Elevation mapping using stereo vision enabled heterogenous multi-agent robotic network

Purpose– Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.Design/methodology/approach– A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.Findings– The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.Research limitations/implications– The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.Practical implications– Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.Originality/value– It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.

Cloud computing has caused a revolution in how companies do business giving them flexibility, scalability, and cost-efficiency like never before. It lets organizations use computing resources through the internet instead of relying on the usual in-house setup making cloud computing a key part of today's IT plans. From new startups to big global companies, businesses are using cloud solutions more and more to boost their ability to adapt cut down on running costs, and come up with new ideas. The cloud ecosystem is big and varied, with different deployment models and service types to suit various business needs. Public, private, and hybrid clouds are the three main deployment models each with its own pros and cons. public clouds, like those from Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, offer affordable scalability and global reach. Private clouds, on the other hand, are for organizations that have strict security or compliance needs giving them dedicated environments. Hybrid clouds mix the best parts of both letting businesses find a balance between security and scalability by combining private and public cloud resources

Building on compute and storage virtualization, Cloud Computing provides scalable, network-centric, abstracted IT infrastructure, platforms, and applications as on-demand services that are billed by consumption. Cloud Service Engineering is the application of a systematic approach to leverage Cloud Computing in the context of the Internet in its combined role as a platform for technical, economic, organizational and social networks. This tutorial introduces concepts and technology of Cloud Computing and Cloud Service Engineering, providing an overview of state-of-the-art in research and practice. We show how to set up a private Cloud that delivers Infrastructure-as-a-Service (IaaS). Eucalyptus and OpenNebula are popular open source software frameworks for creating on-premise Clouds. Promises, challenges and solutions for integrating services of a private Cloud with public Cloud services such as Amazon EC2 and SQS are discussed. We show how the best of both worlds - private and public Clouds - can be combined to build scalable and secure systems.

In cloud computing, private cloud tends to exhibit high controllability but lack scalability, whereas public cloud is just the opposite. The hybrid cloud formed by combining them can effectively balance controllability and scalability, and has been widely adopted in industry for executing workflows. The network connecting public and private clouds goes through Internet and its bandwidth is rather limited in comparison with that inside clouds. Hence, cross-cloud data transmission can become a bottleneck of executing workflows in such environment. Moreover, multiple data communications may contend for bandwidth resources, thereby incurring delay. To address these concerns, this work establishes a scheduling model for workflow execution across public and private clouds, where a queueing mode is innovatively employed to address potential network communication contention. A contention-cognizant list scheduling (CCLS) heuristic equipped with task duplication is devised to minimize workflow makespan. It adopts a novel task sorting attribute to schedule tasks and cross-cloud data communications by using available computation and communication resources, and employs task duplication to obviate the needs for certain data communications. Experiments are conducted with realistic workflows and diverse settings, and the results verify the superiority of CCLS over the existing ones as it can always achieve the best makespan <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A crucial challenge for workflow scheduling across public and private clouds is that the cross-cloud bandwidth resource is relatively limited and multiple data communications may contend for it in practice. However, this communication contention issue has largely been neglected in existing investigations. To advance the state of the art, this paper proposes a communication contention-cognizant scheduling approach based on a queueing mode to minimize the makespan for workflow execution across public and private clouds. The proposed approach can be readily put into use and experimental results show that it performs better than traditional scheduling approaches that fail to consider communication contention.

Cloud computing is a phenomenon that is currently a lot of attention from practitioners of information technology. Cloud computing can be composed of several types of services that are known to Saas, PaaS and IaaS, public and private cloud computing. Companies that do not invest themselves in the information technology infrastructure to use cloud computing services as a solution. By adopting and implementing a cloud computing services company can shift the cost of information technology infrastructure investments become operational costs by purchasing cloud computing services. Nevertheless, cloud computing, especially for the type of private cloud is still a bit of adopting it, whether it is caused by the absence of reference to the implementation or caused by something else. The purpose of this study was to analyze the adoption of cloud computing that suitable and beneficial for the company in its operational activities. The study was conducted by analysis of some of the literature related to cloud computing, benefits and barriers. According to the analysis of the literature can be explained some of the benefits associated with the adoption of cloud computing. Based on several studies that have been done, the benefits of implementing cloud computing is primarily financial savings and benefits of resource management that is the flexibility and scalability of the company's operations. However, some considerations still need to be done for a company that will implement cloud computing and those things are data security issues, legal issues and the implications for the company.

For the increasingly prominent problems of wind turbine maintenance, using edge cloud collaboration technology to construct wind farm equipment operation and maintenance framework is proposed, digital twin is used for fault prediction and diagnosis. Framework consists of data source layer, edge computing node layer, public or private cloud. Data source layer solves acquisition and transmission of wind turbine operation and maintenance data, edge computing node layer is responsible for on-site data cloud computing, storage and data transmission to cloud computing layer, receiving cloud computing results, device driving and control. The cloud computing layer completes the big data calculation and storage from wind farm, except that, based on real-time data records, continuous simulation and optimization, correct failure prediction mode, expert database and its prediction software, and edge node interaction and shared intelligence. The research explains that wind turbine uses digital twin to do fault prediction and diagnosis model, condition assessment, feature analysis and diagnosis, life prediction, combining with the probabilistic digital twin model to make the maintenance plan and decision-making method.

