Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets. To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palosis proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.</p

Position-aware packet loss optimization on service function chain placement

The advent of Network Function Virtualization (NFV) and Service Function Chains (SFCs) unleashes the power of dynamic creation of network services using Virtual Network Functions (VNFs). This is of great interest to network operators since poor service quality and resource wastage can potentially hurt their revenue in the long term. However, the study shows with a set of test-bed experiments that packet loss at certain positions (i.e., different VNFs) in an SFC can cause various degrees of resource wastage and performance degradation because of repeated upstream processing and transmission of retransmitted packets. To overcome this challenge, this study focuses on resource scheduling and deployment of SFCs while considering packet loss positions. This study developed a novel SFC packet dropping cost model and formulated an SFC scheduling problem that aims to minimize overall packet dropping cost as a Mixed-Integer Linear Programming (MILP) and proved that it is NP-hard. In this study, Palosis proposed as an efficient scheme in exploiting the functional characteristics of VNFs and their positions in SFCs for scheduling resources and deployment to optimize packet dropping cost. Extensive experiment results show that Palos can achieve up to 42.73% improvement on packet dropping cost and up to 33.03% reduction on average SFC latency when compared with two other state-of-the-art schemes.</p

Schematic diagram of cell fusion using a sequential nanosecond/microsecond electric field pulse combination.

<p>100-ns-long strong field pulse induced many tiny pores in the cell membrane, particularly in the junction region. After a brief delay, fusion process was followed by a low-field 10-microsecond pulse, which enlarged the pores.</p

Time evolution of the pore radius at three locations selected along the two-cell membrane was shown.

In (a). Blue represented the large cell pole, green represented the midpoint of the two-cell junction region, and red represented the small cell pole. (b) Results of the nanosecond pulses, (c) the microsecond pulses, and (d) the combined nanosecond/microsecond pulses.</p

Cell electrofusion based on nanosecond/microsecond pulsed electric fields - Fig 6

<p>Nanosecond pulse results were shown in (a), the microsecond pulse in (b), and the pulse combination in (c). The dashed purple line represented a pore density of 10¹³ m^-2.</p

Model parameters.

<p>Model parameters.</p

Cell electrofusion based on nanosecond/microsecond pulsed electric fields - Fig 5

<p>(a-c) Two-dimensional pore density distributions along the surface of the two cell membranes. (d) Graph of pore densities along the surface of the two cell membranes. The dashed gray lines indicate the cell contact area.</p

Cell electrofusion based on nanosecond/microsecond pulsed electric fields - Fig 2

<p>(a) Modeled electrical pulse shapes, magnitudes, and pulse width. (b) Geometry of the simulation. The two cells were contacted to each other in a rectangular 200-μm-long by 100-μm-wide frame. The inset was magnifying part of the cell junction area.</p

Cell electrofusion based on nanosecond/microsecond pulsed electric fields - Fig 7

<p>(a) represented the TMV simulation region. In 7(b), the red, black, and the green curves represented the TMV under the nanosecond pulse, the microsecond pulse, and the nanosecond/microsecond pulse combination respectively.</p

Supplemental Material, S1 - Pretreatment of Diabetic Adipose-derived Stem Cells with mitoTEMPO Reverses their Defective Proangiogenic Function in Diabetic Mice with Critical Limb Ischemia

Supplemental Material, S1 for Pretreatment of Diabetic Adipose-derived Stem Cells with mitoTEMPO Reverses their Defective Proangiogenic Function in Diabetic Mice with Critical Limb Ischemia by Kun Lian, Qin Wang, Shuai Zhao, Maosen Yang, Genrui Chen, Youhu Chen, Congye Li, Haokao Gao and Chengxiang Li in Cell Transplantation</p