Trusted Computing and Secure Virtualization in Cloud Computing

Large-scale deployment and use of cloud computing in industry is accompanied and in the same time hampered by concerns regarding protection of data handled by cloud computing providers. One of the consequences of moving data processing and storage off company premises is that organizations have less control over their infrastructure. As a result, cloud service (CS) clients must trust that the CS provider is able to protect their data and infrastructure from both external and internal attacks. Currently however, such trust can only rely on organizational processes declared by the CS provider and can not be remotely verified and validated by an external party. Enabling the CS client to verify the integrity of the host where the virtual machine instance will run, as well as to ensure that the virtual machine image has not been tampered with, are some steps towards building trust in the CS provider. Having the tools to perform such verifications prior to the launch of the VM instance allows the CS clients to decide in runtime whether certain data should be stored- or calculations should be made on the VM instance offered by the CS provider. This thesis combines three components -- trusted computing, virtualization technology and cloud computing platforms -- to address issues of trust and security in public cloud computing environments. Of the three components, virtualization technology has had the longest evolution and is a cornerstone for the realization of cloud computing. Trusted computing is a recent industry initiative that aims to implement the root of trust in a hardware component, the trusted platform module. The initiative has been formalized in a set of specifications and is currently at version 1.2. Cloud computing platforms pool virtualized computing, storage and network resources in order to serve a large number of customers customers that use a multi-tenant multiplexing model to offer on-demand self-service over broad network. Open source cloud computing platforms are, similar to trusted computing, a fairly recent technology in active development. The issue of trust in public cloud environments is addressed by examining the state of the art within cloud computing security and subsequently addressing the issues of establishing trust in the launch of a generic virtual machine in a public cloud environment. As a result, the thesis proposes a trusted launch protocol that allows CS clients to verify and ensure the integrity of the VM instance at launch time, as well as the integrity of the host where the VM instance is launched. The protocol relies on the use of Trusted Platform Module (TPM) for key generation and data protection. The TPM also plays an essential part in the integrity attestation of the VM instance host. Along with a theoretical, platform-agnostic protocol, the thesis also describes a detailed implementation design of the protocol using the OpenStack cloud computing platform. In order the verify the implementability of the proposed protocol, a prototype implementation has built using a distributed deployment of OpenStack. While the protocol covers only the trusted launch procedure using generic virtual machine images, it presents a step aimed to contribute towards the creation of a secure and trusted public cloud computing environment

Do we all really know what a fog node is? Current trends towards an open definition

Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.Postprint (author's final draft

Towards a Flexible, Lightweight Virtualization Alternative

In recent times, two virtualization approaches have become dominant: hardware-level and operating system-level virtualization. They differ by where they draw the virtualization boundary between the virtualizing and the virtualized part of the system, resulting in vastly different properties. We argue that these two approaches are extremes in a continuum, and that boundaries in between the extremes may combine several good properties of both. We propose abstractions to make up one such new virtualization boundary, which combines hardware-level flexibility with OS-level resource sharing. We implement and evaluate a first prototype

Virtual Machine Workloads: The Case for New NAS Benchmarks

Network Attached Storage (NAS) and Virtual Machines (VMs) are widely used in data centers thanks to their manageability, scalability, and ability to consolidate resources. But the shift from physical to virtual clients drastically changes the I/O workloads to seen on NAS servers, due to guest file system encapsulation in virtual disk images and the multiplexing of request streams from different VMs. Unfortunately, current NAS workload generators and benchmarks produce workloads typical to physical machines. This paper makes two contributions. First, we studied the extent to which virtualization is changing existing NAS workloads. We observed significant changes, including the disappearance of file system meta-data operations at the NAS layer, changed I/O sizes, and increased randomness. Second, we created a set of versatile NAS benchmarks to synthesize virtualized workloads. This allows us to generate accurate virtualized workloads without the effort and limitations associated with setting up a full virtualized environment. Our experiments demonstrate that relative error of our virtualized benchmarks, evaluated across 11 parameters, averages less than 10%

Improving virtual memory performance in virtualized environments

Virtual Memory is a major system performance bottleneck in virtualized environments. In addition to expensive address translations, frequent virtual machine context switches are common in virtualized environments, resulting in increased TLB miss rates, subsequent expensive page walks and data cache contention due to incoming page table entries evicting useful data. Orthogonally, translation coherence, which is currently an expensive operation implemented in software, can consume up to 50% of the runtime of an application executing on the guest. To improve the performance of virtual memory in virtualized environments, two solutions have been proposed in this thesis - namely, (1) Context Switch Aware Large TLB (CSALT), an architecture which addresses the problem of increased TLB miss rates and their adverse impact on data caches. CSALT copes with the increased demand of context switches by storing a large number TLB entries. It mitigates data cache contention by employing a novel TLB-aware cache partitioning scheme. On 8-core systems that switch between two virtual machine contexts executing multi-threaded workloads, CSALT achieves an average performance improvement of 85% over a baseline with conventional L1-L2 TLBs and 25% over a baseline which has a large L3 TLB (2) Translation Coherence using Addressable TLBs (TCAT), a hardware translation coherence scheme which eliminates almost all of the overheads associated with address translation coherence. TCAT overlays translation coherence atop cache coherence to accurately identify slave cores. It then leverages the addressable Part-Of-Memory TLB (POM-TLB) to eliminate expensive Inter Processor Interrupts (IPI) and achieve precise invalidations on the slave core. On 8-core systems with one virtual machine context executing multi-threaded workloads, TCAT achieves an average performance improvement of 13% over the kvmtlb baselineElectrical and Computer Engineerin