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As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfillment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.

With increasing urbanization, the application of Internet of things (IoT) technology to city governance has become a trend in architecture, transportation, and healthcare management, making IoT applicable in various domains. This study used IoT to inspect green construction and adopted a front-end sensing system, middle-end wireless transmission, and a back-end multifunctional system structure with cloud management. It integrated civil and electrical engineering to develop environmental monitoring technology and proposed a management information system for the implementation of green engineering. This study collected physical "measurements" of the greening environment on a campus. Ambient temperature and humidity were analyzed to explore the greening and energy-saving benefits of a green roof, a pervious road, and a photovoltaic roof. When the ambient temperature was below 25 °C, the solar panels had an insulation effect on the roof of the building during both 4:00–5:00 and 12:00–13:00, with an optimal insulation effect of 2.45 °C. When the ambient temperature was above 25 °C, the panels had a cooling effect on the roof of the building, whether during 4:00–5:00 or 12:00–13:00, with an optimal cooling effect of 5.77 °C. During the lower temperature period (4:00–5:00), the ecological terrace had an insulation effect on the space beneath, with an effect of approximately 1–3 °C and a mean insulation of 1.95 °C. During the higher temperature period (12:00–13:00), it presented a cooling effect on the space beneath, with an effect of approximately 0.5–9 °C and a mean cooling temperature of 5.16 °C. The cooling effect of the three greening areas on air and ground temperature decreased in the following order: pervious road > photovoltaic roof > ecological terrace. [ABSTRACT FROM AUTHOR]

This paper examines the use of digital video in public safety and surveillance systems. Traditionally video recordings are used by law enforcement to review events retrospectively and for evidential purposes in the pursuance of criminal prosecution. We also examine how, due to the proliferation of cameras around cities, human operators are challenged to monitor these data feeds in real-time and how the emergence of AI and computer vision solutions can process this data. Computer vision can enable the move from a purely reactive to a predictive, real-time analysis platform. As camera numbers and the resolution and framerate of cameras grow, existing network infrastructure frequently causes challenges provisioning low latency, high bandwidth networking to private or public cloud infrastructure for evidential storage. These technical challenges can provide issues for law enforcement providing a data chain of custody to ensure its admissibility during court proceedings. Emerging technologies offer solutions to overcome these challenges: the use of emerging edge compute capabilities, including the use of on-camera and mobile edge compute nodes providing compute capabilities closer to the data source and new software paradigms, including CI/CD methodologies, and the use of micro-services and containerization to manage and deliver applications across the portfolio of devices, at the edge of the network. Using Amazon Web Services as an example, we review how cloud providers are now overcoming challenges in delivering real time video analytics solutions in the classical cloud model, and how they are enabling services and platforms closer to the edge, while delivering the cloud computing experience of scalability and manageability across different edges of the network. [ABSTRACT FROM AUTHOR]

Water loss in distribution networks known as Non-Revenue Water (NRW) is one of the major challenges facing water utilities. In a densely populated city, the acoustic listening method manually conducted by waterworks operators during routine leak pinpointing tasks is vital for NRW reduction. However, this method is considered to be typically labor-intensive, skill-dependent, non-systematic, and sometimes imprecise due to fatigue and inexperience of newly trained staff. This paper presents the development of an AI-based water leak detection system with cloud information management. The system can systematically collect and manage leakage sounds and generate a model used by a mobile application to provide operators with guidance for pinpointing leaking pipes. A leakage sound collection and management system was designed and implemented. Leakage sound datasets were collected from some multiple areas of the Metropolitan Waterworks Authority. Machine learning algorithms including Deep Neural Network (DNN), Convolutional Neural Network (CNN), and Support Vector Machine (SVM), were developed and compared. The results show that the DNN performed better than SVM and as well as CNN, but with less complex structure. DNN was then selected to generate a model used in field trials for pinpointing leakage by novice operators. The field trial results show that the accuracy of the system is above 90% and the results were similar to those conducted by experts.

