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Software bugs in cloud management systems often cause erratic behavior, hindering detection, and recovery of failures. As a consequence, the failures are not timely detected and notified, and can silently propagate through the system. To face these issues, we propose a lightweight approach to runtime verification, for monitoring and failure detection of cloud computing systems. We performed a preliminary evaluation of the proposed approach in the OpenStack cloud management platform, an “off-the-shelf” distributed system, showing that the approach can be applied with high failure detection coverage.

El objetivo del trabajo es la instalación y el estudio de un entorno de gestión de nube en producción con pocos servidores. Para conseguirlo, se utilizan tres equipos, un nodo controlador y dos nodos de cómputo, en los que se instala, desde cero, un sistema operativo donde se lanza el software de gestión Cloud de virtualización OpenStack. Una vez instalado dicho software se trata de incorporar al entorno, un servidor web público y realización de pruebas de carga, mantenimiento y monitorización., The goal of this project is the installation and study of a cloud management environment. In order to achieve this goal, three computers are used, a controller and two compute nodes, in which, an operating system is installed from scratch, where the OpenStack virtualization cloud management software is launched. Once said software is installed, it is a matter of incorporating a public web server into the environment and load, maintenance and monitoring tests are carried out., Grado en Ingeniería en Tecnologías de Telecomunicación

The unified cloud management platform is the core of cloud computing management. According to the technical requirements of the power cloud computing platform, the technical architecture of the power unified cloud management platform is designed, and the functional characteristics of the cloud platform management platform are explained. Combined with the cloud platform components of the State Grid Corporation, the experiment is verified The feasibility and effectiveness of the technical architecture of the cloud management platform.

With the advancement of technology, smart home gradually enters our field of vision. Although after several years of development, there are many application categories, due to the slow development speed and the inconsistency of standards, the penetration rate of the smart home industry is still at a low level. The applicability and convenience of the product are also relatively poor, and the personalized experience needs to be improved.Based on NB-IoT technology, this paper designs and implements a set of mobile remote control smart home system using cloud management. The system consists of hardware equipment, cloud platform and application terminal, and provides personalized life services for users through hardware equipment data reporting and mobile terminal command issuance. The realization method of the smart home system proposed in the paper has a good reference value for the design of the smart home system.

This research provides an approach to exploring a suitable enterprise resource planning system using cloud management architecture for the educational environment. It enables enterprises to get into the competition. Enterprise resource planning for educational firms provides an approach to address the targeted female population. To achieve this goal, a system has been established that has an infrastructure basis on governments, nongovernment organizations (NGOs), universities, and other social service providers. This paper helps to present the architecture of cloud computing for the overall educational environment concerns around the world. This research aims to contribute to women&rsquo, s education with respect to modern technology. It ensures that technology is cost-efficiently available for women&rsquo, s education in view of the availability and consistency of the system and in accordance with goals. An architecture is proposed to solve and take over the limitations that have been faced and are the reasons for the failure of the available systems. After designing the architecture, a survey questionnaire was designed and conducted with students and professionals of Air University, Bahria University, and Preston University.

Vehicular Cloud computing is new paradigm where vehicles collaboratively exchange data and resources to support services and problem-solving in urban environments. Characteristically, such Clouds undergo severe challenging conditions from the high mobility of vehicles, and by essence, they are rather dynamic and complex. Many works have explored the assembling and management of Vehicular Clouds with designs that heavily focus on mobility. However, a mobility-based strategy relies on the geographical position of vehicles and its feasibility has been questioned in some recent works. Therefore, we present a more relaxed Vehicular Cloud management scheme that relies on connectivity. This work models uncertainty and considers every possible chance a vehicle may be available through accessible communication means, such as V2X communications and the vehicle being in the range of RSUs for data transmissions. We utilize the MDP model to track the state of vehicles and when there are connected and available for transmission of the data.

