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Network Function Virtualization (NFV) platforms consume significant energy, introducing high operational costs in edge and data centers. This paper presents a novel framework called GreenNFV that optimizes resource usage for network function chains using deep reinforcement learning. GreenNFV optimizes resource parameters such as CPU sharing ratio, CPU frequency scaling, last-level cache (LLC) allocation, DMA buffer size, and packet batch size. GreenNFV learns the resource scheduling model from the benchmark experiments and takes Service Level Agreements (SLAs) into account to optimize resource usage models based on the different throughput and energy consumption requirements. Our evaluation shows that GreenNFV models achieve high transfer throughput and low energy consumption while satisfying various SLA constraints. Specifically, GreenNFV with Throughput SLA can achieve $4.4\times$ higher throughput and $1.5\times$ better energy efficiency over the baseline settings, whereas GreenNFV with Energy SLA can achieve $3\times$ higher throughput while reducing energy consumption by 50%.

The $\gamma$-gated proton spectra measured in the reactions $^{64}$Ni($^{9}$Be, p2n)$^{70}$Ga and $^{64}$Ni($^{9}$Be, pn)$^{71}$Ga, have been utilized to obtain the nuclear level density (NLD) of $^{71}$Ga and $^{72}$Ga nuclei by using the statistical model (SM) calculations. It is seen that the $\gamma$-gated proton spectrum are reasonably explained by using the large value of the inverse level density parameter ($k$ = 11.2 MeV) in the NLD prescription of the Fermi gas (FG) model. The large value of $k$ is indicative of the rotational enhancement, which is consistent with the earlier results in other mass regions. Furthermore, a rotational enhancement factor has been included in the NLD and used in the SM calculation keeping the systematic value of $k$=8.6 MeV and it explains the $\gamma$-gated proton spectrum nicely. The result clearly indicates the presence of collective enhancement in NLD. Subsequently, the NLD with collective enhancement has been utilized in the TALYS calculation, for the first time, to calculate the $^{71}$Ga(n, $\gamma$)$^{72}$Ga capture cross-section. It is observed that, while the FG model without the collective enhancement in the NLD for $^{72}$Ga under predicts the capture data, with the rotational enhancement correction the FG model over predicts the data by similar amount at higher energies. However, in the energy range of 0.01 MeV to 0.1 MeV, the FG model corrected for rotational enhancement describes the data quite well. Thus, the present work indicates that collective enhancement, whenever required, should be taken into account fro proper description of low energy capture cross section data.

Ground state band of $^{140}$Gd has been investigated following their population in the $^{112}$Sn($^{35}$Cl,~$\alpha$p2n)$^{140}$Gd reaction at 195 MeV of beam energy using a large array of Compton suppressed HPGe clovers as the detection setup. Apart from other spectroscopic measurements, level lifetimes of the states have been extracted using the Doppler Shift Attenuation Method. Extracted quadrupole moment along with the pairing independent cranked Nilsson-Strutinsky model calculations for the quadrupole band reveal that the nucleus preferably attains triaxiality with $\gamma$ = -30$^\circ$. The calculation though shows a slight possibility of rotation around the longest possible principal axis at high spin $\sim$ 30$\hbar$ which is beyond the scope of the present experiment.

The interplay between the Fermi sea anisotropy, electron-electron interaction, and localization phenomena can give rise to exotic many-body phases. An exciting example is an anisotropic two-dimensional (2D) Wigner solid (WS), where electrons form an ordered array with an anisotropic lattice structure. Such a state has eluded experiments up to now as its realization is extremely demanding: First, a WS entails very low densities where the Coulomb interaction dominates over the kinetic (Fermi) energy. Attaining such low densities while keeping the disorder low is very challenging. Second, the low-density requirement has to be fulfilled in a material that hosts an anisotropic Fermi sea. Here, we report transport measurements in a clean (low-disorder) 2D electron system with anisotropic effective mass and Fermi sea. The data reveal that at extremely low electron densities, when the r_s parameter, the ratio of the Coulomb to the Fermi energy, exceeds 38, the current-voltage characteristics become strongly nonlinear at small dc biases. Several key features of the nonlinear characteristics, including their anisotropic voltage thresholds, are consistent with the formation of a disordered, anisotropic WS pinned by the ubiquitous disorder potential.

