This paper focuses on the live migration of virtual machines (VMs) across semi-heterogeneous processors (SH), which share the same ISA but expose different features. Processor incompatibilities in such settings can block or break migrations, limiting resource utilization and operational flexibility. We provide the first detailed study of live migration feasibility across SH processors, yielding several findings valuable to cloud tenants, operators and hypervisor vendors. Building on these insights, we present MigCheck, a simulation tool that predicts the feasibility and outcome of VM migrations across SH processors without requiring costly real migrations. We demonstrate its accuracy and effectiveness through multiple use cases, including machine addition/removal, hypervisor updates, and VM platform adjustments.

Cloud applications based on the "Functions as a Service" (FaaS) paradigm have become very popular. Yet, due to their stateless nature, they must frequently interact with an external data store, which limits their performance. To mitigate this issue, we introduce OFC, a transparent, vertically and horizontally elastic in-memory caching system for FaaS platforms, distributed over the worker nodes. OFC provides these benefits cost-effectively by exploiting two common sources of resource waste: (i) most cloud tenants overprovision the memory resources reserved for their functions because their footprint is non-trivially input-dependent and (ii) FaaS providers keep function sandboxes alive for several minutes to avoid cold starts. Using machine learning models adjusted for typical function input data categories (e.g., multimedia formats), OFC estimates the actual memory resources required by each function invocation and hoards the remaining capacity to feed the cache. We build our OFC prototype based on enhancements to the OpenWhisk FaaS platform, the Swift persistent object store, and the RAM-Cloud in-memory store. Using a diverse set of workloads, we show that OFC improves by up to 82 % and 60 % respectively the execution time of single-stage and pipelined functions.

Virtual machine monitors (VMMs) play a crucial role in the software stack of cloud computing platforms: their design and implementation have a major impact on performance, security and fault tolerance. In this paper, we focus on the latter aspect (fault tolerance), which has received less attention, although it is now a significant concern. Our work aims at improving the resilience of the "pVM-based" VMMs, a popular design pattern for virtualization platforms. In such a design, the VMM is split into two main components: a bare-metal hypervisor and a privileged guest virtual machine (pVM). We highlight that the pVM is the least robust component and that the existing fault-tolerance approaches provide limited resilience guarantees or prohibitive overheads. We present three design principles (disaggregation, specialization, and pro-activity), as well as optimized implementation techniques for building a resilient pVM without sacrificing end-user application performance. We validate our contribution on the mainstream Xen platform.

A plethora of optimized mutex lock algorithms have been designed over the past 25 years to mitigate performance bottlenecks related to critical sections and locks. Unfortunately, there is currently no broad study of the behavior of these optimized lock algorithms on realistic applications that consider different performance metrics, such as energy efficiency and tail latency. In this article, we perform a thorough and practical analysis of synchronization, with the goal of providing software developers with enough information to design fast, scalable, and energy-efficient synchronization in their systems. First, we perform a performance study of 28 state-of-the-art mutex lock algorithms, on 40 applications, on four different multicore machines. We consider not only throughput (traditionally the main performance metric) but also energy efficiency and tail latency, which are becoming increasingly important. Second, we present an in-depth analysis in which we summarize our findings for all the studied applications. In particular, we describe nine different lock-related performance bottlenecks, and we propose six guidelines helping software developers with their choice of a lock algorithm according to the different lock properties and the application characteristics. From our detailed analysis, we make several observations regarding locking algorithms and application behaviors, several of which have not been previously discovered: (i) applications stress not only the lock–unlock interface but also the full locking API (e.g., trylocks, condition variables); (ii) the memory footprint of a lock can directly affect the application performance; (iii) for many applications, the interaction between locks and scheduling is an important application performance factor; (vi) lock tail latencies may or may not affect application tail latency; (v) no single lock is systematically the best; (vi) choosing the best lock is difficult; and (vii) energy efficiency and throughput go hand in hand in the context of lock algorithms. These findings highlight that locking involves more considerations than the simple lock/unlock interface and call for further research on designing low-memory footprint adaptive locks that fully and efficiently support the full lock interface, and consider all performance metrics.

NUMA systems are characterized by Non-Uniform Memory Access times, where accessing data in a remote node takes longer than a local access. NUMA hardware has been built since the late 80's, and the operating systems designed for it were optimized for access locality. They co-located memory pages with the threads that accessed them, so as to avoid the cost of remote accesses. Contrary to older systems, modern NUMA hardware has much smaller remote wire delays, and so remote access costs per se are not the main concern for performance, as we discovered in this work. Instead, congestion on memory controllers and interconnects, caused by memory traffic from data-intensive applications, hurts performance a lot more. Because of that, memory placement algorithms must be redesigned to target traffic congestion. This requires an arsenal of techniques that go beyond optimizing locality. In this paper we describe Carrefour, an algorithm that addresses this goal. We implemented Carrefour in Linux and obtained performance improvements of up to 3.6 relative to the default kernel, as well as significant improvements compared to NUMA-aware patchsets available for Linux. Carrefour never hurts performance by more than 4% when memory placement cannot be improved. We present the design of Carrefour, the challenges of implementing it on modern hardware, and draw insights about hardware support that would help optimize system software on future NUMA systems.

