Uncertainty-aware Decisions in Cloud Computing

Cloud computing provides services that allocate infrastructure resources using the Internet as a medium and data storage on an external server. Small and medium corporations are the foundation of any flourishing economy for a growing nation which seeks new and innovative methods to reduce the way they manage their resources. Over a couple of decades, Information technology (IT) has created a significant impact in improving the lives of people and alsoon the global economy due to tremendous digital transformation. With the growth of the Small and medium corporations, IT is creating some real impact in enabling these industries to undergo adigital transformation of their business processes while they continue to grow. Small and medium enterprises (SME’s) are usually identified as the dominant force for the growth of any country's economy. In the cloud computing environment, the SME's need not have the in-house infrastructure so they can give up on any initial expenditure for setting up and instead they can utilize the resources available on the cloud and pay as per their requirement and usage.This paper presents the results of a comprehensive interpretation to research some of the most commonly used SaaS (Software-as-a-Service) implementations in the domain of Cloud Computing firstly to identify the weaknesses of the traditional computing approach for SME’s, and secondly to identify the aspects of these weaknesses that can be overcome by implementing cloud computing.In this paper, we provided the overview of various cloud computing models and literature survey of these models. This study extends to create an own cloud computing system for small and medium corporations. We will be using Software-as-a-Service (SaaS) approach and see how small and medium corporations can leverage on this for their business operations.

Intelligent techniques for secure financial management in cloud computing

In this age of large-scale, data-intensive systems, involving rich metadata, information management, and equally large-scale computationally hungry algorithms and analytics, the principles of software engineering are often glossed over at the expense of the latest and greatest "buzzword-compliant" technology. The cloud is that technology. But how can we ignore it? Hundreds of millions of dollars of investment, entire software platforms and technologies, thousands of teams of individuals building cloud software, and entire data centers are part of the cloud. Clearly the domain of cloud computing has eclipsed the ability of software engineering's foundational principles to catch up, and with great reason, based on the economic, societal and scientific impact the cloud has had even within its nascence, and based on the rapidly expanding nature of what is truly enveloped within the domain of cloud software. What are those key, foundational software-engineering principles though? Rich, detailed design models: mapping of requirements to those models (and vice versa); architectural styles helping to prescribe and guide the constraints of those models, and successful patterns found effective for constructing systems within that domain, to name a few. The core elements of architecture -- components (the units of computation), connectors (the embodiment of interactions amongst components) and configurations, (detailing how to arrange those components and connectors to form an architecture) -- can form an effective platform for modeling and understanding cloud software. This workshop targets the two aforementioned communities of interest, with the goal of bringing together cloud computing engineers, practitioners, academia and industry alongside software engineering community engineers, practitioners, academia and industry. We understand that there is no time like the present to begin identifying the knowledge gaps, the upcoming future needs and investments, and the research challenges for constructing cloud software. We will discuss a broad variety of topics, ranging from theoretically understanding how to properly migrate non-cloud software applications to the cloud, to understanding what to do when cloud software fails, all the way to several successful cloud architectures and systems that we can draw lessons learned from. We expect that the participants of the workshop will exchange tribal knowledge and passalong that information in the form of an output report identifying the upcoming key challenges for software engineering research as an enabler for the domain of cloud computing. We are looking forward to a set of interesting research papers, and interactive demos of real-world cloud systems, and looking forward to seeing you all in Hawaii in May 2011

Cloud computing is a way for businesses and individuals to It has changed and revolutionized the way we access and use resources. However, efficient resource management in cloud computing systems remains a major challenge due to the scalability, heterogeneity, and dynamic nature of these environments. To address these challenges, artificial intelligence (AI) technology has emerged as an effective solution to improve resource management efficiency. This paper provides an overview of AI-based strategies for efficient resource management in cloud computing systems, services, and applications. This paper first reviews resource management challenges in cloud computing, including scalability, heterogeneity, quality of service requirements, and cost optimization. Below is an overview of the various AI techniques used for resource management. B. Algorithms for machine learning, reinforcement learning, predictive analytics, natural language processing, and genetic algorithms. Next, this paper considers specific AI-based strategies for efficient resource management. These strategies include automated resource provisioning and scaling, intelligent workload planning and task allocation, predictive maintenance and fault detection, and energy-efficient resource management. We also present case studies and applications of AI-driven resource management in various cloud computing scenarios, including large-scale cloud providers, edge computing, serverless computing, and container environments. This paper describes evaluation metrics and performance analysis techniques to evaluate the effectiveness of AI-based resource management approaches. It highlights the importance of ethical considerations, transparency, and explainability in AI-powered resource management systems. Additionally, the integration of AI technologies into existing resource management frameworks is discussed, and future directions are identified, including B. real-time resource optimization and coordination.

