Cloud Tenants Research Articles

F2C: Enabling Fair and Fine-Grained Resource Sharing in Multi-Tenant IaaS Clouds

This paper presents F2C , a cooperative resource management system for Infrastructure-as-a-Service (IaaS) clouds. Inspired by group-buying mechanisms in real product and service markets, F2C advocates a group of cloud tenants (called tenant coalition ) to buy resource capacity in bulk and share the resource pool in the form of virtual machines (VMs). Tenant coalitions leads to vast opportunities for fine-grained resource sharing among multiple tenants. However, resource sharing, especially for multiple resource types, poses several challenging problems in pay-as-you-use cloud environments, such as sharing incentive, free-riding, lying and economic fairness. To address those problems, we propose Reciprocal Resource Fairness (RRF) , a novel resource allocation mechanism to enable fair sharing on multiple resource types within a tenant coalition. RRF is implemented in two complementary and hierarchical mechanisms: inter-tenant resource trading and intra-tenant weight adjustment. RRF satisfies several highly desirable properties to ensure fairness. We implement F2C in Xen platform. The experimental results show F2C is promising for both cloud providers and tenants. For cloud providers, F2C improves VM density and cloud providers’ revenue by 2.2X compared to the current IaaS cloud models. For tenants, F2C improves application performance by 45 percent and guarantees 95 percent economic fairness among multiple tenants.

Twenty Security Considerations for Cloud-Supported Internet of Things

To realize the broad vision of pervasive computing, underpinned by the “Internet of Things” (IoT), it is essential to break down application and technology-based silos and support broad connectivity and data sharing; the cloud being a natural enabler. Work in IoT tends toward the subsystem, often focusing on particular technical concerns or application domains, before offloading data to the cloud. As such, there has been little regard given to the security, privacy, and personal safety risks that arise beyond these subsystems; i.e., from the wide-scale, cross-platform openness that cloud services bring to IoT. In this paper, we focus on security considerations for IoT from the perspectives of cloud tenants, end-users, and cloud providers, in the context of wide-scale IoT proliferation, working across the range of IoT technologies (be they things or entire IoT subsystems). Our contribution is to analyze the current state of cloud-supported IoT to make explicit the security considerations that require further work.

Secure searching on cloud storage enhanced by homomorphic indexing

Enterprise cloud tenants would store their outsourced cloud data in encrypted form for data privacy and security. However, flexible data access functions such as data searching is usually sacrificed as a result. Thus, enterprise tenants demand secure data retrieval and computation solution from the cloud provider, which will allow them to utilize cloud services without the risks of leaking private data to outsiders and even service providers.In this paper, we propose an exclusive-or (XOR) homomorphism encryption scheme to support secure keyword searching on encrypted data for cloud storage. First, this scheme specifies a new data protection method by encrypting the keyword and randomizing it by performing XOR operation with a random bit-string for each session to protect access pattern leakage; Secondly, the homomorphic evaluation key enables the searching evaluation to be on-demand calculated, thus it removes the dependency of key storage on cloud and enhance protection against cloud’s violability; Thirdly, this scheme can effectively protect data-in-transit against passive attack such as access pattern analysis due to the randomization. This scheme also can reduce data leakage to service provider because the homomorphism-key solution instead of key storage on cloud. The above three features have been proved by the experiments and further tested out at Email service which can support secure subject searching. The execution time of one searching process is just in the order of milliseconds. We could get 2–3 times speedup compared to default utility grep with the concern of expensive one-time indexing which can be built off-line in advance.

Cloud-Trust—a Security Assessment Model for Infrastructure as a Service (IaaS) Clouds

The vulnerability of cloud computing systems (CCSs) to advanced persistent threats (APTs) is a significant concern to government and industry. We present a cloud architecture reference model that incorporates a wide range of security controls and best practices, and a cloud security assessment model—Cloud-Trust—that estimates high level security metrics to quantify the degree of confidentiality and integrity offered by a CCS or cloud service provider (CSP). Cloud-Trust is used to assess the security level of four multi-tenant IaaS cloud architectures equipped with alternative cloud security controls. Results show the probability of CCS penetration (high value data compromise) is high if a minimal set of security controls are implemented. CCS penetration probability drops substantially if a cloud defense in depth security architecture is adopted that protects virtual machine (VM) images at rest, strengthens CSP and cloud tenant system administrator access controls, and which employs other network security controls to minimize cloud network surveillance and discovery of live VMs.

