Current Cloud Computing is primarily based on proprietary data centers, where hundreds of thousands of dedicated servers are setup to host the cloud services. In addition to the huge number of dedicated servers deployed in data centers, there are billions of underutilized Personal Computers (PCs), usually used only for a few hours per day, owned by individuals and organizations worldwide. The vast untapped compute and storage capacities of the underutilized PCs can be consolidated as alternative cloud fabrics to provision broad cloud services, primarily infrastructure as a service. This approach, thus referred to as "no data center" approach, complements the data center based cloud provision model. In this paper, we present our opportunistic Cloud Computing system, called cuCloud, that runs on scavenged resources of underutilized PCs within an organization/community. Our system demonstrates that the "no data center" solution indeed works. Besides proving our concept, model, and philosophy, our experimental results are highly encouraging.

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With the prevalence of cloud computing, privacy-preserving database outsourcing has been spotlighted. To preserve both data privacy and query privacy from adversaries, databases need to be encrypted before being outsourced to the cloud. However, there exists the only kNN classification scheme over the encrypted databases in the cloud. Because the existing scheme suffers from high computation overhead, we proposed a secure and efficient kNN classification algorithm that conceals the resulting class label and data access patterns. In addition, our algorithm can support efficient kNN classification by using our encrypted index scheme and the Yao's garbled circuit. We show from our performance analysis that the proposed algorithm achieves about 17 times better performance than the existing scheme, in terms of classification time.

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With cloud computing, the efficient management of resources is of great importance as an increased utilization of the available resources can result in higher scalability and significant energy and cost reductions. Experimental validation of novel resource management strategies is costly and time consuming, and often requires in-depth knowledge of and control over the underlying cloud platform. As a result, many novel strategies are only evaluated by means of simulations, in which the whole cloud computing environment is modelled and simulated. Nonetheless, experimental validation should also be considered during the validation, as these types of experiments can often result in new insights or they can be used to fine-tune some specific parameters. In this paper we present a general approach for the experimental validation of cloud resource management strategies, together with the introduction of a cloud testbed adapter which was designed to facilitate the step from simulations towards experimental validation on physical cloud testbeds. We illustrate our solution by means of two case studies, focusing on two different types of testbeds. The adapter mainly acts as a dispatcher towards specific services of the evaluated cloud setup, and allows researchers to easily validate their ideas without having to dive deep into the complex details of the underlying cloud platform.

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There is a perfect storm of the use of cloud computing, and the growth of Internet of Things (IoT). IoT is about processing data that comes from devices in some way that's meaningful, and cloud computing is about leveraging data from centralized computing and storage. Growth rates of both can easily become unmanageable. We have some problems to solve. In addition, alternatives are being consider to placing everything in the public cloud because the public cloud, in some cases, no longer makes sense.

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In recent years, cloud computing is increasingly abstracting away the operating system, allowing developers to focus higher up the stack on applications, not infrastructure. In this position paper, we analyzed the deficiencies of the traditional operating systems in the cloud environment and the features of existing cloud operating systems. We have proposed an operating system architecture for cloud computing which is called vStarCloud, and implemented a prototype to show that our approach is promising. We describe how common problems for the cloud environment can be effectively solved by POSIV (Portable Operating System Interface of vStarCloud) interface. The prototype of vStarCloud provides the ability of virtual machine management and MapReduce job management. Then we discussed how to cope with the challenges of the new cloud operating systems.

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The enrichment knowledge of Cloud Computing is Green Cloud Computing. The term of Cloud Computing is a globally inter - connected networks of Computing Resources( Servers, Networks, Applications, Hardwares, Softwares). The Green Computing is an Environmental Benefits of eco-friendly usage of Computing Resources. The combination of Green Computing and Cloud computing is Green Cloud Computing. GCC performs both performance and efficiency. The combination of Mobile Computing and Cloud Computing is known as Mobile Cloud Computing. Now, the Computational science is changing to be data-intensive. So, Load balancing is a technique to distribute the load across a given Green Cloud Network Vs Mobile Cloud Network. In this proposed system, the in-depth analysis of Load Balancing Algorithms. The Load of Cloud Balancing is a process of reassigning the total load to the individual nodes in a given network. Then the Comparative study of load balancing algorithms with its quality metrics are summarized.

