Fitzgerald v. Barnstable School Committee: Enforcement of Constitutional Rights

The cloud is a fairly new concept in computer science, altough almost everyone has been or is in contact with it on a regular basis. As more and more systems and applications are migrating from the desktop and into the cloud, keeping a high availability in cloud services is becoming increasingly important. Seeing that the cloud users are dependant on the cloud services being online and accessable via the Internet, creating fault tolerant cloud systems is key to keeping the cloud stable and trustworthy. This thesis researches different aspects of the cloud infrastructure such as virtualization which detaches the services from the physical hardware, scheduling algorithms that decides the mapping of virtual machines onto physical machines and fault tolerance in the context of availability in the cloud. It then proposes a new scheduling algorithm with the purpose of deploying virtual machines in a way that active-passive replication can be sustained

A cooperative approach for distributed task execution in autonomic clouds

Virtualization and distributed computing are two key pillars that guarantee scalability of applications deployed in the Cloud. In Autonomous Cooperative Cloud-based Platforms, autonomous computing nodes cooperate to offer a PaaS Cloud for the deployment of user applications. Each node must allocate the necessary resources for customer applications to be executed with certain QoS guarantees. If the QoS of an application cannot be guaranteed a node has mainly two options: to allocate more resources (if it is possible) or to rely on the collaboration of other nodes. Making a decision is not trivial since it involves many factors (e.g. the cost of setting up virtual machines, migrating applications, discovering collaborators). In this paper we present a model of such scenarios and experimental results validating the convenience of cooperative strategies over selfish ones, where nodes do not help each other. We describe the architecture of the platform of autonomous clouds and the main features of the model, which has been implemented and evaluated in the DEUS discrete-event simulator. From the experimental evaluation, based on workload data from the Google Cloud Backend, we can conclude that (modulo our assumptions and simplifications) the performance of a volunteer cloud can be compared to that of a Google Cluster

Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets

Cloud spot markets rent VMs for a variable price that is typically much lower than the price of on-demand VMs, which makes them attractive for a wide range of large-scale applications. However, applications that run on spot VMs suffer from cost uncertainty, since spot prices fluctuate, in part, based on supply, demand, or both. The difficulty in predicting spot prices affects users and applications: the former cannot effectively plan their IT expenditures, while the latter cannot infer the availability and performance of spot VMs, which are a function of their variable price. To address the problem, we use properties of cloud infrastructure and workloads to show that prices become more stable and predictable as they are aggregated together. We leverage this observation to define an aggregate index price for spot VMs that serves as a reference for what users should expect to pay. We show that, even when the spot prices for individual VMs are volatile, the index price remains stable and predictable. We then introduce cloud index tracking: a migration policy that tracks the index price to ensure applications running on spot VMs incur a predictable cost by migrating to a new spot VM if the current VM's price significantly deviates from the index price.Comment: ACM Symposium on Cloud Computing 201

A Reflective Platform for Highly Adaptive Multi-Cloud Systems

International audienceCloud platforms are increasingly used for hosting a broad diversity of services from traditional e-commerce applications to interactive web-based IDEs. However, we observe that the prolif- eration of offers by Cloud vendors raises several challenges. Developers will not only have to deploy applications for a specific Cloud, but will also have to consider migrating services from one cloud to another, and to manage applications spanning multiple Clouds. In this paper, we therefore report on a first experiment we conducted to build a multi-Cloud system on top of thirteen existing IaaS/PaaS. From this experiment, we advocate for two dimensions of adaptability - design and execution time - that applications for such systems require to exhibit. Finally, we propose a roadmap for future multi-Cloud systems

Risk Assessment for Logistics Applications in Cloud Migration

The increase in the number of cloud data centers is due to an increase in the number of companies migrating to cloud computing. There are many advantages that companies get when migrating to the cloud, but there are also many disadvantages. Multitenancy security and privacy are important challenges for cloud migration users. This study proposes a way to assess the risks that may arise in the cloud migration process for logistics business applications. The research method used is semi-quantitative with a 3-phase approach, namely before migration, during migration, and after migration by considering the criteria for risk aspects and environmental aspects that will have an impact on the company, so that companies can make risk mitigation plans. The results of this study identified 11 (eleven) threats in the cloud that occupy the top ranking and identify as many as 17 (seventeen) indicators obtained from the identification of indicators in the previous model or framework used to assess risks in logistics business applications that will be implemented. migrated to the cloud. Based on the experimental results in this study, the application risk value during migration and after migration has a higher value than before migration, and the risk value during migration are higher than the risk value after migration

Migration Planning Framework for Legacy Systems’ Cloud Migration

Cloud computing is being adopted at a fast phase by organizations all over the world. By utilizing cloud computing, significant benefits compared to traditional on-premises solutions can be achieved. Most of the new systems and applications are built to be cloud native applications but many large organizations still depend on legacy systems that have been built tens of years ago. Migrating these legacy applications or systems to the cloud have become major objective for the organizations. Legacy applications and systems have their own concerns and risks regarding cloud migration and require profound evaluation before the migration process can even be started. In this thesis, literature review was done to identify the possible migration strategies as well as common concerns and risks regarding legacy system’s or application’s cloud migration. Literature review was utilized to build a migration planning framework for legacy system’s or application’s cloud migration. The migration planning framework was validated with a case study. In the case study, a legacy application’s tentative cloud migration was planned by utilizing the created migration planning framework

Live Service Migration in Mobile Edge Clouds

Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed

Cloud Migration: Issues and Developments

Cloud computing is a dynamic paradigm that is influencing activities in virtually all facets of the IT world. It has become quite easy to deploy applications on the cloud. Storage is also available based on user’s needs and can be scaled up or down as required by the user. Computing resources have also been made available on virtual machines. Furthermore, applications are available to users supplied by cloud providers. The activities on the cloud has made migration to the cloud desirable to most organizations and enterprises. Adopting the cloud is expected to reduce cost and the need for investment in computing infrastructure. However, most organizations are still concerned about the likely challenges of migrating to the cloud. The goal of this paper is to provide an insight into cloud computing with respect to migration issues. The paper discusses cloud computing and the benefits of migration. It also examines the challenges of migration. Furthermore, present issues of migration as espoused by the industry are discussed. The survey observed that not much is being discussed in terms of current trends and procedures of migration