Machine Learning-Based Elastic Cloud Resource Provisioning in the Solvency II Framework

The Solvency II Directive (Directive 2009/138/EC) is a European Directive issued in November 2009 and effective from January 2016, which has been enacted by the European Union to regulate the insurance and reinsurance sector through the discipline of risk management. Solvency II requires European insurance companies to conduct consistent evaluation and continuous monitoring of risks—a process which is computationally complex and extremely resource-intensive. To this end, companies are required to equip themselves with adequate IT infrastructures, facing a significant outlay. In this paper we present the design and the development of a Machine Learning-based approach to transparently deploy on a cloud environment the most resource-intensive portion of the Solvency II-related computation. Our proposal targets DISAR®, a Solvency II-oriented system initially designed to work on a grid of conventional computers. We show how our solution allows to reduce the overall expenses associated with the computation, without hampering the privacy of the companies’ data (making it suitable for conventional public cloud environments), and allowing to meet the strict temporal requirements required by the Directive. Additionally, the system is organized as a self-optimizing loop, which allows to use information gathered from actual (useful) computations, thus requiring a shorter training phase. We present an experimental study conducted on Amazon EC2 to assess the validity and the efficiency of our proposal

CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

Real-Time Virtualization and Cloud Computing

In recent years, we have observed three major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications. In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic

Limitations and Solutions for Real-Time Local Inter-Domain Communication in Xen

As computer hardware becomes increasingly powerful, there is an ongoing trend towards integrating complex, legacy real-time systems using fewer hosts through virtualization. Especially in embedded systems domains such as avionics and automotive engineering, this kind of system integration can greatly reduce system weight, cost, and power requirements. When systems are integrated in this manner, network communication may become local inter-domain communication (IDC) within the same host. This paper examines the limitations of inter-domain communication in Xen, a widely used open-source virtual machine monitor (VMM) that recently has been extended to support real-time domain scheduling. We find that both the VMM scheduler and the manager domain can significantly impact real-time IDC performance under different conditions, and show that improving the VMM scheduler alone cannot deliver real-time performance for local IDC. To address those limitations, we present the RTCA, a Real-Time Communication Architecture within the manager domain in Xen, along with empirical evaluations whose results demonstrate that the latency of communication tasks can be improved dramatically from ms to μs by a combination of the RTCA and a real-time VMM scheduler

Leveraging virtualization technologies for resource partitioning in mixed criticality systems

Multi- and many-core processors are becoming increasingly popular in embedded systems. Many of these processors now feature hardware virtualization capabilities, such as the ARM Cortex A15, and x86 processors with Intel VT-x or AMD-V support. Hardware virtualization offers opportunities to partition physical resources, including processor cores, memory and I/O devices amongst guest virtual machines. Mixed criticality systems and services can then co-exist on the same platform in separate virtual machines. However, traditional virtual machine systems are too expensive because of the costs of trapping into hypervisors to multiplex and manage machine physical resources on behalf of separate guests. For example, hypervisors are needed to schedule separate VMs on physical processor cores. Additionally, traditional hypervisors have memory footprints that are often too large for many embedded computing systems. This dissertation presents the design of the Quest-V separation kernel, which partitions services of different criticality levels across separate virtual machines, or sandboxes. Each sandbox encapsulates a subset of machine physical resources that it manages without requiring intervention of a hypervisor. In Quest-V, a hypervisor is not needed for normal operation, except to bootstrap the system and establish communication channels between sandboxes. This approach not only reduces the memory footprint of the most privileged protection domain, it removes it from the control path during normal system operation, thereby heightening security

Defeating network jitter for virtual machines

Virtualization based cloud computing hosts networked applications in virtual machines (VMs), and provides each VM the desired degree of performance isolation using resource isolation mechanisms. Existing isolation solutions address heavily on resource proportionality such as CPU, memory and I/O bandwidth, but seldom focus on resource provisioning rate. Even the VM is allocated with adequate resources, if they can not be provided in a timely manner, problems such as network jitter will be very serious and significantly affect the performance of cloud applications like internet audio/video streaming. This paper systematically analyzes and illustrates the causes of unpredictable network latency in virtualized execution environments. We decouple the design goals of resource proportionality from resource provisioning rate, and adopt divide-and-conquer strategy to defeat network jitter for VMs: (1) in VMM CPU scheduling, we differentiate self-initiated I/O from event-triggered I/O, and individually map them to periodic and aperiodic real-time domains to schedule them together; (2) in network traffic shaping of VMs, we introduce the concept of smooth window to smooth network latency and apply closed-loop feedback control to maintain network resource consumption. We implement our solutions in Xen 4.1.0 and Linux 2.6.32.13. The experimental results with both real-life applications and low-level benchmarks show that our solutions can significantly reduce network jitter, and meanwhile effectively maintain resource proportionality.published_or_final_versionThe 4th IEEE International Conference on Utility and Cloud Computing (UCC 2011), Victoria, NSW, 5-8 December 2011. In Proceedings of the 4th IEEE-UCC, 2011, p. 65-7