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With the increasing trend towards edge and fog computing, the aim of the RECAP simulator is to simulate large scale scenarios in the cloud, fog and edge computing space in order to provide decision and control support for application and data center resource administration. This will be accomplished through the simulation of applications and application subsystems, simulation of infrastructure resources and resource management systems, and experimentation and validation of simulation results. The RECAP simulator and associated models will provide support for understanding and predicting impact on resources, workloads and quality of service (QoS) metrics as well as trade-offs for energy efficiency and cost within cloud, edge and fog computing scenarios, while maintaining the service level agreements (SLAs) of users.

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This track started in 2009 with opening remarks from the Chair observing that the computing cloud evolution depends on research efforts from the infrastructure providers creating next generation hardware that is service friendly, service developers that embed business service intelligence in the computing infrastructure to create distributed business workflow execution services and service providers who assure service delivery on a massive scale with global interoperability. The state of the art architecture and evolution of the cloud at that time was already increasing datacenter complexity by piling up new layers of management over the many layers that already exist. Since then, the scale of distributed applications and their management have taken a new dimension demanding tolerance to wild fluctuations both in workloads and available computing resource pools. There are many calls to go cloud native and architect applications using the many services provided by the cloud providers such as Amazon Web Services. On the other hand, there are also calls for avoiding vendor lock-in by going multi-cloud and becoming cloud agnostic. In this conference there is a new paper that proposes cloud agnostic approach with globally interoperable cloud network using private or public network while reducing the complexity of Virtual machine image motion across clouds. In addition, there are 7 papers describing advances in current distributed and cloud computing practices dealing with quality of service, adaptive algorithms and software defined network architectures.

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Advances in cloud computing have made it a feasible and cost-effective solution to improve the resiliency of enterprise systems. However, the replication approach taken by cloud computing to provide resiliency leads to an increase in the number of ways an attacker can exploit or penetrate the systems. This calls for designing cloud systems that can accurately detect anomalies and dynamically adapt themselves to keep performing mission-critical functions even under attacks and failures. In this paper, we propose a self-adaptive resiliency approach for cloud enterprise systems that employs a live monitoring and moving target defense based approach to automatically detect deviations from normal behavior and reconfigure critical cloud processes through software-defined networking to mitigate attacks and reduce system downtime. The proposed solution is promising to present a unified framework for resilient cloud systems.

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With the advancements in mobile technologies, different compute-intensive tasks are emerging rapidly. However, due to resource constraints, these tasks are facing challenges to execute on mobile devices. As a solution to this problem, cloud migration has been introduced to execute a task on the cloud and then to return the results to the user mobile device. In this paper, a cloud migration decision-making algorithm for compute-intensive tasks has been proposed to determine the feasibility of execution on a cloud server instead of a mobile device. Furthermore, the performances between mobile and cloud executions have been investigated which show that the task completion time can be reduced by a factor of 6 to 8 of mobile service when a cloud server is utilised.

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A primary challenge of the cyberinfrastructure research community is the need to define the Platforms for Science beyond 2020. We analyze major current trends and propose that in order to deliver the Platform for Science in 2020 the dominant research challenge is to manage the convergence of capabilities of traditional HPC systems with richness of Apache Big Data systems. In this vision paper, we purport to examine the relationship between infrastructure for data-intensive computing and that for High Performance Computing and examine possible "convergence" of capabilities.

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20 years after writing Enterprise Application Integration, data integration is still an afterthought when it comes to the cloud deployments I see. Enterprises moving to cloud tend to focus on the move itself, and not as much on what they need when they get there. While this may be a common practice, it's not a best practice. Data integration is needed because we just re-hosted some of our data on a remote cloud service. Thus, the inventory system that's still running on a mainframe in the data center needs to share data with the sales order system that's now on AWS. In other words, our data integration problem domain just got bigger and more complex.