Towards Monitoring-as-a-service for Scientific Computing Cloud applications using the ElasticSearch ecosystem

The INFN computing centre in Torino hosts a private Cloud, which is managed with the OpenNebula cloud controller. The infrastructure offers Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) services to different scientific computing applications. The main stakeholders of the facility are a grid Tier-2 site for the ALICE collaboration at LHC, an interactive analysis facility for the same experiment and a grid Tier-2 site for the BESIII collaboration, plus an increasing number of other small tenants. The dynamic allocation of resources to tenants is partially automated. This feature requires detailed monitoring and accounting of the resource usage. We set up a monitoring framework to inspect the site activities both in terms of IaaS and applications running on the hosted virtual instances. For this purpose we used the ElasticSearch, Logstash and Kibana (ELK) stack. The infrastructure relies on a MySQL database back-end for data preservation and to ensure flexibility to choose a different monitoring solution if needed. The heterogeneous accounting information is transferred from the database to the ElasticSearch engine via a custom Logstash plugin. Each use-case is indexed separately in ElasticSearch and we setup a set of Kibana dashboards with pre-defined queries in order to monitor the relevant information in each case. For the IaaS metering, we developed sensors for the OpenNebula API. The IaaS level information gathered through the API is sent to the MySQL database through an ad-hoc developed RESTful web service. Moreover, we have developed a billing system for our private Cloud, which relies on the RabbitMQ message queue for asynchronous communication to the database and on the ELK stack for its graphical interface. The Italian Grid accounting framework is also migrating to a similar set-up. Concerning the application level, we used the Root plugin TProofMonSenderSQL to collect accounting data from the interactive analysis facility. The BESIII virtual instances used to be monitored with Zabbix, as a proof of concept we also retrieve the information contained in the Zabbix database. In this way we have achieved a uniform monitoring interface for both the IaaS and the scientific applications, mostly leveraging off-the-shelf tools. At present, we are working to define a model for monitoring-as-a-service, based on the tools described above, which the Cloud tenants can easily configure to suit their specific needs.

Trusted domain hierarchical model based on noninterference theory

To ensure the integrity and security of cloud tenants' workload, and to prevent unexpected interference among tenants, cloud platform must make sure that system behaviors are trusted. By analyzing threats that exist in the cloud platform, a novel trusted domain hierarchical model (TDHM) based on noninterference theory was proposed in this paper to solve these problems. First of all, the abstraction modeling of tenants' computing environment and trusted domain (TD) were introduced for designing TDHM with formal methods. Secondly, corresponding constraints for trusted running were given to satisfy security requirements of tenants' TD, and security properties of TDHM ware analyzed. After that, trusted behavior of TD was defined based on these properties, and the decision theorem of that was proved. It illustrated that the design and implementation of TD in cloud followed the model with characteristics of trusted behavior. Finally, the implementation of prototype system was introduced based on our previous work, and evaluation results showed that the performance loss was in the acceptable range.

From Security to Assurance in the Cloud

The cloud computing paradigm has become a mainstream solution for the deployment of business processes and applications. In the public cloud vision, infrastructure, platform, and software services are provisioned to tenants (i.e., customers and service providers) on a pay-as-you-go basis. Cloud tenants can use cloud resources at lower prices, and higher performance and flexibility, than traditional on-premises resources, without having to care about infrastructure management. Still, cloud tenants remain concerned with the cloud’s level of service and the nonfunctional properties their applications can count on. In the last few years, the research community has been focusing on the nonfunctional aspects of the cloud paradigm, among which cloud security stands out. Several approaches to security have been described and summarized in general surveys on cloud security techniques. The survey in this article focuses on the interface between cloud security and cloud security assurance. First, we provide an overview of the state of the art on cloud security. Then, we introduce the notion of cloud security assurance and analyze its growing impact on cloud security approaches. Finally, we present some recommendations for the development of next-generation cloud security and assurance solutions.

Secure big data storage and sharing scheme for cloud tenants

The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud. In this paper, we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers. Instead of protecting the big data itself, the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function. Analysis, comparison and simulation prove that the proposed scheme is efficient and secure for the big data of Cloud tenants.

Service-Oriented Resource Allocation in Clouds: Pursuing Flexibility and Efficiency

The networking-oblivious resource reservation model in today’s public clouds cannot guarantee the performance of tenants’ applications. Virtual networks that capture both computing and networking resource requirements of tenants have been proposed as better interfaces between cloud providers and tenants. In this paper, we propose a novel virtual network model that could specify not only absolute and relative location requirements but also time-varying resource demands. Building on top of our model, we study how to efficiently and flexibly place multiple virtual networks in a cloud, and we propose two algorithms, MIPA and SAPA, which focus on optimizing resource utilization and providing flexible placement, respectively. The mixed integer programming based MIPA transforms the placement problem into the multi-commodity flow problem through augmenting the physical network with shadow nodes and links. The simulated annealing-based SAPA achieves resource utilization efficiency through opportunistically sharing physical resources among multiple resource demands. Besides, SAPA allows cloud providers to control the trade-offs between performance guarantee and resource utilization, and between allocation optimality and running time, and allows tenants to control the trade-off between application performance and placement cost. Extensive simulation results confirm the efficiency of MIPA in resource utilization and the flexibility of SAPA in controlling trade-offs.