Cloud Computing has become a new age technology that has got the high degree of potency nowadays. Cloud Computing uses the concept of virtualization. It also provides resources to applications by allocating virtual machines to specific application. Optimizing resources in the cloud environment is more beneficial, minimizing allocation cost and satisfying client requests are the main purpose of working with VM allocation strategy. So, the resource allocation policies play crucial role for allocating and controlling the resources among several applications in cloud computing environment. This paper illustrates a comparative study on various VM allocation policies. The study and performance analysis of these algorithms are done on the basis of total allocation cost in between VM to Host considering different attributes and different service level agreements (SLA) in the domain of cloud computing.

This article presents a comprehensive exploration of the architecture and various approaches in the domain of cloud computing and software-defined networks. The salient points addressed in this article encompass: Foundational Concepts: An overview of the foundational concepts and technologies of cloud computing, including software-defined cloud computing. Algorithm Evaluation: An introduction and evaluation of various algorithms aimed at enhancing network performance. These algorithms include Intelligent Rule-Based Metaheuristic Task Scheduling (IRMTS), reinforcement learning algorithms, task scheduling algorithms, and Priority-aware Semi-Greedy (PSG). Each of these algorithms contributes uniquely to optimizing Quality of Service (QoS) and data center efficiency. Resource Optimization: An introduction and examination of cloud network resource optimization based on presented results and practical experiments, including a comparison of the performance of different algorithms and approaches. Future Challenges: An investigation and presentation of challenges and future scenarios in the realm of cloud computing and software-defined networks. In conclusion, by introducing and analyzing simulators like Mininet and CloudSim, the article guides the reader in choosing the most suitable simulation tool for their project. Through its comprehensive analysis of the architecture, methodologies, and prevalent algorithms in cloud computing and software-defined networking, this article aids the reader in achieving a deeper understanding of the domain. Additionally, by presenting the findings and results of conducted research, it facilitates the discovery of the most effective and practical solutions for optimizing cloud network resources.

Cloud computing is emerging as more than simply a technology platform but a software engineering paradigm for the future. Hordes of cloud computing technologies, techniques, and integration approaches are widely being adopted, taught at the university level, and expected as key skills in the job market. The principles and practices of the software engineering and software architecture community can serve to help guide this emerging domain. The fundamental goal of the ICSE 2011 Software Engineering for Cloud Workshop is to bring together the diverse communities of cloud computing and of software engineering and architecture research with the hopes of sharing and disseminating key tribal knowledge between these rich areas. We expect as the workshop output a set of identified key software engineering challenges and important issues in the domain of cloud computing, specifically focused on how software engineering practice and research can play a role in shaping the next five years of research and practice for clouds. Furthermore, we expect to share war stories, best practices and lessons learned between leaders in the software engineering and cloud computing communities.