With the change of social structure and management mode, emergency social security incidents have become the focus of social security management. In order to solve the problems of insufficient overall performance and low security in the practical application of cloud management mode in traditional social security emergencies. With the use of improved pick-KX load balancing algorithm and RSA encryption algorithm, this paper optimizes the cloud computing model and applies it to the cloud management mode of emergent social security events. Then the optimized cloud management model is simulated and evaluated by the multi-level fuzzy comprehensive evaluation model. The simulation results reflect that the proposed cloud management model in this paper is able to effectively improve the management effect of social security events. [ABSTRACT FROM AUTHOR]

Vehicular clouds are considered an attractive approach to provide various services such as safety and entertainment applications by sharing resources between vehicles. Due to the free mobility of vehicles, vehicular clouds need to reconstruct by replacing leaving member vehicles with new member vehicles. When multiple member vehicles leave a vehicular cloud at different times, the design of an efficient member vehicle replacement scheme is a very challenging issue on determining the replacement timing. However, the research on the replacement of multiple member vehicles has little interest and is still in its infancy. Therefore, we propose three schemes to replace multiple member vehicles in vehicular clouds at three different replacement timings: MIN, MAX, and AVG. The MIN scheme replaces all of leaving member vehicles at the leaving time of the first leaving member vehicle, while the MAX scheme replaces all of leaving member vehicles at the leaving time of the last leaving member. The AVG scheme replaces all leaving member vehicles at the average time of their leaving times as a compromise between the Min and Max schemes. First, we determine the first leaving time, the last leaving time, and the average leaving time for each scheme by calculating the distance between a cloud requester vehicle and its member vehicles. Next, we choose replacement member vehicles to minimize the wasted resource at the replacement timing in each scheme. Last, we provide the process for releasing the resource of the leaving member vehicles and allocating the resource of the replacement member vehicles in each scheme. Through simulation results conducted in various environments, we compare and evaluate the performance of our three schemes in terms of the success ratio of the cloud maintenance and the amount of the wasted resources. [ABSTRACT FROM AUTHOR]

Edge computing is the concept where the computation is handled at edge-devices. The transfer of the computation from servers to edge-devices will decrease the massive amount of data transfer generated by edge-devices. There are several efficient management tools for setup and connection purposes, but these management tools cannot provide a unified programming system from a single source code/project. Even though it is possible to control each device efficiently, a global view of the computation is missing in a programming project that includes several edge-devices for computation and data analysis purposes, and the devices need to be programmed individually. A generic workflow engine might automate part of the problem using standard interfaces and predefined objects running on edge-devices. Nevertheless, the approach fails in fine-tuning each edge-device since the computation cannot be moved easily among devices. This paper introduces a new compiler architecture to control and program edge-devices from a single source code. The source code can be distributed to multiple edge-devices using simple compiler directives, and the transfer and communication of the source code with multiple devices are handled transparently. Fine-tuning the source code and code movement between devices becomes very efficient in editing and time. The proposed architecture is a lightweight system with fine-tuned computation and distribution among devices. [ABSTRACT FROM AUTHOR]

With increasing urbanization, the application of Internet of things (IoT) technology to city governance has become a trend in architecture, transportation, and healthcare management, making IoT applicable in various domains. This study used IoT to inspect green construction and adopted a front-end sensing system, middle-end wireless transmission, and a back-end multifunctional system structure with cloud management. It integrated civil and electrical engineering to develop environmental monitoring technology and proposed a management information system for the implementation of green engineering. This study collected physical “measurements” of the greening environment on a campus. Ambient temperature and humidity were analyzed to explore the greening and energy-saving benefits of a green roof, a pervious road, and a photovoltaic roof. When the ambient temperature was below 25 °C, the solar panels had an insulation effect on the roof of the building during both 4:00–5:00 and 12:00–13:00, with an optimal insulation effect of 2.45 °C. When the ambient temperature was above 25 °C, the panels had a cooling effect on the roof of the building, whether during 4:00–5:00 or 12:00–13:00, with an optimal cooling effect of 5.77 °C. During the lower temperature period (4:00–5:00), the ecological terrace had an insulation effect on the space beneath, with an effect of approximately 1–3 °C and a mean insulation of 1.95 °C. During the higher temperature period (12:00–13:00), it presented a cooling effect on the space beneath, with an effect of approximately 0.5–9 °C and a mean cooling temperature of 5.16 °C. The cooling effect of the three greening areas on air and ground temperature decreased in the following order: pervious road > photovoltaic roof > ecological terrace.