E-healthcare is an emerging and promising healthcare system to meet the increasing medical demand from aging population. It requires extensive data storage, pervasive data access, and reliable computing resources to support the real-time communication of critical health information and the real-time diagnosis. Recently, cloud computing, including public cloud and private cloud, with both scalability and accessibility is proposed to be integrated in the e-healthcare system. However, in meeting stringent medical requirements, private clouds lack necessary reliability, whereas public clouds suffer from communication delay. In this paper, we first introduce a cooperation framework to address the distinct challenger facing different clouds. It is inspired by the fact that private clouds are geographically deployed and public clouds can be regarded to possess infinite computing resources. In our framework, private clouds are designed to serve parts of local requests to public clouds, and rewarded by receiving help with excess requests. We adopt stochastic control theory to address the failure minimization issues for private clouds under random demand process. We prove the optimality of a policy constructed through recursion. Numerical and simulation results are presented to demonstrate that our proposed scheme can improve the reliability of private clouds, as well as reduce average delay of public clouds.

Cloud computing is of particularly population these days, it is changing the way of delivering and consuming IT resources of kinds. Private cloud computing is more appealing and acceptable than public cloud computing at this stage since there is no security issue which is intensively concerned by all serious users. But on the one hand, currently widely used open source virtualized environment managers can not meet the requirement of private cloud especially small scale private cloud due to their overstaffed functionality and complexity, on the other hand, customized commercial managers always involve a significant mount of fees and it is hard to be integrated by other applications running in the cloud. Based on the deep investigation on virtualization technique especially on Xen, the design and implement of a light-weighted integrated virtualized environment manager (LWIV Manager) is presented in this paper. LWIV Manager provides an easy use and integration way to allocate the computing resources of CPU, memory and network in the cloud. Moreover, a plug-in which gathers public computing resources to scale the capacity of local private cloud in the case of request burst is integrated in our LWIV Manager as well.

Cloud computing can reduce cost significantly because businesses can share computing resources. In recent years Small and Medium Businesses (SMB) have used Cloud effectively for cost saving and for sharing IT expenses. With the success of SMBs, many perceive that the larger enterprises ought to move into Cloud environment as well. Government agency's stove-piped environments are being considered as candidates for potential use of Cloud either as an enterprise entity or pockets of small communities. Cloud Computing is the delivery of computing as a service rather than as a product, whereby shared resources, software, and information are provided to computers and other devices as a utility over a network. Underneath the offered services, there exists a modern infrastructure cost of which is often spread across its services or its investors. As NASA is considered as an Enterprise class organization, like other enterprises, a shift has been occurring in perceiving its IT services as candidates for Cloud services. This paper discusses market trends in cloud computing from an enterprise angle and then addresses the topic of Cloud Computing for NASA in two possible forms. First, in the form of a public Cloud to support it as an enterprise, as well as to share it with the commercial and public at large. Second, as a private Cloud wherein the infrastructure is operated solely for NASA, whether managed internally or by a third-party and hosted internally or externally. The paper addresses the strengths and weaknesses of both paradigms of public and private Clouds, in both internally and externally operated settings. The content of the paper is from a NASA perspective but is applicable to any large enterprise with thousands of employees and contractors.

In stereo vision, disparity map after stereo matching using projection model can get the depth information and 3D information of the original image. In order to improve the accuracy and speed of stereo matching, improving the quality of disparity map becomes the core problem of stereo matching. Firstly, the paper extracted edge information of binocular images using Canny, Scharr and Sobel edge detection algorithm. On this basis, the paper use SGBM algorithm(Semi-global block Matching) and DP algorithm(Dynamic Programming)and CSCA algorithm(Cross-scale Cost Aggregation) respectively calculated the disparity map and its error rate. After analysis, the combination of Canny detection algorithm and CSCA algorithm can eliminate a large number of number of irrelevant information and reduce the amount of data processing. The real scene image is selected to validate the method .Based on the above research results, the method improved the quality of disparity map.

Big Data is a data analysis methodology made possible by current technological and architectural advancements. However, big data requires a significant investment in hardware and processing power, making it prohibitively expensive for small and medium-sized firms to implement. Small and medium-sized enterprises can benefit from cloud computing promise of big data implementation. The MapReduce programming paradigm is used to process large amounts of data. The MapReduce paradigm typically necessitates networked connected storage and parallel computation. The computing requirements of MapReduce programming are frequently beyond the capabilities of small and medium-sized businesses. Cloud computing is network access to computing resources on demand, given by a third party Hardware cost reduction, processing cost reduction, and the flexibility to verify the value of big data are three significant reasons for small and medium-sized organizations to embrace cloud computing for big data technology implementation. Security and loss of control are two main concerns with cloud computing. Key Words: Big data, MapReduce, cloud computing, private cloud, public cloud, and hybrid cloud.

