Multi-Tenant Cloud FPGA: A Survey on Security

With the exponentially increasing demand for performance and scalability in cloud applications and systems, data center architectures evolved to integrate heterogeneous computing fabrics that leverage CPUs, GPUs, and FPGAs. FPGAs differ from traditional processing platforms such as CPUs and GPUs in that they are reconfigurable at run-time, providing increased and customized performance, flexibility, and acceleration. FPGAs can perform large-scale search optimization, acceleration, and signal processing tasks compared with power, latency, and processing speed. Many public cloud provider giants, including Amazon, Huawei, Microsoft, Alibaba, etc., have already started integrating FPGA-based cloud acceleration services. While FPGAs in cloud applications enable customized acceleration with low power consumption, it also incurs new security challenges that still need to be reviewed. Allowing cloud users to reconfigure the hardware design after deployment could open the backdoors for malicious attackers, potentially putting the cloud platform at risk. Considering security risks, public cloud providers still don't offer multi-tenant FPGA services. This paper analyzes the security concerns of multi-tenant cloud FPGAs, gives a thorough description of the security problems associated with them, and discusses upcoming future challenges in this field of study

Cloud Computing

In the recent years, Cloud Computing has become very popular and an interesting subject in the field of science and technology. The research efforts in the Cloud Computing have led to a number of applications used for the convenience in daily life. Cloud Computing is not only providing solutions at the enterprise level but it is also suitable in organizing a centralized database which is accessible from every corner of the world. It is said that, 10 to 15 years later when all the enterprises have adopted the Cloud Computing, there will be no more perception for the data center in the company. The aim of this Master’s thesis “Cloud Computing: Server Configuration and Software Implementation for the Data Collection with Wireless Sensor Nodes” was to integrate the Wireless Sensor Network with Cloud Computing in a such a way that the data received from the Sensor node can be access able from anywhere in the world. To accomplish this task, a Wireless Sensor Network was deployed to measure the environmental conditions such as Temperature, Light and the Sensor’s battery information and the measured values are sent to a web server from where the data can be accessed. The project also includes the software implementation to collect the sensor’s measurements and a Graphical User Interface (GUI) application which reads the values from the sensor network and stores it to the database.fi=Opinnäytetyö kokotekstinä PDF-muodossa.|en=Thesis fulltext in PDF format.|sv=Lärdomsprov tillgängligt som fulltext i PDF-format

Foundations and Technological Landscape of Cloud Computing

The cloud computing paradigm has brought the benefits of utility computing to a global scale. It has gained paramount attention in recent years. Companies are seriously considering to adopt this new paradigm and expecting to receive significant benefits. In fact, the concept of cloud computing is not a revolution in terms of technology; it has been established based on the solid ground of virtualization, distributed system, and web services. To comprehend cloud computing, its foundations and technological landscape need to be adequately understood. This paper provides a comprehensive review on the building blocks of cloud computing and relevant technological aspects. It focuses on four key areas including architecture, virtualization, data management, and security issues

Enabling horizontal scalability in an open source enterprise services bus

Cloud computing is a recent paradigm which describes a new way of consuming and delivering IT Services. In the Platform as a Service (PaaS) model, an underlying infrastructure such as network, operative system or server is provided to the Cloud consumers for either deploying their own applications, or applications supplied by the Cloud provider. In effect, Cloud computing modifies how applications should be built, provided, and consumed, as they may provide or be totally exposed as services, or consume existing third party applications services. The main advantages in Cloud computing are related to dynamic scaling of resources which are able to adapt to changes based on demand of resources and the use of multi-tenancy techniques in order based on sharing of resources between different users towards achieving the economy of scale. The Enterprise Service Bus (ESB) is essential as an integration middleware between application and services within and between multiple Cloud infrastructures. Different communication protocols might be used by application services and it is therefore necessary to have a mediator between them. Several challenges might arise when using an ESB as communication mediator between applications in cloud when to scale in and scale out to optimize resource consumption. The number of ESB instances should vary depending on the load in the Cloud infrastructure. This can be achieved by dynamically scaling in and out multiple ESB instances which constitute the horizontal ESB cluster. In this Master Thesis we focus on enabling horizontal scalability support for an open source multi-tenant aware Enterprise Service Bus (ESB). The investigation is based on two possible scenarios for enabling horizontal scalability: interconnected vs. non interconnected ESB instances. Therefore, in this work we investigate their advantages, disadvantages, and possible challenges and solutions. Based on previous investigations, a realization approach for enabling multi-instance management of a multi-tenant aware ESB is provided

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it

BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows

Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms

5G Network Slicing using SDN and NFV: A Survey of Taxonomy, Architectures and Future Challenges

In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks.Comment: 40 Pages, 22 figures, published in computer networks (Open Access

Optimal VM placement in data centres with architectural and resource constraints

Recent advance in virtualisation technology enables service provisioning in a flexible way by consolidating several virtual machines (VMs) into a single physical machine (PM). The inter-VM communications are inevitable when a group of VMs in a data centre provide services in a collaborative manner. With the increasing demands of such intra-data-centre traffics, it becomes essential to study the VM-to-PM placement such that the aggregated communication cost within a data centre is minimised. Such optimisation problem is proved NP-hard and formulated as an integer programming with quadratic constraints in this paper. Different from existing work, our formulation takes into consideration of data-centre architecture, inter-VM traffic pattern, and resource capacity of PMs. Furthermore, a heuristic algorithm is proposed and its high efficiency is extensively validated