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In edge computing, application components can be placed over a range of computational devices from cloud data centers to nodes at the network edge. Application placement can have significant impact on important metrics like latency and resource utilization. Thus, application placement is an important optimization problem. In edge computing, the characteristics of both the infrastructure and the application may change over time, which may require the dynamic re-optimization of the application placement. Most algorithms suggested so far for the dynamic re-optimization of edge application placement are centralized, i.e., they rely on one entity collecting information from the whole infrastructure and making decisions centrally. However, centralized approaches suffer from limited scalability and are vulnerable to failures. In this paper, we present a decentralized approach for the dynamic re-optimization of edge application placement. We adopt an algorithm of Malek et al. for distributed systems and modify it to make it applicable to edge computing. In this approach, each node makes decisions autonomously, using auctions for coordination. Our empirical results demonstrate that the proposed algorithm is very effective in optimizing edge application placement. In an edge system with 637 edge nodes and 563 end devices, our algorithm achieves 54% higher reduction of application latency than a previous decentralized algorithm. • Optimized placement of application components on cloud and edge devices. • Decentralized optimization algorithm using local knowledge and local modifications. • Auctions and distributed locking for synchronization. • Minimization of application latency. [ABSTRACT FROM AUTHOR]

Scheduling independent tasks on a parallel platform is a widely-studied problem, in particular when the goal is to minimize the total execution time, or makespan (P | | C max problem in Graham's notations). Also, many applications do not consist of sequential tasks, but rather parallel tasks, either rigid, with a fixed degree of parallelism, or moldable, with a variable degree of parallelism (i.e., for which we can decide at the execution on how many processors they are executed). Furthermore, since the energy consumption of data centers is a growing concern, both from an environmental and economical point of view, minimizing the energy consumption of a schedule is a main challenge to be addressed. One can then decide, for each task, on how many processors it is executed, and at which speed the processors are operated, with the goal to minimize the total energy consumption. We further focus on co-schedules, where tasks are partitioned into shelves, and we prove that the problem of minimizing the energy consumption remains NP-complete when static energy is consumed during the whole duration of the application. We are however able to provide an optimal algorithm for the schedule within one shelf, i.e., for a set of tasks that start at the same time. Several approximation results are derived, both with discrete and continuous speed models, and extensive simulations are performed to show the performance of the proposed algorithms. • A model for a theoretical analysis of the energy consumption of a schedule for moldable and rigid tasks. • Most presented scheduling problems are NP-complete. • There exist approximation algorithms for the presented problems (from 1.59 to 3 depending on the problem). • An experimental study of the trade-off between the time complexity of the scheduler and the energy consumed by the schedule. [ABSTRACT FROM AUTHOR]

Energy is a considerable portion of the cost of a cloud data center. In addition to energy, carbon emission tax imposes another cost on data centers, which have different policies in different cities around the world. Most of the cloud data centers are geographically distributed, which can have a huge advantage in reducing such costs. In addition, in recent years, cloud federation, in which multiple cloud providers share their IT infrastructures voluntarily, could play a crucial role in minimizing the energy consumption of cloud data centers. On the other hand, improper VM placement results frequent VM migration, constant turning off/on physical machines, service quality degradation and energy consumption increase. To tackle these weaknesses, we propose a multi-objective algorithm, Called OUR-ACS, to minimize data centers' energy consumption, carbon emission, and the total expenses (energy cost + carbon tax) in a federated environment considering both initial VM placement and VM consolidation approaches. We evaluate the efficiency of our proposed algorithm by using the ClousSim Plus simulator toolkit using real-world datasets. Compared to the competing algorithms, our simulation results indicate that our proposed algorithm could reduce the total energy consumption, carbon emission, and the total costs of multiple cloud data centers by an average of 37.6%, 41%, and 25%, respectively, in a federated environment. • We propose a novel multi-objective cloud federation algorithm considering to minimize three objectives. • We have identified a new problem in VM migration called Migration lock-in status. • We employ new heuristics to make a tradeoff between servers' power consumption and their resource utilization simultaneously. • We conduct a comprehensive simulation under an IaaS cloud infrastructure using a real-world datasets. [ABSTRACT FROM AUTHOR]

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. • The joint problem of VM and vGPU placement based on API remoting is formulated as a multi-objective ILP model. • The proposed heuristic reduces rejection ratio and negative performance impacts of GPU sharing and remote vGPU accesses. • The results are compared against state-of-the-art and commercial solutions for API remoting and GRID technologies. • The effectiveness is studied for low and high arrival rates in slow and fast networks under different scenarios. • The heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively. [ABSTRACT FROM AUTHOR]

The workload of Cloud data centers is constantly fluctuating causing imbalances across physical hosts that may lead to violations of service-level agreements. To mitigate workload imbalances, this work proposes a concurrent agent-based problem-solving technique supported by cooperative game theory capable of balancing workloads by means of live migration of virtual machines (VMs). Nearby agents managing physical hosts are partitioned into coalitions in which agents play coalitional games to progressively balance separate sections of a data center while considering the coalition's benefit of migrating a VM as well as its associated network overhead. Simulation results show that, in general, the proposed coalition-based load balancing mechanism outperformed a load balancing mechanism based on a hill-climbing algorithm used by a top data center vendor when considering altogether (i) the standard deviation of resource usage, (ii) the number of migrations, and (iii) the number of switch hops per migration. • Agents in coalitions progressively balance data center sections. • Supported by a game-theoretical framework, agents cooperate to balance workloads. • Coalitions composed of nearby hosts are used as a basis to balance workloads. • Coalition-aware heuristics consider both the benefit of migrating a VM and its cost. [ABSTRACT FROM AUTHOR]