Many high-performance communicating systems are designed using the event-driven paradigm. As multicore platforms are now pervasive, it becomes crucial for such systems to take advantage of the available hardware parallelism. Event-coloring is a promising approach in this regard. First, it allows programmers to simply and progressively inject support for the safe, parallel execution of multiple event handlers through the use of annotations. Second, it relies on a workstealing algorithm to dynamically balance the execution of event handlers on the available cores. This paper studies the impact of the workstealing algorithm on the overall system performance. We first show that the only existing workstealing algorithm designed for event-coloring runtimes is not always efficient: for instance, it causes a 33% performance degradation on a Web server. We then introduce several enhancements to improve the workstealing behavior. An evaluation using both micro benchmarks and real applications, a Web server and the Secure File Server (SFS), shows that our system consistently outperforms a state-of-the-art runtime (Libasync-smp), with or without workstealing. In particular, our new workstealing improves performance by up to +25% compared to Libasync-smp without workstealing and by up to +73% compared to the Libasync-smp workstealing algorithm, in the Web server case.

High-performance storage systems are evolving towards decentralized commodity clusters that can scale in capacity, processing power, and network throughput. Building such systems requires: (a)Sharing physical resources among applications; (b)Sharing data among applications; (c) Allowing customized views of data for applications. Current solutions satisfy typically the first two requirements through a distributed file-system, resulting in monolithic, hard-to-manage storage systems. In this paper, we present Orchestra, a novel storage system that addresses all three above requirements below the file-system by extending the block layer. To provide customized views, Orchestra allows applications to create semantically-rich virtual block devices by combining simpler ones. To achieve efficient resource and data sharing it supports block-level allocation and byte-range locking as in-band mechanisms. We implement Orchestra under Linux and use it to build a shared cluster file-system. We evaluate it on a 16-node cluster, finding that the flexibility offered by Orchestra introduces little overhead beyond mandatory communication and disk access costs.

To satisfy current and future application needs in a cost effective manner, storage systems are evolving from monolithic disk arrays to networked storage architectures based on commodity components. So far, this architectural transition has mostly been envisioned as a way to scale capacity and performance. In this work we examine how the block-level interface exported by such networked storage systems can be extended to deal with reliability. Our goals are: (a) At the design level, to examine how strong reliability semantics can be offered at the block level; (b) At the implementation level, to examine the mechanisms required and how they may be provided in a modular and configurable manner. We first discuss how transactional-type semantics may be offered at the block level. We present a system design that uses the concept of atomic update intervals combined with existing, block-level locking and snapshot mechanisms, in contrast to the more common journaling techniques. We discuss in detail the design of the associated mechanisms and the trade-offs and challenges when dividing the required functionality between the file-system and the block-level storage. Our approach is based on a unified and thus, non-redundant set of mechanisms for providing reliability both at the block and file level. Our design and implementation effectively provide a tunable, lightweight transactions mechanism to higher system and application layers. Finally, we describe how the associated protocols can be implemented in a modular way in a prototype storage system we are currently building. As our system is currently being implemented, we do not present performance results

Efficient memory allocation and data transfer for cluster-based data-intensive applications is a difficult task. Both changes in cluster interconnects and application workloads usually require timing of the application and network code. We propose separating control and data transfer traffic by accessing data through a DSM-like cluster-wide shared buffer space and only including buffer references in the control messages. Using a generic API for accessing buffers allows for tuning data transfer without changing the application code. A prototype, implemented in the context of a distributed storage system, has been validated with several networking technologies, showing that such a framework can combine performance and flexibility.

Cluster storage systems where storage devices are distributed across a large number of nodes are able to reduce the I/O bottleneck problems present in most centralized storage systems. However, such distributed storage devices are hard to manage efficiently. In this paper, we examine the use of explicit, component-based (command and data) paths between hosts and disks as a vehicle for performing nondisruptive storage system reconfiguration. We describe the mechanisms necessary to perform reconfigurations and show how they can be used to handle two management tasks: migration between network technologies and rebuilding a disk in a mirror. Our approach is validated through initial performance measurements of these two tasks using a prototype implementation. The results show that online reconfiguration is possible at a modest cost

Abstract Resource management in a Grid computing environment raises several technical issues. The monitoring infrastructure must be scalable, flexible, configurable and adaptable to support thousands of devices in a highly dynamic environment where operational conditions are constantly changing. We propose to address these challenges by combining asynchronous communications with reflective component‐based technologies. We introduce DREAM (Dynamic REflective Asynchronous Middleware), a Java component‐based message oriented middleware. Asynchronous communications are used to achieve the scalability and flexibility objectives, whereas the reflective component technology provides the complementary configurability and adaptability features. We argue that this infrastructure is a viable approach to build a resource monitoring infrastructure for Grid computing. Moreover, DREAM makes the monitoring logic accessible from J2EE application servers. This allows the monitoring information to be presented as a Grid service and to integrate within the Open Grid Software Architecture. Copyright © 2004 John Wiley & Sons, Ltd.