Computation Energy Efficiency Maximization for Intelligent Reflective Surface-Aided Wireless Powered Mobile Edge Computing

A wide variety of Mobile Devices (MDs) are adopted in Internet of Things (IoT) environments, resulting in a dramatic increase in the volume of task data and greenhouse gas emissions. However, due to the limited battery power and computing resources of MD, it is critical to process more data with less energy. This paper studies the Wireless Power Transfer-based Mobile Edge Computing (WPT-MEC) network system assisted by Intelligent Reflective Surface (IRS) to enhance communication performance while improving the battery life of MD. In order to maximize the Computation Energy Efficiency (CEE) of the system and reduce the carbon footprint of the MEC server, we jointly optimize the CPU frequencies of MDs and MEC server, the transmit power of Power Beacon (PB), the processing time of MEC server, the offloading time and the energy harvesting time of MDs, the local processing time and the offloading power of MD and the phase shift coefficient matrix of Intelligent Reflecting Surface (IRS). Moreover, we transform this joint optimization problem into a fractional programming problem. We then propose the Dinkelbach Iterative Algorithm with Gradient Updates (DIA-GU) to solve this problem effectively. With the help of convex optimization theory, we can obtain closed-form solutions, revealing the correlation between different variables. Compared to other algorithms, the DIA-GU algorithm not only exhibits superior performance in enhancing the system's CEE but also demonstrates significant reductions in carbon emissions

ChainsFormer: A Chain Latency-aware Resource Provisioning Approach for Microservices Cluster

The trend towards transitioning from monolithic applications to microservices has been widely embraced in modern distributed systems and applications. This shift has resulted in the creation of lightweight, fine-grained, and self-contained microservices. Multiple microservices can be linked together via calls and inter-dependencies to form complex functions. One of the challenges in managing microservices is provisioning the optimal amount of resources for microservices in the chain to ensure application performance while improving resource usage efficiency. This paper presents ChainsFormer, a framework that analyzes microservice inter-dependencies to identify critical chains and nodes, and provision resources based on reinforcement learning. To analyze chains, ChainsFormer utilizes light-weight machine learning techniques to address the dynamic nature of microservice chains and workloads. For resource provisioning, a reinforcement learning approach is used that combines vertical and horizontal scaling to determine the amount of allocated resources and the number of replicates. We evaluate the effectiveness of ChainsFormer using realistic applications and traces on a real testbed based on Kubernetes. Our experimental results demonstrate that ChainsFormer can reduce response time by up to 26% and improve processed requests per second by 8% compared with state-of-the-art techniques

CoScal: Multi-faceted Scaling of Microservices with Reinforcement Learning

The emerging trend towards moving from monolithic applications to microservices has raised new performance challenges in cloud computing environments. Compared with traditional monolithic applications, the microservices are lightweight, fine-grained, and must be executed in a shorter time. Efficient scaling approaches are required to ensure microservices’ system performance under diverse workloads with strict Quality of Service (QoS) requirements and optimize resource provisioning. To solve this problem, we investigate the trade-offs between the dominant scaling techniques, including horizontal scaling, vertical scaling, and brownout in terms of execution cost and response time. We first present a prediction algorithm based on gradient recurrent units to accurately predict workloads assisting in scaling to achieve efficient scaling. Further, we propose a multi-faceted scaling approach using reinforcement learning called CoScal to learn the scaling techniques efficiently. The proposed CoScal approach takes full advantage of data-driven decisions and improves the system performance in terms of high communication cost and delay. We validate our proposed solution by implementing a containerized microservice prototype system and evaluated with two microservice applications. The extensive experiments demonstrate that CoScal reduces response time by 19%-29% and decreases the connection time of services by 16% when compared with the state-of-the-art scaling techniques for Sock Shop application. CoScal can also improve the number of successful transactions with 6%-10% for Stan’s Robot Shop application

Static and Dynamic Properties of Semi-Crystalline Polyethylene.