Reducing energy consumption and optimizing resource usage in large cloud data centers is still an essential target for the current researchers and cloud providers. The state-of-the-art highlights the effectiveness of VM consolidation and live migrations in achieving reasonable solutions. However, most proposals consider only the real-time workload variations to decide whether a host is overloaded or underloaded, or to trigger migration actions. Such approaches may apply frequent and needless VM migrations leading to energy waste, performance degradation, and service-level agreement (SLA) violations. In this paper, we propose a consolidation approach based on the resource utilization prediction to determine the overloaded and underloaded hosts. The prediction method combines a Kalman filter and support vector regression (SVR) to forecast the host's future CPU utilization. Simulations are conducted on Cloudsim using real PlanetLab workloads to verify the performance of our proposal against existing benchmark algorithms. Experimental results demonstrate that our consolidation technique significantly reduces the SLA violation rate, number of VM migrations, and energy consumed in the datacenter. • A multi-step workload prediction model based on Kalman Filter and SVR is proposed. • A dynamic VM consolidation approach based on resource prediction is presented. • A trade-off between SLA violation and energy consumption is pursued. • Analysis study is carried out on the energy consumed by executing our simulation. • Several experiments are conducted on Cloudsim to evaluate our approach. [ABSTRACT FROM AUTHOR]

• An GAN-based method for time-dependent cloud workload generation is proposed. • The method does not rely on any prior knowledge to capture the data distribution. • Spectral normalization is used to stabilize the adversarial training. • Achieving better results than state-of-art models. • Enabled conditional workload generation with simple changes on models. To design repeatable and comparable resource management policies for data centers, researchers mainly conduct experiments in the simulation environment, which requires large-scale workload traces to simulate real scenes. However, issues related to data collection, security and privacy hinder the public availability of cloud workload datasets. Though workload generation is a promising solution, due to the unpredictable time dependency, cloud workloads are difficult to model. In light of this, we propose a novel end-to-end model for time-dependent cloud workload generation using Generative Adversarial Networks, which adopts improved Temporal Convolution Networks and Spectral Normalization to capture the time dependency and stabilize the adversarial training. Experimental results on real cloud datasets demonstrate that our model can efficiently generate realistic workloads that fulfill the diversity, fidelity and usefulness. Further, we also propose a conditional GAN which is trained with labeled data and can generate specific kind of workloads according to the input. [ABSTRACT FROM AUTHOR]

The erasure-coded cross-data center storage system can achieve high disaster tolerance and low redundancy. But as it has large cross-data center update traffic, its data update time is long. In erasure-coded storage systems, each data object is sequentially divided into several stripes, and each stripe consists of several data packets. When erasure-coded stripes do not undergo insertion or deletion, existing work can effectively reduce cross-data center update traffic by performing delta updates—delta update methods can update the old stripe without transferring matched packets that are new and old stripes' duplicate packets with the same offset-within-stripe. However, because existing delta update methods' stripe size is fixed, when a stripe undergoes insertion or deletion, its subsequent stripes' duplicate packets' offset-within-stripe will change. In this scenery, the matched packet number is small, resulting in large cross-data center update traffic. This paper proposes an elastic stripe-based delta update method for erasure-coded cross-data center storage systems (ESDU). Under insertion or deletion, ESDS tries to avoid duplicate packets' offset-within-stripe changing (i.e., maximizing matched packets) by adjusting the stripes' size flexibly according to the duplicate packet locating result. So, it can reduce cross-data center traffic. Moreover, ESDU can optimize stripes' update topology based on the location information of storage nodes to reduce cross-data center update traffic further. In addition, we implement an erasure-coded cross-data center storage system adopting ESDU, called ECESD. Experiments with the workloads derived from EduCoder's real-world trace show that compared with the existing erasure-coded cross-data center storage system adopting the fixed stripe-based delta update method, ECESD reduces average update time by 89.6%. Moreover, compared with a replication-based storage system with a delta update method (HadoopRsync), ECESD achieves an 8.3% shorter update time and much smaller redundancy. • We propose an elastic stripe-based delta update method for erasure-coded cross-data center storage systems, ESDU. • ESDU can shorten data update time by adjusting stripe size and optimizing update topology reasonably. • We implement an erasure-coded cross-data center storage system adopting ESDU called ECESD. • ECESD reduces average update time by 89.6%, compare with existing erasure-coded cross-data center storage systems. • ECESD's average update time is even shorter than existing replication-based cross-data center storage systems. [ABSTRACT FROM AUTHOR]

The k -ary n -cube Q n k serves as an indispensable interconnection network in the design of data center networks, network-on-chips, and parallel computing systems since it possesses numerous attractive properties. In these parallel architectures, the paired (or unpaired) many-to-many m -disjoint path cover (m -DPC) plays a significant role in message transmission. Nevertheless, the construction of m -DPC is severely obstructed by large-scale edge faults due to the rapid growth of the system scale. In this paper, we investigate the existence of paired 2-DPC in Q n k under the partitioned edge fault (PEF) model, which is a novel fault model for enhancing the networks' fault-tolerance related to path embedding problem. We exploit this model to evaluate the edge fault-tolerance of Q n k when a paired 2-DPC is embedded into Q n k. Compared to the other known works, our results can help Q n k to achieve large-scale edge fault-tolerance. • This research studies the existence of paired 2-disjoint path cover in k -ary n -cubes in the presence of large-scale faulty edges. • Our findings generalize the result in [50] from the theoretical perspective. • Our results provide the exponential edge fault-tolerance compared to previous similar works. [ABSTRACT FROM AUTHOR]

