Infrastructure as a service

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers.

Despite the evolution of cloud computing in recent years, the performance and comprehensive understanding of the available private cloud tools are still under research. This paper contributes to an analysis of the Infrastructure as a Service (IaaS) domain by mapping new insights and discussing the challenges for improving cloud services. The goal is to make a comparative analysis of OpenNebula, OpenStack and CloudStack tools, evaluating their differences on support for flexibility and resiliency. Also, we aim at evaluating these three cloud tools when they are deployed using a mutual hypervisor (KVM) for discovering new empirical insights. Our research results demonstrated that OpenStack is the most resilient and CloudStack is the most flexible for deploying an IaaS private cloud. Moreover, the performance experiments indicated some contrasts among the private IaaS cloud instances when running intensive workloads and scientific applications.

Nigeria is a West African country, often referred to as the "Giant of Africa" due to its population of over 177 million and an estimated GDP of over US$587 billion; abundant natural resources including one of the largest natural gas and crude oil reserves. Despite these strong fundamentals, Nigeria has found it difficult to convert her unique advantages into economic and social benefits for the country. This necessitates diversification of the economy which will require huge investments in infrastructure. In China, Nigeria has found a partner and financier. Despite the importance of Chinese finance for African infrastructure, relatively little is known and or ascertain about its socioeconomic value and impacts. The aim of this research work is to identify and discuss the socioeconomic impacts of Chinese Government Finances in Infrastructural Development Projects in Nigeria. Secondary sources of data were used to review literature. The result shows that Infrastructural Development Projects in the long run have direct influence on socioeconomic development, and the majority of economic activities depend one way or the other on road networks, airports, railway transportation systems and power generation projects.

Infrastructure-as-a-service (IaaS) cloud computing is revolutionizing how we approach computing. Compute resource consumers can eliminate the expense inherent in acquiring, managing, and operating IT infrastructure and instead lease resources on a pay-as-you-go basis. IT infrastructure providers can exploit economies of scale to mitigate the cost of buying and operating resources and avoid the complexity required to manage multiple customer-specific environments and applications. The authors describe the context in which cloud computing arose, discuss its current strengths and shortcomings, and point to an emerging computing pattern it enables that they call sky computing.

Cloud computing is the current IT buzzword synonymous with outsourced data center management and agile solution architecture. It has the potential to improve scalability of large enterprise network delivery of services and the capability to revolutionize how data is delivered as a service. At its core, cloud computing is not a new technology but rather a new approach to distributed shared pooling of IT infrastructure linked together to offer centralized IT services on demand. The study results determined that management’s perception of security, cost-effectiveness and IT compliance factors significantly influence their decisions to adopt cloud computing. The results of multiple linear regression analysis testing in this
study showed that managements’ perception of cost effectiveness is more significantly correlated to their decision to adopt cloud computing than it is to security.

We present the design and implementation of AppScale, an open source extension to the Google Ap-pEngine (GAE) Platform-as-a-Service (PaaS) cloud technology. Our extensions build upon the GAE SDK to facilitate distributed execution of GAE applications over Xen-based clusters, including Infrastructureas-a-Service (IaaS) cloud systems such as Amazon's AWS/EC2 and Eucalyptus. AppScale provides a framework with which researchers can investigate the interaction between PaaS and IaaS systems as well as the inner workings of, and new technologies for, PaaS cloud technologies using real GAE applications.

Technology experts and stakeholders say they expect they will ‘live mostly in the cloud’ in 2020 and not on the desktop, working mostly through cyberspace-based applications accessed through networked devices. This will substantially advance mobile connectivity through smartphones and other internet appliances. Many say there will be a cloud-desktop hybrid. Still, cloud computing has many difficult hurdles to overcome, including concerns tied to the availability of broadband spectrum, the ability of diverse systems to work together, security, privacy, and quality of service.

Though the concept of "clouds" is not new, it is undisputable that they have proven a major commercial success over recent years and will play a large part in the ICT domain over the next 10 years or more, as future systems will exploit the capabilities of managed services and resource provisioning further. Clouds are of particular commercial interest not only with the growing tendency to outsource IT so as to reduce management overhead and to extend existing, limited IT infrastructures, but even more importantly, they reduce the entrance barrier for new service providers to offer their respective capabilities to a wide market with a minimum of entry costs and infrastructure requirements -in fact, the special capabilities of cloud infrastructures allow providers to experiment with novel service types whilst reducing the risk of wasting resources.

