Cost effectiveness of commercial computing clouds

Cloud computing is an emerging technological paradigm, which provides computing resources as utility. Like other day-to-day utilities, cloud computing follows pay-as-you-use model, where users are charged according to the usage without regard to where the services are hosted or how they are delivered. Today, majority of companies follow an IT infrastructure-driven business model. With the growing demand, rise in customer base and market place competitions, companies prefer focusing on respective business policies and services they offer, rather than IT management overheads. Therefore, there is a high probability that the future of present day business model may shift to clouds where non-IT companies no longer have to procure, manage, and maintain IT resources. They will host applications and data to the servers, which are deployed by cloud providers, possibly in geographically dispersed locations. However, security is a major challenge before outsourcing any IT needs of business. As cloud provides a multi-tenant virtual computing environment, where competitive businesses may co-exist, hosting of sensitive information for mission-critical applications is of utmost concern. This chapter reviews the recent works reported specifically in the area of data and application security relevant to cloud computing. Some works which use biologically inspired phenomenon to manage security and load balancing in cloud environment, have also been studied. The aim of this chapter is to provide an insight into the present state-of-the-art cloud security problems, proposed solutions, and identify future research directions as well as scopes in various security issues.KeywordsCloud ComputingNetwork CodeCloud ProviderCloud EnvironmentCloud StorageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

E-commerce web sites typically have large fluctuations in their IT resource usage, while rapid elasticity is an essential characteristic of cloud computing. These characteristics make the cloud a good fit for hosting e-commerce web sites. Cloud providers deploy their cloud in their data centers. However, cloud providers usually have a limited number of data center locations around the world. Thus, e-commerce web sites in the cloud may be far away from their customers. Long client-perceived response latency may cause e-commerce web sites to lose business. To solve this problem, we propose a virtual proxy solution to reduce the response latency of the e-commerce site in the cloud. In our approach, a virtual proxy platform is designed to cache applications and data of e-commerce sites. A k-means based table partitioning algorithm is designed to select frequently used data from the database in the cloud. We have used an industrial e-commerce benchmark TPC-W to evaluate the performance of our approach. The experimental results show that our approach can significantly reduce the client-perceived response time.

Cloud computing is the on-demand delivery of IT resources over the Internet with pay-as-you-go pricing. Instead of buying, owning, and maintaining physical data centers and servers, you can access technology services, such as computing power, storage, and databases, on an as-needed basis from a cloud provider like Amazon Web Services (AWS), Microsoft Azure, Google cloud. Cloud computing is one of the most growing technologies. The fundamental idea behind cloud computing is to distribute an array of computing services by unifying and scheduling a pool of computing resources, thereby minimizing the burden on the users and helping them focus on their core businesses. These computing resources are hosted on virtual hosts and distributed on-demand to the users by cloud service providers. For efficient resource utilization, systematic load balancing of incoming user traffic across virtual hosts is imperative. Aim of this paper is to design and implement Network load balancer with cross zone feature which balance incoming user requests and avoid the problem of server down in real scenario. Elastic Load Balancer automatically equally distribute incoming traffic to the available EC2, targets, like Amazon EC2 instances, IP addresses containersand Lambda functions. Load balancer effectively distribute load equally on available server and AZ to improve fault tolerance and availability. It can manage the differing load of your application traffic in one Availability Zone or over different Availability Zones.

In the begin cloud computing was seen as a thing of “startup” and visionary users, but nowadays it is a reality in organizations of any sector, type and size. Investments in data centers are replaced by the consumption of IT resources from a cloud provider. There are several types of virtualization, and that essentially means the abstraction of computational resources for different purposes; one of the ways, widely used, is to create virtual machines, which simulate a complete environment with software and hardware resources for the user. Despite being a technique that has existed for a long time, the growth of cloud computing has made this concept to be widely discussed. Significant innovations in virtualization, distributed computing, high-speed communications, and IoT, have aroused more and more interest in Cloud Computing. In this sense, we will analyze and evaluate the impact of cloud computing and virtualization on organizations.