클라우드 서비스 중개를 위한 가변성 기반의 서비스 명세 기법

As the prevalence of cloud computing increases, various cloud service types have emerged, such as IaaS, PaaS, and SaaS. The growth and diversification of these cloud services has also resulted in the development of technology for cloud service brokers (CSBs), which serve as intermediate cloud services that can assist cloud tenants (users) in deploying services that fit their requirements. In order to broker cloud services, CSBs require the specification of structural models in order to facilitate the analysis and search for cloud services. In this study, we propose a variability-based service analysis model (SAM) that can be used to describe various cloud services. This model is based on the concept of variability in the software product line and represents the commonality and variability of cloud services by binding variants to each variation point that exists in the specification, quality, and pricing of the services. We also propose a virtual cloud bank architecture as a CSB that serves as an intermediate to provides tenants with appropriate cloud services based on the SAM.

Cost-Optimized Joint Resource Allocation in Grids/Clouds With Multilayer Optical Network Architecture

The requirements for large-scale computing, storage, and network capabilities by the business and scientific communities have led to the development of the grid/cloud network. Grid network users can access a shared set of resources for scientific computing tasks. Cloud tenants are offered IT infrastructure through infrastructure as a service. An efficient resource scheduling mechanism across the network, as a result, will improve the resource utilization and also reduce the capital cost of scheduling in the cloud significantly. In this paper, we focus on the joint resource (processor, storage, and network) allocation in the grid/cloud environment. The multilayer optical network architecture is introduced to guarantee the reservation of the network bandwidth resource. We investigate the bandwidth guaranteed joint resource scheduling from the cloud provider's point of view, which is completing the resource scheduling with minimal capital expenditure. The mixed integer linear programming (MILP) formulations and heuristics (best-fit and tabu search) are developed to solve our problems. The results show that both MILP and heuristics work well to solve the problem, and the heuristics are much more time-efficient. In addition, the tabu search method achieves the optimal resource allocation and also reaches a lower blocking rate compared to the best-fit method.

DIV: Dynamic integrity validation framework for detecting compromises on virtual machine based cloud services in real time

With the increasing popularity of cloud services, attacks on the cloud infrastructure also increase dramatically. Especially, how to monitor the integrity of cloud execution environments is still a difficult task. In this paper, a real-time dynamic integrity validation (DIV) framework is proposed to monitor the integrity of virtual machine based execution environments in the cloud. DIV can detect the integrity of the whole architecture stack from the cloud servers up to the VM OS by extending the current trusted chain into virtual machine's architecture stack. DIV introduces a trusted third party (TTP) to collect the integrity information and detect remotely the integrity violations on VMs periodically to avoid the heavy involvement of cloud tenants and unnecessary information leakage of the cloud providers. To evaluate the effectiveness and efficiency of DIV framework, a prototype on KVM/QEMU is implemented, and extensive analysis and experimental evaluation are performed. Experimental results show that the DIV can efficiently validate the integrity of files and loaded programs in real-time, with minor performance overhead.

A Quantitative Evaluation Method Based on Hierarchical Architecture Model of Cloud Service Availability

Today, cloud service has evolved as a network centric, service oriented computing model. Consumers purchase computational resources and network accesses on demand without worrying about the underlying technologies used. However, the concerns of security, reliability and service response availability are also increasing. Although cloud service providers usually committed to the availability scope of Service Level Agreement (SLA), most cloud tenants do not understand the inherent complexity. There is an apparent lack of ability to assess the risks when choosing cloud platform. From the perspective of the cloud computing service layer model, this paper defines and analyses a quantitative evaluation method of the availability of cloud service. And it proposes the concepts of availability unit (AU), availability set (AS), standalone availability (SA) and deployment availability (DA). Also discusses the algorithm relationships among all levels of IaaS, PaaS and SaaS on cloud computing service.