Properties of extruded polymers are strongly affected by molecular structure. For two different semi-crystalline polymers, low-density polyethylene (LDPE) and ultra-high molecular weight polyethylene (UHMWPE), this investigation measures the elastic modulus, plastic flow stress and strain-rate dependence of yield stress. Also, it examines the effect of molecular structure on post-necking tensile fracture. The static and dynamic material tests reveal that extruded UHMWPE has a somewhat larger yield stress and much larger strain to failure than LDPE. For both types of polyethylene, the strain at tensile failure decreases with increasing strain-rate. For strain-rates 0.001⁻3400 s-1, the yield stress variation is accurately represented by the Cowper⁻Symonds equation. These results indicate that, at high strain rates, UHMWPE is more energy absorbent than LDPE as a result of its long chain molecular structure with few branches.This work was partially sponsored by Foundation of State Key Laboratory of Explosion Science and Technology of China under Grant No.KFJJ13-1Z, No. YBKT15-02 and Natural Science Foundation of China under Grant No.11102023. The authors would like to thank Chunmei Liu of the First Research Institute of the China Ministry of Public Security for assistance with the static tensile tests.This is the final version of the article. It first appeared from the Multidisciplinary Digital Publishing Institute via http://dx.doi.org/10.3390/polym804007

Perforation resistance of aluminum/polyethylene sandwich structure

© 2016 Elsevier Ltd. Ballistic tests were performed on two types of polyethylene core sandwich structures (AA6082/LDPE/AA6082 and AA6082/UHMWPE/AA6082) to investigate their perforation resistance. Bulging and dishing deformation of layered plates were compared under low-velocity impact by hemispherical-nosed projectiles. Different impact failure mechanisms leading to perforation were revealed for laminates composed of a pair of aluminum alloy face sheets separated by a polyethylene interlayer. Using the finite element code Abaqus/Explicit, the perforation behavior and distribution of energy dissipation of each layer during penetration were simulated and analysed. The deformation resistance and anti-penetration properties of polyethylene core sandwich structures were compared with those of monolithic AA6082-T6 plates that had the same areal density. Although the polyethylene interlayer enlarged the plastic deformation zone of the back face, the polyethylene core sandwich structure was a little less effective than the monolithic Al alloy target at resisting hemispherical-nosed projectile impact.The authors gratefully acknowledge the Foundation of State Key Laboratory of Explosion Science and Technology of China under Grant No. KFJJ13-1Z, and Natural Science Foundation of China under Grant No. 11102023, 11172071

Seagull optimization algorithm based multi-objective VM placement in edge-cloud data centers

Edge-Cloud Datacenters (ECDCs) have been massively exploited by the owners of technology and industrial centers to satisfy the user demand. At the same time, the amount of energy used by these data centers is considerable. To address this challenge, Virtual machine placement of the ECDCs plays an important role; therefore, assigning Virtual Machines (VM) properly to physical machines (PM) can significantly decrease the amount of energy consumption. The applied assigning technique simultaneously must consider additional objectives involving traffic and power usage of the network elements, which makes it a challenging problem. This paper proposes a multi-objective VM placement approach in edge-cloud data centers, which uses Seagull optimization to optimize power and network traffic together. In this strategy, the network traffic among PMs is reduced by concentrating the communications of VMs on the same PMs to reduce the amount of transferred data through the network and reduce the PMs’ power consumption by consolidating VMs to fewer PMs, which consumes less energy. We evaluate with simulations in CloudSim and test two different network topologies, VL2 (Virtual Layer 2) and three-tier, to validate that the proposed approach can effectively reduce traffic and power consumption in ECDCs. The experimental results show that our proposed method can decrease energy consumption by 5.5% while simultaneously reducing network traffic by 70% and the power consumption of the network components by 80%