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Fog computing extends cloud computing to end devices, in order to better support time dependent, location dependent, massive scale, and latency sensitive applications. In this paper, we propose a fog computing ecosystem, implement a real testbed, and evaluate it with diverse usage scenarios. More specifically, we study three usage scenarios and optimize our fog computing platform for them. These usage scenarios are: (i) content dissemination in challenged networks, (ii) crowd-sourced fog computing, and (iii) programmable Internet-of-Things analytics. We leverage and enhance open source projects to realize our fog computing platform.We then solve optimization problems in each usage scenario with novel algorithms. Sample results show that our proposed algorithms outperform baseline algorithms by at least 30.3%, 20.0%, and 89.4% in terms of the main performance metrics of the three usage scenarios, respectively. Several ongoing tasks aim to improve our fog computing platform for: (i) network resource provision, (ii) system dynamics adaption, and (iii) device availability prediction.

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Multi-cloud computing has been proposed as a way to reduce vendor lock-in, to improve resiliency during outages and geo-presence, to boost performance and to lower costs. However, semantic differences between cloud providers, as well as their heterogeneous management interfaces, make changing from one provider to another very complex and costly. This is quite challenging for the implementation of multi-cloud systems. In this paper, we aim to take advantage of formal methods to define a precise semantics for multi-clouds. We propose fclouds, a formal-based framework for semantic interoperability in multi-clouds. This framework contains a catalogue of formal models that mathematically describe cloud APIs and reason over them. A precise alignment can be described between their concepts, which promotes semantic interoperability.

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Mobile devices are resource restrichtion devices (i.e.) size and facilities are inversely proportional. While executing the computational intensive jobs in mobile devices, power system is getting drained very fast. Computational time of application and power consumption in mobile devices are directly proportional. Cloud computing is emerging technology to solve many problems. Cloud computing are getting ready to give their hands to solve. Mobile computing and cloud computing are joined their hands to have good hope. Computational intensive tasks are offloaded to rich resourceful server. Cloud resources are heterogeneity in nature. It creates a challenge in terms of assigning the offloaded tasks in VM machine in efficient manner. The main objective of this paper is to find optimized mapping between offloaded task and cloud resource. First we executed the workflow in mobile alone. Then we observed that It consumes more time to complete and also gain more energy. Then we conducted the experiment on homogeneous and heterogeneous configurations of virtual machines for scheduling the offloaded task to have a least completion time. Finally we found optimal mapping between offloaded task with virtual machines in terms of cost and power consumption.

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In cloud services, trust management is more important than ever before in the use of information and communica-tion technologies. Obviously, the cloud service business relies on the fact the service users trust cloud service providers. A user who trusts the cloud service believes that utilizing the service is a positive thing and helps in achieving his/her goals. In general, the question of trust measurement proved to be a difficult one due to the dynamic nature of cloud resources. This paper presents a QoS-based trust computing system for SLA guarantee in cloud computing system. By embedding our trust system into the SLA (Service Level Agreement) architecture, trust management system can prepare the best trustworthy resources for each service request in advance, and allocate the best resources to users. Experimental results show that our trust system converges more rapidly and accurately than do existing approaches, thereby verifying that it can effectively take on trust measurement tasks in cloud computing.

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Give full play to the big data in the cloud computing environment application advantages has become an important information service mode of the era of Internet +, paper with information service quality evaluation as the main line, using fuzzy comprehensive evaluation method to analyze a set of cloud computing environment information service quality evaluation process, at the same time, using the case of project construction example analysis, the evaluation of cloud computing environment based on large data information service quality has important reference value.

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Cloud provides resources to the users based on their requirements by using several resource allocation schemes. Reliable resource allocation is one of the major issues of cloud computing. The objective of this paper is to provide a reliable resource allocation approach for cloud computing while minimizing the cost. The existing research works on resource allocation in cloud mostly address cost and resource utilization whereas we address the most crucial feature, which is cloud reliability. The main novelty of our work is that we consider not only reliability but also cost while allocating appropriate resources to the users. The aim of our proposed approach is to maximize reliability while minimizing the cost. In this regard, we propose a heuristic for resource allocation in cloud. We provide several performance analyses to validate our approach and the simulation results show that our approach provides increased reliability while allocating resources to the users.

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Cloud computing has been adapted for various application areas because it can reduce the time required for system development and the cost of hardware. One of the factors that degrades performance stability of applications running in the cloud is “unexpected loads”, caused by interference between Virtual Machines (VMs) coexisting on the same physical machine. In this paper, we propose a VM scaling method that forecasts performance fluctuation caused by unexpected loads to adjust the number of VMs appropriately. We evaluate the proposed method with simulation to verify that the proposed method can improve performance stability of MQTT brokers.