Using Business Intelligence in the cloud is considered a key factor for success in various fields in 2018, about 66 percent of successful organizations in BI already using cloud. 86% of Cloud BI adopters choose Amazon AWS as their first choice, 82% choose Microsoft Azure, 66% choose Google Cloud, and 36% identify IBM Bluemix as their preferred provider of cloud BI services. In recent years, both Business Intelligence and cloud computing have undergone dramatic changes and advancements. The newest capabilities that these recent developments bring forth are introduced. In this paper the latest technologies in the field of Cloud (SaaS) BI is introduced. The paper shows also that many of the current problems in Cloud (SaaS) BI can be solved by enhance the performance and increase the use and acceptance of this technology. Many of the key characteristics of Business Intelligence systems tend to complement those of cloud computing systems and vice versa. Therefore, when integrated properly, these two technologies can be made to strengthen each other's advantages and eliminate each other's weaknesses.

More often than not, a "cloud" refers to an "Infrastructure-as-a-Service" (IaaS) cloud, such as Amazon EC2, where IT infrastructure is deployed in a cloud provider's datacenter in the form of virtual machines. With the growing popularity of IaaS clouds, an ecosystem of tools and technologies is emerging that can transform an organization's existing infrastructure into a private cloud or a hybrid cloud. In this article, we present OpenNebula, an open source virtual infrastructure manager that can be used to deploy virtualized services on both a local pool of resources and on external IaaS clouds, and Haizea, a resource lease manager that can act as a scheduling backend for OpenNebula providing features not found in other cloud software or virtualization-based datacenter management software, such as advance reservations and resource preemption, which we argue to be specially relevant for private and hybrid clouds.

Increasingly, governments are experimenting with ways to provide public goods by involving the private sector in the planning, financing, building and operating of a range of services, facilities, infrastructure, etc. In the geographical literature on neoliberalism this entanglement of the state and markets has been loosely conceptualized as a process of marketization. This concept describes the insertion of markets or market forces into the state and public sector. In this paper we unpack this concept by highlighting the need to think about a range of marketization processes at play across a range of geographies.

Despite the evolution of cloud computing in recent years, the performance and comprehensive understanding of the available private cloud tools are still under research. This paper contributes to an analysis of the Infrastructure as a Service (IaaS) domain by mapping new insights and discussing the challenges for improving cloud services. The goal is to make a comparative analysis of OpenNebula, OpenStack and CloudStack tools, evaluating their differences on support for flexibility and resiliency. Also, we aim at evaluating these three cloud tools when they are deployed using a mutual hypervisor (KVM) for discovering new empirical insights. Our research results demonstrated that OpenStack is the most resilient and CloudStack is the most flexible for deploying an IaaS private cloud. Moreover, the performance experiments indicated some contrasts among the private IaaS cloud instances when running intensive workloads and scientific applications.

The National Institute of Standards and Technology (NIST) defined cloud computing as a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or cloud provider interaction [Def:1]. Cloud computing has the potential to change how organizations manage information technology and transform the economics of hardware and software at the same time. Cloud computing promised to bring a new set of entrepreneurs who could start their venture with zero investment on IT infrastructure. How ever this captivating technology has security concerns which are formidable. The promises of cloud computing, especially public cloud can be shadowed by security breaches which are inevitable. As an emerging information technology area cloud computing should be approached carefully. In this article we will discuss the security and privacy concerns of cloud computing and some possible solutions to enhance the security. Based on the security solutions suggested i have come up with a secured framework for cloud computing.

The research is positioned in the context of the responsibility of archives to preserve important records in an increasingly changing technological environment, and focused on the impact of cloud solutions on archival theory and practice. Authors address several questions which they consider crucial for archival science and community. Results of the survey on the usage of private cloud are given. In view of that, the authors examine if the concept "Archiving-as-a-Service" will require redefinition of archival practice in the new technological and organizational context. Finally, they suggest the need for transition from postcustodial to "postcustodial 2.0" paradigm.