The vigorous development and the increasing popularity of cloud computing highlight the necessity of reducing data center energy consumption and the environmental impact of carbon dioxide emissions. For geographically distributed data centers, cloud servers are connected to the conventional power grid and in addition they are supported by an attached renewable energy source. Since the carbon footprint rate of energy consumption has dynamic differences in space, reducing energy consumption does not mean decrease carbon emission, which indicates that energy consumption and carbon footprint need to be synergistically optimized. In this paper, an energy and carbon-aware algorithm for virtual machine placement is proposed. The goal is to obtain a virtual machine allocation scheme that aims to achieve the trade-off between energy consumption and carbon emissions by improving renewable energy utilization. The experimental results show that the proposed approach is more energy-efficient and greener, which can also maximize the renewable energy utilization with 73.11% while ensuring the SLA violation with 0.2% in comparison to the baseline algorithms. • Propose a novel energy-aware VM placement approach in cloud data centers. • Introduce renewable energy for managing energy consumption and carbon footprint. • Design renewable energy generation prediction method based on EEMD and TCN. • Demonstrate the superiority of the proposed algorithm with five baseline algorithms. [ABSTRACT FROM AUTHOR]

• A deployment strategy for edge nodes based on the analysis of mobility traces. • A performance comparison of the strategy against traditional approaches. • The use of ParticpAct and Google Popular Time. • A data exploration analysis of the data sets describing some mobility features. The main objective of Multi-access Edge Computing (MEC) is to bring computational capabilities at the edge of the network to better support low-latency applications. Such capabilities are typically offered by Edge Data Centers (EDC). The MEC paradigm is not tied to a single radio technology, rather it embraces both cellular and other radio access technologies such as WiFi. Distributed intelligence at the edge for AI purposes requires careful spatial planning of computing and storage resources. The problem of EDC deployment in urban environments is challenging and, to the best of our knowledge, it has been explored only for cellular connectivity so far. In this paper, we study the possibility of deploying EDC without analyzing the expected data traffic load of the cellular network, a kind of information rarely shared by network operators. To this purpose, we propose in this work CLUB, CLUstering-Based strategy tailored on the analysis of urban mobility. We analyze two experimental mobility data sets, and we analyze some mobility features in order to characterize their properties. Finally, we compare the performance of CLUB against state-of-the-art techniques in terms of the outage probability, namely the probability an EDC is not able to serve a request. Our results show that the CLUB strategy is always comparable with respect to our benchmarks, but without using any information related to network traffic. [ABSTRACT FROM AUTHOR]

• We propose a new approach to achieve balance and optimization in resource exploitation and reduce energy consumption in the Cloud. • We propose two successive algorithms of FCA and Fuzzy-FCA to adapt our data according to their type (exact or fuzzy). • We use a mixed environment by integrating the MEC architecture with the cloud to minimize end-to-end latency. • We offer a feasible solution to properly place network services at VNFs. • We apply Fuzzy FCA to place and consolidate the VNFs based on a classification very effective. Virtual Network Function (VNF) is one of the pillars of a Cloud network that separates network functions and their dedicated hardware devices, such as routers, firewalls, and load balancers, to host their services on virtual machines. The VNF is responsible for network services that run on virtual machines and can connect each of them alone or organize themselves into a single enclosure to use all the resources available in that enclosure. This flexibility allows physical and virtual resources to be used in a way that ensures control over power consumption, balance in resource use, and minimizing costs and latency. In order to consolidate VNF groups into a minimum number of Virtual Machine (VM) with estimation of the association relation to a measure of confidence under the context of possibility theory, we propose a new Fuzzy-FCA approach for VNF placement based on Formal Concept Analysis (FCA) and fuzzy logic in mixed environment based on cloud data centers and Multiple access Edge Computing (MEC) architecture. Thus, the inclusion of this architecture in the cloud environment ensures the distribution of compute resources to the end user in order to reduce end-to-end latency. To confirm the effectiveness of our solution, we compared it to one of the best algorithms studied in the literature, namely the MultiSwarm algorithm. The results of the series of experiments carried out show the feasibility and efficiency of our algorithm. Indeed, the harvested results confirm the capability of maximizing and balancing the use of resources, of minimizing the latency and the cost of energy consumption. The performance of our solution in terms of average latency represents 16%, a slight increase compared to MultiSwarm, and an average gain, in runtime, of 49%, compared to the same algorithm. [ABSTRACT FROM AUTHOR]

