Private Cloud Providers Research Articles

Blockchain for Achieving Accountable Outsourcing Computations in Edge Computing

Since the emergence of cloud computing, data owners are being driven to move their intricate data management systems from on-site locations to private cloud providers in order to take advantage of increased flexibility and cost savings. However, sensitive data must be encrypted before being outsourced in order to safeguard data privacy. This renders outdated the conventional method of using data, which relies on plaintext keyword searches. It is therefore crucial to activate an encrypted cloud data search service. In order to meet the effective data retrieval, need, search services must support multi-keyword queries and offer result similarity rating, given the volume of data users and documents stored in cloud storage. Similar works on searchable encryption seldom distinguish between search results and concentrate on single keyword or Boolean keyword search. I define and solve the difficult problem of privacy-preserving multi-keyword ranked search over encrypted cloud data (MRSE) for the first time in this work. I also outline a tight set of privacy requirements that must be met in order for such a safe cloud data utilization system to be implemented.

Energy-Efficient Resource Allocation and Migration in Private Cloud Data Centre

The level of difficulty that can be envisioned in a cloud data center will not grow with convention. As a result, all hosts should have a standard and pervasive collection of memory and communication characteristics in order to lower ownership costs and operate virtual machine instances. This solution includes fundamental foundations and integrated component basics that will allow an IT or federal agency to embrace cloud computing domestically via private virtual cloud data centers. These private cloud data centers would later be developed to purchase and develop IT services on the outside. They are well aware of the obstacles to cloud computing’s acceptance, including concerns about credibility, privacy, interoperability, and marketplaces. In addition, this procedure describes critical standards and collaborations to address these issues. Ultimately, it offers a coherent response to deploying safe data centers using cloud computing services from both a technological and an IT strategic standpoint. To foster creativity, invention, learning, and enterprise, a private data center and cloud computing must be established to combine the activities of different research teams. In the framework of energy-efficient distribution of resources in private cloud data center architecture, we focus on system structure investigations. On the other hand, we want to equip private cloud providers with the current design and performance analysis for energy-efficient resource allocation. The methodology should be adaptable enough to support a wide range of computing systems, as well as on-demand and extensive resource providing approaches, cloud environment scheduling, and bridging the gap between private cloud users and a complete image of offers.

A Two-Stage Auction Mechanism for Cloud Resource Allocation

With the recent growth in the size of cloud computing business, handling the interactions between customers and cloud providers has become more challenging. Auction theory has been proposed to model these interactions due to its simplicity and a good match with real-world scenarios. In this paper, we consider cloud of clouds networks (CCNs) with different types of servers along with customers with heterogeneous demands. For each CCN, a CCN manager is designated to handle the cloud resources. A comprehensive framework is introduced in which the process of resource gathering and allocation is addressed via two stages, where the first stage models the interactions between customers and CCN managers, and the second stage examines the interactions between CCN managers and private cloud providers (CPs). For the first stage, an options-based sequential auction (OBSA) is adapted to the examined market, which is capable of providing truthfulness as the dominant strategy and resolving the entrance time problem. An analytical foundation for OBSAs is presented and multiple performance metrics are derived. For the second stage, two parallel markets are assumed: flat-price and auction-based market. A theoretical framework for market analysis is provided and the bidding behavior of CCN managers is described.

Metrics for improving the management of Cloud environments — Load balancing using measures of Quality of Service, Service Level Agreement Violations and energy consumption

Cloud service providers use load balancing algorithms in order to avoid Service Level Agreement Violations (SLAVs) and wasted energy consumption due to host over- and under-utilization, respectively. Load balancing algorithms migrate VMs between hosts in order to balance host loads. Any Virtual Machines (VMs) that are migrated experience performance degradation which results in lower Quality of Service (QoS) and can possibly result in SLAVs. Hence, an optimal load balancing method should reduce the number of over- and under-utilized hosts with a minimal number of VM migrations. One of the metrics used previously in the literature for evaluating load balancing stated that it equally considered SLAVs caused by both over-utilized hosts and migrations. However, in this paper, we show that, in fact, this metric values keeping the number of migrations low at the expense of an increased number of over-utilized hosts. This disparity is demonstrated by simulation of Google, PlanetLab and Azure data sets in CloudSim. This metric may suit public cloud providers which are focused on minimizing SLAVs and keeping energy costs low, but does not consider the QoS of customer VMs. We propose an alternative metric that considers QoS for the VMs. This alternative metric considers not only performance loss during migration, but also performance degradation due to host over-utilization. Private cloud providers, e.g., IT services within large organizations, often value the performance of their “customer” VMs, i.e., the QoS their organization receives, as well as traditional cloud provider costs, i.e., energy and SLAV costs. Hence, our alternative metric would be more appropriate in these scenarios. We compare and contrast load balancing methods using both the existing, biased metric and our new alternative metric.

