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In the era of the game publishing industry, MAJAMOJO collaborates with game developers in developing game server infrastructure in Indonesia, by realizing the adoption of cloud computing using the Roadmap for Cloud Computing Adaption (ROCCA) method. Cloud computing implementation by following 5 stages of modification of the ROCCA adoption model, namely the analysis, design, adoption, migration, and management stages. By using Amazon Web Service (AWS) Cloud Service (CSP) services. Cloud computing adoption is carried out by developing game server infrastructure as connectivity between the client and the game server. The analysis is carried out by collecting data and interviews from speakers, in the design of the cloud computing technology to be used, the selection of the technology is based on the results of the analysis stage, then the adoption process prepares the cloud infrastructure to be built, based on software selection and setting up servers with recommended specifications, then migration is the core of the cloud computing adoption process, Where the process transfers the system from the physical server to the virtual server, new management will be executed if the adoption and migration process has been completed with the Game Server and Game Client connectivity indicators running normally. With the application of cloud computing based on the characteristics of the cloud itself, it is flexible, scalable, and safe to access over the internet. [ABSTRACT FROM AUTHOR]

Reactive programming is a popular paradigm that has been used as a new solution in our proposed model for security in the cloud. In this context, we have been able to reduce the execution time compared to our previous work for the model proposed in cloud security, where the control of security depending on the ITSS (IT security specialist) of a certain organization based on selecting options. Some of the difficulties we encountered in our previous work while using traditional programming were the coordination of parallel processes and the modification of real-time data. This study provides results for two methods of programming based on the solutions of the proposed model for cloud security, with the first method of traditional programming and the use of reactive programming as the most suitable solution in our case. While taking the measurements in this paper, we used the same algorithms, and we present comparative results between the first and second methods of programming. The results in the paper are presented in tables and graphs, which show that reactive programming in the proposed model for cloud security offers better results compared to traditional programming. [ABSTRACT FROM AUTHOR]

This paper proposes a robust optimization model for probabilistic protection with multiple types of resources to minimize the required backup capacity for each type of resource against multiple random failures of physical machines in a cloud provider. If random failures occur, the required capacities for virtual machines are allocated to the preplanned backup physical machines, which are determined in advance. Probabilistic protection restricts the probability that the workload caused by failures exceeds the backup capacity by a given survivability parameter. We introduce three survivability parameters for central processing unit (CPU), memory, and the entire cloud provider considering both CPU and memory. By using the relationship between the three survivability parameters, the proposed model guarantees probabilistic protection for each resource, CPU and memory, and the entire cloud provider. By adopting the robust optimization technique, we formulate the proposed model as a multi-objective mixed integer linear programming problem. To deal with the multi-objective optimization problem, we apply the lexicographic weighted Tchebycheff method with which a Pareto optimal solution is obtained. We show that our proposed model reduces the average value between the backup capacity ratios of CPU and memory compared with the conventional model. A multi-objective simulated annealing (MOSA) and nondominated sorting genetic algorithm II (NSGA-II) are adopted to solve larger size problems. By using them, approximate solutions are obtained for larger size problems. In addition, we find that NSGA-II searches for solutions more effectively than MOSA, in our backup capacity allocation problem. [ABSTRACT FROM AUTHOR]

Task scheduling and data replication are highly coupled resource management techniques that are widely used by cloud providers to improve the overall system performance and ensure service level agreement (SLA) compliance while preserving their own economic profit. However, balancing the trade-off between system performance and provider profit is very challenging. In this paper, we propose a novel scheduling algorithm called Bottleneck and Cost Value Scheduling (BCVS) algorithm coupled with a novel dynamic data replication strategy called Correlation and Economic Model-based Replication (CEMR). The main goal is to improve data access effectiveness in order to meet service level objectives in terms of response time SLORT and minimum availability SLOMA, while preserving the provider profit. The BCVS algorithm focuses on reducing system bottleneck situations caused by data transfer when the CEMR focuses on preventing future SLA violations and guaranteeing a minimum availability. An economic model is also proposed to estimate the cloud provider profit. Simulation results indicate that the proposed combination of scheduling and replication algorithms offers higher monetary profit for the cloud provider by up to 30% compared to existing strategies. Moreover, it allows better performance. [ABSTRACT FROM AUTHOR]