The raid population growth and increasing number of private vehicle ownership in countries like India have largely contributed to vehicle congestion. Inappropriate vehicle parking areas are considered as one of the root causes for slow moving traffic and has invoked the attention of the administration officials. The model proposed here serves a three-stage parking system using the Internet of Things. The first stage consists of an automated gate connected to an onscreen vehicle counter. The second stage consists of sensors that are installed at each parking slot, connected with a cloud platform by using a microprocessor board. These sensors send real-time data to the cloud by keeping track of the status of each parking slot and provide an online platform for the user to check the availability of parking slots. The final stage of the system provides an application interface that sends the user’s parking details through e-mail and displays the same on the application.

Beijing Gas Group used the Internet of things technology to build its Internet of things cloud platform. In the field of intelligent gas metering management and intelligent gas pipe network management, it has created the first complete NB-IoT intelligent application case in the domestic urban gas industry. The multi-layer defense system constructed by the information security design from the intelligent terminal to the application system effectively guarantees the data acquisition and remote control process of the massive NB-IoT gas intelligent terminal. The Internet of things cloud platform as a whole refers to the technical requirements of three levels of national information security protection. The security management subsystem of the Internet of things supports hardware encryption on security chips and software encryption. It can flexibly combine symmetric and asymmetric algorithms that meet the national security standards according to the needs of various business scenarios, and realize the encryption, decryption and signature verification processing of various data.

CISCO MERAKI, una empresa de TI que se maneja en la nube, en donde se proveen servicios de conmutación, seguridad, LAN inalámbrica, entre otros; los cuales son gestionados de forma centralizada a través de la Internet. Uno de los muchos servicios prestados por esta empresa conocida a nivel mundial por gestión de redes, la cual busca beneficiar a sus clientes y satisfacer las diferentes necesidades de empresas a través de redes clásicas o redes que se encuentra sobre la nube. La cual se puede aplicar en cualquier tipo de empresa, por ende se da a conocer los beneficios y las características adecuadas para empresas de TI que gestionan proyectos, describiendo características propias de los proyectos, fases y procesos. CISCO MERAKI, a cloud-driven IT company, where switching, security, wireless LAN services are provided, which are centrally managed over the Internet. One of the many services provided by this company known worldwide for network management, which seeks to benefit its customers and meet the different needs of companies through classic networks or networks located on the cloud. Which can be applied in any type of company, therefore the benefits and the appropriate characteristics for IT companies that manage projects are disclosed, describing characteristics of the projects, phases and processes. Ingeniero de Telecomunicaciones http://unidadinvestigacion.usta.edu.co Pregrado

Network Function Virtualization (NFV) is a network architecture built to run on commercial off-the-shelf (COTS) servers in a virtualized environment. Lower layers network function of telecom network elements like PHY, MAC and Bearer are hosted as Virtualized Network Function (VNF). NFV Management and Orchestration (MANO) referred as cloud platform allocates, monitors and controls the cloud-computing resources. In the Telecom world, resources are statically provisioned so that the resource utilization is optimal in order to meet the KPIs. Owing to this resource dimensioning and to secure a stable deployment cost, customers require a static configuration of cloud system resources like CPU, memory and network for VNFs. When these cloud resources are over-provisioned in view of higher resource utilization, the VNF instances will have workloads contributing to active and lean periods based on user applications requirement (throughput, latency) in the network. In a cloud environment, ad-hoc workloads, like performance testing, monitoring(vProbe), diagnostics etc., need to be deployed occasionally. In this paper, we propose an intelligent cloud platform that can enable an operator to exploit the static resources which are under-utilized. We experimented with Auto-Regressive Integrated Moving Average (ARIMA) model and our results show that the proposed intelligent cloud platform is able to forecast CPU, memory, and network utilization with an accuracy of 95.4%, 93.7% and 96.2% respectively. Further, VNFs are able to run additional workload during lean period with average CPU, memory, and network utilization improved by 38%, 19% and 20% respectively.

