OpenStack Private Cloud Research Articles

Building and Operating a Large-Scale Enterprise Data Analytics Platform

Over the last three years we have been running a large-scale data processing platform for applying analytics to corporate data at scale on an OpenStack private cloud instance. Our platform makes a wide variety of corporate data assets, such as sales, marketing, customer information, as well as data from less conventional sources such as weather, news and social media available for analytics purposes to hundreds of globally distributed teams across the company. We control every layer in the stack from the processing engines down to the hardware. Here we report our experiences in building and operating such a system. We describe our technical choices and describe how they evolved as we observed the actual workloads created by users.

Online Smart Disguise: real-time diversification evading coresidency-based cloud attacks

Security is a major challenge in Cloud Computing. In this paper, we propose an Online Smart Disguise Framework (OSDF). OSDF employs dynamic, proactive, real-time moving-target defense against cloud attacks. OSDF relies on two main pillars. The first, is a behavior obscuring module that frequently live-migrates virtual machines (VMs) between heterogeneously configured compute nodes to avoid co-residency and virtualization based attacks. The second module limits attack dispersion between same-host VMs by migrating maliciously behaving VMs to remote isolated compute node acting as a quarantine zone. The second module is guided by a smart intrusion detection system that monitors the VM system calls searching for suspicious activities. To evaluate OSDF efficiency and effectiveness on limiting attack dispersion, we devised the vulnerable, exposed, attacked, recovered model based on the susceptible, exposed, infected, recovered (SEIR) epidemic model. The SEIR model is an epidemiological model commonly used to investigate disease dispersion on cooperative communities. The implementation of OSDF is tested on OpenStack private cloud. Simulation results show the effectiveness of OSDF MTD approach in decreasing the number of attacked VMs even for fast-spreading worms. Furthermore, NAS Parallel Benchmark is used to evaluate OSDF efficiency for cloud-hosted VMs running both stateful and stateless applications.

Towards optimal resource provisioning for Hadoop-MapReduce jobs using scale-out strategy and its performance analysis in private cloud environment

Cloud computing always provides IT resources on demand basis, without additional waiting time. Therefore, data analytics is one of the most significant areas that can be benefited from Cloud Computing. MapReduce programs in the cloud computing to optimize the resource provisioning and finish the MapReduce jobs with quantified time. The efficacy as well as the accuracy of performance of the performance model based on regression used for predicting the MapReduce job completion time has been suggested in our OpenStack private cloud Hadoop cluster using linear regression method. In order to satisfy the user jobs with deadline requirements, Cloud service providers do not have a resource provisioning technique or polices. The contemporary system requires a cloud user to estimate the needed quantity of resources for running jobs in the cloud. Our proposed scalability strategy of Scale-Out methods used to obtain the accurate prediction of job completion times through our experimental results shows the performance level of MapReduce benchmark in the open stack private cloud. The regression based performance model predicting and evaluating the execution time of 5 popular MapReduce benchmark applications over our private cloud environment with better resource utilization which depicts 99% of accuracy results.

Software Defined Networking challenges and future direction: A case study of implementing SDN features on OpenStack private cloud

Cloud computing provides services on demand instantly, such as access to network infrastructure consisting of computing hardware, operating systems, network storage, database and applications. Network usage and demands are growing at a very fast rate and to meet the current requirements, there is a need for automatic infrastructure scaling. Traditional networks are difficult to automate because of the distributed nature of their decision making process for switching or routing which are collocated on the same device. Managing complex environments using traditional networks is time-consuming and expensive, especially in the case of generating virtual machines, migration and network configuration. To mitigate the challenges, network operations require efficient, flexible, agile and scalable software defined networks (SDN). This paper discuss various issues in SDN and suggests how to mitigate the network management related issues. A private cloud prototype test bed was setup to implement the SDN on the OpenStack platform to test and evaluate the various network performances provided by the various configurations.