The increasing problem of bacterial resistance, particularly with quinolone-resistant Escherichia coli (QnR eco) poses a serious global health issue. We collected data on QnR eco resistance rates and detection frequencies from 2014 to 2021 via the China Antimicrobial Resistance Surveillance System, complemented by meteorological and socioeconomic data from the China Statistical Yearbook and the China Meteorological Data Service Centre (CMDC). Comprehensive nonparametric testing and multivariate regression models were used in the analysis. Our analysis revealed significant regional differences in QnR eco resistance and detection rates across China. Along the Hu Huanyong Line, resistance rates varied markedly: 49.35 in the northwest, 54.40 on the line, and 52.30 in the southeast (P = 0.001). Detection rates also showed significant geographical variation, with notable differences between regions (P < 0.001). Climate types influenced these rates, with significant variability observed across different climates (P < 0.001). Our predictive model for resistance rates, integrating climate and healthcare factors, explained 64.1% of the variance (adjusted R-squared = 0.641). For detection rates, the model accounted for 19.2% of the variance, highlighting the impact of environmental and healthcare influences. The study found higher resistance rates in warmer, monsoon climates and areas with more public health facilities, but lower rates in cooler, mountainous, or continental climates with more rainfall. This highlights the strong impact of climate on antibiotic resistance. Meanwhile, the predictive model effectively forecasts these resistance rates using China's diverse climate data. This is crucial for public health strategies and helps policymakers and healthcare practitioners tailor their approaches to antibiotic resistance based on local environmental conditions. These insights emphasize the importance of considering regional climates in managing antibiotic resistance. [Display omitted] • E. coli 's resistance rates change with climate and Hu line. • Region-specific health strategies against antibiotic resistance is needed. • The model has a higher explanatory power. [ABSTRACT FROM AUTHOR]

• End-to-End solution of memory reliability in data center environment combing ML-models and engineering tools. • Memory error pattern modeled as images to apply CNN models. • Providing a visualization tool for memory errors on cell level. • Providing an MLOps framework for operating the CNN model in production. • Concept for self-supervised data labeling. With the trend towards multi-socket server systems, the demand for random access memory (RAM) per server increased. The consequence are more DIMM sockets per server. Since every dual in-line memory module (DIMM), which comprises a series of dynamic random-access memory integrated circuits, has a probability of failure, RAM issues became a dominant failure pattern for servers. The concept introduced in this work contributes to improving the reliability of data centers by avoiding RAM failures and mitigating their impact. For this purpose, an ML-driven framework is provided to estimate the probability of memory failure for each RAM module. The ML framework is based on structural information between correctable (CE) and uncorrectable errors (UE). In a common memory scenario, a corrupted bit within a module can be restored by redundancy using an error correction code (ECC), resulting in a CE. However, if there is more than one corrupted bit within a group of bits covered by the ECC, the information cannot be restored, resulting in a UE. Consequently, the related task requesting the memory content, and the corresponding service may crash. There is evidence that UEs have a CE history and structural relation between the CEs. However, for the case of UEs without a CE history or of a false decision of the ML framework, we extend the total framework by engineering measures to mitigate the impact of a UE by avoiding kernel panic and using backups. The engineering measures use a mapping between physical and logical memory addresses. [ABSTRACT FROM AUTHOR]

• FPGA performance characterization using parametrizable OpenCL benchmarks. • High resource utilization and performance on Intel and Xilinx FPGAs. • Ability to also measure the performance of devices and tools. Emerging high-level tools lead to a reduced development time for applications on FPGA accelerators while still producing high-quality results. This is one reason for the increased adoption of FPGAs in data center applications which emphasizes the need for a benchmark suite to enable the comparison of FPGA architecture, programming tools, runtimes, and libraries. Because of the lack of such a benchmark suite, we have developed an OpenCL-based open-source implementation of the HPCC benchmark suite for Xilinx and Intel FPGAs. In an in-depth evaluation, we show that the benchmarks allow to quantify the impact of HBM2 memory in comparison to FPGAs with DDR and to analyze differences in the arithmetic units on current FPGA architectures. Power measurements indicate, that not all benchmark implementations can utilize the full potential of the FPGAs in terms of power efficiency. We are continuing to optimize and port the benchmark for new generations of FPGAs and design tools and we encourage active participation to create a valuable tool for the community. [ABSTRACT FROM AUTHOR]

• Model: Proposed a new model to handle different SLAs of workloads and guarantee the worst-case delay of delay-tolerant workload. • Algorithm: Designed an O (1) time online control algorithm OSDG for each slot to achieve the long term optimization objective. • Analysis and Experiment: Proved our algorithm achieves an explicit [ O (1 / V) , O (V) ] cost-delay tradeoff and validated its performance. More and more cloud data centers are turning to leverage on-site renewable energy to reduce power cost for sustainable development. But how to effectively coordinate the intermittent renewable energy with workload remains to be a great challenge. This paper investigates the problem of workload scheduling for power cost minimization under the constraints of different Service Level Agreements (SLAs) of delay tolerant workload and delay sensitive workload for green data centers in a smart grid. Different from the existing studies, we take into consideration of the impact of zero price in the smart grid and the cost of on-site renewable energy. To handle the randomness of workload, electricity price and renewable energy availability, we first formulate the problem as a constrained stochastic problem. Then we propose an efficient online control algorithm named ODGWS (Online Delay-Guaranteed Workload Scheduling) which makes online scheduling decisions achieve a bounded guarantee from the worst scheduling delay for delay tolerant workload. Compared with the existing solutions, our ODGWS decomposes the problem into that of solving a simple optimization problem within each time slot in O (1) time without needing any future information. The rigorous theoretical analysis demonstrates that our algorithm achieves a [ O (1 V) , O (V) ] cost-delay tradeoff, where V is a balance parameter between the cost optimality and service quality. Extensive simulations based on real-world traces are done to evaluate the performance of our algorithm. The results show that ODGWS saves about 5% average power cost compared with the baseline algorithms. [ABSTRACT FROM AUTHOR]