Cloud computing gives a rich set of IT services that are provided to a customer over a network on a leased basis and with the flexibility to rescale or down their service requirements. Usually Cloud Computing services are given by a third party provider who owns the infrastructure. Cloud Computing holds the potential to eliminate the necessities for high quality infrastructure. Those components are virtualized computers. This would allow multi-fold increase within the capacity and capabilities of the present and new software. The resources can be dynamically allowing also for an optimum resource by a pay-per-use model it means customized Service Level. The user can access the data from anywhere just with the help of an internet connection. To access this computing, the user should be authenticated through by providing their identification credentials like Userid and password for security purposes. In a cloud computing environment, the complete data resides over a group of networked resources, enabling the info to be accessed through virtual machines. Despite the potential gains achieved from the cloud computing, the organizations are slow in accepting it thanks to security issues and challenges relevant to it. Security is one in every of the main issues which hamper the expansion of cloud. There are many challenges also there for adopting cloud computing like well managed service level agreement (SLA), privacy, interoperability and reliability. This research paper presents the main concept of cloud computing, the various cloud models and therefore the overview of the cloud computing architecture. It simply states that cloud computing means accessing, retrieving and fetching the data and programs over the internet rather than the computer's hard disk.

Despite the evolution of cloud computing in recent years, the performance and comprehensive understanding of the available private cloud tools are still under research. This paper contributes to an analysis of the Infrastructure as a Service (IaaS) domain by mapping new insights and discussing the challenges for improving cloud services. The goal is to make a comparative analysis of OpenNebula, OpenStack and CloudStack tools, evaluating their differences on support for flexibility and resiliency. Also, we aim at evaluating these three cloud tools when they are deployed using a mutual hypervisor (KVM) for discovering new empirical insights. Our research results demonstrated that OpenStack is the most resilient and CloudStack is the most flexible for deploying an IaaS private cloud. Moreover, the performance experiments indicated some contrasts among the private IaaS cloud instances when running intensive workloads and scientific applications.

With an increasing number of providers claiming to offer Cloud infrastructures, there is a lack in the community for a common terminology, accompanied by a clear definition and classification of Cloud features. We conduct in this paper a survey on a selection of Cloud providers, and propose a taxonomy of eight important Cloud computing elements covering service type, resource deployment, hardware, runtime tuning, business model, middleware, and performance. We conclude that the provisioning of Service Level Agreements as utilities, of open and interoperable middleware solutions, as well as of sustained performance metrics for high-performance computing applications are three elements with the highest need of further community research.

Optimalisasi penggunaan teknologi khususnya teknologi informasi untuk layanan publik perlu dilakukan sebagai usaha unutk meningkatkan kualitas tata kelola pemerintahan yang akuntabel,transparan, efektif dan efisien. Pemerintah sudah mengeluarkan kebijakan tentang mekanisme pengadaan barang dan jasa secara elektronik melalui unit Layanan Pengadaan Secara Elektronik. Dalam perkembangannya LPSE ini dikembangkan lagi infrastruktur dan arsitekturnya menggunakan teknologi cloud computing khususnya sebagai infrastructure as a service untuk meningkatkan availabilitas dan reabilitas layanan pengadaan secara elektronik khususnya di daerah, yang sering mengeluhkan ganguan teknis. Makalah ini mencoba menganalisa akuntabilitas upaya optimalisasi penggunaan cloud computing infrastructure as a service pada sistem LPSE untuk mengetahui sistem yang dikembangkan ini masih sesuai dengan tujuan yang diamanatkan oleh aturan tentang Pengadaan Barang/Jasa Pemerintah. Dengan metode deskriptif dan pendekatan kualitatif, data dikumpulkan melalui studi literature serta didapatkan dari wawancara terbatas dari narasumber yang terlibat dalam pengembangan cloud computing ini. Dari hasil analisa bahwa pengembangan cloud computing infrastructure as a service ini sudah sesuai dengan tujuan pengadaan secara elektronik dalam hal meningkatkan akuntabilitas dan transparansi, meningkatkan ketersediaan (availability) dan kehandalan (reliability) sistem layanan, serta meningkatkan akses layanan yang real time. Selain itu juga efektifitas biaya pengembangan infrastruktur.

Kata kunci : cloud computing,LPSE,Iaas,ketersediaan,kehandalan.

