Analysis of Risks and Security Requirements in Public Cloud

A company planning to implement a public cloud solution should be aware of the risks associated with cloud computing. This is due to the rapidly evolving threats to cloud security that are emerging at an increasing rate in recent years, also to multi-tenant and open nature of the public cloud. Cloud computing is a powerful technology in IT environment and to take advantage of all the benefits of cloud computing, it is necessary to have security and privacy requirements for the implementation and use of the cloud. A risk analysis must be done initially with a cloud provider in order to identify the existing vulnerabilities and to implement the measures to counter those threats. This paper explores the key issues surrounding cloud security, and passes on valuable guidance about not only information security and broadly risk. Also, it will help providers to make informed security decisions about their diverse cloud set-ups and better understand how to reap all the benefits of cloud without compromising their organizations’ security. As well, it discusses the security requirements, and various risks related to the public cloud.

Cloud computing technology is increasingly important in the industry with the promise of increased performance, dynamic resource allocation capabilities and provides a convincing opportunity for organizations to outsource their IT infrastructure under the pay-per-use model offered by many public cloud providers. But there are concerns when selecting between the private cloud and the public cloud that must be taken into account. Many SME enterprises with scarce in-house IT support and limited knowledge about different cloud technologies encounter difficulties in making the most appropriate choice on 'buying' a private cloud or 'renting' a public cloud service. Through a number of interviews with potential cloud adopters from the industry, this paper identifies the most relevant factors concerning cloud adoption and develops a cloud deployment model assessment method called Cloud Deployment Selection Model (CDSM). The model has been validated in real case studies, and recommendations derived have been compared with real adoption cases. Result shows it is able to accurately recommend a suitable cloud deployment model based on the factors identified from many SME organizations, an important tool for SMEs to decide between the private or the public cloud solution.

Scalable applications deployed in public clouds can be built from a combination of custom software components and public cloud services. To meet performance and/or cost requirements, such applications can scale-out/in their components during run-time. When higher performance is required, new component instances can be deployed on newly allocated cloud services (e.g., virtual machines). When the instances are no longer needed, their services can be deallocated to decrease cost. However, public cloud services are usually billed over predefined time and/or usage intervals, e.g., per hour, per GB of I/O. Thus, it might not be cost efficient to scale-in public cloud applications at any moment in time, without considering their billing cycles. In this work we aid developers of scalable applications for public clouds to monitor their costs, and develop cost-aware scalability controllers. We introduce a model for capturing the pricing schemes of cloud services. Based on the model we determine and evaluate the application's costs depending on its used cloud services and their billing cycles. We further evaluate cost efficiency of cloud applications, analyzing which application component is cost efficient to deallocate and when. We evaluate our approach on a scalable platform for IoT, deployed in Flexiant1, one of the leading European public cloud providers. We show that cost-aware scalability can achieve higher application stability and performance, while reducing its operation costs.

Virtual Machine (VM) consolidation provides a promising approach to save energy and improve resource utilization in cloud data centers. However, aggressive consolidation of VMs may lead to performance degradation of data centers and even Service Level Agreement (SLA) violation. Therefore, it is very important to solve the tradeoff between reduction of SLA violation level and energy costs. In this paper, we propose a Host State 3-order Markov Chain prediction model for SLA-aware and energy efficient consolidation of VMs in cloud data centers. We propose two adaptive utilization thresholds based on a robust statistical method for host state estimate, and our model uses them to build a 3-order Markov Chain Transition Matrix manually for host state detection. We implement our algorithms on Cloudsim simulator for comparative study. The experimental results show that our model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLA Violation by at most 98.69% and the metric of Energy by at most 21.88% for real world workload.

Cloud computing is a key technology for online service providers. However, current online service systems experience performance degradation due to the heterogeneous and time-variant incoming of user requests. To address this kind of diversity, we propose a hierarchical approach for resource management in hybrid clouds, where local private clouds handle routine requests and a powerful third-party public cloud is responsible for the burst of sudden incoming requests. Our goal is to answer (1) from the online service provider’s perspective, how to decide the local private cloud resource allocation, and how to distribute the incoming requests to private and/or public clouds; and (2) from the public cloud provider’s perspective, how to decide the optimal prices for these public cloud resources so as to maximize its profit. We use a Stackelberg game model to capture the complex interactions between users, online service providers and public cloud providers, based on which we analyze the resource allocation in private clouds and pricing strategy in public cloud. Furthermore, we design efficient online algorithms to determine the public cloud provider’s and the online service provider’s optimal decisions. Simulation results validate the effectiveness and efficiency of our proposed approach.