Cloud computing as an emerging computing model provides computing resources as general utilities for the end-users through the Internet. This paper presents a market structure to address the competition among cloud providers in the wholesale electricity market and cloud computing market (CCM). The cloud providers compete to serve the customers in the CCM while they bid for the electricity demand in the wholesale market. The proposed market structure highlights the interdependence among electricity and cloud computing infrastructure systems by introducing the dynamic game with complete information among cloud providers in the wholesale market and the CCM. At each infrastructure system, the operation strategies of the market participants are determined by forming bilevel optimization problems in which the upper-level problem maximizes the payoff of the market participants while the lower-level problem represents the corresponding market settlement process. Independent system operator ensures the security and reliability of the network in the wholesale market while the demand and supply are balanced in the CCM to determine the price of the offered computing resources. A case study illustrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The constant increase in the growth of the cloud market creates new challenges for cloud service providers. One such challenge is the need to avoid possible service level agreement (SLA) violations and their consequences through good SLA management. Researchers have proposed various frameworks and have made significant advances in managing SLAs from the perspective of both cloud users and providers. However, none of these approaches guides the service provider on the necessary steps to take for SLA violation abatement; that is, the prediction of possible SLA violations, the process to follow when the system identifies the threat of SLA violation, and the recommended action to take to avoid SLA violation. In this paper, we approach this process of SLA violation detection and abatement from a risk management perspective. We propose a Risk Management-based Framework for SLA violation abatement (RMF-SLA) following the formation of an SLA which comprises SLA monitoring, violation prediction and decision recommendation. Through experiments, we validate and demonstrate the suitability of the proposed framework for assisting cloud providers to minimize possible service violations and penalties. [ABSTRACT FROM AUTHOR]

Media Edge Cloud Data Centers (MEC-DCs) that are interconnected by a Metro Network were selected as infrastructure to enhance the Quality of Experience (QoE) for end users of multimedia applications. Unlike the traditional Data Centers, MEC-DCs, which are kept closer to the user, have limited availability of resources at a given Data Center. Therefore, it is of paramount importance for Infrastructure service providers to efficiently dimension and use the media resources in an environment where the applications have high resource demand and the infrastructure has limited availability. To perform this task dynamically, we first propose a resource allocation strategy that considers the physical characteristics of the networking layer while minimizing the costs of deploying media applications. Second, we analyze the different configurations of the networking layer in order to enhance the use of MEC-DCs resources and the QoE for the end-users. Simulation results show a clear advantage of this proposed optimization-based approach over the benchmarks in terms of provisioning costs, blocking ratio and resource use. [ABSTRACT FROM AUTHOR]

The acceleration of technical change in the fast moving electronics market increases the uncertainty and risk for IT providers. Influenced by new IT provisioning concepts such as cloud computing, providers are looking to identify stable guidelines and success factors within existing and new business models. The authors have conducted an intensive analysis of the business model characteristics of 45 providers in the cloud market that are critical to success. A cloud business model framework with 105 characteristics was used to systemize the business models, and the data was analyzed statistically in regard to indicators for success. The results revealed 42 success-related business model characteristics, and a cluster analysis led to three common combinations of characteristics that describe meta types of cloud business models. The most promising meta type is a specialized cloud provider with customer-oriented branch solutions, while small-scale newcomers with aggregation services experience difficulties to be competitive. To evaluate and verify the results and the success of each business model type, 12 expert interviews were conducted. The interview statements were aggregated and summarized to offer recommendations for action and a prediction for the success of cloud business models. [ABSTRACT FROM AUTHOR]

Invention of internet has given boost to the new emerging business called the Cloud Computing. Cloud computing has given advantages to make way for reducing operational costs. It gives companies and individuals to get various services without investing in traditional way, user pay for the service they use, for the time they use them. In this paper we will try to discuss how telecom operators, as a leader in providing telecom services, can do to be competitive with multinational companies in market as cloud service providers. As companies already have infrastructure, it gives them a head start in the emerging field. Also we will try to explain model to use in the vendor, which will help telecom operators launch cloud services. [ABSTRACT FROM AUTHOR]

Cloud infrastructure and platform services of the cloud are progressively getting to be plainly well known everywhere throughout the world, however, resource scheduling and allocation of multiple virtual machines on clouds are still a difficult task to attain. Streamlining of these issues can be useful in enhancing the vitality savings and load adjusting in substantial datacenters. Since the request of assets from client fluctuates with time, therefore, the load on cloud generally remains low during normal hours and remains high during peak hours. A solitary cloud service provider might not have obliged assets to satisfy client's solicitations amid the pinnacle hour and on contrary, there may be some providers with under-utilized resources. These difficulties can be overcome by using cloud federation which allows outsourcing at peak time i.e., underutilized providers can rent their resources to different IaaS (Infrastructure as a service) providers. Resource allocation and scheduling also have an impact in federated clouds, assets can be purchased from different individuals of cloud federation. Existing practices cloud confederation and resource management are a bit intricate, which makes them less dynamic in nature and in some cases decreases the revenue and profit of cloud service provider. A prototype of new method for cloud confederation has expounded which focuses to enhance the gain for cloud service provider. Our parameters include free assets to be sold, a number of outsourced resources, the fare of maintaining servers, the fare of third party resources, and workload. [ABSTRACT FROM AUTHOR]

