Dian Shen

Towards a fast and secure design for enterprise‐oriented cloud storage systems

With the rapid development of information technology, enormous volumes of data are being generated by enterprises at all times. The management and storage of these large‐scale data have always been challenging enterprises. As these data are usually shared among users in a collaborative manner, secure data access and access performance are 2 key concerns for data storage of enterprises. However, current solutions fail to meet the requirements of enterprises since they suffer from the following drawbacks: (1) they do not support fine‐grained access control and cannot meet the strict secure data access requirements of enterprises, and (2) they suffer from the unpredictable access latency. Thus in this paper, we propose Frostor, an enterprise‐oriented cloud storage system, which addresses the secure data access issue through a user account and IP‐based fine‐grained access control mechanism, and guarantees the access performance via a two‐level performance optimization mechanism. We further implement Frostor and deploy it on the testbed environment in a real data center. Extensive evaluations have shown that Frostor implements fine‐grained access control, while achieving a significant reduction (≥60%) on access latency.

Game Theory based Dynamic Resource Allocation for Hybrid Environment with Cloud and Big Data Application

Virtualization based cloud and big data applications have been widely adopted in various fields. Because deploying the big data applications on the cloud will cause obvious performance degradation, the cloud and big data applications are provided with fixed resource separately. However, the traditional fixed resource allocation mechanism has two drawbacks: (1) low resource utility and (2) unresponsiveness to the performance degradation. To address these drawbacks, the cloud and big data hybrid environment is designed, where fair resource allocation is used to ensure fairness between cloud and big data applications while virtual machine migration is used to make each virtual machine in cloud application reach its own satisfactory. Herein, game theory is used to model the conflict and negotiation between cloud and big data applications. Firstly, the Nash Equilibrium is used to discover the best strategy for both applications. Secondly, as for virtual machine migration, we use Nash Bargaining game to present the situation where virtual machines compete for more resources allocation while their minimal demand is ensured. Finally, experiments are carried out to prove that the hybrid environment outperforms the traditional method both in resource utility and application performance.

A Novel Solution of Cloud Operating System Based on X11 and Docker

With the fast development of cloud technologies, cloud-based operating systems are becoming increasingly popular, while several enterprise products, such as Chrome OS and SUNDE, are released continuously. According to the way of usage, the existing cloud-based operating systems can be divided into two categories: browser-driven operating system and virtualization-based operating system. However, traditional cloud-based operating systems have some disadvantages: As the functionality of browser is limited, it is difficult to transplant traditional desktop software to browser based cloud operating system; on the other hand, as for systems which are based on remote virtual machines (VMs) and Virtual Network Console (VNC), the system performance is constrained by the limited network bandwidth, which will also lead to performance degradation. To solve these problems, this paper designs AntOS, a full-featured cloud operating system. AntOS takes advantage of Docker and X11 technique, to reduce the load of network over 50% while increasing CPU utilization by 45%, which can effectively support the private cloud scenario in enterprises.

Virtual network fault diagnosis mechanism based on fault injection

Diagnosing faults in virtual networks is always a popular research area. Existing researches primarily focus on diagnosing faults in physical networks, while they could not identify the faults introduced by virtual networks. Besides, the high complexity of algorithms and the requirement for modifying hardware may limit their scope of use. To address these drawbacks, in this paper, we propose a novel approach to diagnose faults in virtual networks. The rational of our approach is that the faults can be identified when located in the packet traces, with the knowledge that the possible known faults that can happen in that location. To achieve this goal, we apply packet marking, fault injection and machine learning techniques to provide precise fault diagnosis. Experimental results show that our approach can efficiently identify 73% of the faults while for virtual network-specific faults, our approach can diagnose 86% of them. Our system can also support real-time or near real-time fault analysis.

Superblock: An Application-Aware Dynamic Partition Strategy for Large-Scale Graph

The emergence of large-scale graph data has posed essential challenges for processing them efficiently. The fundamental step for effectively processing the graph is to partition the graph and distribute the relevant parts on multiple workers for parallel computing. The existing partition strategies may suffer from the following problems: 1) They ignore the certain application features, making the partition not satisfy the application needs, which may cause performance degradation, 2) Because of the ignorance of the applications features, current partition strategies are not dynamic to meet the needs from different applications. In this paper, the Superblock partition strategy, an application-aware dynamic partition strategy for large-scale data is proposed to solve the above problems. It pre-partitions the graph into blocks and then extracts the application features and combines the blocks into Superblocks. The Superblock will be re-constructed when new application arrives as well. Experiments are performed using some common graph algorithms to confirm that the Superblock partition strategy can boost the performance of various data processing application on large-scale graph data and be dynamic enough to alter the partitions for different applications.

Cost-Effective Virtual Machine Image Replication Management for Cloud Data Centers

Cloud computing offers infrastructure as a service to deliver large amount of computation and storage resources, in which fast provisioning of virtual machine(VM) instances has significant impacts on the overall system performance and elasticity. In this paper, we analyze the characteristics of image provisioning by studying the traces collected from the real-world cloud data centre. From the analysis results, we observe that the overloaded and dynamic requests for some popular images result in degradation and fluctuation of performance and availability of the system. Addressing this issue, we propose a stochastic model based on queueing theory, which captures the main factors in image provisioning to optimize the number and placement of image replication, so as to manage the VM images in a cost-effective manner. We implement our theoretical model based on open-source cloud platform and carry out trace driven evaluation to validate its effectiveness. The evaluation results show that our system is cost-effective and can achieve high and stable performance in VM provisioning while remaining high availability under different test scenarios.

Energy-Efficient Resource Allocation Model with QoS Assurance for Ubiquitous and Heterogeneous Environment

Ubiquitous computing is considered as a promising technological path of innovation. In order to support various kinds of ubiquitous applications, the service providers have to build up networked infrastructures consisting of heterogeneous computing appliances and sensors. With the growth in the number of devices and the increasing complexity of the infrastructure, it becomes an important concern and challenge in the design and management of ubiquitous infrastructure to allocate the resources to maximize the total utilization and minimize the energy consumption, that is, in the energy-efficient manner. Addressing this issue, this paper proposes a resource allocation model, with the goal to minimize the energy consumption of the infrastructure, while achieving Quality of Service (QoS) requirements. We first propose a stochastic model using queueing theory and then model this problem as a nonlinear programming problem subject to a number of inequality constraints and develop heuristic solutions to solve it. Simulation results are presented to show the effectiveness of our approach.