A reliable energy supply is critical to every aspect of modern society, and so the U.S. Department of Homeland Security, along with the North American Energy Reliability Corporation and the U.S. Federal Energy Regulatory Commission, has established emergency management and business continuity standards for the nation’s energy producers and distributors. Because of these compliance standards, and to conform to accepted business practices, Seattle City Light instituted an information technology business continuity program as part of their strategic planning. As with most industries, energy production and distribution is heavily reliant on information technology for both the core functions as well as for the associated daily business. It is of particular importance for the monitoring of the power grid; remote sensing and control of production and distribution facilities; sale of excess power and purchase of power during high demand; operation and maintenance of the consumer power system; dispatching and safety of power crews; and for sundry business operations, such as human resources, billing, communications, etc. These operational and business information technology functions may be interrupted by damage to communications systems from winter ii
storms or earthquakes, vandalism, terrorism, hacking or, ironically, loss of power. This case study examines the Seattle City Light Information Technology Business Continuity and Disaster Recovery Program and describes the historical narratives of the decision-making process for, and design and implementation of, the program. The study utilizes qualitative description of the process, as well as lessons learned and best practices, and will quantitatively analyze the costs of the program implementation vs. the costs of non-compliance and interruption of supply. The program will be compared to available literature, with discussion, conclusions, and suggestions for the industry as a whole and for other industries facing similar compliance and business requirements.

Cloud Computing is middle-of-road for many technology companies for computing resources. Cloud computing is classified into three kinds Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), Infrastructure-as-a-Service (IaaS). Cloud platforms are pay-as-you-use, on-demand, easy to design, deploy, data storage, backup, and other utilities. In the year 2015 Platform-as-a-Service came into buzz it established its strong roots to provide technical service. This paper gives an overview of cloud computing and focuses on platform-as-a-Service (PaaS) along with an example of the Force.com platform. The paper also discusses the architecture, stack implementation, pros, and cons of Force.com.

Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users' demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users' tasks by getting too many resources.

Traditionally , computing infrastructure has been a substantial burden for companies. But what happens when business are unchained from their physical infrastructure and allowed to focus on business goal ? The model of infrastructure as a service and cloud computing bring us closer to this possibility. In this paper we discussed the growth of the web based service industry. The myriad of service now available over the internet rely on data center spread around the world. One of the major new trends in computing the rise of what is known as cloud computing, in witch not only service are offered remotely over the internet, but also a full menu of computing resource such a computing power, storage, databases, and many other resources.

More often than not, a “cloud” refers to an “Infrastructure-as-a-Service” (IaaS) cloud, such as Amazon EC2, where IT infrastructure is deployed in a cloud provider's datacenter in the form of virtual machines. With the growing popularity of IaaS clouds, an ecosystem of tools and ...

This document provides information about the proposed architectural framework for Cloud Infrastructure as a Service (IaaS) provisioning model that includes the following components: the Composable Services Architecture (CSA) that intends to provide a conceptual and methodological framework for developing dynamically configurable virtualised infrastructure services; the Infrastructure Services Modeling Framework (ISMF) that provides a basis for the infrastructure resources virtualisation and management, ...

One of the main challenges faced by current users of infrastructure-as-a-service (IaaS) clouds are the difficulties to estimate cloud resources according to their application needs. Even though cloud platforms are elastic and provide fast means to acquire or release resources it is important to understand the best ways to do that considering a diversity of providers with many different resource types and prices. This work reports on an experimental investigation conducted on a popular cloud benchmark based on a social network application running on top of the Amazon EC2 cloud. Our experiments aim at finding cost-effective ways to select the different EC2 resource types and deployment configurations based on the demand imposed to the application (measured in number of simultaneous users) and identify the configuration that gives the best return in terms of its cost per user.

A novel conceptual framework is presented in this paper with an aim to standardize and virtualize Internet of Things(IoT) infrastructure through deploying OpenFlow technology. The framework can receivee services based on context information leaving the current infrastructure unchanged. This framework allows the active collaboration of heterogeneous devices and protocols. Moreover it is capable to model placement of physical objects, manage the system and to collect information for services deployed on an IoT infrastructure. Our proposed IoT virtualization is applicable to a random topology scenario which makes it possible to 1) share flow sensors resources 2) establish multioperational sensor networks, and 3) extend reachability within the framework without establishing any further physical networks. Flow sensors achieve better results comparable to the typical sensors with respect to packet generation, reachability, simulation time, throughput, energy consumption point of view. Eve...

Effective use of existing network and IT infrastructure can be achieved by providing combined network and IT resources on-demand as infrastructure services that are capable of supporting complex technological processes, scientific experiments, and collaborative groups of researchers and applications. This paper provides a short overview of existing standards and technologies and refers to ongoing projects. We also describe experiences in developing an architectural framework and tools for combined on-demand network and ...

Cloud computing is an emerging paradigm which allows the on-demand delivering of software, hardware, and data as services. As cloud-based services are more numerous and dynamic, the development of e cient service provisioning policies become increasingly challenging. Game theoretic approaches have shown to gain a thorough analytical understanding of the service provisioning problem.