Cloud computing is a relatively new technology by the creation of which, companies and organizations transmit their services to cloud domains in order to do their tasks. With the companies and service provider organizations, profiteers seem not to neglect these areas and with different approaches try to do threats on users information and data security. Hence, it is necessary to adopt an approach relying on cloud providers to distinguish valid from the invalid cloud provider. Recognizing valid and invalid cloud providers can be issued as a classifying subject. In this paper, a support vector machine (SVM) algorithm is used in order to classify cloud providers. The features of SVM are a good generalization, ability to classify input pattern, optimal general pattern, and learning capability. The proposed method converts data to learning vector each of which has a corresponding output value and the ability to find the optimal amount in the non-linear and linear atmosphere. In this research, a data set of 1018 samples was used to classify cloud providers each of which has 10 features of cloud providers. To evaluate the performance of the proposed approach, 80 percent of the data set is randomly considered as a training set and 20 percentages as a test set. The results demonstrated that the proposed approach is efficient as compared to the meta-heuristic algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing is analyzed from the viewpoint of personal data protection. Recommendations for cloud providers and cloud customers on implementing the principles of personal data processing are considered. Some provisions of the project of an international standard on the protection of personal data in the cloud computing are formulated. [ABSTRACT FROM AUTHOR]

This paper presents a novel economic model to regulate capacity sharing in a federation of hybrid cloud providers (CPs). The proposed work models the interactions among the CPs as a repeated game among selfish players that aim at maximizing their profit by selling their unused capacity in the spot market but are uncertain of future workload fluctuations. The proposed work first establishes that the uncertainty in future revenue can act as a participation incentive to sharing in the repeated game. We, then, demonstrate how an efficient sharing strategy can be obtained via solving a simple dynamic programming problem. The obtained strategy is a simple update rule that depends only on the current workloads and a single variable summarizing past interactions. In contrast to existing approaches, the model incorporates historical and expected future revenue as part of the virtual machine (VM) sharing decision. Moreover, these decisions are not enforced neither by a centralized broker nor by predefined agreements. Rather, the proposed model employs a simple grim trigger strategy where a CP is threatened by the elimination of future VM hosting by other CPs. Simulation results demonstrate the performance of the proposed model in terms of the increased profit and the reduction in the variance in the spot market VM availability and prices. [ABSTRACT FROM AUTHOR]

Resource provisioning in Cloud providers is a challenge because of the high variability of load over time. On the one hand, the providers can serve most of the requests owning only a restricted amount of resources, but this forces to reject customers during peak hours. On the other hand, valley hours incur in under-utilization of the resources, which forces the providers to increase their prices to be profitable. Federation overcomes these limitations and allows providers to dynamically outsource resources to others in response to demand variations. Furthermore, it allows providers with underused resources to rent them to other providers. Both techniques make the provider getting more profit when used adequately. Federation of Cloud providers requires having a clear understanding of the consequences of each decision. In this paper, we present a characterization of providers operating in a federated Cloud which helps to choose the most convenient decision depending on the environment conditions. These include when to outsource to other providers, rent free resources to other providers (i.e., insourcing), or turn off unused nodes to save power. We characterize these decisions as a function of several parameters and implement a federated provider that uses this characterization to exploit federation. Finally, we evaluate the profitability of using these techniques using the data from a real provider. [ABSTRACT FROM AUTHOR]

Cloud computing is quickly becoming the next wave of technological evolution as a new approach to providing IT capabilities needed by business. Driving interest and investment in cloud computing is the revolutionary change to the economic model. Cloud computing also promises to allow IT to respond more quickly to the needs of the business. Key tenets of cloud computing include being on-demand and self-service. This shift to the way that a business engages IT services creates new challenges including regulating how internal business units purchase cloud services. How does a business assess cloud providers services for security, privacy, and service levels? The purpose of this study is to develop an instrument for evaluating a cloud provider’s transparency of security, privacy, and service level competencies via its self-service web portals and web publications, and then to empirically evaluate cloud service providers to measure how transparent by using the instrument. [ABSTRACT FROM PUBLISHER]