ABSTRACT
Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.
Subject(s)
Algorithms , Cloud ComputingABSTRACT
An experimental characterization of crop residue biomass blends to evaluate their energy potential was conducted using an experimental approach in a commercial scale downdraft gasifier. Corncobs, rice husks, sesame stalks and cotton gin refuse were used to study the effect of mixture proportions on equivalence ratio, gasification temperature, syngas lower heating value (LHV), and cold gas efficiency (CGE). Using an experimental mixture design, thirty-two sample blends were evaluated in an Ankur WBG-30 downdraft gasifier with 30 kg/h feed supply coupled with a syngas purification system, temperature sensors and a gas chromatograph. Syngas composition CO, H2, CH4, N2 and CO2 are presented for each blend. It was found that temperature, syngas composition, syngas lower heating value and cold gas efficiency were negatively affected as the proportion of rice husks in the mixture was increased. It was possible to reach CGE values up to 57.91% and LHV up to 4460 kJ/kg under certain blending conditions. A higher percentages of rice husks caused a considerable increase in the variability of the equivalence ratio resulting in suboptimal gasification conditions.
Subject(s)
Garbage , Biomass , Colombia , Family Characteristics , HeatingABSTRACT
BACKGROUND: Coffee samples adulterated with roasted corn and roasted soybean were analyzed using a voltammetric electronic tongue equipped with a polypyrrole sensor array. METHODS: Coffee samples were adulterated in concentrations of 2%, 5%, 10% and 20% of roasted corn and roasted soybean; 5 replicates of each were used. The discrimination capacity of a voltammetric electronic tongue elaborated with a polypyrrole sensor array, was evaluated by principal component analysis and cluster analysis, while the capacity to perform quantitative determinations was carried out by partial least squares. RESULTS: The results obtained by the application of principal component analysis showed an excellent ability to discriminate adulterated samples. Additionally, the classifications obtained by cluster analysis was concordant with those obtained by principal component analysis. On the other hand, the evaluation of the ability to quantitatively analyze the adulterated samples showed that the polypyrrole sensor array provides sufficient information to allow quantitative determinations by partial least squares regression. CONCLUSIONS: It could be concluded that the voltammetric electronic tongue used in this work allows the suf- ficient analysis of coffee samples adulterated with roasted corn and roasted soybean.
