Migrating medical communications software to a multi-tenant cloud environment

The rise of cloud computing has paved the way for many new applications. Many of these new cloud applications are also multi-tenant, ensuring multiple end users can make use of the same application instance. While these technologies make it possible to create many new applications, many legacy applications can also benefit from the added flexibility and cost-savings of cloud computing and multi-tenancy. In this paper, we describe the steps required to migrate a. NET-based medical communications application to the Windows Azure public cloud environment, and the steps required to add multi-tenancy to the application. We then discuss the advantages and disadvantages of our migration approach. We found that the migration to the cloud itself requires only a limited amount of changes to the application, but that this also limited the benefits, as individual instances would only be partially used. Adding multi-tenancy requires more changes, but when this is done, it has the potential to greatly reduce the cost of running the application

Multi-tenancy in cloud computing

As Cloud Computing becomes the trend of information technology computational model, the Cloud security is becoming a major issue in adopting the Cloud where security is considered one of the most critical concerns for the large customers of Cloud (i.e. governments and enterprises). Such valid concern is mainly driven by the Multi-Tenancy situation which refers to resource sharing in Cloud Computing and its associated risks where confidentiality and/or integrity could be violated. As a result, security concerns may harness the advancement of Cloud Computing in the market. So, in order to propose effective security solutions and strategies a good knowledge of the current Cloud implementations and practices, especially the public Clouds, must be understood by professionals. Such understanding is needed in order to recognize attack vectors and attack surfaces. In this paper we will propose an attack model based on a threat model designed to take advantage of Multi-Tenancy situation only. Before that, a clear understanding of Multi-Tenancy, its origin and its benefits will be demonstrated. Also, a novel way on how to approach Multi-Tenancy will be illustrated. Finally, we will try to sense any suspicious behavior that may indicate to a possible attack where we will try to recognize the proposed attack model empirically from Google trace logs. Google trace logs are a 29-day worth of data released by Google. The data set was utilized in reliability and power consumption studies, but not been utilized in any security study to the extent of our knowledge

MulTe: A Multi-Tenancy Database Benchmark Framework

Multi-tenancy in relational databases has been a topic of interest for a couple of years. On the one hand, ever increasing capabilities and capacities of modern hardware easily allow for multiple database applications to share one system. On the other hand, cloud computing leads to outsourcing of many applications to service architectures, which in turn leads to offerings for relational databases in the cloud, as well. The ability to benchmark multi-tenancy database systems (MT-DBMSs) is imperative to evaluate and compare systems and helps to reveal otherwise unnoticed shortcomings. With several tenants sharing a MT-DBMS, a benchmark is considerably different compared to classic database benchmarks and calls for new benchmarking methods and performance metrics. Unfortunately, there is no single, well-accepted multi-tenancy benchmark for MT-DBMSs available and few efforts have been made regarding the methodology and general tooling of the process. We propose a method to benchmark MT-DBMSs and provide a framework for building such benchmarks. To support the cumbersome process of defining and generating tenants, loading and querying their data, and analyzing the results we propose and provide MULTE, an open-source framework that helps with all these steps

Cloud computing - The effect of generalized spring tensor algorithm on load balancing

© 2014 IEEE. In business world, competitors use innovative approaches to improve their performance and profits. Cloud computing is one of these creative concepts that allowed companies to further taking advantage of their potential. Cloud computing is assisting companies to execute their business plans more efficiently. As cloud computing has multi-tenancy structure, availability and efficiency of the resources is essential foundation of the cloud architecture. Recent studies showed that, optimized cloud computing could be seen as an elastic network of resources that are interacting with each other, to minimize the waiting time and utilize the throughput. Therefore load balancing and resource management can be highlighted as the main concerns in cloud computing as they are impacting the network performance directly. This research aims to discuss the current challenges existing in load balancing algorithms. Different metrics and policies of the relevant load balancer algorithms have been investigated and as a result, collective behavior has been proposed as a new policy for classification of elasticity mechanism in load balancing

A survey of denial-of-service and distributed denial of service attacks and defenses in cloud computing

Cloud Computing is a computingmodel that allows ubiquitous, convenient and on-demand access to a shared pool of highly configurable resources (e.g., networks, servers, storage, applications and services). Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are serious threats to the Cloud services’ availability due to numerous new vulnerabilities introduced by the nature of the Cloud, such as multi-tenancy and resource sharing. In this paper, new types of DoS and DDoS attacks in Cloud Computing are explored, especially the XML-DoS and HTTP-DoS attacks, and some possible detection and mitigation techniques are examined. This survey also provides an overview of the existing defense solutions and investigates the experiments and metrics that are usually designed and used to evaluate their performance, which is helpful for the future research in the domain