Alioth: A Machine Learning Based Interference-Aware Performance Monitor for Multi-Tenancy Applications in Public Cloud

Multi-tenancy in public clouds may lead to co-location interference on shared resources, which possibly results in performance degradation of cloud applications. Cloud providers want to know when such events happen and how serious the degradation is, to perform interference-aware migrations and alleviate the problem. However, virtual machines (VM) in Infrastructure-as-a-Service public clouds are black-boxes to providers, where application-level performance information cannot be acquired. This makes performance monitoring intensely challenging as cloud providers can only rely on low-level metrics such as CPU usage and hardware counters. We propose a novel machine learning framework, Alioth, to monitor the performance degradation of cloud applications. To feed the data-hungry models, we first elaborate interference generators and conduct comprehensive co-location experiments on a testbed to build Alioth-dataset which reflects the complexity and dynamicity in real-world scenarios. Then we construct Alioth by (1) augmenting features via recovering low-level metrics under no interference using denoising auto-encoders, (2) devising a transfer learning model based on domain adaptation neural network to make models generalize on test cases unseen in offline training, and (3) developing a SHAP explainer to automate feature selection and enhance model interpretability. Experiments show that Alioth achieves an average mean absolute error of 5.29% offline and 10.8% when testing on applications unseen in the training stage, outperforming the baseline methods. Alioth is also robust in signaling quality-of-service violation under dynamicity. Finally, we demonstrate a possible application of Alioth's interpretability, providing insights to benefit the decision-making of cloud operators. The dataset and code of Alioth have been released on GitHub.Comment: Accepted by 2023 IEEE International Parallel & Distributed Processing Symposium (IPDPS

IBP reduction coefficients made simple

We present an efficient method to shorten the analytic integration-by-parts (IBP) reduction coefficients of multi-loop Feynman integrals. For our approach, we develop an improved version of Leinartas' multivariate partial fraction algorithm, and provide a modern implementation based on the computer algebra system Singular. Furthermore, We observe that for an integral basis with uniform transcendental (UT) weights, the denominators of IBP reduction coefficients with respect to the UT basis are either symbol letters or polynomials purely in the spacetime dimension $D$. With a UT basis, the partial fraction algorithm is more efficient both with respect to its performance and the size reduction. We show that in complicated examples with existence of a UT basis, the IBP reduction coefficients size can be reduced by a factor of as large as $\sim 100$. We observe that our algorithm also works well for settings without a UT basis.Comment: minor changes, typos correcte

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability

Optimal Allocation of Generalized Power Sources in Distribution Network Based on Multi-Objective Particle Swarm Optimization Algorithm

Optimal allocation of generalized power sources in distribution network is researched. A simple index of voltage stability is put forward. Considering the investment and operation benefit, the stability of voltage and the pollution emissions of generalized power sources in distribution network, a multi-objective optimization planning model is established. A multi-objective particle swarm optimization algorithm is proposed to solve the optimal model. In order to improve the global search ability, the strategies of fast non-dominated sorting, elitism and crowding distance are adopted in this algorithm. Finally, tested the model and algorithm by IEEE-33 node system to find the best configuration of GP, the computed result shows that with the generalized power reasonable access to the active distribution network, the investment benefit and the voltage stability of the system is improved, and the proposed algorithm has better global search capability

Hydrogen Production by Water Electrolysis: Progress and Suggestions

The increasing demand for carbon emission reduction has drawn wide attention on the green hydrogen-manufacturing technology. Hydrogen production by water electrolysis based on renewable energies has the lowest carbon emission among the main hydrogen manufacturing methods. This study summarizes the hydrogen demand, hydrogen industry planning, and demonstrations of hydrogen production by water electrolysis. The water electrolysis technology is analyzed, including alkaline water electrolysis and proton exchange membrane (PEM) water electrolysis. Research reveals that improving electrocatalyst activity, catalyst utilization, bipolar plate surface treatment, and electrolytic bath structures helps optimize the performance of PEM electrolytic baths and lower equipment cost. The PEM water electrolysis has high operating current density, low energy consumption, and high output pressure; therefore, it accommodates the fluctuation of renewable energy power generation and can be easily combined with renewable energy consumption. Considering the technical characteristics of hydrogen transportation and electrolytic hydrogen production as well as hydrogen transportation demand in China, a solution for green hydrogen generation and long-distance transportation is proposed. High

Experimental Study on Critical Membrane Water Content of Proton Exchange Membrane Fuel Cells for Cold Storage at −50 °C

Membrane water content is of vital importance to the freezing durability of proton exchange membrane fuel cells (PEMFCs). Excessive water freezing could cause irreversible degradation to the cell components and deteriorate the cell performance and lifetime. However, there are few studies on the critical membrane water content, a threshold beyond which freezing damage occurs, for cold storage of PEMFCs. In this work, we first proposed a method for measuring membrane water content using membrane resistance extracted from measured high frequency resistance (HFR) based on the finding that the non-membrane resistance part of the measured HFR is constant within the range of membrane water content of 2.98 to 14.0. Then, freeze/thaw cycles were performed from −50 °C to 30 °C with well controlled membrane water content. After 30 cycles, cells with a membrane water content of 8.2 and 7.7 exhibited no performance degradation, while those higher than 8.2 showed significant performance decay. Electrochemical tests revealed that electrochemical surface area (ECSA) reduction and charge transfer resistance increase are the main reasons for the degradation. These results indicate that the critical membrane water content for successful cold storage at −50 °C is 8.2

Influence of calcium and phosphate on pH dependency of arsenite and arsenate adsorption to goethite

In the environment, simultaneous presence of arsenic (As) of different oxidation states is common, which hampers our understanding of As behavior. In the current study, the pH dependency of arsenite (As(III)) and arsenate (As(V)) adsorption to goethite under the influence of calcium (Ca2+) (as a major cation) and phosphate (PO4 3−) (as a major anion) was studied, and the reliability of the CD-MUSIC model prediction was tested. The results show that the presence of the major ions led in general to a weaker and more complicated pH dependency of As adsorption. Calcium promoted As(V) adsorption especially at high pH, which can reverse the direction of the pH dependency. The presence of Ca2+ can even decrease As(III) adsorption when As(V) and/or PO4 3− are present. Phosphate competed strongly with both As(III) and As(V) in their adsorption, especially at intermediate and low pH. In the multi-component system, As(III) adsorbs weaker than As(V) over the environmental relevant pH range, therefore it is often the dominant As species in solution and soluble As(III) concentration generally decreases with increasing pH. In the same pH range, As(V) adsorption shows a complicated pH dependency. Soluble As(V) reaches a minimum around pH 6 at high concentration of major bivalent cations (e.g. Ca2+), whereas soluble As(V) will decrease with pH at low bivalent cation concentrations. The experimental results can be reliably predicted and explained with the CD-MUSIC model. The outcome of this study can provide understanding needed in the risk assessment and remediation of As contaminated soils and water