Collaborative Layer-wise Discriminative Learning in Deep Neural Networks

Intermediate features at different layers of a deep neural network are known to be discriminative for visual patterns of different complexities. However, most existing works ignore such cross-layer heterogeneities when classifying samples of different complexities. For example, if a training sample has already been correctly classified at a specific layer with high confidence, we argue that it is unnecessary to enforce rest layers to classify this sample correctly and a better strategy is to encourage those layers to focus on other samples. In this paper, we propose a layer-wise discriminative learning method to enhance the discriminative capability of a deep network by allowing its layers to work collaboratively for classification. Towards this target, we introduce multiple classifiers on top of multiple layers. Each classifier not only tries to correctly classify the features from its input layer, but also coordinates with other classifiers to jointly maximize the final classification performance. Guided by the other companion classifiers, each classifier learns to concentrate on certain training examples and boosts the overall performance. Allowing for end-to-end training, our method can be conveniently embedded into state-of-the-art deep networks. Experiments with multiple popular deep networks, including Network in Network, GoogLeNet and VGGNet, on scale-various object classification benchmarks, including CIFAR100, MNIST and ImageNet, and scene classification benchmarks, including MIT67, SUN397 and Places205, demonstrate the effectiveness of our method. In addition, we also analyze the relationship between the proposed method and classical conditional random fields models.Comment: To appear in ECCV 2016. Maybe subject to minor changes before camera-ready versio

Fortaleza: The emergence of a network hub

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies

Chromosome location and characterization of genes for grain protein content in Triticum dicoccoides

Dissertação de Mestrado em História, apresentada à Faculdade de Letras da Universidade de Coimbra.No início do século XX, com a instalação da República portuguesa, surgiu a oportunidade de se construir um lar judaico num território português de além-mar. Angola foi uma forte possibilidade. Vários fatores contribuíram para que tal oportunidade fosse possível. Os constantes massacres feitos ao povo judaico em variadíssimos países europeus, nomeadamente nos países de leste, e as dificuldades encontradas por Theodor Herzl, fundador do movimento sionista, para a edificação do desejado Estado judaico na Palestina, levou a que alguns líderes judaicos começassem a estudar outras hipóteses para o estabelecimento da comunidade judaica para além da Palestina. Era urgente encontrar uma solução para que o sofrimento dos judeus terminasse. Por outro lado, é preciso não esquecer que Portugal se debatia com uma grande questão, a necessidade de ocupar efetivamente as suas colónias, a fim de contrariar as pretensões alemãs e inglesas. A hipótese de criar uma colónia judaica em Angola, como forma de enfrentar as aspirações alheias, e a necessidade de valorizar aquele território, faziam da colonização judaica uma boa solução para Portugal. Tendo em conta as dificuldades de um povoamento de Angola com elementos naturais da metrópole, devido à fraca capacidade financeira do Estado português e a razões sociais e mentais, a colonização judaica aparecia como uma alternativa viável. No entanto, este projeto não se concretizaria. Serão identificados os fatores internos e externos que levaram a que este projeto não fosse posto em prática e tratar-se-á da posterior criação do Estado de Israel na Palestina, depois da Segunda Guerra Mundial.In the early XXth century, with the establishment of Portuguese Republic, arises an opportunity of setting a jewish home in a portuguese land overseas. Angola was a strong possibility. Several factors contributed for the possibility of such opportunity. The constant massacres the Jewish people suffered in numerous different European countries, mainly in Eastern countries, and the difficulties encountered by Theodor Herlz, founder of the Zionist movement, in order to build the desired Jewish State in Palestine, drove some of jewish leaders to study other options to establish the home of Jewish people beyond Palestine. It was urgent to find a solution for the suffering of the Jews to end. On the other hand, it’s important not to forget that Portugal was struggling with a big question, the necessity of effectively settling its colonies, in order to fight back the German and English pretensions. The possibility of creating a jewish colony in Angola as a way to prevent third-parties aspirations and the need of increase the value of Angola land turned the jewish settlement into a good solution to Portugal. As the colonization of Angola with natives from the metropolis appeared hard to reach, due to a poor financial capacity of the portuguese State as well as social and mental factors, the Jewish colonization appeared as a viable alternative. However, this project wasn’t meant to achieve the goal. We’ll describe the internal and external factors that caused the failure of this project and we’ll talk about the creation, later on, of the Jewish State in Palestine, after Second World Wa