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Cloud-computing is an enticing topic; it developed very quickly. Numerous huge cloud organizations, alike as Amazon, Yahoo, Google, offer many cloud-services and have many users. Cloud-computing is an Internet-based computing approach, where the software, resources and the applications are shared between many-to-many computing devices. Cloud Load-balancing (CLB) takes the wealth of the cloud's scalability and the physically to meet rerouted workload and to improve overall availability. In the addition of tasks goods and traffic distribution, CLB technology provides fitness checks to the cloud applications. We used GA approach to handling the LB in cloud-computing. Our proposed work is more appropriate than the current techniques work, as we executed the cloudlets in less time and performing the load-balancing in more profitability.

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Cloud computing has become pervasive nowadays. Additionally, cloud computing customers increasingly demand the use of accelerators such as CUDA GPUs. This has motivated that Amazon, for example, provides virtual machine instances comprising up to 16 NVIDIA GPUs. However, the use of GPUs in cloud computing deployments is not exempt from important concerns. In order to overcome many of these concerns, the remote GPU virtualization technique can be used. In this paper we present the design of a live demo to be used in exhibitions in order to show the benefits of using such a virtualization technique in the context of cloud computing systems. The demo was designed to be technically sound at the same time that it draws the attention of attendees. The demo was successfully used in the recent SuperComputing'16 exhibition, attracting more than 100 people to the booth.

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This electronic document is about to calculate the performance of cloud computing for distributed data center using cloud-sim software programme, The simulation and performance analysis will be done by using the cloud analyst tool. It is easy to evaluate new strategies in utilization of clouds considering policies, scheduling algorithms, load balancing policies, etc. It can also be used to assess competence of strategies from various perspectives such as cost, application execution time etc. It also supports evaluation of Green IT policies. User can use it as building blocks for simulated Cloud environment and can add new policies for scheduling, load balancing, and new scenarios. It shows that it is flexible enough to be used as a library that allows you to write a desired scenario by writing a Java program.

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Cloud computing is now-a-days the most trending field in Information technology which facilitates the users to access everything they need on the internet from software's to the resources. The user just need to request to the server to access the required requirement. Different architecture of cloud computing includes parallel and distributed computing which enable the remote area users to access the resources efficiently. Client from different geographical areas generate the request to the server and in response server has to assign the tasks to the available servers for efficiently working. As the distributed environment is growing, problems of load balancing is also emerging and trying to seek our attention. Different algorithms are used in cloud computing to face this challenge of loading the balance of data among servers. Some of them are discussed in this article and simulation is performed using the cloud analyst tool to see the performance comparison among them. Round robin is found best among the being compared.

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With emergence of the smartphone, peoplegain access to several applications that make their workeasier. Likewise, one of the top trended app searcheswhich we do is location tracking or accessing location ofthe other user in the cloud computing environment. Inrecent past, these accesses are done without user'sknowledge and accord. In this paper, we present thestudy about runtime location access of users in the cloudcomputing environment. With this, we have thepossibility of the location accessibility of users both viaonline as well as offline. Our main goal is to get users'location for safety purpose. Through the online, we haveseveral apps which show the user's location but not mucheffective. We introduce the novel Mobile-Based LocationTracking without Internet (MLTWI). Our approachtracks the location of the user by applying latitude andlongitude information. The latitude and longitudeinformation are collected using our designed latitude andlongitude (LL) algorithm. Based on the realexperimental tests and simulation results, we validatedthat our scheme outperforms to known approaches.

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A key benefit of connecting edge and cloud computing is the capability to achieve high-throughput under high concurrent accesses, mobility support, real-time processing guarantees, and data persistency. For example, the elastic provisioning and storage capabilities provided by cloud computing allow us to cope with scalability, persistency and reliability requirements and to adapt the infrastructure capacity to the exacting needs based on the amount of generated data.