More often than not, a “cloud” refers to an “Infrastructure-as-a-Service” (IaaS) cloud, such as Amazon EC2, where IT infrastructure is deployed in a cloud provider's datacenter in the form of virtual machines. With the growing popularity of IaaS clouds, an ecosystem of tools and ...

Cloud Computing is ever growing technology in the internet era. It provides sharing the resources among the various users and it also provides various services to the user wherever they need. Cloud computing provides services on the Platform, Infrastructure, Software and Application as pay-as-use manner. Under the cloud environment of Infrastructure as a service the resource scheduling are not reliable because of heterogeneity and availability of environment. The genetic algorithm is a powerful technique used for resource scheduling, but Genetic Algorithms are not efficient for all the problems. A Parallel Genetic Algorithm is used for scheduling the resources in efficient manner. The proposed system uses Hybridization PGA for scheduling. HPGA uses the combinations of Master-Slave Parallelization and Coarse-Grained algorithm for resource scheduling. HPGA is an optimized scheduling algorithm for resource scheduling.

Cloud computing is the current IT buzzword synonymous with outsourced data center management and agile solution architecture. It has the potential to improve scalability of large enterprise network delivery of services and the capability to revolutionize how data is delivered as a service. At its core, cloud computing is not a new technology but rather a new approach to distributed shared pooling of IT infrastructure linked together to offer centralized IT services on demand. The study results determined that management’s perception of security, cost-effectiveness and IT compliance factors significantly influence their decisions to adopt cloud computing. The results of multiple linear regression analysis testing in this study showed that managements’ perception of cost effectiveness is more significantly correlated to their decision to adopt cloud computing than it is to security.

Cloud computing is an increasingly popular paradigm for accessing computing resources. A popular class of computing clouds is Infrastructure as a Service (IaaS) clouds, exemplified by Amazon's Elastic Computing Cloud (EC2). In these clouds, users are given access to virtual machines on which they can install and run arbitrary software, including database systems. Users can also deploy database appli- ances

With the rapid and growing volume of Green House Gas (GHG) we may soon cross a tipping point where there may be dramatic climatic catastrophes such that governments will be forced to order the shutdown of coal powered electrical production or mandate carbon neutrality across all sectors of society. On the other hand, in the event of such a development, the future Internet and Grid infrastructure may become absolutely essential for communications and a replacement for travel and for the delivery of critical services such as health, education, research, etc. Information, Computer and Telecommunication (ICT) technologies produce 2-3% of the world's GHG emissions through the consumption of electricity largely produced by coal plants. This rate of GHG is expected to double in the next few years and is clearly unsustainable. Therefore it is critical that any future Internet and Grid infrastructure be designed not only to survive, but also be sustained, through an age where no additional GHG emissions will be allowed. Converged optical networks, Grid and cloud services hosted at zero carbon renewable energy sites using Infrastructure as a Service (IaaS) where each network and computer element is represented to a user as a configurable virtual service will allow for the deployment of what are often referred to as "follow the sun or follow the wind" optical network and Grid architectures where the network and Grid topology and Grid resources availability and location are constantly changing depending on local availability states of the wind or sun.

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers.

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems. These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years. However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers.

The availability of Infrastructure-as-a-Service (IaaS) computing clouds gives researchers access to a large set of new resources for running complex scientific applications. However, exploiting cloud resources for large numbers of jobs requires significant effort and expertise. In order to make it simple and transparent for researchers to deploy their applications, we have developed a virtual machine resource manager (Cloud Scheduler) for distributed compute clouds. Cloud Scheduler boots and manages the user-customized virtual machines in response to a user's job submission. We describe the motivation and design of the Cloud Scheduler and present results on its use on both science and commercial clouds.

Using virtual machines as a resource provisioning mechanism offers multiple benefits, most recently exploited by "infrastructure-as-a-service" clouds, but also poses several scheduling challenges. More specifically, although we can use the suspend/resume/migrate capability of virtual machines to support advance reservation of resources efficiently, by using suspension/resumption as a preemption mechanism, this requires adequately modeling the time and resources consumed by these operations to ensure that preemptions are completed before the start of a reservation. In this work we present a model for predicting various runtime overheads involved in using virtual machines, allowing us to efficiently support advance reservations. We extend our lease management software, Haizea, to use this new model in its scheduling decisions, and we use Haizea with the OpenNebula virtual infrastructure manager so the scheduling decisions will be enacted in a Xen cluster. We present both physical and simulated experimental results showing the degree of accuracy of our model and the long-term effects of variables in our model on several workloads.

