The electrochemical oxidation of vanillic acid, o-coumaric acid and protocatechuic acid, three representative toxic phenolics in olive mill wastewater, was studied using carbon felt cathode in the electro-Fenton system. Results obtained, in the presence or absence of UV support, were compared throughout the degradation processes up to mineralization. It was demonstrated that all three phenolic compounds reacted completely with hydroxyl radicals and degraded efficiently. It was shown in the photoelectro-Fenton process that the degradation and mineralization efficiency of the phenolic compounds were enhanced by the effect of UV light, especially at the later stages of the degradation processes.
Energy consumption is an increasingly important concern in data centers. Today, nearly half of the energy in data centers is consumed by the cooling infrastructure. Existing policies on thermally-aware workload allocation do not consider applications that include many tasks (or threads) running on a large set of nodes with significant communication among the tasks. Such jobs, however, constitute most of the cycles in high performance computing (HPC) domain, and have started to appear in other data centers as well. Job allocation strongly affects the performance of such communication-intensive applications. Communication-aware job allocation methods exist, but they focus solely on performance and do not consider cooling energy. This paper proposes a novel job allocation methodology to jointly minimize communication cost and cooling energy consumption in data centers. We formulate and solve the joint optimization problem using binary quadratic programming. Our joint optimization algorithm reduces cooling energy by 16.4% on average with only a 2.66% average increase in application running time compared to solely performance-aware allocations. To further optimize the communication cost, we develop a Charm++ based framework that extracts the communication behavior of applications. We then integrate our job allocation policy with recursive coordinate bisection (RCB) based task mapping method to place highly-communicating tasks in close proximity. Experimental results show that task mapping further decreases the communication cost by up to 20.9% compared to assuming all-to-all communication, a popular assumption in much of the prior work. 20 temperature is sufficient to cool the data center [4,5].Performance is another first-order constraint in data center design and management. Communication-intensive parallelized applications constitute most of HPC workloads, and have started to occupy more cycles in other data centers following the efforts in designing parallelized large-scale applications in many domains. These applications run 25 on multiple nodes with intensive communication between the threads, with job running times in the range of minutes, hours, or even days. In particular, the performance of a communication-intensive application is sensitive to the specific allocation and mapping onto the compute nodes. In other words, communication cost has a significant impact on system performance [6,7,8], with long communication distances and communication 30 resource contention increasing the overall delay. Leung et al. have shown a 2X slow down for two communication-intensive jobs when hand-placing them such that their communication paths overlap significantly [6].Most of the existing performance-aware job allocation algorithms focus on minimizing the average number of communication hops between communicating nodes (e.g., [7,8,35 9, 10]). For data centers running transactional enterprise loads, performance-aware job allocation algorithms focus on satisfying the response time constraints imposed by service level agreemen...
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