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The development of Cloud computing applications has initiated a substantial influence on the data center expansion around the world. This expansion has led to an increase in energy consumption within the different data centers. Aware of the environmental and financial impact of the energy efficiency, several research studies have tackled the problem of the energy efficiency at the level of the hardware as well as scheduling algorithms. However, the majority of the proposed solutions such as Dynamic Voltage and Frequency Scaling (DVFS) have demonstrated a negative effect on response time where most Cloud applications required a high responsiveness. This paper suggests an energy efficiency solution for Cloud computing by applying a fast and smart algorithm. Firstly, the paper provides an evaluation of the energy efficiency within the Cloud environment through the Taguchi experience plan. Secondly, the article introduces a bee colony scheduling algorithm which aims to optimize the energy efficiency while guaranteeing an optimum response time. The validation results acquired from the GreenCloud simulator emphasize the effectiveness of the suggested scheduling analysis methodology.

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Cloud computing is a technology that can provide many benefits in the field of education. The utilization of cloud computing technology is a necessity in the college environment, where collaborative learning is a main feature. Google Apps is one of the largest cloud computing service providers, offering multiple collaboration facilities. Measurable and planned adaptations to Google Apps can provide many benefits for higher education institutions. How is measurable and planned adaptation of cloud computing technology in higher education institutions? How to implement Google Apps to support collaboration activities at higher education institutions? With the five-step method of adopting cloud computing strategies at higher education institutions, it can be explored the benefits of using Google Apps to support collaborative activities at higher education institutions.

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A Cloud federation is a collaboration of organizations sharing data hosted on their private cloud infrastructures in order to exploit a common business opportunity. However, the adoption of cloud federations is hindered by member organizations' concerns on sharing their data with potentially competing organizations. For cloud federations to be viable, federated organizations' privacy concerns should be alleviated by providing mechanisms that allow organizations to control which users from other federated organizations can access which data. We propose the architecture of a novel identity and access management system part of FaaS, a cloud federation service developed by the H2020 SUNFISH project. Our system allows federated organizations to enforce attribute-based access control policies on their data in a privacy-preserving fashion. Users are granted access to federated data when their identity attributes match the policies, but without revealing their attributes in clear. The architecture relies on two novel technologies, blockchain and Intel SGX hardware platform to guarantee integrity of the policy evaluation process.

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Nanostructures, atoms and molecules are the key matters of nanotechnology. It is applicable in almost all the domain of applied science which includes physics, chemistry, biology, material science, computer science, bio technology and so on. There are huge requirement of nanotechnology in computing and information technology. Nanotechnology has wider scope in computing specially in core computing i.e. use of nano-materials rather silicon chip. Today the size of every kind of electronic devices becomes smaller and for this nanotechnology has played a vital role. The bulk or top-down technology is another important one where applications nanotechnology in computing and information technology taken place. The modern computing and information technology generation seeks more nanotechnology applications for smarter and smaller electronic gadgets. In recent past top down technology is core and very much important criteria for the nanotechnologist and information scientist and technologist are also putting efforts on creation of products by the use of this. In the field of Information Technology, Cloud computing is a kind of mechanism for software and hardware practices empowered by virtualization. The architecture of cloud basically uses less and minimum uses of computer, hardware as well as IT Delivery and in this case application of Nanotechnology are no doubt important and valuable. In recent past Nanotechnology has also created a new avenue for another IT technology i.e. Big Data. In this paper basics of Nanotechnology including its applications in computing and information technology etc have well illustrated. Cloud computing-Big Data Management and their current opportunities in connections with Nanotechnology has also well defined. The paper also prepared with the aspects of issues and challenges as well.

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Edge computing has been proposed for new types of cloud services, which need computing infrastructure at the network edge. Driven by important use cases from the Internet of Things (IoT) domain, edge cloud computing has also a huge business potential. Edge computing devices are already operational in many industrial and consumer-oriented scenarios. A typical characteristic of these solutions is, however, that the hardware and software platforms in use are proprietary and closed. The interaction among the different vendor platforms, and their extension with third party services, in general, is not well supported. As a consequence, the opportunities for third party providers to build new tailored edge services on these platforms face significant barriers. In this paper we argue to build a collaborative edge cloud deployed on home servers. We position the proposed system and describe its building elements, where we use Docker containers for provisioning the services.

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Cloud computing data security and privacy is always the area of concern for the cloud users and providers. The user identity, credentials and data privacy etc., are some of the issues for which user needs to assure about proper control by the provider. A tool to assess and select the cloud security based on the user demands is need in a cloud environment. Here a data security measurement tool is proposed that will be used to measure the cloud computing data security. Parameters for measuring the data security are identified and various cloud services are analyzed to measure the security against these parameters. Cloud Security Alliance report is also used to check the adequacy and accuracy of the cloud data security measure.