To improve the user experience in Cloud systems, it is of major interest for Cloud management tools to be able to automatically detect and notify anomalies in the behavior of services executed in virtual machines in a non-intrusive manner. To this end, this paper presents KDetect, an unsupervised learning algorithm to detect anomalies in periodic time series using clustering techniques. Using unlabeled time series representing the resource consumption of an unknown service executing in a virtual machine as input, KDetect is able to identify patterns that correspond to anomalies in the behavior of that service, assuming that the service behaves normally most of the time. Our evaluation, run with data extracted from a production dataset and coming from virtual machines exhibiting very different normal and abnormal behaviors, shows that KDetect is able to achieve a very high accuracy in the detection of anomalies (with more than 98% of recall and precision in most cases).

The dynamicity and complexity of clouds highlight the importance of automated root cause analysis solutions for explaining what might have caused a security incident. Most existing works focus on either locating malfunctioning clouds components, e.g., switches, or tracing changes at lower abstraction levels, e.g., system calls. On the other hand, a management-level solution can provide a big picture about the root cause in a more scalable manner. In this paper, we propose DOMINOCATCHER, a novel provenance-based solution for explaining the root cause of security incidents in terms of management operations in clouds. Specifically, we first define our provenance model to capture the interdependencies between cloud management operations, virtual resources and inputs. Based on this model, we design a framework to intercept cloud management operations and to extract and prune provenance metadata. We implement DOMINOCATCHER on OpenStack platform as an attached middleware and validate its effectiveness using security incidents based on real-world attacks. We also evaluate the performance through experiments on our testbed, and the results demonstrate that DOMINOCATCHER incurs insignificant overhead and is scalable for clouds.

The Test Command and Control System or C2 system is an important part of equipment testing system, and its operation directly determines the success or failure of the task. With the increasing number of tasks, existing C2 system’s resources and architectures have becoming difficult to meet the need to support multiple C2 operations simultaneously in a short period of time. With the help of cloud management platform, combined with virtualization, software definition network, security group and other technologies, this study proposed a method of building multi-tasking C2 system construction based on virtual machines, and successfully ran a number of parallel C2 systems on a physical machine according to this method. Through a Burn-In test, the system’s performance is good. The study providing a solution for multitasking C2 of equipment testing.

Many modern day cloud services are composites of multiple smaller services working correctly together. This design has become increasingly prevalent due to the rise of the microservices application architecture, as well as service chaining in Network Function Virtualization (NFV). Future composite applications and services will be deployed on multi-tier clouds where their constituent microservices may be geographically spread over different regions. To optimize the delivery of such composites, the constituent microservices must be placed in locations where their clients, which may be other microservices, are able to meet certain QoS constraints. We propose an architecture and present a prototype system for incorporating network metrics into the auto-scaling and scheduling decisions of cloud management systems. Given a service with QoS constraints, our system monitors the network metrics (e.g. latency and bandwidth) of their clients. If a particular client is unable to receive the required latency or bandwidth of the service, our system auto-scales the service and strategically places the new instance(s) in a location capable of meeting the service quality, and re-directs traffic to the new instance.

The advent and rapid rise of network technology and cloud computing have led to new opportunities for ushering in a new era in telehealth. Thanks to the Internet of Things (IoT) and advances in 5G communication, telehealth is expanding and shows no signs of slowing down. It provides patients including elderly and disabled patients with convenient and easy access to healthcare services across space and time. However, the continuous real-time transmission of health information over networks also exposes private data to the risk of being intercepted by third parties. The privacy of the primary individual patient must be managed under the protection of the patient&rsquo, s anonymous key while storing, transferring, sharing, and adding privacy rights. A question arises: How can we design a secure communication environment for remote access control to personal privacy matters? The patient&rsquo, s electronic medical record is protected by the patient&rsquo, s private key, and our scheme provides a real anonymous design for the patient with absolute autonomy over their privacy. Each update of the cloud medical records is patient-led and performed in a secure tunnel. As a result, this study reveals that the cloud-based iPatient privacy copyright management fully controlled by an individual patient is indeed safe and effective.