With ever increasing usage of IT infrastructure around the globe, the increase in energy consumption and carbon emission is a worrisome situation for everyone because of its direct effect on environment. The environment is being dangerously impacted by Information Technology infrastructure extensive use and its waste. Cloud computing has emerged as one step further solution to deploy all virtualized IT resources as per need on self-service basis as a rental method for the users. Different cloud deployment methods have emerged according to the need of users, institutions and communities. In last decade virtualization has been a milestone in enabling optimal usage of information technology resources. Cloud computing has provided a platform for deploying everything as a Service in IT, which has changed the economics of IT based business and organizations from Capital Expenditure to Operational Expenditure. For enabling Cloud services we first need to understand our existing infrastructure. In second step we should be able to virtualize it for optimal usage. Then with the help of existing cloud applications we can deliver and deploy information technology as a resource at various levels such as infrastructure, platform, and software. Keywords; cloud computing, carbon emission, energy consumption, IT waste, virtualization. The Advancement in the field of information technology has enabled us to optimize the use of physical infrastructure with the help of virtualization, most of which is only used in specific applications like vitalizing a local area network on switch or vitalizing a compute resource to run more than one operating system on a desktop. The benefits of same has not been completely implemented and utilized in industrial environments where we require a large amount of IT resources. The advantage of using cloud is to enable complete utilization of Information technology resources, where most of the resources are underutilized. The cost involved in maintenance and management of those resources is huge which can be saved due to centralized management of private cloud infrastructure. We create private cloud keeping in mind. • Availability and performance requirements for the cloud • Infrastructure sizing, performance and continual availability requirements • The need for data security or isolation • The appropriate user interface With advent of IT as a service in cloud computing technology energy consumption and related carbon emissions and waste are considerably lesser than in-house implementation. Technology in Cloud Computing creates an able structure for sharing resources with the help of virtualization among many; it greatly decreases or abolishes the need for most products, equipment, utilities and facilities. The results are more sustainable IT infrastructures and processes which help in keeping environment safe. • Highest resource consumption and effectiveness • Reduced energy consumption by up to 95 % with the help of virtualization which optimizes the use of physical infrastructure and thereby helps IT to be delivered as a service. • Displaced utility costs Workstation utility costs are cut by 90% due to change of capital expenditure into operational expenditure in industries or organizations across the world. • Reduced electronic waste as No more batteries and minimal software/hardware waste. • Reduced waste overall due to consumption of computational power as service, no more as infrastructure. • Reduced ecological impact of workstations as compared to thin clients as anything related to computation can be provided as a service on a client connected with internet to the cloud provider.

NASA scenarios for lunar and planetary missions include robotic vehicles that function in both teleoperated and semi-autonomous modes. Under teleoperation, on-board stereo cameras may provide 3-D scene information to human operators via stereographic displays; likewise, under semi-autonomy, machine stereo vision may provide 3-D information for obstacle avoidance. In the past, the slow speed of machine stereo vision systems has posed a hurdle to the semi-autonomous scenario; however, recent work at JPL and other laboratories has produced stereo systems with high reliability and near real-time performance for low-resolution image pairs. In particular, JPL has taken a significant step by achieving the first autonomous, cross-country robotic traverses (of up to 100 meters) to use stereo vision, with all computing on-board the vehicle. Here, we describe the stereo vision system, including the underlying statistical model and the details of the implementation. The statistical and algorithmic aspects employ random field models of the disparity map, Bayesian formulations of single-scale matching, and area-based image comparisons. The implementation builds bandpass image pyramids and produces disparity maps from the 60×64 level of the pyramids at rates of up to two seconds per image pair. All vision processing is done in one 68020 augmented with Datacube image processing boards. We argue that the overall approach provides a unifying paradigm for practical, domain-independent stereo ranging. We close with a discussion of practical and theoretical issues involved in evaluating and extending the performance of the stereo system.

NASA scenarios for lunar and planetary missions include robotic vehicles that function in both teleoperated and semi-autonomous modes. Under teleoperation, on-board stereo cameras may provide 3-D scene information to human operators via stereographic displays; likewise, under semi-autonomy, machine stereo vision may provide 3-D information for obstacle avoidance. In the past, the slow speed of machine stereo vision systems has posed a hurdle to the semi- autonomous scenario; however, recent work at JPL and other laboratories has produced stereo vision systems with high reliability and near real-time performance for low-resolution image pairs. In particular, JPL has taken a significant step by achieving the first autonomous, cross- country robotic traverses (of up to 100 meters to use stereo vision, with all computing on- board the vehicle. This paper describes the stereo vision system, including the underlying statistical model and the details of the implementation. The statistical and algorithmic aspects employ random field models of the disparity map, Bayesian formulations of single-scale matching, and area-based image comparisons. The implementations builds Laplacian image pyramids and produces disparity maps from the 60X64 level of the pyramids at rates of up to two seconds per image pair. All vision processing is done on one 68020 augmented with Datacube image processing boards. The author argues that the overall approach provides a unifying paradigm for practical, domain-independent stereo ranging.

Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory