Virtual time-aware virtual machine systems

Discrete dynamic system models that track, maintain, utilize, and evolve virtual time are referred to as virtual time systems (VTS). The realization of VTS using virtual machine (VM) technology offers several benefits including fidelity, scalability, interoperability, fault tolerance and load balancing. The usage of VTS with VMs appears in two ways: (a) VMs within VTS, and (b) VTS over VMs. The former is prevalent in high-fidelity cyber infrastructure simulations and cyber-physical system simulations, wherein VMs form a crucial component of VTS. The latter appears in the popular Cloud computing services, where VMs are offered as computing commodities and the VTS utilizes VMs as parallel execution platforms. Prior to our work presented here, the simulation community using VM within VTS (specifically, cyber infrastructure simulations) had little awareness of the existence of a fundamental virtual time-ordering problem. The correctness problem was largely unnoticed and unaddressed because of the unrecognized effects of fair-share multiplexing of VMs to realize virtual time evolution of VMs within VTS. The dissertation research reported here demonstrated the latent incorrectness of existing methods, defined key correctness benchmarks, quantitatively measured the incorrectness, proposed and implemented novel algorithms to overcome incorrectness, and optimized the solutions to execute without a performance penalty. In fact our novel, correctness-enforcing design yields better runtime performance than the traditional (incorrect) methods. Similarly, the VTS execution over VM platforms such as Cloud computing services incurs large performance degradation, which was not known until our research uncovered the fundamental mismatch between the scheduling needs of VTS execution and those of traditional parallel workloads. Consequently, we designed a novel VTS-aware hypervisor scheduler and showed significant performance gains in VTS execution over VM platforms. Prior to our work, the performance concern of VTS over VM was largely unaddressed due to the absence of an understanding of execution policy mismatch between VMs and VTS applications. VTS follows virtual-time order execution whereas the conventional VM execution follows fair-share policy. Our research quantitatively uncovered the exact cause of poor performance of VTS in VM platforms. Moreover, we proposed and implemented a novel virtual time-aware execution methodology that relieves the degradation and provides over an order of magnitude faster execution than the traditional virtual time-unaware execution.Ph.D

Synchron - An API and Runtime for Embedded Systems

Programming embedded systems applications involve writing concurrent, event-driven and timing-aware programs. Traditionally, such programs are written in low-level machine-oriented programming languages like C or Assembly. We present an alternative by introducing Synchron, an API that offers high-level abstractions to the programmer while supporting the low-level infrastructure in an associated runtime system and one-time-effort drivers.Embedded systems applications exhibit the general characteristics of being (i) concurrent, (ii) I/O–bound and (iii) timing-aware. To address each of these concerns, the Synchron API consists of three components - (1) a Concurrent ML (CML) inspired message-passing concurrency model, (2) a message-passing–based I/O interface that translates between low-level interrupt based and memory-mapped peripherals, and (3) a timing operator, syncT, that marries CML’s sync operator with timing windows inspired from the TinyTimber kernel.We implement the Synchron API as the bytecode instructions of a virtual machine called SynchronVM. SynchronVM hosts a Caml-inspired functional language as its frontend language, and the backend of the VM supports the STM32F4 and NRF52 microcontrollers, with RAM in the order of hundreds of kilobytes. We illustrate the expressiveness of the Synchron API by showing examples of expressing state machines commonly found in embedded systems. The timing functionality is demonstrated through a music programming exercise. Finally, we provide benchmarks on the response time, jitter rates, memory, and power usage of the SynchronVM

System Support for Managing Invalid Bindings

Context-aware adaptation is a central aspect of pervasive computing applications, enabling them to adapt and perform tasks based on contextual information. One of the aspects of context-aware adaptation is reconfiguration in which bindings are created between application component and remote services in order to realize new behaviour in response to contextual information. Various research efforts provide reconfiguration support and allow the development of adaptive context-aware applications from high-level specifications, but don't consider failure conditions that might arise during execution of such applications, making bindings between application and remote services invalid. To this end, we propose and implement our design approach to reconfiguration to manage invalid bindings. The development and modification of adaptive context-aware applications is a complex task, and an issue of an invalidity of bindings further complicates development efforts. To reduce the development efforts, our approach provides an application-transparent solution where the issue of the invalidity of bindings is handled by our system, Policy-Based Contextual Reconfiguration and Adaptation (PCRA), not by an application developer. In this paper, we present and describe our approach to managing invalid bindings and compare it with other approaches to this problem. We also provide performance evaluation of our approach

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%