The effect of insider attack on the e-Healthcare system can lead to false examination of patient’s health records which have led to unaccountability of data usage and high financial cost as a result of data breaches in the e-healthcare without a highly efficient detection approach. A number of health centers have been faced with legal and reputational consequences as a result. This therefore requires the proposition of an efficient technique that can make this problem addressed most especially eHealth systems on the cloud environment as operations are currently operating with cloud services. Until such approaches are proposed, health records could be attacked and peradventure lead to poor treatment of patients due to misinformation and hence causing the death of individuals. This need serves as a key motivation for this research. In this, we proposed a new framework for detecting insider attacks in Cloud-based Healthcare system using watermarking extraction and logging detection technique. The framework was implemented using Microsoft Azure, Opennebula cloud management software emulation, PhP web scripting language, MySQL database server. The approach gave an output of the number of activities performed by users with the permission update of legal and illegal intrusion into the system using an audit trail. The approach showed high level of precision, recall and accuracy which makes it performance excellent to implement from the evaluation conducted at the end of the research

The securing of sophisticated artifacts by computerized forensics is a crucial phase in the forensic process. There is no such system for protecting and analyzing software systems in support of daily forensic knowledge, as illustrated in previous studies. When a customer or law enforcement department needs evidence, it is essential to come back to CSPs to show that and collect that irrespective of the condition the details might be in. Cloud data actually exists in a computer case, and depending on the CSP, is inevitable. This poses similar issues in itself, inquiry assumes partnership, pause response, unreasonable evidence and possible single intent of failure. While prior inquiries have recommended an IaaS framework, this relies on the compilation and availability of VM files at a rather simple level, and a key point of view of the approach is to incorporate the CSP as a core part of the structure in general and ensure additional data is obtained from cloud management. Nevertheless, the problem of reliance on the CSP and its associated matters is not addressed in this way. This paper briefly introduces a innovative solution that integrates accessing the virtual hard disk from the cloud environment at any given time with no requirement to use the CSP or some alteration to the basic basis of the CSP. Such an method will be tested experimentally in order to see its achievability. In this article, three experiments are performed to investigate the tolerability, efficiency of the solution and to provide a deeper understanding of both the technical recommendations resulting from such a method about the day-to-day function of a cloud framework and the costs.

International audience; A self-adaptive service can maintain its QoS requirements in the presence of dynamic environment changes. To develop a self-adaptive service, service engineers have to create self-adaptation logic encoding when the service should execute which adaptation actions. However, developing self-adaptation logic may be difficult due to design time uncertainty ; e.g., anticipating all potential environment changes at design time is in most cases infeasible. Online reinforcement learning addresses design time uncertainty by learning suitable adaptation actions through interactions with the environment at runtime. To learn more about its environment, reinforcement learning has to select actions that were not selected before, which is known as exploration. How exploration happens has an impact on the performance of the learning process. We focus on two problems related to how a service's adaptation actions are explored: (1) Existing solutions randomly explore adaptation actions and thus may exhibit slow learning if there are many possible adaptation actions to choose from. (2) Existing solutions are unaware of service evolution, and thus may explore new adaptation actions introduced during such evolution rather late. We propose novel exploration strategies that use feature models (from software product line engineering) to guide exploration in the presence of many adaptation actions and in the presence of service evolution. Experimental results for a self-adaptive cloud management service indicate an average speed-up of the learning process of 58.8% in the presence of many adaptation actions, and of 61.3% in the presence of service evolution. The improved learning performance in turn led to an average QoS improvement of 7.8% and 23.7% respectively.

Cloud computing is readily being adopted by enterprises due to its following benefits: ability to provide better service to customers, improved flexibility, lower barrier to entry for an enterprise, lower maintenance cost on IT service, availability etc. However, the interaction between cloud service provider and customer is not well-defined yet. Understanding of the service offered while approaching cloud computing paradigm and also understanding of the required actions during the period of receiving a cloud service e.g. provision of new resources, scaling up/down, billing, etc. remains a concern for the enterprises. This paper proposes a segregated interaction model to manage the receiving of a cloud service in a hierarchical way.