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Along with the growing popularisation of Cloud Computing. Cloud storage technology has been paid more and more attention as an emerging network storage technology which is extended and developed by cloud computing concepts. Cloud computing environment depends on user services such as high-speed storage and retrieval provided by cloud computing system. Meanwhile, data security is an important problem to solve urgently for cloud storage technology. In recent years, There are more and more malicious attacks on cloud storage systems, and cloud storage system of data leaking also frequently occurred. Cloud storage security concerns the user's data security. The purpose of this paper is to achieve data security of cloud storage and to formulate corresponding cloud storage security policy. Those were combined with the results of existing academic research by analyzing the security risks of user data in cloud storage and approach a subject of the relevant security technology, which based on the structural characteristics of cloud storage system.

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Ever since the completion of the Human Genome Project in 2003, the human genome has been represented as a linear sequence of 3.2 billion base pairs and is referred to as the "Reference Genome". Since then it has become easier to sequence genomes of individuals due to rapid advancements in technology, which in turn has created a need to represent the new information using a different representation. Several attempts have been made to represent the genome sequence as a graph albeit for different purposes. Here we take a look at the Variant Calling Format (VCF) file which carries information about variations within genomes and is the primary format of choice for genome analysis tools. This short paper aims to motivate work in representing the VCF file as Directed Acyclic Graphs (DAGs) to run on a cloud in order to exploit the high performance capabilities provided by cloud computing.

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Since the performance improvement has always been in demand but the ever increasing energy/ power consumption of computing systems and emission of carbon dioxide in the environment has limited this improvement of performance. Therefore, there is much need of optimizing the power consumption of such systems. Considering cloud computing to be the focus of this review, we need to make them energy efficient. Up to now, Data Centers have contributed a lot in making cloud computing powerful. Thus, there is an increasing demand for Data Centers and this will tend to increase in future as well. This increase in demand has increased the energy consumption by data centers. Hence, there is an utmost need to take sufficient measures to lower the risk factors associated with increased energy demand. The fundamental objective of this review is to analyze the various techniques that have been implemented so far to reduce the consumption of energy by cloud computing systems. These techniques mainly focus on the virtualization of machines that has proved the revolutionary application area of green IT.

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Cloud computing is basically excogitated from the word internet, which is an advance technology. Cloud computing enables us to access services and infrastructure over the cloud through internet and also permits secure sharing of resources. The use of computing resources as a service with the help of internet is called Cloud Computing. It has started to gain vision in corporate data centers. In recent years, cloud computing is including grid computing because utilization of virtualization at data center could increase. With increase in the number of user and data, there is a need of data confidentiality as well as authenticity of users connected to the cloud so as to protect them from each other as well as from the hackers too

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Applications that span across multiple clouds are often found to be vulnerable to security threats. Such highly heterogeneous environments need a fine-grained access control mechanism like Attribute-based Access Control (ABAC) for enforcing security. A first step towards successfully deploying ABAC is to define an appropriate set of access control rules that establish the desired inter-cloud accesses. This becomes more challenging when the access requirements vary with time or the users and objects are updated quite frequently. We study the problem of formulation of an optimal set of ABAC rules for granting inter-cloud accesses in a dynamic environment. The problem being NP-Hard, we propose heuristic solutions. Extensive experiments on benchmark datasets show encouraging results.

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One of the key challenges faced by consumers is to efficiently manage and monitor the quality of cloud services. To manage service performance, consumers have to validate rules embedded in cloud legal contracts, such as Service Level Agreements (SLA) and Privacy Policies, that are available as text documents. Currently this analysis requires significant time and manual labor and is thus inefficient. We propose a cognitive assistant that can be used to manage cloud legal documents by automatically extracting knowledge (terms, rules, constraints) from them and reasoning over it to validate service performance. In this paper, we present this Question and Answering (Q&A) system that can be used to analyze and obtain information from the SLA documents. We have created a knowledgebase of Cloud SLAs from various providers which forms the underlying repository of our Q&A system. We utilized techniques from natural language processing and semantic web (RDF, SPARQL and Fuseki server) to build our framework. We also present sample queries on how a consumer can compute metrics such as service credit.

