namely: (i) we design the main building blocks and supporting services of the LiFo architecture by explicitly accounting for the multiple constraints on the per-exit maximum inference delays of the supported CDNN; (ii) we develop an adaptive algorithm for the minimum-energy distributed joint allocation and reconfiguration of the available computing-plus-networking resources of the LiFo platform. Interestingly enough, the designed algorithm is capable to self-detect (typically, unpredictable) environmental changes and quickly self-react them by properly re-configuring the available computing and networking resources; and, (iii) we design the main building blocks and related virtualized functionalities of an Information Centric-based networking architecture, which enables the LiFo platform to perform the aggregation of spatially-distributed IoT sensed data. The energy-vs.-inference delay LiFo performance is numerically tested under a number of IoT scenarios and compared against the corresponding ones of some state-of-the-art benchmark solutions that do not rely on the Fog support.

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“…Science DMZ implementation experiences to support e-science in Brazil are shared in [13]. Multipath TCP (MPTCP) is a promising transport technology that can introduce higher throughput and robustness than single-path TCP [14,15]. The Science DMZ can take advantage of MPTCP in the case of path failure, at the cost of resource reservation.…”

Section: Related Workmentioning

confidence: 99%

Scalable design and algorithm for science DMZ by considering the nature of research traffic

et al. 2020

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This paper explores the nature of scientific research traffic on the Korea research environment open network. Based on these investigations, we propose a scalable design and algorithm for the science demilitarization zone (DMZ). The proposed design allows users to share a data-transfer node (DTN), which is essential but costly equipment in the Science DMZ. The proposed iterative greedy algorithm attempts to minimize the peak traffic of the shared DTNs. By considering state-of-the-art DTN and practical research traffic, the proposed design and algorithm achieve up to 79% capital expenditure (CAPEX) reduction from that of a reference design of the Science DMZ where a DTN is allocated per user. The proposed algorithm achieves a 5-order-of-magnitude reduction in computation time at the cost of acceptable CAPEX overheads compared to those of the minimum-CAPEX solutions.

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Fog-Supported Delay-Constrained Energy-Saving Live Migration of VMs Over MultiPath TCP/IP 5G Connections

Cited by 22 publications

References 41 publications

A Memory-Efficient Transmission Scheme for Multi-Homed Internet-of-Things (IoT) Devices

A Memory-Efficient Transmission Scheme for Multi-Homed Internet-of-Things (IoT) Devices

Learning-in-the-Fog (LiFo): Deep Learning Meets Fog Computing for the Minimum-Energy Distributed Early-Exit of Inference in Delay-Critical IoT Realms

Scalable design and algorithm for science DMZ by considering the nature of research traffic

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