Traditional ways of managing information technology (IT) service providers are no longer applicable as companies use more and more services provisioned in the cloud. Therefore, organizations are looking for new ways to manage their relationship with cloud providers. The shift from IT-as-a-product to IT-as-a-service puts clients in a continued dependency on cloud service providers (CSPs), making provider management a critical factor for companies’ success. In this paper, we (1) identify cloud-specific challenges in managing CSPs, (2) develop a corresponding process framework for CSP management, and (3) discuss and extend this framework. Our final cloud management framework comprises ten processes for effective CSP management based on a literature study and twelve expert interviews. Furthermore, we unpack three major contingency factors, i.e., client–provider ratio, specificity, and service delivery model, which influence the reasonability and configuration of the cloud management processes. Drawing on two specific cases from our interview study, we explicate the contingency factors’ influence. Thus, our paper contributes to cloud sourcing research by deepening the understanding of client–provider relationships and by introducing a viable CSP management instrument contingent on three salient factors of cloud service provisioning.

Cloud services are transforming the roles of knowledge workers and the ICT Operations and Services that support knowledge workers. Changes to ICT governance, architecture, management, and operations are all occurring. ICT is becoming more complex with increased risk from vendor management, public and private cloud management, and management of legacy systems across outsourcers, vendors, and in-house resources. ICT Operations and Services are transitioning from a “physical device focus” to a “virtual focus” with the management of complex interconnected role-based datasets across platforms, suppliers, and sources. Consequently, the risk profile of ICT to a business is increasing. The changes in ICT Operations and Services occurring from the adoption of cloud services are reviewed in this chapter.

Problem definition: Cloud computing is recognized as a critical driver of information technology–enabled innovations. The operations management (OM) community, however, has not been exposed enough to the essential operations problems that arise from the management of cloud value chains. Academic/practical relevance: In this paper, we examine recent research on cloud value chains and explore future research opportunities from an OM perspective. In particular, we focus on major operations management challenges facing a cloud provider in three problem domains: (1) cloud computing resource management, (2) pricing in the cloud computing marketplaces, and (3) capacity planning and management of cloud supply chains. Methodology: We describe prevalent business models and management practices in the cloud value chains, discuss recent research from OM that falls into each of the three problem domains mentioned, and point out opportunities for future research. Results: We note that cloud computing operations are driven by demand that exhibits distinct characteristics, including complex workflow, demand redundancy, multifeatured characteristics, multidimensional resource requirement, and nonstationarity. On the supply side, cloud computing operations also exhibit distinct characteristics, including heterogeneous resources, packing constraints, preconfigured (“bundled”) supply, technology risks, and cost uncertainty. These characteristics of demand and supply are not all prevalent in other operations. Managerial implications: Cloud computing operations not only share many features with classic OM problems, but also bring new challenges and innovative business models. Thus, OM tools and research have the potential to provide vital insights into cloud computing operations and impact management practices in the cloud industry, which, in turn, can stimulate much innovative research from the OM perspective. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1178 .

Against the background of the rapid development of big data and big data analysis technologies, cloud computing service providers provide services to enterprises and individuals. In many large enterprises, by setting up a cloud computing platform environment, multiple services within the enterprise are placed on the cloud platform to provide resource sharing services for various departments. With the dramatic improvement in computing performance of computer clusters, big data and cloud computing technologies are maturing. We combine cloud computing with financial management services, and use modern technical means to innovate financial management models; we will change the financial management model of enterprises by building financial sharing service centers, thereby providing more efficient financial management services. Based on the research of cloud computing technology, this paper expounds the application of cloud computing in financial sharing, and explores the transformation of financial management models in the context of cloud computing. Fully clarify the principles and procedures for the construction of financial sharing service centers based on cloud computing, and discuss the basic structure of financial cloud management. Finally, we discuss the operation model of financial management based on cloud computing to provide a reference for researchers in financial management.

The logging service on cloud is a critical component for administrators who maintain applications and solutions for end users. The service is usually a central server deployment to accept log messages from leaf computing nodes. Since several leaf computing nodes and their usages are dynamically changed for applications and solutions, the amount of generated log messages is also changed. This paper proposes the architecture and design for Distributed Logging Service (DLS) which can distribute processing powers and storage resources to leaf computing nodes with Distributed Hash Table (DHT). Those nodes generate log messages and also have DLS components locally. The evaluation results with the emulated environment show the feasibility of DLS with the scalability.