An online sequential procurement mechanism under uncertain demands in multi-cloud environment

The uncertainty of demands brings challenges for the private cloud providers, leading to low utilization of resources during periods of low-demand and low quality of service during periods of peak-demand, which has attracted much attention. In this paper, taking account into both uncertainty of demands and budget constraint, we design an online sequential procurement auctions of residual resources, which helps the busy cloud provider make an irrevocable decision about how to purchase resources during period of uncertain peak-demand. The crucial part of the mechanism is the seller accepting-rule based on a value-density threshold which is learned dynamically from the historical information. Given the condition that all the sellers are myopic, we prove that the mechanism is truthful, budget feasible and individual rational. Furthermore, we obtain the competitive ratio of the proposed mechanism when the demands of the BCP are δ-degree balance. Using real data from parallel computing centers, we construct 60 scenarios in six data settings, in which we compare our mechanism with average budget allocation and offline proportional sharing mechanism, the results show that in more than 85% scenarios the proposed mechanism has better performance than allocation with average budget, and it improves more than 20% valuation on average for the buyer, even if we use the estimate value of balance degree δ.

Are Cloud Service Brokers (CSBs) Still Relevant?

Cloud Service Brokers (CSBs) rose in importance due to the diverse number of public and private cloud providers, and the need to manage the consumption of those services within an enterprise. As you can see in Figure 1, CSBs sit between the service consumers, which is typically an application, and the cloud service provider. CSBs provide the following services.

Enhancing fairness in P2P cloud federations

This work presents a non-monetary market system mechanism for resource sharing within a decentralized lightweight Peer-to-Peer (P2P) federation of private cloud providers. The mechanism is intended to enable collaborative peers to take appropriate decisions guaranteeing suitable levels of satisfaction and fairness as well as isolation of non-collaborative peers. The proposed scheme leverages the notion of the Network of Favors, formerly advanced as an incentive mechanism for P2P opportunistic desktop grids in which peers are concerned primarily with their satisfaction, not fairness. An enhancement of the Network of Favors is presented here that consists in the introduction of a control loop mechanism limiting the amount of resources that a private cloud is willing to provide to other members of federation, thereby indirectly keeping resource contention at an adequate level for discouraging free riding and guaranteeing an appropriate degree of fairness with only minor impact on the satisfaction levels of collaborative peers.

ATLAS user analysis on private cloud resources at GoeGrid

User analysis job demands can exceed available computing resources, especially before major conferences. ATLAS physics results can potentially be slowed down due to the lack of resources. For these reasons, cloud research and development activities are now included in the skeleton of the ATLAS computing model, which has been extended by using resources from commercial and private cloud providers to satisfy the demands. However, most of these activities are focused on Monte-Carlo production jobs, extending the resources at Tier-2. To evaluate the suitability of the cloud-computing model for user analysis jobs, we developed a framework to launch an ATLAS user analysis cluster in a cloud infrastructure on demand and evaluated two solutions. The first solution is entirely integrated in the Grid infrastructure by using the same mechanism, which is already in use at Tier-2: A designated Panda-Queue is monitored and additional worker nodes are launched in a cloud environment and assigned to a corresponding HTCondor queue according to the demand. Thereby, the use of cloud resources is completely transparent to the user. However, using this approach, submitted user analysis jobs can still suffer from a certain delay introduced by waiting time in the queue and the deployed infrastructure lacks customizability. Therefore, our second solution offers the possibility to easily deploy a totally private, customizable analysis cluster on private cloud resources belonging to the university.

Analysis of the Join Performance in Vertically Distributed Cloud Databases

Cloud Computing becomes interesting for enterprises across all branches. Renting computing capabilities from external providers avoids initial investments, as only those resources have to be paid that were used eventually. Especially in the context of “Big Data” this pay-as-you-go accounting model is particularly important. The dynamically scalable resources from the Cloud enable enterprises to store or analyze these huge amounts of unstructured data without using their own hardware infrastructure. However, Cloud Computing is currently facing severe data security and protection issues. These challenges require new ways to store and analyze data, especially when huge data volumes with sensitive data are stored at external locations. The presented approach separates data on database table level into independent chunks and distributes them across several clouds. Hence, this work is a contribution to a more secure and resilient cloud architecture as multiple public and private cloud providers can be used independently to store data without losing data security and privacy constraints.

Dynamic Cloud Deployment of a MapReduce Architecture

Cloud-based MapReduce services process large datasets in the cloud, significantly reducing users' infrastructure requirements. Almost all of these services are cloud-vendor-specific and thus internally designed within their own cloud infrastructures, resulting in two important limitations. First, cloud vendors don't let developers see and evaluate how the MapReduce architecture is managed internally. Second, users can't build their own private cloud-infrastructure-based offerings or use different public cloud infrastructures for deploying MapReduce services. The authors' proposed framework enables the dynamic deployment of a MapReduce service in virtual infrastructures from either public or private cloud providers.