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Cloud Computing is an emerging technology which plays an important role in the digital world. Scheduling and load balancing are the two major issues in cloud computing. Scheduling is the process of allocating the cloud resources to the users whereas Load balancing is the efficient utilization of servers and ensure the availability of Cloud Servers to the users at any time. The proposed work ENNEGCC 3D gives a novel 3-Dimension Neural Network Predictor model to estimate the workload. The server statuses are dynamically changed according to the availability. ENNEGCC 3D work emphasizes Green Cloud Computing by reduced power Consumption and Heat. The packet drop is also into consideration and reduced the packet lost due to server availability. The experiment is carried out using 6 different processors using Cloud SIM Tool. ENNEGCC 3D avoids unwanted status change in server thus greatly reduces the power consumption.

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Data-intensive cloud computing is used to manage a large amount of data. Transaction management is one of the most considerable aspect to any database management system. ACID-based transaction processing is allowed for static cloud computing environment. But for moving cloud environment it is difficult to maintain all the ACID properties. As a result for moving cloud environment CAP theorem is considered. So, in order to achieve both Availability and Partition tolerance, it becomes difficult to maintain consistency for cloud database. This paper includes an algorithm for preserving consistency in data-intensive cloud computing environment. The simulation results that our proposed algorithm CBA(Consistency Based Algorithm) gives better result rather than the existing one.

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Cloud computing is an increasing field in studies and industry today, which includes virtualization, distributed computing, net, software program and web services. A cloud consists of several factors consisting of customers, records centers and disbursed servers, net and it includes fault tolerance, excessive availability, effectiveness, scalability, flexibility, reduced overhead for users, decreased fee of ownership, on demand offerings and etc. The offerings of cloud computing are becoming ubiquitous, and function the primary source of computing power for special packages like corporations and personal computing programs. In this paper we delivered the novel load balancing algorithm the use of fuzzy technique in cloud computing, in which load balancing is a middle and hard problem in Cloud Computing. The processor pace and assigned load of Virtual Machine (VM) are used to stability the burden in cloud computing thru fuzzy logic.

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In the era of Big Data analytics predictive modeling plays an important role to predict the future demand and behavior by using historical data. As majority of the IT companies running behind cloud services, the cloud service providers like Amazon, Google cloud, Microsoft Azure etc may be interested to know the future demand of the computing resources so that they can derive new pricing schemes to gain more profit. The providers have different pricing schemes to charge for computing resources e.g., Amazon provides three pricing schemes, namely, on-demand pricing, reserved pricing and auction pricing in the same way Microsoft has different schemes like Pay-As-You-Go Subscriptions, Prepaid Subscriptions. The paper presents survey of role of predictive modeling in cloud service pricing. The survey result clearly shows that predictions made by various author are closer to actual outcomes, which highlights the importance of predictive modeling to forecast future demand of cloud computing resources and deciding the price of resources.

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Cloud federation is a novel concept that has been drawing attention from research and industry. However, there is a lack of solid proposal that can be widely adopted in practice to guarantee adequate governance of federations, especially in the Public Sector contexts due to legal requirements. In this paper, we propose an innovative governance approach that ensures distributed and democratic control in cloud federations. Starting from FaaS, a recent cloud federation proposal, we propose a blockchain infrastructure for the federation registry that implements the proposed governance approach.

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Cloud computing is the rising technology in everyday life. Cloud computing is a boon on IT area. Everyone moving on cloud technology from IT companies to military, single users to multiple users. Number of clients began utilizing cloud innovation and expanding gradually. Cloud services can access from anywhere without any demarcation, the only need is the internet. Virtualization is the essential feature of cloud computing. These virtual machines are made on the premise of client requests. In virtualization the jobs processes and execute randomly. So some jobs required more time to execute while some required less time. Job who required long time makes other small jobs to wait for longer time. To swamp this situation paper suggests different scheduling algorithms. In the proposed framework for execution of scheduled jobs if the current virtual machines can't execute jobs having more prominent mips rate as contrast with virtual machine mips then the framework will create another virtual machine on the same physical machine which will be equipped for executing work.

