http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

We present Mesos, a platform for sharing commodity clusters between multiple diverse cluster computing frameworks, such as Hadoop and MPI. Sharing improves cluster utilization and avoids per-framework data replication. Mesos shares resources in a fine-grained manner, allowing frameworks to achieve data locality by taking turns reading data stored on each machine. To support the sophisticated schedulers of today's frameworks, Mesos introduces a distributed two-level scheduling mechanism called resource offers. Mesos decides how many resources to offer each framework, while frameworks decide which resources to accept and which computations to run on them. Our results show that Mesos can achieve near-optimal data locality when sharing the cluster among diverse frameworks, can scale to 50,000 (emulated) nodes, and is resilient to failures.

/pdf/mesos-a-platform-for-fine-grained-resource-sharing-in-the-1mr3h7jksr.pdf

Mesos: a platform for fine-grained resource sharing in the data center

The cloud heralds a new era of computing where application services are provided through the Internet. Cloud computing can enhance the computing capability of mobile systems, but is it the ultimate solution for extending such systems' battery lifetimes?

Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?

Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development.

Cloud Computing: Issues and Challenges

One of the many definitions of "cloud" is that of an infrastructure-as-a-service (IaaS) system, in which IT infrastructure is deployed in a provider's data center as virtual machines. With IaaS clouds' growing popularity, tools and technologies are emerging that can transform an organization's existing infrastructure into a private or hybrid cloud. OpenNebula is an open source, virtual infrastructure manager that deploys virtualized services on both a local pool of resources and external IaaS clouds. Haizea, a resource lease manager, can act as a scheduling back end for OpenNebula, providing features not found in other cloud software or virtualization-based data center management software.

Virtual Infrastructure Management in Private and Hybrid Clouds

Cloud computing systems fundamentally provide access to large pools of data and computational resources through a variety of interfaces similar in spirit to existing grid and HPC resource management and programming systems.  These types of systems offer a new programming target for scalable application developers and have gained popularity over the past few years.  However, most cloud computing systems in operation today are proprietary, rely upon infrastructure that is invisible to the research community, or are not explicitly designed to be instrumented and modified by systems researchers. In this work, we present Eucalyptus -- an open-source software framework for cloud computing that implements what is commonly referred to as Infrastructure as a Service (IaaS); systems that give users the ability to run and control entire virtual machine instances deployed across a variety physical resources. We outline the basic principles of the Eucalyptus design, detail important operational aspects of the system, and discuss architectural trade-offs that we have made in order to allow Eucalyptus to be portable, modular and simple to use on infrastructure commonly found within academic settings.  Finally, we provide evidence that Eucalyptus enables users familiar with existing Grid and HPC systems to explore new cloud computing functionality while maintaining access to existing, familiar application development software and Grid middle-ware.

/pdf/the-eucalyptus-open-source-cloud-computing-system-2ahud2ixme.pdf

The Eucalyptus Open-Source Cloud-Computing System

In this paper, we consider the problem of modeling machine availability in enterprise-area and wide-area distributed computing settings Using availability data gathered from three different environments, we detail the suitability of four potential statistical distributions for each data set: exponential, Pareto, Weibull, and hyperexponential In each case, we use software we have developed to determine the necessary parameters automatically from each data collection

To gauge suitability, we present both graphical and statistical evaluations of the accuracy with each distribution fits each data set For all three data sets, we find that a hyperexponential model fits slightly more accurately than a Weibull, but that both are substantially better choices than either an exponential or Pareto

These results indicate that either a hyperexponential or Weibull model effectively represents machine availability in enterprise and Internet computing environments

/pdf/modeling-machine-availability-in-enterprise-and-wide-area-1fo759sl1g.pdf

Modeling machine availability in enterprise and wide-area distributed computing environments

We present a framework for making computation offloading decisions in computational grid settings in which schedulers determine when to move parts of a computation to more capable resources to improve performance. Such schedulers must predict when an offloaded computation will outperform one that is local by forecasting the local cost (execution time for computing locally) and remote cost (execution time for computing remotely and transmission time for the input/output of the computation to/from the remote system). Typically, this decision amounts to predicting the bandwidth between the local and remote systems to estimate these costs. Our framework unifies such decision models by formulating the problem as a statistical decision problem that can either be treated "classically" or using a Bayesian approach. Using an implementation of this framework, we evaluate the efficacy of a number of different decision strategies (several of which have been employed by previous systems). Our results indicate that a Bayesian approach employing automatic change-point detection when estimating the prior distribution is the best-performing approach.

/pdf/using-bandwidth-data-to-make-computation-offloading-3gtjvrb19r.pdf

Using bandwidth data to make computation offloading decisions

Most space-sharing parallel computers presently operated by high-performance computing centers use batch-queuing systems to manage processor allocation. Because these machines are typically “space-shared,” each job must wait in a queue until sufficient processor resources become available to service it. In production computing settings, the queuing delay (experienced by users as the time between when the job is submitted and when it begins execution) is highly variable. Users often find this variability a drag on productivity as it makes planning difficult and intellectual continuity hard to maintain.

/pdf/qbets-queue-bounds-estimation-from-time-series-1yubz0z441.pdf

QBETS: queue bounds estimation from time series

Today's scientific workflows use distributed heterogeneous resources through diverse grid and cloud interfaces that are often hard to program. In addition, especially for time-sensitive critical applications, predictable quality of service is necessary across these distributed resources. VGrADS' virtual grid execution system (vgES) provides an uniform qualitative resource abstraction over grid and cloud systems. We apply vgES for scheduling a set of deadline sensitive weather forecasting workflows. Specifically, this paper reports on our experiences with (1) virtualized reservations for batchqueue systems, (2) coordinated usage of TeraGrid (batch queue), Amazon EC2 (cloud), our own clusters (batch queue) and Eucalyptus (cloud) resources, and (3) fault tolerance through automated task replication. The combined effect of these techniques was to enable a new workflow planning method to balance performance, reliability and cost considerations. The results point toward improved resource selection and execution management support for a variety of e-Science applications over grids and cloud systems.

/pdf/vgrads-enabling-e-science-workflows-on-grids-and-clouds-with-44sd5k5waa.pdf

Daniel Nurmi

Papers

The Eucalyptus Open-Source Cloud-Computing System

Modeling machine availability in enterprise and wide-area distributed computing environments

Using bandwidth data to make computation offloading decisions

QBETS: queue bounds estimation from time series

VGrADS: enabling e-Science workflows on grids and clouds with fault tolerance