Cloud computing is revolutionizing the IT industry by enabling them to offer access to their infrastructure and application services on a subscription basis. As a result, several enterprises including IBM, Microsoft ,Google, and Amazon have started to offer different Cloud services to their customers. Due to the vast diversity in the available Cloud services, from the customer’s point of view, it has become difficult to decide whose services they should use and what is the basis for their selection. Currently, there is no framework that can allow customers to evaluate Cloud offerings and rank them based on their ability to meet the user’s Quality of Service (QoS) requirements. In this work, we propose a framework and a mechanism that measure the quality and prioritize Cloud services. Such a framework can make a significant impact and will create healthy competition among Cloud providers to satisfy their Service Level Agreement (SLA) and improve their QoS. We have shown the applicability of the ranking framework using a case study.
The ever-increasing status of the cloud computing hypothesis and the budding concept of federated cloud computing have enthused research efforts towards intellectual cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Cloud computing is a novel paradigm for the provision of computing infrastructure, which aims to shift the location of the computing infrastructure to the network in order to reduce the maintenance costs of hardware and software resources. Cloud computing systems vitally provide access to large pools of resources. Resources provided by cloud computing systems hide a great deal of services from the user through virtualization. In this paper, the cloud data center is modelled as queuing system with a single task arrivals and a task request buffer of infinite capacity.
Computing is undergoing a substantial shift from client/server to the cloud. The enthusiasm for cloud infrastructures is not only present in the business world, but also extends to government agencies. Managers of both segments thus need to have a clear view of how this new era will evolve in the coming years, in order to appropriately react to a changing economic and technological environment. In this study, we explore the dynamic equilibrium of cloud computing adoption through the application of Mean Field Games. In our formulation, each agent (i.e., each firm or government agency) arbitrates between “continuing to implement the traditional on-site computing paradigm” and “moving to adopt the cloud computing paradigm”. To decide on his level of moving to the cloud computing paradigm, each agent will optimize a total cost that consists of two components: the effort cost of moving to the cloud computing paradigm and the adoption cost of implementing the cloud computing paradigm. In the formulation, the adoption cost is linked to the general trend of decisions on the computing paradigm adoption. Thus, an agent’s optimal level of transition to the cloud computing paradigm is not only dependent on his own effort and adoption costs but also affected by the general trend of adoption decisions. The problem is solved by a system of partial differential equations (PDEs) ,that is, mean field games PDEs, which consists of a backward PDE, the Hamilton Jacobi Bellman equation for a controlled problem, and a forward Fokker-Planck equation transported by the optimal control from the backward HJB equation. Thus ,the solution to the forward Fokker-Planck equation enables us to study the dynamic evolution of the density of the cloud computing adoption. It therefore allows us to investigate the impact of the general trend of technology adoption decisions on a firm’s optimal decision of technology transition.
The notion of Cloud computing has not only reshaped the feld of distributed systems but also fundamentally changed how businesses utilize computing today. While Cloud computing provides many advanced features, it still has some shortcomings such as the relatively high operating cost for both public and private Clouds. The area of Green computing is also becoming increasingly important in a world with limited energy resources and an ever-rising demand for more computational power. In this paper a new framework is presented that provides efcient green enhancements within a scalable Cloud computing architecture. Using power-aware scheduling techniques, variable resource management, live migration, and a minimal virtual machine design, overall system efciency will be vastly improved in a data center based Cloud with minimal performance overhead.
The computational humanity is flattering extremely bulky and multifaceted. Cloud computing is becoming one of the most expanding methodologies in the computing industry. It is a novel approach for the deliverance of IT services on the World Wide Web. This model provides computing resources in the puddle for consumers, all the way through Internet. In cloud computing, resource allocation and scheduling of numerous aggregate web services is an imperative and demanding quandary. This paper estimates the various network resource allocation strategies and their applications in Cloud Computing Environment. A brief description for network resource allocation in Cloud Computing, based on differentially adapted dynamic proportions, has also been done. This paper addresses and categorizes the foremost challenges normal to the resource allocation progress of cloud computing in terms of diverse types of resource allocation techniques.
The distributed and open structure of cloud computing and services becomes an attractive target for potential cyber-attacks by intruders. The traditional Intrusion Detection and Prevention Systems (IDPS) are largely inefficient to be deployed in cloud computing environments due to their openness and specific essence. This paper surveys, explores and informs researchers about the latest developed IDPSs and alarm management techniques by providing a comprehensive taxonomy and investigating possible solutions to detect and prevent intrusions in cloud computing systems. Considering the desired characteristics of IDPS and cloud computing systems, a list of germane requirements is identified and four concepts of autonomic computing self-management, ontology, risk management, and fuzzy theory are leveraged to satisfy these requirements.
The success of cloud computing as a platform for deploying webapplications has led to a deluge of applications characterized by small data footprints with unpredictable access patterns. A scalable multitenant database management system (DBMS) is therefore an important component of the software stack for platforms supporting these applications. Elastic load balancing and efficient database migration techniques are key requirements for effective resource utilization and operational cost minimization. Our vision is a DBMS where multitenancy is viewed as virtualization in the database layer, and elasticity is a first class notion with the same stature as scalability, availability etc. We analyze the various models of database multitenancy, formalize the forms of migration, and identify the design space and research goals for an autonomic and elastic multitenant database.
In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
Importance and development advantage of the Internet of Things industry chain is discussed. The basic ideas and methods of the combination of cloud computing and the Internet of things are described. Combined with cloud computing, the unified Internet of Things operation management system framework is established. The framework of the new platform middleware layer uses the cloud computing technology, which greatly improves the operating efficiency of the Internet of things. Based on the framework, Internet of Things business operations platform construction scheme is proposed, it can be used as reference of the construction of the Internet of things platform design.
Multi-tenancy helps service providers to save costs, improve resource utilization, and reduce service customization and maintenance time by sharing of resources and services. On the other hand, supporting multi-tenancy adds more complexity to the shared application’s required capabilities. Security is a key requirement that must be addressed when engineering new SaaS applications or when re-engineering existing applications to support multi-tenancy. Traditional security (re)engineering approaches do not fit with the multitenancy application model where tenants and their security requirements emerge after the system was first developed. Enabling, runtime, adaptable and tenant-oriented application security customization on single service instance is a key challenging security goal in multi-tenant application engineering. In this paper we introduce TOSSMA, a TenantOriented SaaS Security Management Architecture. TOSSMA allows service providers to enable their tenants in defining, customizing and enforcing their security requirements without having to go back to application developers for maintenance or security customizations. TOSSMA supports security management for both new and existing systems. Service providers are not required to write security integration code to use a specific security platform or mechanism. In this paper, we describe details of our approach and architecture, our prototype implementation of TOSSMA, give a usage example of securing a multi-tenant SaaS, and discuss our evaluation experiments of TOSSMA.