The workloads of social computing are tremendous, bursty and unpredictable. A hybrid cloud, which combines a private cloud and a public cloud, is a promising solution to processing the workloads. For most corporations, they leverage one public cloud. However, with fierce competition among public cloud providers, public cloud services change frequently, which may lead to service unavailability and a less cost-effective hybrid cloud solution. As a result, leveraging multiple public clouds in the hybrid cloud is a potential solution. In this paper, we identify such a problem in current hybrid cloud and analyze the necessity of load balancing for hybrid cloud applications. Focusing on cost minimization and performance guarantee, we propose a Least Cost per Connection (LCC) algorithm so as to choose the most cost-effective clouds along with adapting changes among multiple public clouds. The simulation results show that our solution can significantly decrease the outsourcing cost as well as guarantee Quality of Service (QoS) of applications.

An increasing demand is seen from enterprises to host and dynamically manage middlebox services in public clouds in order to leverage the same benefits that network functions provide in traditional, in-house deployments. However, today's public clouds provide only a limited view and programmability for tenants that challenges flexible deployment of transparent, software-defined network functions. Moreover, current virtual network functions can't take full advantage of a virtualized cloud environment, limiting scalability and fault tolerance. In this paper we review and evaluate the current infrastructural limitations imposed by public cloud providers and present the design and implementation of GNFC, a cloud-based Network Function Virtualization (NFV) framework that gives tenants the ability to transparently attach stateless, container-based network functions to their services hosted in public clouds. We evaluate the proposed system over three public cloud providers (Amazon EC2, Microsoft Azure and Google Compute Engine) and show the effects on end-to-end latency and throughput using various instance types for NFV hosts.

Online service is used to be as Pay-Per-Use in Cloud computing. Service user need not be in a long time contract with cloud service providers. Service level agreements (SLAs) are understandings marked between a cloud service providers and others, for example, a service user, intermediary operator, or observing operators. Since cloud computing is an ongoing technology giving numerous services to basic business applications and adaptable systems to manage online agreements are significant. SLA maintains the quality-of-service to the cloud user. If service provider fails to maintain the required service SLA is considered to be SLA violated. The main aim is to minimize the SLA violations for maintain the QoS of their cloud users. In this research article, a toolbox is proposed to help the procedure of exchanging of a SLA with the service providers that will enable the cloud client in indicating service quality demands and an algorithm as well as Negotiation model is also proposed to negotiate the request with the service providers to produce a better agreement between service provider and cloud service consumer. Subsequently, the discussed framework can reduce SLA violations as well as negotiation disappointments and have expanded cost-adequacy. Moreover, the suggested SLA toolkit is additionally productive to clients so clients can secure a sensible value repayment for diminished QoS or conceding time. This research shows the assurance level in the cloud service providers can be kept up by as yet conveying the services with no interruption from the client's perspective

Security and privacy concerns are increasingly important when massive data processing and transferring becomes a reality in the era of the Sixth Generation (6G) Networks. Under this circumstance, it becomes a trend that the enterprises tend to host their data and services on private clouds dedicated to their own use, rather than the public cloud services. However, in contrary to the well-investigated total cost of ownership (TCO) for public clouds, the analytic research on the cost of purchase and operation for private clouds is still a blank. In this work, we first review the state-of-the-art TCO literature to summarize the models, tools, and cost optimization techniques for public clouds. Based on our survey, we envision the TCO modeling and optimization for private clouds by comparing the differences of features between public and private clouds.

Given the wide distribution of software and its current growth, security is inevitable, and software systems have become more complex. To avoid the complexity and protect valuable assets of software systems, researchers, and practitioners in the fields of security requirement engineering (SRE) and security assurance advise incorporating security requirements early in software development life cycle (SDLC). Nowadays, security requirements specification and identification are an active research area for ensuring the effectiveness of software systems. However, security requirement identification is a challenging task due to sheer numbers of threats and attacks to a software system. Previous researchers have identified different SRE techniques, each looking at the same problem from a different perspective. Therefore, a literature review conducted on SRE techniques. We compared and analyzed different SRE techniques to find the most suitable SRE techniques for researchers and practitioners. Selected SRE techniques compared based on various parameters, such as different attributes, requirement elicitation, requirement analysis, project size, and integration of standards. The literature also indicates that among SRE approaches, security quality requirements engineering (SQAURE), secure tropos, security requirements engineering process (SREP), and comprehensive lightweight application security process (CLASP) are the most used SRE approaches that focus on activities such as threats, security requirements identification, and security objectives identification. Unfortunately, several SRE approaches fail to explicitly integrate the security standard and security assurance perspective. Three questions developed for this study: Question 1 is based on comparison of existing SRE approaches, Question 2 is based on SRE with security requirements elicitation and analysis, and Question 3 highlights the incorporation of security requirements assurance by SRE approaches implicitly or explicitly. The researcher must work with the developer to easily adopt SRE approaches to elicit and ensure security requirements for the software system.

