Cloud Computing Platform Research Articles

Long Term Aquatic Vegetation Dynamics in Longgan Lake Using Landsat Time Series and Their Responses to Water Level Fluctuation

Aquatic vegetation in shallow freshwater lakes are severely degraded worldwide, even though they are essential for inland ecosystem services. Detailed information about the long term variability of aquatic plants can help investigate the potential driving mechanisms and help mitigate the degradation. In this paper, based on Google Earth Engine cloud-computing platform, we made use of a 33-year (1987–2019) retrospective archive of moderate resolution Landsat TM, ETM + and OLI satellite images to estimate the extent changes in aquatic vegetation in Longgan Lake from Middle Yangtze River Basin in China using the modified enhanced vegetation index, including emerged, floating-leaved and floating macrophytes. The analysis of the long term dynamics of aquatic vegetation showed that aquatic vegetation were mainly distributed in the western part of the lake, where lake bottom elevation ranged from 11 to 12 m, with average water depth of less than 1 m in spring. The vegetation area variation for the 33-year period were divided into six stages. In years with heavy precipitation, the vegetation area decreased sharply. In the following years, the area normally restored. Aquatic vegetation area had a significant negative correlation with the spring water level and summer water level. The results showed that aquatic vegetation was negatively affected when water depth exceeded 2.5 m in May and 5 m in summer. It is recommended that water depth remain close to 1 m in spring and close to 3 m in summer for aquatic vegetation growth. Our study provide quantitative evidence that water-level fluctuations drive vegetation changes in Longgan Lake, and present a basis for sustainable lake restoration and management.

Security Protection Scheme of Brain-Like System Based on Cloud Computing Platform

The functions of human being’s brains are very difficult to simulation by computers, therefore many scientists are focusing on research of brain-like system (or robotic brain) based on cloud computing platform, which can simulate the human being’s hearing, vision, thinking and action. However, due to the security incidents of the internet occur frequently, the robotic brain based on the cloud computing platform may be seriously harmed by the security incidents. Therefore, this paper designs and implements of the overall security solution of the robotic brain based on cloud platform. The cloud platform intranet is divided into the intranet trusted zone and the DMZ zone, and strict filtering strategies are set in the zone, and firewalls and firewalls are set up in the DMZ zone and the external network zone. Monitor incoming network traffic at the network interface, and deal with abnormal traffic. At the same time, it audits security events in the intranet, conducts the statistical analysis on content security, server security, and application control, and blocks suspicious attacking IP. A series of safety measures are adopted to ensure the safety of the internal network of the robotic brain based on cloud platform.

Communication Security Design of Brain-Like System Based on Cloud Computing Platform

This paper mainly studies the communication security problems of brain-like system (or robotic brain) based on cloud computing platform, and designs a more convenient and safer communication mechanism for the robotic brain, which includes password algorithm design, digital signature design before login, data exchange design during login process, and security protection design against network attacks. Only the legitimate users can communicate with the robotic brain to ensure that the control authority of the robotic brain is not illegally stolen. In the authentication process, the key agreement must be carried out first, the user name and password of the legitimate user must be encrypted and sent, and the encryption algorithm must be guaranteed to be unable to be deciphered under the existing technical conditions. For the important communication data, the encrypted transmission is required, the encryption key is determined through negotiation, the encryption algorithm must be safe and reliable, and the encryption and decryption speed must be guaranteed. This process requires ensuring that communication data is not stolen illegally, and the data can be verified to avoid tampering and illegal data insertion. The users may control the robotic brain to work, and ensure its safety during the communication process.

DDoS Detection and Protection Based on Cloud Computing Platform

Currently, the distributed denial service attacks (DDoS) are increased rampantly on the internet, the threshold of such attacks is relatively low for the malicious attackers. Therefore, the DDoS attacks are serious threats to the security availability of the cloud computing. Aiming at the threats, this paper studies the malicious traffic identification and the detection scheme of the denial service attack under the soft-ware-defined Network (SDN), which uses the SDN forwarder to distinguish the DDoS attack traffic and adopt the corresponding filtering means to achieve protection for the distributed denial service attack. This paper firstly implements cloud platform resource calls, then an attack detection technology based on information entropy is proposed and implemented to carry out the DDoS attack detection, because the size of the entropy value can show the discrete or aggregated characteristics of the current data set, which can be used to detect abnormal data traffic. At last, the experiments are also carried out to verify and analyze the effectiveness of the DDoS attack detection and the protection methods.