AstroSat is India's first dedicated multi-wavelength space observatory launched by the Indian Space Research Organisation (ISRO) on 28 September 2015. After launch, the AstroSat Science Support Cell (ASSC) was set up as a joint venture of ISRO and the Inter-University Centre for Astronomy and Astrophysics (IUCAA) with the primary purpose of facilitating the use of AstroSat, both for making observing proposals and for utilising archival data. The ASSC organises meetings, workshops and webinars to train users in these activities, runs a help desk to address user queries, provides utility tools and disseminates analysis software through a consolidated web portal. It also maintains the AstroSat Proposal Processing System (APPS) which is deployed at ISSDC, a software platform central to the workflow management of AstroSat operations. This paper illustrates the various aspects of ASSC functionality., Comment: Accepted for publication in the Journal of Astrophysics & Astronomy (JOAA)

With the proliferation of data movement across the Internet, global data traffic per year has already exceeded the Zettabyte scale. The network infrastructure and end-systems facilitating the vast data movement consume an extensive amount of electricity, measured in terawatt-hours per year. This massive energy footprint costs the world economy billions of dollars partially due to energy consumed at the network end-systems. Although extensive research has been done on managing power consumption within the core networking infrastructure, there is little research on reducing the power consumption at the end-systems during active data transfers. This paper presents a novel cross-layer optimization framework, called Cross-LayerHLA, to minimize energy consumption at the end-systems by applying machine learning techniques to historical transfer logs and extracting the hidden relationships between different parameters affecting both the performance and resource utilization. It utilizes offline analysis to improve online learning and dynamic tuning of application-level and kernel-level parameters with minimal overhead. This approach minimizes end-system energy consumption and maximizes data transfer throughput. Our experimental results show that Cross-LayerHLA outperforms other state-of-the-art solutions in this area.

Memory or transistor devices based on electron's spin rather than its charge degree of freedom offer certain distinct advantages and comprise a cornerstone of spintronics. Recent years have witnessed the emergence of a new field, valleytronics, which seeks to exploit electron's valley index rather than its spin. An important component in this quest would be the ability to control the valley index in a convenient fashion. Here we show that the valley polarization can be switched from zero to one by a small reduction in density, simply tuned by a gate bias, in a two-dimensional electron system. This phenomenon arises fundamentally as a result of electron-electron interaction in an itinerant, dilute electron system. Essentially, the kinetic energy favors an equal distribution of electrons over the available valleys, whereas the interaction between electrons prefers single-valley occupancy below a critical density. The gate-bias-tuned transition we observe is accompanied by a sudden, two-fold change in sample resistance, making the phenomenon of interest for potential valleytronic transistor device applications. Our observation constitutes a quintessential demonstration of valleytronics in a very simple experiment.

What are the ground states of an interacting, low-density electron system? In the absence of disorder, it has long been expected that as the electron density is lowered, the exchange energy gained by aligning the electron spins should exceed the enhancement in the kinetic (Fermi) energy, leading to a (Bloch) ferromagnetic transition. At even lower densities, another transition to a (Wigner) solid, an ordered array of electrons, should occur. Experimental access to these regimes, however, has been limited because of the absence of a material platform that supports an electron system with very high-quality (low disorder) and low density simultaneously. Here we explore the ground states of interacting electrons in an exceptionally-clean, two-dimensional electron system confined to a modulation-doped AlAs quantum well. The large electron effective mass in this system allows us to reach very large values of the interaction parameter $r_s$, defined as the ratio of the Coulomb to Fermi energies. As we lower the electron density via gate bias, we find a sequence of phases, qualitatively consistent with the above scenario: a paramagnetic phase at large densities, a spontaneous transition to a ferromagnetic state when $r_s$ surpasses 35, and then a phase with strongly non-linear current-voltage characteristics, suggestive of a pinned Wigner solid, when $r_s$ exceeds $\simeq 38$. However, our sample makes a transition to an insulating state at $r_s\simeq 27$, preceding the onset of the spontaneous ferromagnetism, implying that, besides interaction, the role of disorder must also be taken into account in understanding the different phases of a realistic dilute electron system.

A fundamental concept in physics is the Fermi surface, the constant-energy surface in momentum space encompassing all the occupied quantum states at absolute zero temperature. In 1960, Luttinger postulated that the area enclosed by the Fermi surface should remain unaffected even when electron-electron interaction is turned on, so long as the interaction does not cause a phase transition. Understanding what determines the Fermi surface size is a crucial and yet unsolved problem in strongly interacting systems such as high-$T_{c}$ superconductors. Here we present a precise test of the Luttinger theorem for a two-dimensional Fermi liquid system where the exotic quasi-particles themselves emerge from the strong interaction, namely for the Fermi sea of composite fermions (CFs). Via direct, geometric resonance measurements of the CFs' Fermi wavevector down to very low electron densities, we show that the Luttinger theorem is obeyed over a significant range of interaction strengths, in the sense that the Fermi sea area is determined by the density of the \textit{minority carriers} in the lowest Landau level. Our data also address the ongoing debates on whether or not CFs obey particle-hole symmetry, and if they are Dirac particles. We find that particle-hole symmetry is obeyed, but the measured Fermi sea area differs quantitatively from that predicted by the Dirac model for CFs., Comment: 6 pages, 4 figures