Due to the large migration of applications from traditional computing environments to cloud systems, task scheduling has become a well-known research topic. Further, assigning tasks is an essential step in the server consolidation that enables clients’ needs to be mapped to suitable resources, such as Physical Machines (PMs) or Virtual Machines (VMs). The difficulty lies in using the available shared computing resources for various tasks without degrading the quality of service (QoS) or increasing the carbon footprint. In addition, the cloud provider needs to use a good task strategy based on the requirements of the client and the IT resources capacity. The best resource utilization and quickest task execution completion times are essential metrics for a cloud provider to maintain the optimum performance of the cloud system. The appropriate solution distributes tasks to the available virtual machine while applying optimization techniques such as the artificial bee colony (ABC), the cuckoo search (CS) algorithm, and particle swarm optimization (PSO). This paper will analyze the algorithms mentioned above based on energy consumption and resource utilization.KeywordsServer consolidationCloud computingResource efficiencyMetaheuristic algorithmsEnergy consumptionPerformance parametersCloudSim

Introduction In every working area, assets are exposed to risk of various harms. In the information technology area, there is risk of damaging or losing resources. Risk assessment is a process that determines what information resources require protection, and it documents potential risks from IT security failures that may cause loss of information confidentiality, integrity, or availability. The purpose of risk assessment is to help management create appropriate strategies and controls for the steering of information assets (BSI Standard 100-1,2,3), (ENISA Regulation). IT resources are tangible assets (such as hardware, equipment, buildings), data, documents, software, users, and intangible resources (reputation, confidence, etc.) Management of the information system (IS) is a component of the company management and makes its inherent part. The planning of the continuity of IS operation and development must be entirely coordinated with the same activities on the company level. Both short-term and long-term strategies must be harmonized within the framework of business, development, changes, and planning. Risk management and its maintenance on an acceptable level is part of the work process and its development. Its basic characteristic must be continuity (Nikolic, 2007). The chosen acceptable risk level after the implementation of particular measures also means a decision on balance referring to costs concerning the achievement of the selected risk level and benefits, i.e., profit of the company. Methodology The methodology of risk assessment implies recognized procedure (Macdonald, 2004), detailed system description, identification of hazards, defining causes of emphasized hazards, risk assessment, measures for risk reduction and mitigation, risk reassessment according to remaining hazards, and conducting measures for maintaining risk at an accepted level. Figure 1 displays the methodology of risk assessment with elements of the risk management system. Risk assessment in the IT area can be conducted by way of various methods. We use the method established and developed by our expert team (Nikolic & Laban 2008). The method was created for the occupational health and safety area, and is successfully applied in the IT area as described in Nikolic and Ruzic-Dimitrijevic (2009). This method enables the achievement of one of the main goals of risk assessment: risk assessment for every employee, workplace, and work environment, i.e., the assessment of all risks. The work environment can be a company, building, floor, or office. It can also be a plant, building site, park, field, or any system, as well as an information system and its components, such as hardware, communication equipment (network), and system software. The workplace in an information system is application software with data as final products, and it can be considered in the same way as in the occupational health and safety area. IT resources are buildings, hardware, communication equipment, software, data, and, of course, people who take part in IT processes. Risk assessment is carried out on more levels (items in the work environment). Software also has several levels--environmental software, operating system, program supported application, and finally a single application--as well as the data used and gained in application executing. Risk assessment of each level involves conducting appropriate protective measures in order to obtain the layered protection. Therefore, the risk assessment of an information system (IS) is conducted through the risk assessment of every application and through the evaluation of the system's state. Risk identification involves the knowledge about the organization (internal and external), as well as earlier experiences about risk issues. The evaluation of risk classifies risk in certain categories (quantitative and/or qualitative) according to which adequate measures are carried out. …

Provisioning extra resources is necessary when the local resources are not sufficient to meet the user requirements. Commercial Cloud providers offer the extra resources to users in an on demand manner and in exchange of a fee. Therefore, scheduling policies are required that consider resources’ prices as well as user’s available budget and deadline. Such scheduling policies are known as market-oriented scheduling policies. However, existing market-oriented scheduling policies cannot be applied for Cloud providers because of the difference in the way Cloud providers charge users. In this work, we propose two market-oriented scheduling policies that aim at satisfying the application deadline by extending the computational capacity of local resources via hiring resource from Cloud providers. The policies do not have any prior knowledge about the application execution time. The proposed policies are implemented in Gridbus broker as a user-level broker. Results of the experiments achieved in real environments prove the usefulness of the proposed policies.

Small, Medium, and Micro enterprises (SMMEs) usually do not have adequate funds to acquire ICT infrastructure and often use cloud computing. In this paper, the authors discuss the implementation of virtual enterprises (VE) to enable SMMEs to respond quickly to customers’ demands and market opportunities. The virtual enterprise model is based on the ability to create temporary co-operations and realize the value of a short term business opportunity that the partners cannot fully capture on their own. The model of virtual enterprise is made possible through virtualisation technology, which is a building block of cloud computing. To achieve a common goal, enterprises integrate resources, organisational models, and process models. Through the virtual business operating environment offered by cloud computing, the SMMEs are able to increase productivity and gain competitive advantage due to the cost benefit incurred. In this paper, the authors propose a virtual enterprise enabled cloud enterprise architecture based on the concept of virtual enterprise at both business and technology levels. The business level comprises of organisational models, process models, skills, and competences whereas the technology level comprises of IT resources.