Virtual machine (VM) consolidation in Cloud computing environments provides a great opportunity for energy saving. However, the obligation of providing suitable quality of service to end users leads to the necessity in dealing with energy-performance tradeoff. In this paper, we propose a redesigned energy-aware heuristic framework for VM consolidation to achieve a better energy-performance tradeoff. The main contribution of us is a Service Level Agreement (SLA) violation decision algorithm, which is used to decide a host is overload with SLA violation or not. Finally, we have evaluated our framework through simulation on large-scale experiments driven by workload traces from more than a thousand VMs, and the results show that our framework outperforms previous work, which has 11.8%~27% decrease in energy consumption, 57.9%~78.4% decrease in SLA violation, 63.2%~84.1% decrease in energy-performance metric which is a product metric of the energy consumption and SLA violation.

The advent of cloud computing has made access to computing infrastructure available to millions of users that face resource constraints. In the context of high performance computing (HPC), public cloud resources have emerged as a cost‐effective alternative to expensive on‐premises clusters. However, there are several challenges and limitations in adopting this approach. This paper proposes HPC@Cloud , a provider‐agnostic open‐source software toolkit that facilitates the migration, testing, and execution of HPC applications in public clouds. The toolkit takes advantage of various fault tolerance technologies to enable the use of inexpensive transient cloud infrastructure, commonly known as “spot” instances. Also, it features integration with singularity containers, allowing users to run complex applications on virtual HPC clusters in a portable and reproducible way. Finally, it provides a data‐based empirical approach to estimating cloud infrastructure costs for HPC workloads. The results obtained on two public cloud providers (AWS and Vultr) show that: (i) HPC@Cloud can efficiently build virtual HPC clusters on the cloud; (ii) the new adaptive fault tolerance strategy outperforms other existing strategies based on blocking restoration; (iii) the integration of singularity containers into HPC@Cloud improves the portability of HPC applications to public clouds with negligible performance penalty to the applications; (iv) the proposed cost prediction approach can estimate the cost of running the applications on AWS and Vultr with up to 93% accuracy on average.

Public cloud providers offer access to hardware resources and users rent resources by choosing among many VM sizes. While users choose the CPU core count and main memory size per VM, they cannot specify last-level cache (LLC) requirements. LLC is typically shared among all cores of a modern CPU causing cache contention and performance interference among co-located VMs. Consequently, a user's only way to avoid this interference is purchasing a full-server VM to prevent co-tenants. Although researchers have studied LLC partitioning and despite its availability in commodity processors, LLC QoS has not been offered to public cloud users today. Existing techniques rely mostly on performance profiling, which is not feasible in public cloud settings with opaque VMs. Moreover, prior work does not address how to deliver differentiated LLC allocations at scale. In this work, we develop CacheSlicer, the first system that provides cluster-level support for LLC management in a public cloud. We show how to provide LLC allocations in a major public cloud provider to enable differentiated VM categories, from which users select VMs that match their workloads. We integrate it into the Azure VM scheduler and show its effectiveness through extensive evaluations.

Service Level Agreements (SLA) represents the principal means to control the Quality of Service (QoS). In cloud computing, various advanced SLA strategies are used; some techniques among others try to avoid the SLA violations. Taking into account the cloud consumer contextual parameters is a promising way to predict and avoid costly SLA violations. Therefore, in this paper, we propose a context-aware system for the SLA in order to guarantee the QoS. We create a contextual ontology to introduce the semantic meaning of the cloud consumer contextual parameters. In addition, using reasoning techniques, we (1) predict SLA violations and we (2) choose and apply the corrective actions in autonomous manner. This allows getting a proactive adjustment of the cloud service execution and respecting the SLA parameters.

Cloud computing is a promising technology for the realization of large, scalable, and on-demand provisioned computing infrastructures. Currently, many enterprises are adopting Clouds to achieve high performance and scalability for their applications while maintaining low costs. Service provisioning in the Cloud is based on a set of predefined nonfunctional properties specified and negotiated by means of Service Level Agreements (SLAs). Cloud workloads are dynamic and change constantly. Thus, in order to reduce steady human interactions, self-manageable Cloud techniques are required to comply with the agreed customers' SLAs. Flexible and reliable management of SLAs is of paramount importance for both, Cloud providers and consumers. On one hand, the prevention of SLA violations avoids penalties that are costly to providers. On the other hand, based on flexible and timely reactions to possible SLA violation threats, user interaction with the system can be minimized enabling Cloud computing to take roots as a flexible and reliable form of on-demand computing. Furthermore, a trade-off has to be found between proactive actions that prevent SLA violations and those that reduce energy consumption, i.e., increase energy efficiency. In this talk we discuss how the application of Cloud computing technologies can support the work of scientists working in the field of high-throughput sequencing, while at the same time optimizing utilization of resource and increasing energy efficiency.