Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

On the Benefit of Virtualization: Strategies for Flexible Server Allocation

Virtualization technology facilitates a dynamic, demand-driven allocation and migration of servers. This paper studies how the flexibility offered by network virtualization can be used to improve Quality-of-Service parameters such as latency, while taking into account allocation costs. A generic use case is considered where both the overall demand issued for a certain service (for example, an SAP application in the cloud, or a gaming application) as well as the origins of the requests change over time (e.g., due to time zone effects or due to user mobility), and we present online and optimal offline strategies to compute the number and location of the servers implementing this service. These algorithms also allow us to study the fundamental benefits of dynamic resource allocation compared to static systems. Our simulation results confirm our expectations that the gain of flexible server allocation is particularly high in scenarios with moderate dynamics

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

Elastic Resource Management in Distributed Clouds

The ubiquitous nature of computing devices and their increasing reliance on remote resources have driven and shaped public cloud platforms into unprecedented large-scale, distributed data centers. Concurrently, a plethora of cloud-based applications are experiencing multi-dimensional workload dynamics---workload volumes that vary along both time and space axes and with higher frequency. The interplay of diverse workload characteristics and distributed clouds raises several key challenges for efficiently and dynamically managing server resources. First, current cloud platforms impose certain restrictions that might hinder some resource management tasks. Second, an application-agnostic approach might not entail appropriate performance goals, therefore, requires numerous specific methods. Third, provisioning resources outside LAN boundary might incur huge delay which would impact the desired agility. In this dissertation, I investigate the above challenges and present the design of automated systems that manage resources for various applications in distributed clouds. The intermediate goal of these automated systems is to fully exploit potential benefits such as reduced network latency offered by increasingly distributed server resources. The ultimate goal is to improve end-to-end user response time with novel resource management approaches, within a certain cost budget. Centered around these two goals, I first investigate how to optimize the location and performance of virtual machines in distributed clouds. I use virtual desktops, mostly serving a single user, as an example use case for developing a black-box approach that ranks virtual machines based on their dynamic latency requirements. Those with high latency sensitivities have a higher priority of being placed or migrated to a cloud location closest to their users. Next, I relax the assumption of well-provisioned virtual machines and look at how to provision enough resources for applications that exhibit both temporal and spatial workload fluctuations. I propose an application-agnostic queueing model that captures the resource utilization and server response time. Building upon this model, I present a geo-elastic provisioning approach---referred as geo-elasticity---for replicable multi-tier applications that can spin up an appropriate amount of server resources in any cloud locations. Last, I explore the benefits of providing geo-elasticity for database clouds, a popular platform for hosting application backends. Performing geo-elastic provisioning for backend database servers entails several challenges that are specific to database workload, and therefore requires tailored solutions. In addition, cloud platforms offer resources at various prices for different locations. Towards this end, I propose a cost-aware geo-elasticity that combines a regression-based workload model and a queueing network capacity model for database clouds. In summary, hosting a diverse set of applications in an increasingly distributed cloud makes it interesting and necessary to develop new, efficient and dynamic resource management approaches

Server-Based Desktop Virtualization

Virtualization can be accomplished at different layers in the computational stack and with different goals (servers, desktops, applications, storage and network). This research focuses on server-based desktop virtualization. According to the Gartner group, the main business drivers for adopting desktop virtualization are: application compatibility, business continuity, security and compliance, mobility and improved productivity [15]. Despite these business drivers, desktop virtualization has not been widely adopted. According to a survey conducted by Matrix42, only 5% of desktop computers are virtualized [37]. The research deals with the challenges preventing the wider adoption of server-based desktop virtualization while focusing on two of the main virtualization architectures: session-based desktop virtualization (SBDV) and virtual desktop infrastructure (VDI). The first chapter introduces some of the challenges faced by large organizations in their efforts to create a cost effective and manageable desktop computing environment. The second chapter discusses two of the main server-based desktop virtualizations (VDI and SBDV), illustrating some of the advantages and disadvantages in these different architectures. The third chapter focuses on some of the technical challenges and provides recommendations regarding server-based desktop virtualization. In the fourth chapter, measurements are conducted for the utilization and performance of SBDV on different 3 user profiles (light, heavy and multimedia). Data and results collected from desktop assessment and lab are used to formulate baselines and metrics for capacity planning. According to the conducted measurements, it is concluded that light and heavy profiles can be virtualized using SBDV, while for multimedia profiles, additional capacity planning and resource allocation are required. Multimedia profiles can be virtualized with VDI considering client-side rendering to avoid network bandwidth congestion. While the research focuses on VDI and SBDV, it highlights few points related to client access devices (CADs). CADs are one of the main components in the desktop virtualization stack (OS virtualization, session virtualization, application virtualization, connection broker, CADs and user data and profiles). The latter chapter of the research focuses on conclusions and future work toward greater levels of adoption of VDI and SBDV

Cloud-Based Game Server Infrastructure AT PT. Games Karya Nusantara (Majamojo)

In the era of the game publishing industry, MAJAMOJO collaborates with game developers in developing game server infrastructure in Indonesia, by realizing the adoption of cloud computing using the Roadmap for Cloud Computing Adaption (ROCCA) method. Cloud computing implementation by following 5 stages of modification of the ROCCA adoption model, namely the analysis, design, adoption, migration, and management stages. By using Amazon Web Service (AWS) Cloud Service (CSP) services. Cloud computing adoption is carried out by developing game server infrastructure as connectivity between the client and the game server. The analysis is carried out by collecting data and interviews from speakers, in the design of the cloud computing technology to be used, the selection of the technology is based on the results of the analysis stage, then the adoption process prepares the cloud infrastructure to be built, based on software selection and setting up servers with recommended specifications, then migration is the core of the cloud computing adoption process, Where the process transfers the system from the physical server to the virtual server, new management will be executed if the adoption and migration process has been completed with the Game Server and Game Client connectivity indicators running normally. With the application of cloud computing based on the characteristics of the cloud itself, it is flexible, scalable, and safe to access over the internet