Cache-Aware Real-Time Virtualization

Virtualization has been adopted in diverse computing environments, ranging from cloud computing to embedded systems. It enables the consolidation of multi-tenant legacy systems onto a multicore processor for Size, Weight, and Power (SWaP) benefits. In order to be adopted in timing-critical systems, virtualization must provide real-time guarantee for tasks and virtual machines (VMs). However, existing virtualization technologies cannot offer such timing guarantee. Tasks in VMs can interfere with each other through shared hardware components. CPU cache, in particular, is a major source of interference that is hard to analyze or manage. In this work, we focus on challenges of the impact of cache-related interferences on the real-time guarantee of virtualization systems. We propose the cache-aware real-time virtualization that provides both system techniques and theoretical analysis for tackling the challenges. We start with the challenge of the private cache overhead and propose the private cache-aware compositional analysis. To tackle the challenge of the shared cache interference, we start with non-virtualization systems and propose a shared cache-aware scheduler for operating systems to co-allocate both CPU and cache resources to tasks and develop the analysis. We then investigate virtualization systems and propose a dynamic cache management framework that hierarchically allocates shared cache to tasks. After that, we further investigate the resource allocation and analysis technique that considers not only cache resource but also CPU and memory bandwidth resources. Our solutions are applicable to commodity hardware and are essential steps to advance virtualization technology into timing-critical systems

SDN-Based Network Intrusion Detection as DDoS defense system for Virtualization Environment

Nowadays, DDoS attacks are often aimed at cloud computing environments, as more people use virtualization servers. With so many Nodes and distributed services, it will be challenging to rely solely on conventional networks to control and monitor intrusions. We design and deploy DDoS attack defense systems in virtualization environments based on Software-defined Networking (SDN) by combining signature-based Network Intrusion Detection Systems (NIDS) and sampled flow (sFlow). These techniques are practically tested and evaluated on the Proxmox production Virtualization Environment testbed, adding High Availability capabilities to the Controller. The evaluation results show that it promptly detects several types of DDoS attacks and mitigates their negative impact on network performance. Moreover, it also shows good results on Quality of Service (QoS) parameters such as average packet loss about 0 %, average latency about 0.8 ms, and average bitrate about 860 Mbit/s

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

Grid Infrastructure for Domain Decomposition Methods in Computational ElectroMagnetics

The accurate and efficient solution of Maxwell's equation is the problem addressed by the scientific discipline called Computational ElectroMagnetics (CEM). Many macroscopic phenomena in a great number of fields are governed by this set of differential equations: electronic, geophysics, medical and biomedical technologies, virtual EM prototyping, besides the traditional antenna and propagation applications. Therefore, many efforts are focussed on the development of new and more efficient approach to solve Maxwell's equation. The interest in CEM applications is growing on. Several problems, hard to figure out few years ago, can now be easily addressed thanks to the reliability and flexibility of new technologies, together with the increased computational power. This technology evolution opens the possibility to address large and complex tasks. Many of these applications aim to simulate the electromagnetic behavior, for example in terms of input impedance and radiation pattern in antenna problems, or Radar Cross Section for scattering applications. Instead, problems, which solution requires high accuracy, need to implement full wave analysis techniques, e.g., virtual prototyping context, where the objective is to obtain reliable simulations in order to minimize measurement number, and as consequence their cost. Besides, other tasks require the analysis of complete structures (that include an high number of details) by directly simulating a CAD Model. This approach allows to relieve researcher of the burden of removing useless details, while maintaining the original complexity and taking into account all details. Unfortunately, this reduction implies: (a) high computational effort, due to the increased number of degrees of freedom, and (b) worsening of spectral properties of the linear system during complex analysis. The above considerations underline the needs to identify appropriate information technologies that ease solution achievement and fasten required elaborations. The authors analysis and expertise infer that Grid Computing techniques can be very useful to these purposes. Grids appear mainly in high performance computing environments. In this context, hundreds of off-the-shelf nodes are linked together and work in parallel to solve problems, that, previously, could be addressed sequentially or by using supercomputers. Grid Computing is a technique developed to elaborate enormous amounts of data and enables large-scale resource sharing to solve problem by exploiting distributed scenarios. The main advantage of Grid is due to parallel computing, indeed if a problem can be split in smaller tasks, that can be executed independently, its solution calculation fasten up considerably. To exploit this advantage, it is necessary to identify a technique able to split original electromagnetic task into a set of smaller subproblems. The Domain Decomposition (DD) technique, based on the block generation algorithm introduced in Matekovits et al. (2007) and Francavilla et al. (2011), perfectly addresses our requirements (see Section 3.4 for details). In this chapter, a Grid Computing infrastructure is presented. This architecture allows parallel block execution by distributing tasks to nodes that belong to the Grid. The set of nodes is composed by physical machines and virtualized ones. This feature enables great flexibility and increase available computational power. Furthermore, the presence of virtual nodes allows a full and efficient Grid usage, indeed the presented architecture can be used by different users that run different applications