As on miniature devices to military applications, the cloud computing plays a vital role. Building efficient cloud management systems will lead to improve extraordinary features in the applications of cloud services. In cloud atmosphere, enormous tasks are performed simultaneously; an effectual task scheduling is very important role to get better performance of the cloud system. An assortment of cloud-based task scheduling algorithms is offered that schedule the user's task to resources for implementation. The innovation of cloud computing, conventional scheduling algorithms cannot gratify the cloud's requirements, the researchers are frustrating to modify conventional algorithms that can accomplish the cloud needs similar to rapid elasticity, resource pooling and on-demand self-service. Also, the priority becomes an important task when dealing with critical functionality systems. Real world invocations are needed to make an efficient selection in cloud services through a collection of functionally equivalent services. This research aims to detect a novel method to predict the system functionality without consuming more time and less expensive for implementation. Investigation on swarm intelligence-based task scheduling is presented. This will improve the power consumption by reducing overloads when more services opt for a single load. Experiments were carried out to test the effectiveness of the method.

Transmission of data via internet is one of the great purposes of cloud computing is popular for transferring and storing data via internet. The user can make use of cloud services through broker. But since the job initialization is dynamic the concepts like scheduling of jobs and task management is of crucial importance. The main reason is that the users can request the use of same resources at a time. Thus, managing the resources over the requests is important so that all users can access the resources. Although generic scheduling techniques like FCFS or priority scheduling are available in many ways they fall short in they’re purposeless, due to their own disadvantages. The algorithms have higher waiting time and high turnaround time, which is not that efficient when the number of jobs in cloud environments is very large. Here a hybrid of shortest job first and priority based Scheduling is used and implemented in a cloud environment and analyzed. The conclusions are noted down and they are promising enough. The average waiting time and turnaround time are greatly reduced and highly increased the efficiency of cloud management of resources. Cloud computing includes an online exchange of data, resources, and information, where the users are in constant need of resources. This causes congestion of network, starvation or at worst case a deadlock. To counter these problems occurring a new methodology of hybrid algorithm has been proposed by implementing two techniques namely, shortest job first and priority-based scheduling.

Clouds offer significant advantages over traditional cluster computing architectures including flexibility, high-availability, ease of deployment, and on-demand resource allocation -- all packed up in an attractive pay-as-you-go economic model for the users. However, cloud users are often forced into vendor lock-in due to the use of incompatible APIs, cloud-specific services, and complex pricing models of the cloud service providers (CSPs). Cloud management platforms (CMPs), supporting hybrid and multi-cloud deployment, offer an answer by providing a unified abstract interface to multiple cloud platforms. Nonetheless, modelling applications to use multi-clouds, automated resource selection based on user requirements from various available CSPs, cost optimization, security, and runtime adaptation of deployed applications still remain a challenge. In this tutorial, we provide a practical introduction to multi-cloud application modelling, configuration, deployment, and adaptation. We survey existing CMPs, compare their features and modelling methods. Finally, we provide a practical hands-on training for getting your applications ready for multi-cloud using selected tools. By the end of this tutorial, attendees should be able to understand the benefits of the multi-cloud approach, and prepared to deploy their first managed multi-cloud application.

More businesses and organizations are choosing clouds to accommodate their workload, e.g., annalistic departments of enterprises use public/private cloud to deploy the analysis function of data, and telecommunication operators deploy Virtual Network Functions (NFVs) such as virtual Evolved Packet Core (vEPC) on clouds to provide telecommunication services. When utilizing the cloud, the cloud user is concerned about the functionality, reliability, performance, etc. of the cloud-based function, which we call the intent or the service requirements. However, to meet the intent, the user needs to tell the cloud resource orchestrator/controller what kinds of Virtual Machines (VMs) are needed and the amount of computing resources necessary to allocate to each VM, which we call resource requirements. In our preliminary work [1], we addressed this problem and proposed an Intent-Based Cloud Management (IBCM) framework to "translate" the cloud user's service requirements into resource requirements. In this work, we have focused on realizing a Resource Design Framework (RDF) for IBCM that decides the needed computing resource amount to fulfill the cloud user' intent/ service requirements. The main contributions of this work are as follows: (1) We have systematically analyzed factors that need to be taken into consideration when designing the resources. (2) On the basis of the analysis, we propose the architecture of RDF that is applicable for various cloud-based applications and scenarios. To the best of knowledge, this is the first work to address this issue. (3) We have validated the proposed RDF in an experimental cloud-based machine learning scenario. The experiment results show our RDF is able to infer the performance and operation state with over 90% precision, thus enabling resources to be precisely designed in accordance with performance requirements and operation policy. (4) In addition to precise resource design, we also proposed a service level agreement (SLA) violation prevention mechanism for RDF and verified its effectiveness.