Modern virtualized data centers often rely on virtual machine (VM) migrations to consolidate workload on a single machine for energy saving. But VM migrations have many drawbacks, including performance degradation, service disruption etc. Hence, many approaches have been proposed to minimize the overhead when migrations occur. In contrast, this work aims to proactively avoid migrations from happening in the first place. We have proposed a novel consolidation aware scheduling algorithm to minimize the number of migrations for batch processing systems by taking advantage of the prior knowledge of consolidation strategy and job information. We show the problem can be formulated as an integer linear programming (ILP) problem, and an effective heuristic solution can be found by a genetic algorithm. Both real and synthetic workload traces were used to evaluate our methods. Experimental results showed that, after comparing with two popular job scheduling algorithms, our approach has reduced the number of migrations by more than 25%. • Proactively avoid VM migration in virtualized data centers. • A co-design between consolidation and scheduling decisions. • Construct an ILP formulation to find an optimal solution. • Apply genetic algorithm to find a heuristic solution. • Using real world workload traces from parallel computing cluster for experimentation. [ABSTRACT FROM AUTHOR]

Coflow is a network abstraction used to represent communication patterns in data centers. The coflow scheduling problem encountered in large data centers is a challenging NP -hard problem. Many previous studies on coflow scheduling mainly focus on the single-core model. However, with the growth of data centers, this single-core model is no longer sufficient. This paper addresses the coflow scheduling problem within heterogeneous parallel networks, which feature an architecture consisting of multiple network cores running in parallel. In this paper, two polynomial-time approximation algorithms are developed for the flow-level scheduling problem and the coflow-level scheduling problem in heterogeneous parallel networks, respectively. For the flow-level scheduling problem, the proposed algorithm achieves an approximation ratio of O (log ⁡ m / log ⁡ log ⁡ m) when all coflows are released at arbitrary times, where m represents the number of network cores. On the other hand, in the coflow-level scheduling problem, the proposed algorithm achieves an approximation ratio of O (m (log ⁡ m / log ⁡ log ⁡ m) 2) when all coflows are released at arbitrary times. Moreover, we propose a heuristic algorithm for the flow-level scheduling problem. Simulation results using synthetic traffic traces validate the performance of our algorithms and show improvements over the previous algorithm. • Algorithm achieves O (log ⁡ m / log ⁡ log ⁡ m) approximation ratio for flow-level scheduling, where m is network cores. • For coflow-level scheduling, algorithm achieves O (m (log ⁡ m / log ⁡ log ⁡ m) 2) approximation ratio. • Moreover, this paper proposes a heuristic algorithm for the flow-level scheduling problem. [ABSTRACT FROM AUTHOR]

As an essential infrastructure to support cloud computing services, data center networks (DCNs) are primarily used for transmitting and storing data. BCube is a large-scale DCN architecture that can effectively handle the massive data generated by network end devices due to the explosive growth of the Internet. However, as server failures in DCNs become more frequent, ensuring reliable data communication in BCube is critical. In this paper, we first establish a conditional local safe model in BCube, which divides the fault-free nodes in sub-BCube by adding some restrictions, and effectively avoids communication obstacles that may arise from faulty nodes. Then, based on the proposed model, a fault-tolerant unicast path algorithm is designed, which can construct a reliable data transmission path in the local safe sub-BCube of BCube and can tolerate more faulty nodes than existing algorithms. Finally, we conduct simulation experiments to verify that our algorithm can construct the shortest path with a high probability and achieve almost complete success in data transmission when the number of faulty nodes does not exceed half of the total nodes in BCube. • We propose a conditional local safe model in the data center network BCube. • We prove the existence of fault-free paths with local safe information in BCube. • We construct a fault-tolerant unicast algorithm in BCube based on the above model. [ABSTRACT FROM AUTHOR]

To promote the intelligent and accurate management of river basins, especially large basins which involve many catchments, it is highly required to develop a useful platform to effectively coordinate arithmetic resources and data, and simultaneously help to make decisions based on the real-time calculation. In this study, a multi-centre cloud platform architecture called 3L4C was constructed, which includes a Cloud-edge-terminal Layer (3L), data centre, model centre, control centre, and customer-service centre (4C). Data fusion technology and an air-land-water coupled model were constructed. Based on HTML5, JavaScript, and Java, an integrated water environment management platform was created and applied to the Three Gorges Reservoir Basin, China. The platform was tested and successfully used for automatic water quality prediction, water environment pollution analysis and control, early warning of abnormal water quality, and emergency water pollution incident evaluation. This platform quickly and accurately forecasts and perfectly displays past, present and future state of the water environment, and offers beneficial support for management decisions in various water environment departments. • A cloud platform architecture for environmental management decision-making. • Three Gorges Reservoir Basin Water Environment Management Platform. • Integrate water environment data from multiple sources. [ABSTRACT FROM AUTHOR]

