分析了基于处理器硬件虚拟化技术实现的KVM子系统的架构。针对KVM跟踪独立事件信息的局限性,提出一种新的KVM事件跟踪机制(kvmtrace)来达到性能调节的目的,并使用relayfs接口进行了设计与实现。同时探讨了Linux kernel Markers实现机制及其在kvmtrace的实际应用。

Kernel-based virtual machine事件跟踪机制的设计与实现

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users’ retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is $\mathcal {NP}$ NP -complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

Online Collaborative Data Caching in Edge Computing

The increase in high-precision, high-sample-rate telemetry timeseries poses a problem for existing timeseries databases which can neither cope with the throughput demands of these streams nor provide the necessary primitives for effective analysis of them. We present a novel abstraction for telemetry timeseries data and a data structure for providing this abstraction: a time-partitioning version-annotated copy-on-write tree. An implementation in Go is shown to outperform existing solutions, demonstrating a throughput of 53 million inserted values per second and 119 million queried values per second on a four-node cluster. The system achieves a 2.9× compression ratio and satisfies statistical queries spanning a year of data in under 200ms, as demonstrated on a year-long production deployment storing 2.1 trillion data points. The principles and design of this database are generally applicable to a large variety of timeseries types and represent a significant advance in the development of technology for the Internet of Things.

/pdf/btrdb-optimizing-storage-system-design-for-timeseries-1t5natrvp0.pdf

BTrDB: optimizing storage system design for timeseries processing

The topic of serverless computing has proved to be a controversial subject both within academic and industrial communities. Many have praised the approach to be a platform for a new era of computing and some have argued that it is in fact a step backward. Though, both sides agree that there exist challenges that must be addressed in order to better utilize its potentials. This paper surveys existing challenges toward vast adoption of serverless services and also explores some of the challenges that have not been thoroughly discussed in the previous studies. Each challenge is discussed thoroughly and a number of possible directions for future studies is proposed. Moreover, the paper reviews some of the unique opportunities and potentials that the serverless computing presents.

Serverless Computing: A Survey of Opportunities, Challenges and Applications

Eliminating duplicate data in primary storage of clouds increases the cost-efficiency of cloud service providers as well as reduces the cost of users for using cloud services. Existing primary deduplication techniques either use inline caching to exploit locality in primary workloads or use post-processing deduplication running in system idle time to avoid the negative impact on I/O performance. However, neither of them works well in the cloud servers running multiple services or applications for the following two reasons: Firstly, the temporal locality of duplicate data writes may not exist in some primary storage workloads thus inline caching often fails to achieve good deduplication ratio. Secondly, the post-processing deduplication allows duplicate data to be written into disks, therefore does not provide the benefit of I/O deduplication and requires high peak storage capacity. This paper presents HPDedup, a Hybrid Prioritized data Deduplication mechanism to deal with the storage system shared by applications running in co-located virtual machines or containers by fusing an inline and a post-processing process for exact deduplication. In the inline deduplication phase, HPDedup gives a fingerprint caching mechanism that estimates the temporal locality of duplicates in data streams from different VMs or applications and prioritizes the cache allocation for these streams based on the estimation. HPDedup also allows different deduplication threshold for streams based on their spatial locality to reduce the disk fragmentation. The post-processing phase removes duplicates whose fingerprints are not able to be cached due to the weak temporal locality from disks. Our experimental results show that HPDedup clearly outperforms the state-of-the-art primary storage deduplication techniques in terms of inline cache efficiency and primary deduplication efficiency.

HPDedup: A Hybrid Prioritized Data Deduplication Mechanism for Primary Storage in the Cloud

Host-side flash caching has emerged as a promising solution to the scalability problem of virtual machine (VM) storage in cloud computing systems, but it still faces serious limitations in capacity and endurance. This paper presents CloudCache, an on-demand cache management solution to meet VM cache demands and minimize cache wear-out. First, to support on-demand cache allocation, the paper proposes a new cache demand model, Reuse Working Set (RWS), to capture only the data with good temporal locality, and uses the RWS size (RWSS) to model a workload's cache demand. By predicting the RWSS online and admitting only RWS into the cache, CloudCache satisfies the workload's actual cache demand and minimizes the induced wear-out. Second, to handle situations where a cache is insufficient for the VMs' demands, the paper proposes a dynamic cache migration approach to balance cache load across hosts by live migrating cached data along with the VMs. It includes both on-demand migration of dirty data and background migration of RWS to optimize the performance of the migrating VM. It also supports rate limiting on the cache data transfer to limit the impact to the co-hosted VMs. Finally, the paper presents comprehensive experimental evaluations using real-world traces to demonstrate the effectiveness of CloudCache.

/pdf/cloudcache-on-demand-flash-cache-management-for-cloud-1p3kransyy.pdf

CloudCache: on-demand flash cache management for Cloud Computing

Virtualization enables flexible application delivery and efficient resource consolidation, and is pervasively used to build various virtualized systems including public and private cloud computing systems. Many applications can benefit from computing on virtualized systems, including those that are time sensitive, but it is still challenging for existing virtualized systems to deliver application-desired timeliness. In particular, the lack of awareness between VM host- and guest-level schedulers presents a serious hurdle to achieving strong timeliness guarantees on virtualized systems. This paper presents RTVirt, a new solution to time-sensitive computing on virtualized systems through cross-layer scheduling. It allows the two levels of schedulers on a virtualized system to communicate key scheduling information and coordinate on the scheduling decisions. It enables optimal multiprocessor schedulers to support virtualized time-sensitive applications with strong timeliness guarantees and efficient resource utilization. RTVirt is prototyped on a widely used virtualization framework (Xen) and evaluated with diverse workloads. The results show that it can meet application deadlines (99%) or tail latency requirements (99.9th percentile) nearly perfectly; it can handle large numbers of applications and dynamic changes in their timeliness requirements; and it substantially outperforms the existing solutions in both timeliness and resource utilization.

https://dl.acm.org/doi/pdf/10.1145/3190508.3190527

Jorge Cabrera

Papers

CloudCache: on-demand flash cache management for Cloud Computing

RTVirt: enabling time-sensitive computing on virtualized systems through cross-layer CPU scheduling