Office 365: Migrating and Managing Your Business in the Cloud

Written for the IT professional and business owner, this book provides the business and technical insight necessary to migrate your business to the cloud using Microsoft Office 365. This is a practical look at cloud migration and the use of different technologies to support that migration. Numerous examples of cloud migration with technical migration details are included. Cloud technology is a tremendous opportunity for an organization to reduce IT costs, and to improve productivity with increased access, simpler administration and improved services. Those businesses that embrace the advantages of the cloud will receive huge rewards in productivity and lower total cost of ownership over those businesses that choose to ignore it. The challenge for those charged with implementing Microsoft Office 365 is to leverage these advantages with the minimal disruption of their organization. This book provides practical help in moving your business to the Cloud and covers the planning, migration and the follow on management of the Office 365 Cloud services

Evaluating cloud database migration options using workload models

A key challenge in porting enterprise software systems to the cloud is the migration of their database. Choosing a cloud provider and service option (e.g., a database-as-a-service or a manually configured set of virtual machines) typically requires the estimation of the cost and migration duration for each considered option. Many organisations also require this information for budgeting and planning purposes. Existing cloud migration research focuses on the software components, and therefore does not address this need. We introduce a two-stage approach which accurately estimates the migration cost, migration duration and cloud running costs of relational databases. The first stage of our approach obtains workload and structure models of the database to be migrated from database logs and the database schema. The second stage performs a discrete-event simulation using these models to obtain the cost and duration estimates. We implemented software tools that automate both stages of our approach. An extensive evaluation compares the estimates from our approach against results from real-world cloud database migrations

Decision Support Tools for Cloud Migration in the Enterprise

This paper describes two tools that aim to support decision making during the migration of IT systems to the cloud. The first is a modeling tool that produces cost estimates of using public IaaS clouds. The tool enables IT architects to model their applications, data and infrastructure requirements in addition to their computational resource usage patterns. The tool can be used to compare the cost of different cloud providers, deployment options and usage scenarios. The second tool is a spreadsheet that outlines the benefits and risks of using IaaS clouds from an enterprise perspective; this tool provides a starting point for risk assessment. Two case studies were used to evaluate the tools. The tools were useful as they informed decision makers about the costs, benefits and risks of using the cloud.Comment: To appear in IEEE CLOUD 201

A cloud migration decision support system for SMEs in Tamil Nadu (India) using AHP

Cloud computing is a new computing paradigm which has the potential to speed up Information Technology (IT) adoption among SMEs in developing economies. Its successful implementation can present SMEs with various benefits like reduced IT costs, high scalability and faster time to market. In recent years, many research have been carried in both academia and in businesses to explain the features, benefits and risks of cloud adoption. Literature review reveals that there are existing frameworks available to support cloud migration. However, there is very little literature available to support cloud migration decisions, which covers the whole cloud migration process for SMEs in Tamil Nadu. This paper aims to fill that gap by presenting the SME decision makers (DMs) with a decision support tool. The proposed cloud migration decision support system (CMDSS) will be based on Analytical Hierarchy Process (AHP) and will aid decision makers to make cloud migration decision effectively by suggesting a path from where to start, to how to complete the migration

Cloud migration

Migrating on-premises applications to cloud environment has become a popular task for organizations. In this thesis, cloud migration is defined to be an action where one or more parts of an application are migrated to a cloud platform. Multiple motivations are mentioned for such migration like reducing costs, flexibility, and scalability of the application. This thesis also goes through different strategies for cloud migration. After that, a literature-based, generalized migration process for cloud migration was created. This created migration process was then validated against case process. Phases in the case process were investigated through interviews. Interviews were done in two parts. First, all interviewees were interviewed one at a time. From these interviews, a draft of the case process was done. This draft was then validated and supplemented with a group interview. After creating both processes, they were compared. It was found that the literature-based process had a lot of similarities with the case process. Also, it was found that the case process had a few tasks that were not mentioned in the literature-based process. These tasks were discussing future of the application, estimating workload and project end date, defining migration scope, and familiarizing customers with application. These can be said to be important tasks, and they should have been in the literature-based process too

Optimizing Virtual Machine I/O Performance in Cloud Environments

Maintaining closeness between data sources and data consumers is crucial for workload I/O performance. In cloud environments, this kind of closeness can be violated by system administrative events and storage architecture barriers. VM migration events are frequent in cloud environments. VM migration changes VM runtime inter-connection or cache contexts, significantly degrading VM I/O performance. Virtualization is the backbone of cloud platforms. I/O virtualization adds additional hops to workload data access path, prolonging I/O latencies. I/O virtualization overheads cap the throughput of high-speed storage devices and imposes high CPU utilizations and energy consumptions to cloud infrastructures. To maintain the closeness between data sources and workloads during VM migration, we propose Clique, an affinity-aware migration scheduling policy, to minimize the aggregate wide area communication traffic during storage migration in virtual cluster contexts. In host-side caching contexts, we propose Successor to recognize warm pages and prefetch them into caches of destination hosts before migration completion. To bypass the I/O virtualization barriers, we propose VIP, an adaptive I/O prefetching framework, which utilizes a virtual I/O front-end buffer for prefetching so as to avoid the on-demand involvement of I/O virtualization stacks and accelerate the I/O response. Analysis on the traffic trace of a virtual cluster containing 68 VMs demonstrates that Clique can reduce inter-cloud traffic by up to 40%. Tests of MPI Reduce_scatter benchmark show that Clique can keep VM performance during migration up to 75% of the non-migration scenario, which is more than 3 times of the Random VM choosing policy. In host-side caching environments, Successor performs better than existing cache warm-up solutions and achieves zero VM-perceived cache warm-up time with low resource costs. At system level, we conducted comprehensive quantitative analysis on I/O virtualization overheads. Our trace replay based simulation demonstrates the effectiveness of VIP for data prefetching with ignorable additional cache resource costs

Atlas: Hybrid Cloud Migration Advisor for Interactive Microservices

Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions.Comment: To appear at EuroSys 202