The recent increase in availability of Infrastructure-as-a-Service (IaaS) computing clouds provides a new way for researchers to run complex scientific applications. However, using cloud resources for a large number of research jobs requires significant effort and expertise. Furthermore, running jobs on many different clouds presents even more difficulty. In order to make it easy for researchers to deploy scientific applications across many cloud resources, we have developed a virtual machine resource manager (Cloud Scheduler) for distributed compute clouds. In response to a user's job submission to a batch system, the Cloud Scheduler manages the distribution and deployment of user-customized virtual machines across multiple clouds. We describe the motivation for and implementation of a distributed cloud using the Cloud Scheduler that is spread across both commercial and dedicated private sites, and present some early results of scientific data analysis using the system.

Infrastructure as a service (IaaS) systems offer on demand virtual infrastructures so reliably and flexibly that users expect a high service level. Therefore, even with regards to internal IaaS behaviour, production clouds only adopt novel ideas that are proven not to hinder established service levels. To analyse their expected behaviour, new ideas are often evaluated with simulators in production IaaS system-like scenarios. For instance, new research could enable collaboration amongst several layers of schedulers or could consider new optimisation objectives such as energy consumption. Unfortunately, current cloud simulators are hard to employ and they often have performance issues when several layers of schedulers interact in them. To target these issues, a new IaaS simulation framework (called DISSECT-CF) was designed. The new simulator’s foundation has the following goals: easy extensibility, support energy evaluation of IaaSs and to enable fast evaluation of many scheduling and IaaS internal behaviour related scenarios. In response to the requirements of such scenarios, the new simulator introduces concepts such as: a unified model for resource sharing and a new energy metering framework with hierarchical and indirect metering options. Then, the comparison of several simulated situations to real- life IaaS behaviour is used to validate the simulator’s functionality. Finally, a performance comparison is presented between DISSECT-CF and some currently available simulators.

The evolution of cloud computing over the past few years is potentially one of the major advances in the history of computing. However, if cloud computing is to achieve its potential, there needs to be a clear understanding of the various issues involved, both from the perspectives of the providers and the consumers of the technology. While a lot of research is currently taking place in the technology itself, there is an equally urgent need for understanding the business-related issues surrounding cloud computing. In this article, we identify the strengths, weaknesses, opportunities and threats for the cloud computing industry. We then identify the various issues that will affect the different stakeholders of cloud computing. We also issue a set of recommendations for the practitioners who will provide and manage this technology. For IS researchers, we outline the different areas of research that need attention so that we are in a position to advice the industry in the years to come. Finally, we outline some of the key issues facing governmental agencies who, due to the unique nature of the technology, will have to become intimately involved in the regulation of cloud computing.

the number of open source cloud management platforms is increasing day-by-day. The features of these software vary significantly and this creates a difficulty for the cloud consumers to choose the software based on their business and scientific requirements. This paper evaluates Eucalyptus and CloudStack, the two most popular open source platforms used to build private Infrastructure as a service (IaaS) clouds. The performance of virtual machines (VMs) initiated and managed by Eucalyptus and CloudStack are evaluated in terms of CPU utilization, memory bandwidth, disk I/O access speed, and network performance using suitable benchmarks. Different VM management operations such as add, delete and live migration are also assessed to determine which cloud solution is more suitable than other to be adopted as a private cloud solution. As a further performance testing, a simple web application has been implemented on the both clouds to evaluate their suitability in web application hosting.

Cloud computing offers a powerful abstraction that provides a scalable, virtualized infrastructure as a service where the complexity of fine-grained resource management is hidden from the end-user. Running data analytics applications in the cloud on extremely large data sets is gaining traction as the underlying infrastructure can meet the extreme demands of scalability. Typically, these applications (e.g., business intelligence, surveillance video searches) leverage the MapReduce framework that can decompose a large computation into a set of smaller parallelizable computations. More often than not the underlying storage architecture for running a MapReduce application is based on an Internet-scale filesystem, such as GFS, which does not provide a standard (POSIX) interface.

Today, cloud computing is one of the increasingly popular technology where the customer can use the resources of the cloud services providers to perform their tasks and only pay for the resources they use. The customer virtual machines in the cloud are vulnerable to different types of attacks. In this paper we propose techniques for securing customer virtual machines from