Software-defined networks for resource allocation in cloud computing: A survey

Cloud computing is viewed as a cost-effective and scalable way of providing computing resources for both large and small organizations. However, as cloud storage is outsourced it is highly susceptible to information security risks. The insider threat may become particularly insidious with the predilection towards cloud computing. Insiders have a significant advantage, as not only do they have knowledge about vulnerabilities in policies, networks or systems but they also have the requisite capability. An `insider' is any individual who has legitimate access to an organization's information technology infrastructure whereas an `insider threat' uses the authority granted to him/her for illegitimate gain. Fundamentally, the insider threat concern is a complex issue, as the problem domain intersects the social, technical, and socio-technical dimensions. From a cloud-computing perspective, the concept of the insider is multi-contextual and consequently propagates more opportunities for malfeasance. The definition of an insider changes from context to context; an insider is someone who works within an organization that uses a cloud-based system and it also includes a user that works for a cloud service provider. Clearly, the concept of the insider within the cloud-computing domain is amorphous. This chapter intends to define the insider threat and identify the various types of insider threats that exist within the cloud-computing domain. This chapter considers the challenges involved in managing the insider threat and possible mitigation strategies including authentication schemes within cloud-based systems. To this end, this chapter also considers the various mitigation strategies that exist within the technical, social and sociotechnical domains in order to identify gaps for further research.

This research work identifies the national and international related work to identify the cutting-edge areas of research problems in the domain of cloud computing , mobile software and multimedia data for its security and cyber forensics. The explanatory research identifies the statement of problems with its aim, objectives, and the scope of the work to be conducted into the proposed cyber forensic laboratory. The hardware and software requirements for the cybercrime digital forensic laboratory are identified to conduct cybercrime research in the domain of cyber security for the recommendation of security. The Centre of excellence for computer cybercrime digital forensics will be useful for reading, writing, research, and development to give practical and feasible solutions for building digital forensic tools in the cutting domain of computing systems such as Cloud Computing, Web Applications, Mobile communication, and Multimedia Systems. The hardware and software requirements for setting up the forensic laboratory are identified and the budget expected to set the centre of excellence for a cybercrime investigation laboratory is approximately Rs.7,79,83,434.39/- which may vary depending on versions and updates to hardware and software’s.

Keywords: risk management framework; risk assessment; cloud migration; security; analytic hierarchy process (AHP); business value

Service Level Agreement (SLA) represents a means of regulating and controlling the interaction between service providers and their customers. In the first part of this paper, we use Bigraphical Reactive Systems (BRS) to model customers, offered services by different providers and the SLA between them. Concretely, we use bigraphs, the static structure of BRS, to model these entities and to describe their relationships. In addition, we propose a set of reaction rules to show the evolution of their states during the different stages of the SLA's lifecycle. In the second part, we apply these models in the domain of cloud computing. Cloud computing architecture is usually represented as a stack of different layers. We show that the proposed models can be applied to any computing model (e.g., Software as a Service, Platform as a Service and Infrastructure as service layer) and they allow describing the SLA across the cloud stack layers. We show also that these models can represent the composition of services from several layers to offer complete solutions to end users.

Cloud computing is a paradigm for enabling remote, on-demand access to a set of configurable computing resources as a service. The pay-per-use model enables service providers to offer their services to customers in different Quality-of-Service (QoS) levels. These QoS parameters are used to compose some bipartisan Service Level Agreement (SLA) between a service provider and a service consumer. A main challenge for a service provider is to manage SLAs for its service consumers, i.e. automatically determine the appropriate resources required from the lower layer in order to respect the QoS requirements of his consumers. This paper proposes an optimization framework driven by consumer preferences to address the SLA dependencies problem across the different cloud layers as well as the need of flexibility and dynamicity required by the domain of Cloud computing. Our approach aims to select the optimal vertical business process designed by cross-layer cloud services, enforcing SLA dependencies between layers. Based on Constraint Programming (CP), our approach can take into account dynamic QoS parameters in a flexible manner to compose the best vertical business process. Experimental results demonstrate the flexibility and effectiveness of our approach.

Current data centers consume huge amount of power to face the increasing network traffic. Therefore energy efficient processors are required that can process the cloud applications efficiently without consuming excessive power. This paper presents a performance evaluation of the processors that are mainly used in high performance embedded systems in the domain of cloud computing. Several representative applications based on the widely used MapReduce framework are mapped in the embedded processor and are evaluated in terms of performance and energy efficiency. The results shows that high performance embedded processors can achieve up to 7.8x better energy efficiency than the current general purpose processors in typical MapReduce applications.Keywordscloud computinggreen computingembedded processorsmapreducedata centers

Development and analysis of a three phase cloudlet allocation algorithm