• An optical band-pass filter-assisted offset DSB direct-detection receiver is proposed for low-cost data center interconnect applications. • The proposed scheme uses an OBPF to filter out the optical carrier as the local oscillator for the receiver. As such, signal-signal beat interference can be eliminated. • Compared with the previous offset DSB CADD scheme, the optimal CSPR of the proposed scheme can be reduced to less than 0 dB, and the OSNR sensitivity can be significantly improved. • The effectiveness of the proposed offset DSB self-coherent detection is fully verified by simulation. The numerical results show the potential of proposed scheme. The offset double-sideband (DSB) scheme has attracted attention because it can achieve field recovery at low carrier-to-signal power ratio (CSPR) and improve the spectral efficiency (SE) of self-coherent detection. In this paper, an offset DSB filter-assisted direct detection (FADD) scheme is proposed and studied for the first time, which uses an optical bandpass filter (OBPF) to filter out the optical carrier as the local oscillator for the receiver. As such, signal-signal beat interference (SSBI) can be eliminated. Since the offset DSB signal has a large frequency gap, the requirement for the bandwidth of the OBPF is not high. The performance of the proposed offset DSB FADD scheme is evaluated for 16-QAM signals. It is comprehensively compared with offset DSB carrier-assisted differential detection (CADD) scheme in terms of OSNR sensitivity, optimal CSPR, and receiver power sensitivity. In addition, the effect of frequency gap compression is significant as SSBI is completely eliminated from the system, thereby utilizing as many low-frequency resources as possible and improving SE. Finally, the proposed offset DSB FADD scheme is resilient to the wavelength offset up to several GHz between the transmitter laser and the center wavelength of the OBPF. [ABSTRACT FROM AUTHOR]

Meat consumption and convenience food are both located at the heart of contemporary, industrialized, unhealthy and unsustainable food systems. In this article, we study the intersections between convenience food and 'meatification' of diets, focusing on the 'pølse'—an umbrella term including both hotdogs and a range of sausages—as the epitome of convenience food in Norway. We explore how the pølse is embedded in Norwegian food practices, and why it is considered convenient in different contexts. In doing so, we seek to explain how pølse eating is co-shaped by socio-material scripting processes that further entrench meat in food practices and complicate meat-reduction efforts. The analysis is based on 52 in-depth household interviews and autophotography in four geographical contexts in Norway, in addition to 22 park interviews and survey data centering on household food and meat practices. We use a theoretical apparatus combining social practice theory, foodscapes and socio-material scripts to analyse the conveniencization of pølse. The articles demonstrates how meat consumption and convenience food become entwined in specific social practices, and how conveniencization intersects with practices of care, notions of class, social expectations and normativity. Moreover, we show that despite the range of plant-based 'pølse' substitutes on offer, meat-eaters remain skeptical to its taste, and substitutes rather seem to offer a way into established social occasions for non-meat eaters than a way out of meat eating. [ABSTRACT FROM AUTHOR]

Data center is a very important infrastructure to support the development of information technology, and its development and increment are very remarkable. However, with the rapid and large-scale development of data centers, the problem of energy consumption turns to be also very prominent. Under the background of global carbon peak and carbon neutrality, developing green and low-carbon data centers has become an inevitable trend. This paper reviews and analyzes the policies and their roles in promoting China's green development of data centers in the past 10 years, summarizes the current situation of the implementation of green data center projects in China and gives the changes of PUE limits of data centers under the policy constraints. Application of green technologies is an important measure for energy-saving and low-carbon development of data centers, so encouraging innovation and application of green technologies in data center is also a priority task in relevant policies. This paper points out the green and low-carbon technology system of data centers, further summarizes energy-saving and carbon-reducing technologies in IT equipment, cooling system, power supply and distribution system, lighting, intelligent operation and maintenance, and provides an outlook on the future green development of data centers. • China's green data center policy in recent 10 years has played an important role in promoting the development of green data centers. • Summarizes the status of green data centers under policy guidance. • Provides energy saving and carbon reduction technology path for data centers. • Prospects for the future development of green data centers. [ABSTRACT FROM AUTHOR]

Since 1970, decametric observations of the Sun and Jupiter have been acquired on a daily basis from the radio observatory of Nançay, France. The resulting database now forms the worldwide largest set of low frequency observations of the Solar Corona between 10 and 100 MHz and of the Jovian magnetosphere between 10 and 40 MHz. The Nançay Decameter Interferometer (NDI), a two-antenna interferometer, acquired pioneer observations from 1970 to 1978, using analog radio receivers tested on the Arecibo giant radiotelescope. The large Nançay Decameter array (NDA), a phased array of 144 helical antennas, was then constructed in the late 1970s as a ground-based support to the Voyager mission and started routine observations in January 1978 in replacement of the NDI, using similar spectrometers. The NDA is still in operation today and its observations, recorded in a digital format since 1990, are public and daily made accessible to the heliospheric and magnetospheric communities at https://www.obs-nancay.fr/reseau-decametrique/. Before 1990, though, the Nançay decametric observations were recorded on analog devices, such as 35-mm films, magnetic tapes and other paper supports, physically archived at the Observatory of Paris, on its site of the Observatory of Meudon, where they could only be accessed on demand. The NDA team recently undertook a multi-year effort to inventory all the Nançay decametric archives, to refurbish and digitize them in order to preserve and promote this historical data collection, most notably by extending the current public database over the 1970–1990 time interval. We digitized the full collection of 35-mm films, corresponding to a total film length of ∼ 45. 5 km, with a specific high resolution scanner. Each roll was scanned by a series of pixel-to-pixel contiguous images recorded in TIF format with adequate metadata and naming convention. The final ∼ 40 Tb data volume was archived at Paris Astronomical Data Centre and the physical films were refurbished to be archived in a perennial way. We also digitized the associated observations sheets. Finally, we took advantage of this project to also digitize a sample of 35-mm films extracted from four other historical solar data collections hosted at the Observatory of Paris and dealing with two distinct Nançay radiotelescopes and two Lyot heliographs located at the Observatories of Meudon and Haute-Provence. The digitized, documented, Nançay decametric archives have been publicly released to the community in 2020 at http://archives-decametriques.obspm.fr. After having described the instruments, the data collections and the digitization pipeline, this articles ends with a few scientific case studies, focussing on the search for Jupiter-satellite decametric emissions. For instance, the digitized NDA archives suggest the presence of Gan-A and B emissions, while these were first reported in 2018. This work is a concrete example of how historical astronomical measurements can be preserved and archived along the IAU recommendation, to the benefit of the astronomical community. [ABSTRACT FROM AUTHOR]