Copyright of Global Journal of Economics & Business is the property of Refaad for Studies, Research & Development and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber’s applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers’ SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads.

IoT generates considerable amounts of data, which often requires leveraging cloud computing to effectively scale the costs of transferring and computing these data. The concern regarding cloud security is more severe because many devices are connected to the cloud. It is important to automatically monitor and control these resources and services to efficiently and securely deliver cloud computing. The writable virtual machine introspection (VMI) technique can not only detect the runtime state of a guest VM from the outside but also update the state from the outside without any need for administrator efforts. Thus, the writable VMI technique can provide the benefit of high automation, which is helpful for automated cloud management. However, the existing writable VMI technique produces high overhead, fails to monitor the VMs distributed on different host nodes, and fails to monitor multiple VMs with heterogeneous guest OSes within a cloud; therefore, it cannot be applied for automated and centralized cloud management. In this paper, we present CloudVMI, which is a writable and crossnode monitoring VMI framework that can overcome the aforementioned issues. CloudVMI solves the semantic gap problem by redirecting the critical execution of system calls issued by the VMI program into the monitored VM. It has strong practicability by allowing one introspection program to inspect heterogeneous guest OSes and to monitor VMs distributed on remote host nodes. Thus, CloudVMI can be directly applied for automated and centralized cloud management. Moreover, we implement some defensive measures to secure CloudVMI itself. To highlight the writable capability and practical usefulness of CloudVMI, we implement four applications based on CloudVMI. CloudVMI is designed, implemented, and systematically evaluated. The experimental results demonstrate that CloudVMI is effective and practical for cloud management and that its performance overhead is acceptable compared with existing VMI systems.

Summary: Cloud computing and its service models, such as Platform as a Service (PaaS), have changed the way that computing resources are allocated to Information and Communications Technology enterprises and users. Although multiple cloud providers support dynamic service provisioning, it is necessary to facilitate the management of the cloud infrastructure and applications in order to allow the continuous refinement of cloud models. Therefore, issues are raised regarding the cloud orchestration, including the flexible portability and interoperability of cloud applications among multiple cloud providers. Having said that, there is a need for a standardized design and management of the cloud use cases (during the creation of scenarios, application's deployment, and patching) to ensure efficient applications' migration between different providers. This paper proposes an artifact, GITS, a generic input template for CloudSim and other cloud simulators. GITS can be provided by PaaS offering to manage the creation, monitoring, administration, and patching of infrastructure and applications in the cloud. GITS defines the cloud schema that can be used with conforming cloud models and independent cloud providers; thus, portability and interoperability can be enabled in PaaS cloud models. GITS focuses on the architecture‐based modeling for cloud infrastructure and application not only in terms of computational resources but also in terms of high availability properties associated with infrastructure and applications. The main objective of the GITS template is to provide the cloud user with a modular, simple, readable, and reusable model that still supports the essential components and provide them with the ability to control the applications' execution, deployment, and other management needs in addition to the allocation environment. This paper describes GITS usage, specifically as an input template for CloudSim. [ABSTRACT FROM AUTHOR]

Copyright of HMD: Praxis der Wirtschaftsinformatik is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Enterprise applications are being increasingly deployed on cloud infrastructures. Often, a cloud service provider (SP) enters into a Service Level Agreement (SLA) with a cloud subscriber, which specifies performance requirements for the subscriber's applications. An SP needs systematic Service Level Planning (SLP) tools that can help estimate the resources needed and hence the cost incurred to satisfy their customers' SLAs. Enterprise applications typically experience bursty workloads and the impact of such bursts needs to be considered during SLP exercises. Unfortunately, most existing approaches do not consider workload burstiness. We propose a Resource Allocation Planning (RAP) technique, which allows an SP to identify a time varying allocation plan of resources to applications that satisfies bursts. Extensive simulation results show that the proposed RAP variants can identify resource allocation plans that satisfy SLAs without exhaustively generating all possible plans. Furthermore, the results show that RAP can permit SPs to more accurately determine the capacity required for meeting specified SLAs compared to other competing techniques especially for bursty workloads. [ABSTRACT FROM AUTHOR]