Evaluation of the Spatiotemporal Variations in the Eco-environmental Quality in China Based on the Remote Sensing Ecological Index

The eco-environment is dynamic and shows a continuous process of long-term change. It is helpful for policymakers to know the status of the regional eco-environment through accurate evaluations of the history and current situation of the regional eco-environment. The remote sensing ecological index (RSEI) model of China was established in this study by using four indexes: wetness, greenness, dryness, and heat. Knowledge granulation of the RSEIs were carried out, and a method to determine the weights of the knowledge granulation entropy of the indexes based on their characteristics was proposed. This study used Moderate Resolution Image Spectroradiometer (MODIS) data from the Google Cloud Computing Platform to study and calculate the eco-environmental quality of China from 2000–2017. The overall eco-environmental quality in China tended to improve from 2000–2017, although there were large areas of ecological degradation from 2009–2014. The eco-environment of eastern China was better than that of western China. Most of the national ecological areas were third-level ecological areas, which had moderate environmental quality. Dryness was the most important factor affecting the quality of the eco-environment, followed by greenness, which reflected the increasing environmental damage caused by human activities in China in recent years.

Upward Expansion of Supra-Glacial Debris Cover in the Hunza Valley, Karakoram, During 1990 ∼ 2019

Supra-glacial debris cover is key to glacier ablation through increasing (thin debris layer) or decreasing (thick debris layer) melt rates, thereby regulating the mass balance of a glacier and its meltwater runoff. The thickening or lateral expansion of supra-glacial debris cover correlates with a reduction of glacier ablation and, consequently, runoff generation, which is also considered to be an influential factor on the rheology and dynamics of a glacierized system. Studies on supra-glacial debris cover have recently attracted wide attention especially for glaciers in the Himalayas and Karakoram, where the glaciers have heterogeneously responded to climate change. In this study, we used 32 images from the Landsat Thematic Mapper, Enhanced Thematic Mapper Plus and Operational Land Imager archive, going back to 1990, which are available on the Google Earth Engine cloud-computing platform, to map the supra-glacial debris cover in the Hunza Valley, Karakoram, Pakistan based on a band ratio segmentation method (normalized difference snow index [NDSI] < 0.4), Otsu thresholding and machine learning algorithms. Compared with manual digitization, the random forest (RF) model was found to have the greatest accuracy in identifying supra-glacial debris, with a Kappa coefficient of 0.94 ± 0.01 and an overall accuracy of 95.5 ± 0.9%. Overall, the supra-glacial debris cover in the study area showed an increasing trend, and the total area expanded by 8.1–21.3% for various glaciers from 1990 to 2019. The other two methods (Otsu thresholding and NDSI < 0.4) generally overestimated the supra-glacial debris covered area, by 36.3% and 18.8%, respectively, compared to that of the RF model. The supra-glacial debris cover has migrated upward on the glaciers, with intensive variation near the equilibrium-line altitude zone (4500–5500 m a.s.l.). The increase in ice or snow avalanche activity at high altitudes may be responsible for this upward expansion of supra-glacial debris cover in the Hunza Valley, which is attributed to the combined effect of temperature decrease and precipitation increase in the study area.

Identification of Short-Rotation Eucalyptus Plantation at Large Scale Using Multi-Satellite Imageries and Cloud Computing Platform

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.

Intelligent Health Vessel ABC-DE: An Electrocardiogram Cloud Computing Service

The severe challenges of the fast aging population and the prevalence of cardiovascular diseases highlight the needs for effective solutions supporting more accurate and affordable medical diagnosis and treatment. Recent advances in cloud computing have inspired numerous designs of cloud-based health care services. In this paper, we developed a cloud-computing platform monitored by physicians, which can receive 12-lead ECG records and send back diagnostic reports to users. Aiming to lessen the physicians’ workload, we implemented an analysis algorithm that can identify abnormal heart rate, irregular heartbeat, abnormal amplitude, atrial fibrillation and abnormal ECG in it. A large number of testing samples were used to evaluate performance. Our algorithm achieved a TPR95 (specificity under the condition of negative predictive value being equal to 95 percent) of 68.5 percent and 0.9317 AUC (area under the ROC curve) for classification of normal and abnormal ECG records and a sensitivity of 98.51 percent and specificity of 98.26 percent for atrial fibrillation classification, comparable to the state-of-the-art results for each subject. The proposed ECG cloud computing service has been applied in Hunan Jinshengda Aerial Hospital Network and it now can receive and analyze ECG records in real time.

An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform

The cloud computing paradigm has the ability to adapt to new technologies and provide consistent cloud services. These features have led to the widespread use of the paradigm, making it necessary for the underlying computer infrastructure to cope with the increased demand and the high number of end users. Platforms often use classical mathematical models for this purpose, helping assign computational resources to the services provided to the final user. Although this kind of model is valid and widespread, it can be refined through intelligent techniques. Therefore, this research presents a novel system consisting of a multi-agent system, which integrates a case-based reasoning system. The resulting system dynamically allocates resources within a cloud computing platform. This approach, which is distributed and scalable, can learn from previous experiences and produce better results in each resource allocation. A model of the system has been implemented and tested on a real cloud platform with successful results.

Data-driven agriculture for rural smallholdings

Spatial information science has a critical role to play in meeting the major challenges facing society in the coming decades, including feeding a population of 10 billion by 2050, addressing environmental degradation, and acting on climate change. Agriculture and agri-food value-chains, dependent on spatial information, are also central. Due to agriculture's dual role as not only a producer of food, fibre and fuel, but also as a major land, water and energy consumer, agriculture is at the centre of both the food-water-energy-environment nexus and resource security debates. The recent confluence of a number of advances in data analytics, cloud computing, remote sensing, computer vision, robotic and drone platforms, and IoT sensors and networks have lead to a significant reduction in the cost of acquiring and processing data for decision support in the agricultural sector. When combined with cost-effective automation through development of swarm farming technologies, the technology has the potential to decouple productivity and cost efficiency from economies of size, reducing the need to increase farm size to remain economically viable. We argue that these pressures and opportunities are driving agricultural value-chains towards high-resolution data-driven decision-making, where even decisions made by small rural landowners can be data-driven. We survey recent innovations in data, especially focusing on sensor, spatial and data mining technologies with a view to their agricultural application; discuss economic feasibility for small farmers; and identify some technical challenges that need to be solved to reap the benefits. Flexibly composable information resources, coupled with sophisticated data sharing technologies, and machine learning with transparently embedded spatial and aspatial methods are all required.

Adaptive Language Processing Based on Deep Learning in Cloud Computing Platform

With the continuous advancement of technology, the amount of information and knowledge disseminated on the Internet every day has been developing several times. At the same time, a large amount of bilingual data has also been produced in the real world. These data are undoubtedly a great asset for statistical machine translation research. Based on the dual-sentence quality corpus screening, two corpus screening strategies are proposed first, based on the double-sentence pair length ratio method and the word-based alignment information method. The innovation of these two methods is that no additional linguistic resources such as bilingual dictionary and syntactic analyzer are needed as auxiliary. No manual intervention is required, and the poor quality sentence pairs can be automatically selected and can be applied to any language pair. Secondly, a domain adaptive method based on massive corpus is proposed. The method based on massive corpus utilizes massive corpus mechanism to carry out multidomain automatic model migration. In this domain, each domain learns the intradomain model independently, and different domains share the same general model. Through the method of massive corpus, these models can be combined and adjusted to make the model learning more accurate. Finally, the adaptive method of massive corpus filtering and statistical machine translation based on cloud platform is verified. Experiments show that both methods have good effects and can effectively improve the translation quality of statistical machines.

Data Processing Platform of Cloud Computing and Its Performance Analysis Based on Photoelectric Hybrid Interconnection Architecture

Data processing platform is the core support platform of cloud computing. The use of electric interconnection architecture will increase the complexity of network topology, while optical interconnection architecture is ideal, so cloud computing platform based on optical interconnection has become a research hotspot. The distributed optical interconnection architecture of cloud computing data processing platform is focused on. Combining the hybrid mechanism of optical circuit switching and electric packet switching, it can meet a variety of traffic requirements. Meanwhile, it improves the switching mechanism, communication strategy, and router structure. Moreover, considering that the hybrid optoelectronic interconnection architecture can improve the network delay and throughput, but there is still a problem of network consumption. Combined with the network characteristics of the data processing platform (wireless mesh structure) of cloud computing, the network topology algorithm is studied, and the relationship between the topology and the maximum number of allocable channels is analyzed. Furthermore, the equation of topological reliability calculation is defined, and the optimization model of topological design is proposed, according to which the data processing platform of cloud computing is further optimized under the photoelectric hybrid interconnection architecture. During the experiment, before topology optimization, by changing the message length, it is found that adding optical circuit switching can help achieving large capacity and new type of transmission, and can effectively reduce the time delay. After topology optimization structure is adopted, the photoelectric hybrid-data processing platform of cloud computing without topology optimization is compared. It is found that under different reliability constraints, the throughput and end-to-end delay of the network are significantly improved, which proves that the data processing platform of cloud computing based on the photoelectric hybrid interconnection architecture is a feasible cloud computing platform.

Does Selection of Open Source Cloud Computing Platforms is a Confusing Task?

Over the last few years the majority of small and large companies moving to cloud computing to develop IT solutions for businesses. It is technology which provides distributed and dynamically shared computing resources using certain operating techniques. In the revolution of information technology, cloud computing is becoming a key paradigm. Cloud computing further classified as Public, Private and Hybrid Cloud. It provides three services which are categorized as Infrastructure-as-Service, Platform-as-Service and Software-as-Service. Open source cloud management platforms providing Infrastructure-as-a-service are now commonly used because of the fastest growth of cloud. Many open source softwares are available for deploying public or private cloud. This paper provides a brief review and comparison of five well-known open source cloud software i.e. OpenNebula, Eucalyptus, OpenStack, Nimbus and CloudStack providing IaaS on the basis of their similar features and technology used. After reviewing the importance and features, we have found OpenStack Cloud Platform is more reliable and useful for the enterprises and organization because of its feature and rapid improvements in its features. The distinction in this paper is believed to help people to choose the suitable open source software according to their need.

RETRACTED ARTICLE: Cloud management architecture to improve the resource allocation in cloud IAAS platform

Cloud computing (CC) is an attractive emerging technology due to offering services based on-demand by the process of virtualization. Since CC platform offers services based on-demand it has been widely used in the field of various emerging IT infrastructure. In cloud platform each application is run in individual virtual machine (VM) for execution of services within the host. Since cloud platform operates on on-demand service it need to cope with multiple application in single time hence it is necessary to adopt an effective approach for balancing memory utilization in cloud network. For effective utilization of available memory existing approaches uses probability distribution method for allocating resources in cloud platform but still there exists a lack of utilization of available memory in cloud platform. This paper aims to develop an effective approach for dynamic memory allocation in VM in cloud platform. For memory allocation among VM in cloud platform proposed approach uses cloud vertical elasticity manager (CVEM), memory reporter (MR), memory over subscription granter (MOG). The MOG uses a scheduler to allocate the memory in a dynamic way inside a host. Finally, we adopt host elasticity rule to balance the available memory to allocate dynamically the memory inside an available host in cloud.

PySpark-Based Optimization of Microwave Image Reconstruction Algorithm for Head Imaging Big Data on High-Performance Computing and Google Cloud Platform

For processing large-scale medical imaging data, adopting high-performance computing and cloud-based resources are getting attention rapidly. Due to its low–cost and non-invasive nature, microwave technology is being investigated for breast and brain imaging. Microwave imaging via space-time algorithm and its extended versions are commonly used, as it provides high-quality images. However, due to intensive computation and sequential execution, these algorithms are not capable of producing images in an acceptable time. In this paper, a parallel microwave image reconstruction algorithm based on Apache Spark on high-performance computing and Google Cloud Platform is proposed. The input data is first converted to a resilient distributed data set and then distributed to multiple nodes on a cluster. The subset of pixel data is calculated in parallel on these nodes, and the results are retrieved to a master node for image reconstruction. Using Apache Spark, the performance of the parallel microwave image reconstruction algorithm is evaluated on high-performance computing and Google Cloud Platform, which shows an average speed increase of 28.56 times on four homogeneous computing nodes. Experimental results revealed that the proposed parallel microwave image reconstruction algorithm fully inherits the parallelism, resulting in fast reconstruction of images from radio frequency sensor’s data. This paper also illustrates that the proposed algorithm is generalized and can be deployed on any master-slave architecture.

A Comparative Study of Cloud Computing Platforms

This article offers a comparison and analysis of three of the most widely used cloud computing platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The research focuses on the most important aspects of each platform, such as the virtual machines they offer, the storage choices they provide, the database services they provide, and the serverless computing capabilities they provide. In addition to this, the paper discusses issues like as cost, availability, security, and scalability, as well as some of the positives and negatives associated with using each platform. The literature review that is included in this paper highlights some of the most important findings from recent studies on cloud computing platforms. These findings include the widespread use of Amazon Web Services (AWS), the difficulties that are encountered by organisations in the management of cloud computing resources, and the significance of aspects such as reliability, security, and cost when selecting a cloud computing platform. This article's overarching objective is to offer businesses a comprehensive introduction to AWS, Microsoft Azure, and Google Cloud Platform (GCP), and to assist those businesses in making well-informed choices regarding which cloud platform is best suited to meet their requirements.

Management of humanitarian relief operations using satellite big data analytics: the case of Kerala floods

Disasters lead to breakdown of established Information and Communication Technology (ICT) infrastructure. ICT breakdown obstructs the channel to gather real-time last mile information directly from the disaster-stricken communities and thereby hampers the agility of humanitarian supply chains. This creates a complex, chaotic, uncertain, and restrictive environment for humanitarian relief operations, which struggles for credible information to prioritize and deliver effective relief services. In this paper, we discuss how satellite big data analytics built over real-time weather information, geospatial data and deployed over a cloud-computing platform aided in achieving improved coordination and collaboration between rescue teams for humanitarian relief efforts in the case of 2018 Kerala floods. The analytics platform made available to the stakeholders involved in the rescue operations led to timely logistical planning and execution of rescue missions. The developed platform improved the accuracy of information between the distressed community and the stakeholders involved and thereby increased the agility of humanitarian logistics and relief supply chains. This research proves the utility of fusing data sources that are normally sitting as islands of information using big data analytics to prioritize humanitarian relief operations.

Optimizing Mass-Scale Multi-Screen Video Delivery

We discuss the design of a mass-scale streaming video delivery system, targeting devices with different decoding, playback, and network capabilities. This system incorporates several advanced tools: dynamic packaging, dynamic devices detection, dynamic manifest generation, rules engine, analytics engine, and context-aware encoding (CAE). We explain how this system is implemented using the current cloud computing and content delivery network platforms and how it is tuned to achieve optimal end-to-end performance, delivering the best possible user experience with minimized transcoding, bandwidth, and storage costs.

Joint Task Offloading and Resource Allocation in UAV-Enabled Mobile Edge Computing

Mobile edge computing (MEC) is an emerging technology to support resource-intensive yet delay-sensitive applications using small cloud-computing platforms deployed at the mobile network edges. However, the existing MEC techniques are not applicable to the situation where the number of mobile users increases explosively or the network facilities are sparely distributed. In view of this insufficiency, unmanned aerial vehicles (UAVs) have been employed to improve the connectivity of ground Internet of Things (IoT) devices due to their high altitude. This article proposes an innovative UAV-enabled MEC system involving the interactions among IoT devices, UAV, and edge clouds (ECs). The system deploys and operates a UAV properly to facilitate the MEC service provisioning to a set of IoT devices in regions where the existing ECs cannot be accessible to IoT devices due to terrestrial signal blockage or shadowing. The UAV and ECs in the system collaboratively provide MEC services to the IoT devices. For optimal service provisioning in this system, we formulate an optimization problem aiming at minimizing the weighted sum of the service delay of all IoT devices and UAV energy consumption by jointly optimizing UAV position, communication and computing resource allocation, and task splitting decisions. However, the resulting optimization problem is highly nonconvex and thus, difficult to solve optimally. To tackle this problem, we develop an efficient algorithm based on the successive convex approximation to obtain suboptimal solutions. Numerical experiments demonstrate that our proposed collaborative UAV-EC offloading scheme largely outperforms baseline schemes that solely rely on UAV or ECs for MEC in IoT.

Cloud Computing and Local Chip-Based Dynamic Economic Dispatch for Microgrids

Dynamic economic dispatch in microgrids is usually realized in a centralized energy management system (EMS). However, centralized systems are subject to single-point failure problems. In rural areas and islands, a failed EMS cannot be recovered in a timely manner due to lack of technical support. In this paper, we propose a cloud and edge computing-based framework to realize dynamic economic dispatch, which is conducted on a local Digital Signal Processor (DSP) chip and a remote cloud computing platform (CCP). Based on the multi-parametric programming algorithm, the dispatch process is divided into two parts: offline calculation and real-time decision making. These calculation tasks can be conducted on the remote cloud computing platform and the DSP chip of the inverter in an arbitrary renewable generator, respectively. The tests are carried out on the Amazon Web Service (AWS) EC2 instance and an autonomous microgrid with DSP TMS320F28335 chip. The results show that the proposed method can obtain the same results as the conventional method, with significantly improved reliability.