• A network architecture supporting ONUs' discretionary peer-to-peer communication is created, and a skillful wavelength allocation strategy is proposed. • The proposed architecture improves the flexibility and efficiency of direct communication, and reduces the cost of manufacturing and maintenance of network. • The proposed strategy improves the utilization of wavelength resources, and weakens the Rayleigh backscattering in signal transmission. This paper proposes a low-cost access network architecture supporting flexible point-to-point communication between any optical network units (ONUs). By sharing transceivers between upstream/downstream communication and ONU communication, the number of transceivers required in ONU can be decreased. Coupled with using passive components instead of active transmitters and adopting modular design of nodes for integration, the cost of manufacturing and maintenance of network equipment can be significantly reduced. In addition, by skillfully allocating wavelengths, not only wavelength multiplexing can be realized and thereby improving the utilization of network wavelength resources, but also the Rayleigh backscattering (RB) in signal transmission can be effectively weakened. Finally, by the simulation experiment, the effectiveness of the proposed architecture is demonstrated. [ABSTRACT FROM AUTHOR]

Divergence-free vector fields play an important role in many types of problems, including the incompressible Navier-Stokes equations and the equations for magnetohydrodynamics. In the discrete setting, these fields are often obtained by projection, resulting in a discrete approximation of the continuous field that is discretely divergence-free. For many applications, such as tracing particles, this discrete field must then be extended to the entire region using interpolation. This interpolated field is continuous and differentiable (almost everywhere), but in general it will not be divergence-free. In this paper, we construct approximation schemes with the property that discretely divergence-free data interpolates to an analytically divergence-free vector field. Our focus is on data stored in a MAC grid layout that is divergence free under the second order central difference stencil, a case that is common in projection methods for the Navier-Stokes equations. While existing schemes with this property are known, they tend to be global (the interpolated value at a point depends on data stored on the grid far from that point) or discontinuous. We construct C 0 and C 1 continuous approximation schemes for 2D and 3D that are local and satisfy the divergence-free property. We also construct interpolating versions of the schemes that reproduce the MAC data at face centers. All eight schemes are explicit piecewise polynomials over small stencils. • We develop divergence-free interpolation schemes on a uniform grid. • We present a general construction for divergence-free interpolation schemes. • The schemes are local, piecewise polynomial, and continuous. • The simpler schemes are as efficient as non-divergence-free schemes. [ABSTRACT FROM AUTHOR]

Objectives: To study the incidence and trends of non-epithelial ovarian cancer (OC) in the United States as compared to Asia. Methods: Data were obtained from the United States Cancer Statistics (USCS) Public Use Databases and the Taiwan Cancer Registry of Taiwan Health and Welfare Data Center (HWDC) from 2001 to 2017. SEER*Stat 8.3.9, Joinpoint regression program 4.8.0.1, and Excel were used to calculate the incidence and trends. Results: From 2001 to 2017, the incidence of germ cell and sex cord-stromal tumors in the US was 0.42 and 0.26 per 100,000, while In Asia, the incidence was 0.74 and 0.33 per 100,000, respectively. While the average annual percent change (AAPC) has been increasing for non-epithelial OC in both the US and Asia, the increase is markedly greater in Asia. In Asia, the incidence is increasing at three times the rate for sex cord-stromal tumors (1.7 vs 5.7 AAPC) and at nearly 5.5 times the rate for germ cell tumors (0.3 vs 1.6 AAPC), compared to the US. For germ cell tumors, the peak age incidence is younger in the US versus Asia (20-24 years vs 25-29 years, respectively). While for sex cord-stromal tumors, the peak age incidence is older in the US versus Asia (65-69 years vs 50-54 years, respectively). Conclusions: The overall incidence of germ cell and sex cord-stromal OCs is increasing in both the US and Asia. However, it is approximately 50 percent higher in Asia as compared to the US. Further investigation should investigate the role that genetic and environmental factors play in explaining these trends. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the feasibility of international clinical trials in rare ovarian cancers by first studying the incidence and trends of the rare histologic subtypes in the United States (US) and Asia. Methods: Data were obtained from the United States Cancer Statistics (USCS) Public Use Databases and Taiwan Cancer Registry of Taiwan Health and Welfare Data Center (HWDC) for a diagnosis of ovarian clear cell carcinoma, endometrioid carcinoma, mucinous carcinoma, or carcinosarcoma between 2001 to 2017. SEER*Stat 8.3.9, Joinpoint regression program 4.8.0.1, and Excel were used to calculate the incidence and trends. Results: In 2017, the incidence of ovarian clear cell carcinoma, endometrioid carcinoma, mucinous carcinoma, or carcinosarcoma was 0.81, 0.51, 0.47, or 0.25 per 100,000 in the US, and 1.58, 1.17, 1.08, or 0.15 per 100,000 in Asia, respectively. Over the last 18 years, the average annual percent change (AAPC) in clear cell carcinoma did not change in the US and increased by 6.2% in Asia. The AAPC for ovarian endometrioid carcinoma decreased by 2.7% in the US and increased by 2.9% in Asia. Although the rate of carcinosarcoma is increasing in both the US and Asia, it is 16-fold higher in Asia compared with the US, with an AAPC for ovarian carcinosarcoma of 6.4% in Asia versus 0.4% in the US. The rate of ovarian mucinous carcinoma decreased in both the US and Asia with an AAPC of 2.0% versus 0.9%, respectively. Conclusions: The AAPC of ovarian clear cell carcinoma, endometrioid carcinoma, and carcinosarcoma is increasing at higher rates in Asia than in the US, providing justification to utilize global recruitment strategies for clinical trial investigational treatments and interventions in rare ovarian cancers. [ABSTRACT FROM AUTHOR]

