Workload-aware live storage migration for clouds

The emerging open cloud computing model will provide users with great freedom to dynamically migrate virtualized computing services to, from, and between clouds over the wide-area. While this freedom leads to many potential benefits, the running services must be minimally disrupted by the migration. Unfortunately, current solutions for wide-area migration incur too much disruption as they will significantly slow down storage I/O operations during migration. The resulting increase in service latency could be very costly to a business. This thesis presents a novel storage migration scheduling algorithm that can greatly improve storage I/O performance during wide-area migration. Our algorithm is unique in that it considers individual virtual machine's storage I/O workload such as temporal locality, spatial locality and popularity characteristics to compute an efficient data transfer schedule. Using a trace-driven framework, we show that our algorithm provides large performance benefits across a wide range of popular virtual machine workloads

Tailoring the Cyber Security Framework: How to Overcome the Complexities of Secure Live Virtual Machine Migration in Cloud Computing

This paper proposes a novel secure live virtual machine migration framework by using a virtual trusted platform module instance to improve the integrity of the migration process from one virtual machine to another on the same platform. The proposed framework, called Kororā, is designed and developed on a public infrastructure-as-a-service cloud-computing environment and runs concurrently on the same hardware components (Input/Output, Central Processing Unit, Memory) and the same hypervisor (Xen); however, a combination of parameters needs to be evaluated before implementing Kororā. The implementation of Kororā is not practically feasible in traditional distributed computing environments. It requires fixed resources with high-performance capabilities, connected through a high-speed, reliable network. The following research objectives were determined to identify the integrity features of live virtual machine migration in the cloud system: To understand the security issues associated with cloud computing, virtual trusted platform modules, virtualization, live virtual machine migration, and hypervisors; To identify the requirements for the proposed framework, including those related to live VM migration among different hypervisors; To design and validate the model, processes, and architectural features of the proposed framework; To propose and implement an end-to-end security architectural blueprint for cloud environments, providing an integrated view of protection mechanisms, and then to validate the proposed framework to improve the integrity of live VM migration. This is followed by a comprehensive review of the evaluation system architecture and the proposed framework state machine. The overarching aim of this paper, therefore, is to present a detailed analysis of the cloud computing security problem, from the perspective of cloud architectures and the cloud service delivery models. Based on this analysis, this study derives a detailed specification of the cloud live virtual machine migration integrity problem and key features that should be covered by the proposed framewor

Adaptive live VM migration over a WAN: modeling and implementation

Recent advances in virtualization technology enable high mobility of virtual machines and resource provisioning at the data-center level. To streamline the migration process, various migration strategies have been proposed for VM live migration over a local-area network (LAN). The most common solution uses memory pre-copying and assumes the storage is shared on the LAN. While applied to a wide-area network (WAN), the VM live migration algorithms need a new design philosophy to address the challenges of long latency, limited bandwidth, unstable network conditions and the movement of storage. This paper proposes a three-phase fractional hybrid pre-copy and post-copy solution for both memory and storage to achieve highly adaptive migration over a WAN. In this hybrid solution, we selectively migrate an important fraction of memory and storage in the pre-copy and freeze-and-copy phase, while the rest (non-critical data set) is migrated during post-copying. We propose a new metric called performance restoration agility, which considers both the downtime and the VM speed degradation during the post-copy phase, to evaluate the migration process. We also develop a profiling framework and a novel probabilistic prediction model to adaptively find a predictably optimal combination of the memory and storage fractions to migrate. This model-based hybrid solution is implemented on Xen and evaluated in an emulated WAN environment. Experimental results show that our solution wins over all others in adaptiveness for various applications over a WAN, while retaining the responsiveness of post-copy algorithms.published_or_final_versio

Leveraging AI for Predictive Migration Planning and Automated Data Transfer: Ensuring Optimal Cloud Resource Allocation and Data Integrity

This research paper explores the potential groundbreaking impact of Artificial Intelligence (AI) on the migration of applications from on-premises infrastructure to public cloud platforms. The utilization of predictive migration planning and automated data transfer highlights the potential of artificial intelligence (AI) in enhancing the efficiency of cloud resource allocation, safeguarding data integrity, and mitigating issues related to migration

Cloud scheduling optimization: a reactive model to enable dynamic deployment of virtual machines instantiations

This study proposes a model for supporting the decision making process of the cloud policy for the deployment of virtual machines in cloud environments. We explore two configurations, the static case in which virtual machines are generated according to the cloud orchestration, and the dynamic case in which virtual machines are reactively adapted according to the job submissions, using migration, for optimizing performance time metrics. We integrate both solutions in the same simulator for measuring the performance of various combinations of virtual machines, jobs and hosts in terms of the average execution and total simulation time. We conclude that the dynamic configuration is prosperus as it offers optimized job execution performance

Data transformation as a means towards dynamic data storage and polyglot persistence

Legacy applications have been built around the concept of storing their data in one relational data store. However, with the current differentiation in data store technologies as a consequence of the NoSQL paradigm, new and possibly more performant storage solutions are available to all applications. The concept of dynamic storage makes sure that application data are always stored in the most optimal data store at a given time to increase application performance. Additionally, polyglot persistence aims to push this performance even further by storing each different data type of an application in the data store technology best suited for it. To get legacy applications into dynamic storage and polyglot persistence, schema and data transformations between data store technologies are needed. This usually infers application redesigns as well to support the new data stores. This paper proposes such a transformation approach through a canonical model. It is based on the Lambda architecture to ensure no application downtime is needed during the transformation process, and after the transformation, the application can continue to query in the original query language, thus requiring no application code changes

Manipulation of Online Reviews: Analysis of Negative Reviews for Healthcare Providers

There is a growing reliance on online reviews in today’s digital world. As the influence of online reviews amplified in the competitive marketplace, so did the manipulation of reviews and evolution of fake reviews on these platforms. Like other consumer-oriented businesses, the healthcare industry has also succumbed to this phenomenon. However, health issues are much more personal, sensitive, complicated in nature requiring knowledge of medical terminologies and often coupled with myriad of interdependencies. In this study, we collated the literature on manipulation of online reviews, identified the gaps and proposed an approach, including validation of negative reviews of the 500 doctors from three different states: New York and Arizona in USA and New South Wales in Australia from the RateMDs website. The reviews of doctors was collected, which includes both numerical star ratings (1-low to 5-high) and textual feedback/comments. Compared to other existing research, this study will analyse the textual feedback which corresponds to the clinical quality of doctors (helpfulness and knowledge criteria) rather than process quality experiences. Our study will explore pathways to validate the negative reviews for platform provider and rank the doctors accordingly to minimise the risks in healthcare