As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and... Show More

As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the static VM placement. Then the authors propose a two-level runtime reconfiguration policy, including the local adjustment and the parallel migration, to minimize the reconfiguration cost. Finally, the authors implement a prototype to validate and evaluate the proposed mechanism with a set of preliminary experiments, which shows that our work can maximize the resource utilization while effectively reducing the cost of the runtime reconfiguration.

With the on-demand ability of cloud computing, the performance requirement of a cloud application can be satisfied by adding a certain amount of computing resources to or removing some from the... Show More

With the on-demand ability of cloud computing, the performance requirement of a cloud application can be satisfied by adding a certain amount of computing resources to or removing some from the application in response to the workload fluctuation. However, the problem of the availability of application influenced by VM-based physical relative locations during resource scaling process is a challenge and has not been widely discussed yet. In this paper, the authors present a novel availability-based computing model to describe availability attribute of one application in the hierarchical topology of clouds. Moreover, the authors propose an availability-aware scaling mechanism by performing both vertical and horizontal resizing to explore how and where to allocate computing resource. Simulation results indicate that our model captured the availability of cloud applications properly and the proposed dynamic scaling approach achieves the objectives of meeting availability demands and minimizing the total cost.

Virtualization is commonly used for efficient operation of servers in datacenters. The autonomic management of virtual machines enhances the advantages of virtualization. Therefore, for the... Show More

Virtualization is commonly used for efficient operation of servers in datacenters. The autonomic management of virtual machines enhances the advantages of virtualization. Therefore, for the development of such management, it is important to establish a method to accurately detect the performance degradation in virtual machines. This paper proposes a method that detects degradation via passive measurement of traffic exchanged by virtual machines. Using passive traffic measurement is advantageous because it is robust against heavy loads, non-intrusive to the managed machines, and independent of hardware/software platforms. From the measured traffic metrics, performance state is determined by a machine learning technique that algorithmically determines the complex relationships between traffic metrics and performance degradation from training data. The feasibility and effectiveness of the proposed method are confirmed experimentally.

Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools.... Show More

Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.

In Infrastructure-as-a-Service (IaaS) clouds, stepping-stone attacks via hosted virtual machines (VMs) are critical for the credibility. This type of attack uses compromised VMs as stepping stones... Show More

In Infrastructure-as-a-Service (IaaS) clouds, stepping-stone attacks via hosted virtual machines (VMs) are critical for the credibility. This type of attack uses compromised VMs as stepping stones for attacking the outside hosts. For self-protection, IaaS clouds should perform active responses against stepping-stone attacks. However, it is difficult to stop only outgoing attacks at edge firewalls, which can only use packet headers. In this paper, we propose a new self-protection mechanism against stepping-stone attacks, which is called xFilter. xFilter is a packet filter running in the virtual machine monitor (VMM) underlying VMs and achieves pinpoint active responses by using VM introspection. VM introspection enables xFilter to directly obtain information on packet senders inside VMs. On attack detection, xFilter automatically generates filtering rules based on packet senders. To make packet filtering with VM introspection efficient, we introduced several optimization techniques. Our experiments showed that the performance degradation due to xFilter was usually less than 16%.