The study of the impact of high temperature and drought on the yield of major staple crops can provide important scientific support for the decision-making of agricultural sustainable development. Based on the temperature and precipitation data of the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA 5 for northern China, this paper calculates three indexes, the standard precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and the extreme degree-day (EDD), from 1979 to 2017. Monthly SPI and monthly SPEI were calculated at 1 – to – 12 month lags, and EDD was calculated per crop growth season. The yield of winter wheat, spring wheat and summer maize in each province of the study area from 1979 to 2017 was de-trended, and the relative fluctuation of the yield of the three crops was calculated. The change trends of SPI, SPEI and EDD were analysed using the Mann–Kendall test and Sen's slope. The single and interactive effects of high temperature and drought on crop yield were studied using multidimensional Copula function. The results show that: 1) Both high temperature and drought stress in northern China show an increasing trend. The drought trend in the study area detected based on SPEI was greater than the drought trend detected by SPI. The difference between SPEI and SPI in the winter wheat growing season was smaller than that in the spring wheat and maize growing seasons. 2) With the increase in EDD and the decrease of SPI/SPEI values, the probability of negative yield fluctuation gradually increased, and the probability of positive yield fluctuation gradually decreased. Under the same drought and high temperature conditions, the probability of yield fluctuation varies among different crops and different provinces. Drought has a greater impact on crop yield than high temperature. Both the single and interactive effects of drought and high temperature on yield are nonlinear. 3) Irrigation can effectively alleviate the impact of drought and high temperature on yield. In heavily irrigated provinces, the effects of both high temperature and drought on crop yield are not obvious. • The impact of high temperature and drought stress on yield is analysed by a probability model. • The effects of high temperature and drought on yield are non-linear. • Irrigation can offset some of the effects of high temperature and drought on yield. • SPEI may overestimate the impact of drought on yield while SPI may underestimate. [ABSTRACT FROM AUTHOR]

• The minimum MCMSA problem is proposed and investigated in EODCNs. • Various proportions of secure services are considered in the proposed problem. • An ILP and a heuristic algorithm are developed for the DST-MCMSA scheme. • We compare the performance of the proposed methods with other existing methods. Multicast provisioning is important for supporting applications such as data backup and cloud computing in elastic optical data center networks (EODCNs). As the traffic demand from these applications is quickly increasing, it is important to reduce the resource usage and implement cost-effective multicasting. By mapping multiple traffic flows into a single light-tree, the light-tree-based multicast service aggregation can reduce the number of required transceivers while accommodating many multicast requests. However, it may increase the spectrum usage, and it is not always feasible because some subscription-based or secure multicast services in the network should be sent only to authorized users. In this paper, we focus on the problem of routing, modulation level selection, spectrum, and transceiver assignment for multicast services with different security levels. We study potential performance gains resulting from applying a distributed sub-tree scheme to multi-class multicast service aggregation, realizing reduced overall cost. By formulating the mathematical models using an integer linear program (ILP), we obtain results for a small-scale network. We also propose a heuristic algorithm for large-scale networks, and results show that our proposed algorithm can be beneficial in terms of both spectrum and transceiver utilization in EODCNs. [ABSTRACT FROM AUTHOR]

The heatwave is a disastrous hazard having significant impacts on health and society. This study analyses the heatwave hazards and risk for India's current and future scenarios using socioeconomic vulnerability and temperature datasets during the summer (April–June) season. The Census of India (CoI) 2011 datasets were considered to assess current vulnerability and projected from the SocioEconomic Data And Application Center (SEDAC) population at Shared Socioeconomic Pathway (SSP) 4 for future vulnerability. Whereas IMD temperature data used for hazard assessment for the present scenario (1958–2005) while projected temperature data from regional earth system model REMO-OASIS-MPIOM (ROM) were used for the future (2006–2099) scenario. The study exhibited the most hazardous, vulnerable, and risk-prone regions identified as the south-eastern coast and Indo-Gangetic plains and some populous districts with metropolitan regions (Mumbai, Delhi, and Kolkata) under the current scenario. The coupled model ROM has efficiently captured the critical districts with higher and lower risk, showing its future projection capability. The study highlighted that the heatwave hazard-risk would significantly worsen in future scenarios in all districts under enhanced global warming and largely affecting the districts in the eastern and middle Indo-Gangetic plains and Malabar region. The present study will provide sufficient insights into designing mitigation strategies and future adaptive planning for the heatwave risk, which is one of the targets under Sustainable Development Goal 13 (Goal 13: Climate Action). • The present and future vulnerability and risk are assessed over India using a regional earth system model ROM. • The ROM efficiently captured the high and low heatwave risk districts of India. • Southern coastal regions and Indo-Gangetic plains are at higher risk during the baseline period 1980–2005. • The heatwave risk will exacerbate in all districts of India with the highest increment over Indo-Gangetic plains. [ABSTRACT FROM AUTHOR]