Over the last decade, cloud computing has changed the IT world, mainly in the ways of storing, accessing and securing the users' data. However, there is no single cloud management platform in the market that can be used to manage different clouds (ex: AWS and AZURE), together with container communication between the clouds, to cater different cloud user requirements. We have proposed a platform for cloud service providers that automates the multi-cloud management related services with following modules: (1) communication, (2) resource allocation and (3) log view. Our results show that, using our proposed modules, the multi-cloud management related services can be automated efficiently and reliably.

Cloud computing offers many types of computer related services without the direct active management of their users. Cloud Service Providers (CSPs) are responsible to manage these services such as placement of services in the cloud, resource allocation, network monitoring etc. The cloud service provider is required to monitor the network traffic, predict the dynamic traffic changes, and scale out the resources accordingly. We have proposed a platform for cloud service providers that automates the cloud management related services with following modules: (1) traffic monitoring, (2) traffic prediction, (3) virtual service instances placement and (4) traffic load balancing. We have used continuous and periodic approaches for traffic monitoring, Auto-Regressive Integrated Moving Average (ARIMA) model for traffic prediction, Randomized Weighted Majority Algorithm (RWMA) for virtual service instances placement and a threshold-based approach for load balancing. In this paper, we are presenting the performances of our cloud management platform, specially an evaluation of the algorithms used in above mentioned modules. Our results show that, using our proposed modules, the cloud management related services can be automated efficiently and reliably.

Live Virtual Machine (VM) migration across data centers is an important technology to facilitate cloud management and deepen the cooperation between cloud providers. Without the support of a shared storage system between data centers, migrating the storage data (i.e. virtual disk) of a VM becomes the bottleneck of live VM migration over Wide Area Network (WAN) due to the contradiction between the low bandwidth of the Internet and the comparatively large size of VM storage data. According to previous studies, many inter- and intra-VM duplicated blocks exist between VM disk images. Therefore, data deduplication is widely used for accelerating VM storage data migration. However, it must make a trade-off between computation cost and transmission benefit. Existing approaches are fragile as they explore only the static data feature of image files without much consideration on data semantics. They may adversely influence on migration performance when the benefit resulting from data deduplication cannot remedy its computation overhead. In this paper, we propose a new space-efficient VM image structure—three-layer structure. According to different functions and features, the data of a VM are separated into an Operating System (OS) layer, a Working Environment (WE) layer, and a User Data (UD) layer. Based on this structure, we design a novel VM migration system—LayerMover. It mainly focuses on improving migration performance through optimizing the data deduplication technique. Our experimental results show that three-layer image structure can improve data sharing between VMs, and the similarity ratio between WE images can reach 70%. The tests for LayerMover indicate that it can be significantly beneficial to VM migration across data centers, especially when multiple VMs which share base images are migrated.