Evaporated $\alpha$-spectra have been measured in coincidence with low energy discrete $\gamma$-rays from residual nucleus $^{68}$Zn populated in the reaction $^{64}$Ni($^9$Be,$\alpha$n)$^{68}$Zn at $E(^9$Be) = 30 MeV producing $^{73}$Ge compound nucleus. Low energy $\gamma$-gated $\alpha$-particle spectra, for the first time, have been used to extract the nuclear level density (NLD) for the intermediate $^{69}$Zn nucleus in the excitation energy range of E $\approx$ 5-20 MeV. The slope of NLD as a function of excitation energy for $^{69}$Zn matches nicely with the slope determined from RIPL estimates for NLD at low energies and the NLD from neutron resonance data. Extracted inverse NLD parameter (k = A/$\widetilde{a}$) has been used to determine the nuclear level density parameter value $a$ at neutron separation energy $S_n$ for $^{69}$Zn. Total cross-section of $^{68}$Zn(n,$\gamma$) capture reaction as a function of neutron energy is then estimated employing the derived $a(S_n)$ in the reaction code TALYS. It is found that the estimated neutron capture cross-section agrees well with the available experimental data without any normalization. The present result indicates that experimentally derived nuclear level density parameter can constrain the statistical model description of astrophysical capture cross-section and optimize the uncertainties associated with the astrophysical reaction rate

The low-lying non-yrast states in $^{114}$Te have been investigated using the Indian National Gamma Array through the fusion-evaporation reaction $^{112}$Sn($^{4}$He, 2n) at a beam energy of 37 MeV. Eight new $\gamma$-transitions have been placed in the level scheme to establish the quasi-$\gamma$ band in this nucleus. Spin and parity of several excited states have been assigned from the present spectroscopy measurements. The comparison of experimental results on the observed bands with the Interacting Boson Model (IBM) and Triaxial Projected Shell Model (TPSM) confirming the existence of the quasi-$\gamma$ band structure in the $^{114}$Te nucleus.

The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5x for the achieved end-to-end performance.

Solid solutions of perovskite oxides (KNbO3)1-x+(La2NiMnO6)x (x=0, 0.1, 0.2 and 0.3) with a variation of band gap (1.33-3.6 eV) have been introduced as a promising photovoltaic absorber. The structural characterization of the prepared samples was carried out using X-ray diffraction (followed by Rietveld refinement) and Raman experiment. As the doping percentage of the monoclinic La2NiMnO6 in the solid-solution increases, the crystal structure of host KNbO3 becomes more symmetric from orthorhombic to cubic. A large reduction in the particle size has also been observed in the solid solutions in comparison to the pure KNbO3. The band gap (~ 1.33 eV) of the synthesized solid solution x=0.1 is found to be very close to the Shockley-Queisser band gap value of 1.34 eV, which suggests the promising photovoltaic possibility in this material. Photoluminescence (PL) emission spectra reveal a strong PL quenching in the solid-solutions in comparison to the KNbO3. The overall structural and optical studies suggest the promising photovoltaic possibility in KNbO3/ La2NiMnO6 solid solution., Comment: 6 pages, 10 Figures, 1 Table

Mobile data traffic (cellular + WiFi) will exceed PC Internet traffic by 2020. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which depend on the network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, to the best of our knowledge, we are first to provide an in-depth analysis of the effects of application-layer data transfer protocol parameters on the energy consumption of mobile phones. We propose a novel model, called FastHLA, that can achieve significant energy savings at the application layer during mobile network I/O without sacrificing the performance. In many cases, our model achieves performance increase and energy saving simultaneously., Comment: arXiv admin note: text overlap with arXiv:1805.03970 and substantial text overlap with arXiv:1707.06826

Fast, reliable, and efficient data transmission across wide-area networks is a predominant bottleneck for data-intensive cloud applications. This paper introduces OneDataShare, which is designed to eliminate the issues plaguing effective cloud-based data transfers of varying file sizes and across incompatible transfer end-points. The vision of OneDataShare is to achieve high-speed data communication, interoperability between multiple transfer protocols, and accurate estimation of delivery time for advance planning, thereby maximizing user-profit through improved and faster data analysis for business intelligence. The paper elaborates on the desirable features of OneDataShare as a cloud-hosted data transfer scheduling and optimization service, and how it is aligned with the vision of harnessing the power of the cloud and distributed computing. Experimental evaluation and comparison with existing real-life file transfer services show that the transfer throughout achieved by OneDataShare is 6.5 times greater.