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Cloud computing offers the potential for productivity, cost savings and innovation advantages to organizations. To utilize these benefits, to facilitate wide-scale cloud adoption and to embrace cloud computing, we must address many cloud security challenges. Attackers can compromise the vulnerable hosts and can either take over their resources or use them as stepping stones for future attacks. Open ports can be the most prominent gateway for many threats and potential attacks. Security communities, reports and surveys often state that port scans can be considered as precursors to an attack. In this paper, we performed an analysis pinpointing the risks of open ports in a cloud environment. We examined the problem by utilizing Amazon Web Services (AWS), showing that both individual and pairs of open ports on cloud servers can expose organizations to increased security risks. We discussed the security guidelines and potential best practices to address them.

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Scheduling in cloud computing is an essential requirement to execute the tasks given as per the requirements. The scheduler orders the tasks according to the steps mentioned under the scheduling algorithms. The scheduling algorithms need to ensure that the cloud is functioning to the optimum efficiency as well as the tasks are given equal weightage. The way of execution of a scheduling algorithm differs from one another and hence a system, if established, to compare these scheduling algorithms will help the cloud service providers to work better and in a more efficient way as per the user requirements. In fact, it is very important to have a standard comparison system amongst the scheduling algorithms, as per now, there is no particular research done to compare and contrast the scheduling algorithm. This paper develops a set of environments and scenarios in order to differentiate the functioning of the scheduling algorithms. The following scheduling algorithms are studied: First Come First Serve, Shortest Job First, Opportunistic Load Balancing, and Generalized Priority Algorithm. They were put to test under the various environment and scenarios and were evaluated based on parameters such as Cost and Makespan. Based on these studies, an intelligent tool was developed which takes in the user preferences, such as the number of jobs, their preference of time metrics and cost metrics and suggests the scheduler about the number of VMs, and the Scheduling algorithm which should be used to accommodate the user requirements.

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Statistics show the volume of Earth Observation (EO) data increases in the exponential level during the past decade. As the new generation computing platform to meet the big data challenge, cloud computing significantly facilitates the large-scale EO data processing depending on its powerful computing capability. In this paper, we propose a Cloud WPS architecture integrating the cloud computing environment and OGC Web Services. Based on the architecture, we implement the architecture using GeoBrain Cloud, an Apache Cloudstack based private cloud computing platform, and a series of state-of-the-art open-source libraries and software. The result suggests that Web Processing Services and cloud computing environment could be successfully integrated by applying the proposed architecture.

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Cloud computing allows not only to obtain computing resources on-demand but also to store large amounts of data (big data) with a high level of fault tolerance. Nevertheless, data confidentiality for users of hybrid and public clouds is cannot guaranteed in full. Cloud providers have full access to user data, which threatens to compromise the integrity of data. Existing methods of providing security consider methods to increase the speed and reduce the load during authorization and data encryption. The paper proposes a method that describes the use of separate services outside the cloud for authentication, data management and metadata storage to eliminate the possibility of obtaining unauthorized access to data, and the use of metadata to perform integrity control. The developed method is being used to create a stand based on OpenStack and two services on separate servers. The owner of the database limits the access to data that is stored in an encrypted form and does not allow provider to interact with database.

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In the age of fast communication and processing, cloud has become an essential commodity for the tech industry and has been a boon for devices with lesser hardware capability. Cloud server serves as cheap, easy way to process data and saves the hardware production costs of devices. Cloud Computing requires huge computing resources and is an upcoming trend. The mobile device should be used to the maximum till the time the cloud server is faster for that amount of load. With higher loads, cloud computing must employ load balancing methods. Efficient load balancing scheme ensures providing resources for efficient resource utilization.

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Cloud computing is a large scale and heterogeneous distributed systems which are utilized to store and access the data easily. The clients are requesting and receive their resources as per the requirements. The number of users and services are increasing tremendously day by day. Thus, the resource management in the cloud is a challenging task. Hence, they required some automated strategies for dynamic provisioning to provide services which are secure and reliable. The autonomic resource provisioning is used to reduce financial and environmental cost with more efficiency. This paper presents different types of autonomic resource management approaches. It gives the overall advantages and limitations of various autonomic provisioning techniques. This paper would benefit both Cloud users and researchers to better understand about autonomic resource management.

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