Blockchain-Based Trust Auction for Dynamic Virtual Machine Provisioning and Allocation in Clouds

Cloud computing uses virtualization technology to provide users with different types of resources in the form of services. The third party plays a crucial role in coordinating cloud market between cloud providers and users. As for providing services or trading, the extra broker fees are required for the middleman because the third party facilitates transactions. Moreover, there is no guarantee that the third party is trusted, which can lead to information leakage, data tampering, and unfair trading. Blockchain technology is an emerging technology that can store and communicate data between entities that unnecessarily trust each other. To resolve the problems, this paper presents the blockchain-based trust and fair system and develops the smart contract of auction and transaction. The prototype system is implemented based on the Hyperledger Fabric. The experimental results prove the feasibility of the scheme.

Intelligent Virtual Machine Provisioning in Cloud Computing

Virtual machine (VM) provisioning is a common and critical problem in cloud computing. In industrial cloud platforms, there are a huge number of VMs provisioned per day. Due to the complexity and resource constraints, it needs to be carefully optimized to make cloud platforms effectively utilize the resources. Moreover, in practice, provisioning a VM from scratch requires fairly long time, which would degrade the customer experience. Hence, it is advisable to provision VMs ahead for upcoming demands. In this work, we formulate the practical scenario as the predictive VM provisioning (PreVMP) problem, where upcoming demands are unknown and need to be predicted in advance, and then the VM provisioning plan is optimized based on the predicted demands. Further, we propose Uncertainty-Aware Heuristic Search (UAHS) for solving the PreVMP problem. UAHS first models the prediction uncertainty, and then utilizes the prediction uncertainty in optimization. Moreover, UAHS leverages Bayesian optimization to interact prediction and optimization to improve its practical performance. Extensive experiments show that UAHS performs much better than state-of-the-art competitors on two public datasets and an industrial dataset. UAHS has been successfully applied in Microsoft Azure and brought practical benefits in real-world applications.

Fault Tolerance Policy for Resilient Private Cloud Environment

Cloud Computing, being a delivery model is swiftly moving ahead by being adopted by small and large organization alike. This new model opens up many research challenges. As, cloud computing services are offered over the Internet on pay-per-use basis, it is very essential to provide fault tolerant services to the users. To ensure high availability, data centers are replicated. The process of replication is costly but in terms reliability it overtakes the cost factors. Vast amount of work has been undertaken in fault tolerance in other computing environments but they cannot be applied directly to the cloud. This gives an opportunity for new, effective solutions. In this paper, we propose policies for delivering fault tolerant services for private cloud computing environment related to virtual machine allocations. The experimental test results and policies derived are described with respect to virtual machine provisioning.