Mathematical modeling of high temperature and high-pressure dense membrane separation of hydrogen from gasification

The dairy industry faces significant challenges in data integration and analysis, which are critical for informed decision-making, operational optimization, and sustainability. Data integration-combining data from diverse sources, such as herd management systems, sensors, and diagnostics-remains difficult due to the lack of standardization, infrastructure barriers, and proprietary concerns. This commentary explores these issues based on insights from a multidisciplinary group of stakeholders, including industry experts, researchers, and practitioners. Key challenges discussed include the absence of a national animal identification system in the US, high IT resource costs, reluctance to share data due to competitive disadvantages, and differences in global data handling practices. Proposed pathways forward include developing comprehensive data integration guidelines, enhancing farmer awareness through training programs, and fostering collaboration across industry, academia, and technology providers. Additional recommendations involve improving data exchange standards, addressing interoperability issues, and leveraging advanced technologies, such as artificial intelligence and cloud computing. Emphasis is placed on localized data integration solutions for farm-level benefits and broader research applications to advance sustainability, traceability, and profitability within the dairy supply chain. These outcomes provide a foundation for achieving streamlined data systems, enabling actionable insights, and fostering innovation in the dairy industry.

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

Outsourcing data processing and storage to the cloud is a persistent trend in the last years. Cloud computing offers many advantages like flexibility in resource allocation, cost reduction and high availability. However, when sensitive information is handed to a third party, security questions are raised since the cloud provider and his employees are not fully trusted. Standard security mechanisms like transport encryption and regular audits alone cannot solve the issue of insider attacks. Additional cryptographic techniques are required. In this paper, we build upon an existing proxy for secure database outsourcing. We address potential side-channels and weaknesses, which are later analyzed and mitigated. Furthermore, we take a look at trusted execution environments (TEEs) like Intel Software Guard Extensions (SGX) and show how they can be applied to allow for secure execution in the secure database outsourcing case.

Simplifying the Use of Clouds for Scientific Computing with Everest

Heterogeneity in secondary characteristics of different HPC target platforms is the focus of this paper. Clusters, grids, and (IaaS) clouds may appear straightforward to configure to be interchangeable - but our experiences with mainstream parallel codes for CFD demonstrate that secondary attributes - support software, interconnect type, availability, access, and cost - expose heterogeneous aspects that impact overall effectiveness of application execution. The emergence of clouds as alternatives to grids and local resources for parallel HPC codes portends computing as a in science and engineering domains. Our experiences provide preliminary insights into characterizing these different types of platforms to which users typically have access - and show where the tradeoffs can be, in terms of deployment effort, actual and nominal costs, application performance, and availability (both in terms of resource size and time to gain access). For our test application, we report that each of the platforms to which we had access had its particular benefits and drawbacks in terms of the above attributes. More generally, our experiences may provide an example preview into what developers and users can expect when selecting a utility provider and specific instance thereof for a particular run of their application.

Recent decades have seen tremendous increases in the quantity of empirical ecological data collected by individual investigators, as well as through research networks such as FLUXNET (Baldocchi et al., 2001). At the same time, advances in computer technology have facilitated the development and implementation of large and complex land surface and ecological process models. Separately, each of these information streams provides useful, but imperfect information about ecosystems. To develop the best scientific understanding of ecological processes, and most accurately predict how ecosystems may cope with global change, integration of empirical and modeling approaches is necessary. However, true integration - in which models inform empirical research, which in turn informs models (Fig. 1) - is not yet common in ecological research (Luo et al., 2011). The goal of this workshop, sponsored by the Department of Energy, Office of Science, Biological and Environmental Research (BER) program, was to bring together members of the empirical and modeling communities to exchange ideas and discuss scientific practices for increasing empirical - model integration, and to explore infrastructure and/or virtual network needs for institutionalizing empirical - model integration (Yiqi Luo, University of Oklahoma, Norman, OK, USA). The workshop included presentations and small group discussions that coveredmore » topics ranging from model-assisted experimental design to data driven modeling (e.g. benchmarking and data assimilation) to infrastructure needs for empirical - model integration. Ultimately, three central questions emerged. How can models be used to inform experiments and observations? How can experimental and observational results be used to inform models? What are effective strategies to promote empirical - model integration?« less