Defragmenting the cloud using demand-based resource allocation

Current public cloud offerings sell capacity in the form of pre-defined virtual machine (VM) configurations to their tenants. Typically this means that tenants must purchase individual VM configurations based on the peak demands of the applications, or be restricted to only scale-out applications that can share a pool of VMs. This diminishes the value proposition of moving to a public cloud as compared to server consolidation in a private virtualized datacenter, where one gets the benefits of statistical multiplexing between VMs belonging to the same or different applications. Ideally one would like to enable a cloud tenant to buy capacity in bulk and benefit from statistical multiplexing among its workloads. This requires the purchased capacity to be dynamically and transparently allocated among the tenant's VMs that may be running on different servers, even across datacenters. In this paper, we propose two novel algorithms called BPX and DBS that are able to provide the cloud customer with the abstraction of buying bulk capacity. These algorithms dynamically allocate the bulk capacity purchased by a customer between its VMs based on their individual demands and user-set importance. Our algorithms are highly scalable and are designed to work in a large-scale distributed environment. We implemented a prototype of BPX as part of VMware's management software and showed that BPX is able to closely mimic the behavior of a centralized allocator in a distributed manner.

CloudAC: a cloud‐oriented multilayer access control system for logic virtual domain

The security issue has been a challenging concern for cloud computing because of the multitenant usage model. In cloud, each application normally runs on a dynamic coalition that is composed by multiple virtual machines (VMs) running on different virtualised service nodes, which the authors called logic virtual domain (LVD). Moreover, the owners of cloud applications, who are also the tenants of cloud, would specify some security policies to control the access to those resources that they have paid for. Therefore the owners of cloud infrastructures have to provide the tenants with the mechanism to correctly configure and enforce the access control policies on resources that are from multiple service nodes, to meet the security requirements from cloud applications. To address the above challenge, this study presents the design and implementation about a multilayer access control architecture for LVD, named CloudAC, aiming to provide isolation control, information flow control and resource-sharing control among multiple VMs on Xen virtualisation platforms in cloud computing environment. The theory and technology this research formed will provide reliable security guarantee for resource configuration and application deployment on LVDs.

Design and implementation of a trusted monitoring framework for cloud platforms

Virtualization is a pillar technology in cloud computing for multiplexing computing resources on a single cloud platform for multiple cloud tenants. Monitoring the behavior of virtual machines (VMs) on a cloud platform is a critical requirement for cloud tenants. Existing monitoring mechanisms on virtualized platforms either takes a complete VM as the monitoring granularity, such that they cannot capture the malicious behaviors within individual VMs, or they focus on specific monitoring functions that cannot be used for heterogeneous VMs concurrently running on a single cloud node. Furthermore, the existing monitoring mechanisms have made an assumption that the privileged domain is trusted to act as expected, which causes the cloud tenants’ concern about security because the privileged domain in fact could not act as the tenants’ expectation. We design a trusted monitoring framework, which provides a chain of trust that excludes the untrusted privileged domain, by deploying an independent guest domain for the monitoring purpose, as well as utilizing the trusted computing technology to ensure the integrity of the monitoring environment. Moreover, the feature of fine-grained and general monitoring is also provided. We have implemented the proposed monitoring framework on Xen, and integrated it into OpenNebula. Our experimental results show that it can offer expected functionality, and bring moderate performance overhead.

Semantic-aware multi-tenancy authorization system for cloud architectures

Cloud computing is an emerging paradigm to offer on-demand IT services to customers. The access control to resources located in the cloud is one of the critical aspects to enable business to shift into the cloud. Some recent works provide access control models suitable for the cloud; however there are important shortages that need to be addressed in this field. This work presents a step forward in the state-of-the-art of access control for cloud computing. We describe a high expressive authorization model that enables the management of advanced features such as role-based access control (RBAC), hierarchical RBAC (hRBAC), conditional RBAC (cRBAC) and hierarchical objects (HO). The access control model takes advantage of the logic formalism provided by the Semantic Web technologies to describe both the underlying infrastructure and the authorization model, as well as the rules employed to protect the access to resources in the cloud. The access control model has been specially designed taking into account the multi-tenancy nature of this kind of environment. Moreover, a trust model that allows a fine-grained definition of what information is available for each particular tenant has been described. This enables the establishment of business alliances among cloud tenants resulting in federation and coalition agreements. The proposed model has been validated by means of a proof of concept implementation of the access control system for OpenStack with promising performance results.

Design and implementation of caching services in the cloud

Data caching is a key paradigm for improving the performance of web services in terms of both end-user latency and database load. Such caching is becoming an essential component of any application or service designed for the cloud platform. In order to allow hosted applications to benefit from caching capabilities while avoiding dependence on explicit implementations and idiosyncrasies of internal caches, the caching services should be offered by a cloud provider as an integral part of its platform-as-a-service portfolio. We highlight various challenges associated with supporting cloud-based caching services, such as identifying the appropriate metering and service models, performance management, and resource sharing across cloud tenants. We also describe how these challenges were addressed by our prototype implementation, which is called Simple Cache for Cloud (SC2). We demonstrate the effectiveness of these techniques by experimentally evaluating our prototype on a synthetic multitenant workload.