Data security is the major problem in cloud computing. To overcome this problem in the existing work Password can be used as a key to encrypt and decrypt the data in cloud environment. Some of the limitations having Password system because it is not secured, and easily forgotten. In order to overcome these problems the proposed technique utilizes effective data storage using biometric-based authentication to support the user authentication for the cloud environment. For user authentication here we are considering iris and fingerprint. Initially the feature values are extracted from the iris and fingerprint using local binary pattern. In order to improve the security Extracting the feature value of fingerprint and iris and it is given input to the hybrid Genetic Algorithm and Particle swarm optimization algorithm to find the best solution using Cross over mutation technique. Best solution value can be act as a key for encrypting and decrypting data using Triple Data Encryption Standard Algorithm. Finally encrypted data can be stored in cloud using cloud simulator in the Working platform of net beans in java. Finally randomly tested with 5 fingerprint and 5 Iris image for the purpose of man in the middle attack. After tested with fingerprint and iris proposed Hybrid Genetic algorithm with Particle swarm optimization algorithm having less attack compared with the existing Particle swarm optimization algorithm. So the intruder cannot be able to access the data in cloud environment.

The proliferation of cloud computing relied on the virtualization of the compute and storage resources and provisioning them dynamically according to users’ needs on a pay-per-use model. Massive cloud providers have geo-distributed cloud data centers to ensure service reliability, availability and satisfy user’s need. Therefore, cloud management systems are necessary to increase the profit of cloud providers and to improve the quality-of service demanded by users. This paper focuses on an energy-efficient method to solve the problem of allocating data-intensive workloads in geographically distributed data centers. The workload’s tasks are characterized by large data transfer times than their execution times. The problem formulated as a nonlinear programming optimization problem. Then, to find an optimal solution to the problem, meta-heuristic genetic algorithm is proposed. The designed heuristic takes into account the cost of the data transfer time from the storage location to the compute servers as well as the workload makespan on the available hosts. Extensive simulations using the CloudSim simulator are conducted to evaluate the efficacy of the proposed allocation method and how it performs with respect to other methods in the literature. Our results show significant enhancements in energy consumption while respecting the user’s QoS.

A Cloud computing allows to users to get computing resources and services over the internet in the form of an on-demand service using virtualization technology. While cloud resources are built in different locations so they need new strategies to manage the running of them. These strategies may contain more than one algorithm. In cloud computing system, scheduling system is the one of the main keys of system’s performance. The scheduling system doesn’t include task scheduling strategies only, but also it contains fault tolerance and load balancing strategies. Scheduling system is used in cloud management and handling failure. In this study, we discuss some scheduling algorithms and their advantages and disadvantages. Also we guide readers to choose the best for achieving the high quality.

OpenStack is arguably the most popular open-source cloud orchestration software available today. Its user-base spans from large corporations and large scale service providers to SMEs. OpenStack manages infrastructure resources e.g. compute, storage and networking. While customers get a fully functional cloud, an incumbent fault-tolerance analysis and suggestion system is absent from this otherwise robust cloud management system. In this work, we demonstrate a system for automated fault-tolerance (FT) analysis of network applications hosted on OpenStack. Faults are modeled by random server and/or service shutdowns. Aim of the FT Analyzer is to equip cloud tenants with information to determine optimum configuration for their applications before firing up the service. FT Analyzer helps tenants maintain maximum up-time for services and data and preemptively tests the virtual network based applications for any issues that can have a negative impact on performance in post deployment scenario.

The Industrial Internet of Things (IIOT) is a new generation intelligent system based on the real-time network and embedded system. It combines the global Internet with the new capabilities of direct control of the physical world to promote the rapid development of manufacturing industry and other industries. In this paper, a four-layer IIOT architecture, that is perception executive layer (data acquisition), cognitive layer (business logic, identification, classification), network layer (communication layer), and control layer (external participation), is proposed aiming to the design goal to realize the intelligent management and remote control of motorcycle ’s endurance test. This architecture has a good guiding role for the design and development of IIOT and service-oriented architecture. On this basis, the paper builds the new Internet of Thing test platform management system with the ability of digital intelligent control, intelligent perception and remote intensive management. The system includes a management platform and five sub-system: front-end data acquisition system, control feedback and processing system, data transmission system, database management system, and cloud management system. By improving the interface capability of external (detection) parameters and establishing protocol relationships, the system become an open system with easy management, upgrading, extension, and compatibility. Thus realized the remote monitoring and control management on the endurance test process, improved the ability of cloud services through the cloud monitoring and management platform, and achieved the test control and management systematic, modular, digital, and intelligent.