How can you help the compiler do its job better and when does it make sense to do the optimizations manually? In this post we try to give answers to those questions, and as you will see, the answer is not simple to give.
The Open Time Server (OTS) is an Open, Scalable and Validated reference architecture that can be deployed in Data Centers or in an edge environments.
'home.arpa.' is designated for non-unique use in residential home networks.
We propose PAIO, the first general-purpose framework that enables system designers to build custom-made Software-Defined Storage (SDS) data plane stages. It provides the means to implement storage optimizations adaptable to different workflows and user-defined policies, and allows straightforward integration with existing applications and I/O layers. PAIO allows stages to be integrated with modern SDS control planes to ensure holistic control and system-wide optimal performance. We demonstrate the performance and applicability of PAIO with two use cases. The first improves 99th percentile latency by 4x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments
Designing and implementing efficient parallel priority schedulers is an active research area. An intriguing proposed design is the Multi-Queue: given n threads and m≥n distinct priority queues, task insertions are performed uniformly at random, while, to delete, a thread picks two queues uniformly at random, and removes the observed task of higher priority. This approach scales well, and has probabilistic rank guarantees: roughly, the rank of each task removed, relative to remaining tasks in all other queues, is O(m) in expectation. Yet, the performance of this pattern is below that of well-engineered schedulers, which eschew theoretical guarantees for practical efficiency. We investigate whether it is possible to design and implement a Multi-Queue-based task scheduler that is both highly efficient and has analytical guarantees. We propose a new variant called the Stealing Multi-Queue (SMQ), a cache-efficient variant of the Multi-Queue, which leverages both queue affinity -- each thread has a local queue, from which tasks are usually removed; but, with some probability, threads also attempt to steal higher-priority tasks from the other queues -- and task batching, that is, the processing of several tasks in a single insert / delete step. These ideas are well-known for task scheduling without priorities; our theoretical contribution is showing that, despite relaxations, this design can still provide rank guarantees, which in turn implies bounds on total work performed. We provide a general SMQ implementation that can surpass state-of-the-art schedulers such as Galois and PMOD in terms of performance on popular graph-processing benchmarks. Notably, the performance improvement comes mainly from the superior rank guarantees provided by our scheduler, confirming that analytically-reasoned approaches can still provide performance improvements for priority task scheduling.
Software caches optimize the performance of diverse storage systems, databases and other software systems. Existing works on software caches automatically resort to fully associative cache designs. Our work shows that limited associativity caches are a promising direction for concurrent software caches. Specifically, we demonstrate that limited associativity enables simple yet efficient realizations of multiple cache management schemes that can be trivially parallelized. We show that the obtained hit ratio is usually similar to fully associative caches of the same management policy, but the throughput is improved by up to X5 compared to production-grade caching libraries, especially in multi-threaded executions.
Linux gained support for VRF interfaces in Linux 4.3. And since Alpine 3.13, we have supported managing VRFs and binding services to them, primarily for the purpose of low-cost service isolation
Go library providing algorithms optimized to leverage the characteristics of modern CPUs
Amazon Ion is a richly-typed, self-describing, hierarchical data serialization format offering interchangeable binary and text representations. The text format (a superset of JSON) is easy to read and author, supporting rapid prototyping. The binary representation is efficient to store, transmit, and skip-scan parse. The rich type system provides unambiguous semantics for long-term preservation of data which can survive multiple generations of software evolution.
A standard technique from the hashing literature is to use two hash functions h1(x) and h2(x) to simulate additional hash functions of the form gi(x) = h1(x) + ih2(x). We demonstrate that this technique can be usefully applied to Bloom filters and related data structures. Specifically, only two hash functions are necessary to effectively implement a Bloom filter without any loss in the asymptotic false positive probability. This leads to less computation and potentially less need for randomness in practice.
The answer is yes, you can write a server that does no syscalls for handling requests, if you are willing to forego memory allocations, don’t count asynchronous syscalls as being “real” syscalls and are also willing to use 100% of one CPU for the process and some unspecified amount of CPU for the kernel thread. Clearly this is not really a viable approach for production-quality applications, but it was fun to see how low we could go
The reason is because this entire problem is built on a flawed premise. Asking what the most user-friendly way to implicitly insert overflows is is like asking what the driest kind of water is. All the answers are unsatisfactory because you are asking the wrong question in the first place. The right question is “Why do you even need multiple integer types in the first place?”
Filibuster is the prototype implementation of Service-level Fault Injection Testing (ACM SoCC ‘21) written in Python. Service-level Fault Injection Testing is a technique for identifying resilience issues in microservice-based applications in development, before code ships to production. Filibuster has been designed to be easy to use, lightweight, and able to be integrated into a continuous integration environment, like GitHub Actions or Amazon’s CodeBuild CI/CD environment.
Modern key-value (KV) stores adopt the LSM-tree as the core data structure for managing KV pairs, but suffer from high write and read amplifications. Existing LSM-tree optimizations often make design trade-offs and are unable to simultaneously achieve high performance in writes, reads, and scans. To resolve the design tensions, we propose DiffKV, which builds on KV separation to carefully manage the ordering for keys and values. DiffKV manages keys using the conventional LSM-tree with fully-sorted ordering (within each level of the LSM-tree), while managing values with partially-sorted ordering with respect to the fully-sorted ordering of keys in a coordinated way for preserving high scan performance. We further propose fine-grained KV separation to differentiate KV pairs by size, so as to realize balanced performance under mixed workloads. Experimental results show that DiffKV can simultaneously achieve the best performance in all aspects among existing LSM-tree-optimized KV stores.
An open source collection of Design Principles and methods.
This tool is a syscall fault injector built on top of eBPF that has no requirements on the target machine other than a kernel version good enough to support the required features
Build distroless images with apk! Witchery is a collection of tools which can be composed to build distroless images. It is intended to be portable for use with any package management tooling, but is primarily focused on apk-based distributions at the moment, such as Alpine.
SLO Tracker allows you to balance service reliability with the pace of innovation. You can define the target SLO and use corresponding SLIs to track Error Budget. The error budget thus forms a control mechanism for diverting attention to stability as needed
This is the API job people generally refer to as IAM. There are a bunch of different ways to do the IAM job, and they're all fun to nerd out about.
Facebook’s graph store TAO, like many other distributed data stores, traditionally prioritizes availability, efficiency, and scalability over strong consistency or isolation guarantees to serve its large, read-dominant workloads. As product developers build diverse applications on top of this system, they increasingly seek transactional semantics. However, providing advanced features for select applications while preserving the system’s overall reliability and performance is a continual challenge. In this paper, we first characterize developer desires for transactions that have emerged over the years and describe the current failure-atomic (i.e., write) transactions offered by TAO. We then explore how to introduce an intuitive read transaction API. We highlight the need for atomic visibility guarantees in this API with a measurement study on potential anomalies that occur without stronger isolation for reads. Our analysis shows that 1 in 1,500 batched reads reflects partial transactional updates, which complicate the developer experience and lead to unexpected results. In response to our findings, we present the RAMP-TAO protocol, a variation based on the Read Atomic Multi-Partition (RAMP) protocols that can be feasibly deployed in production with minimal overhead while ensuring atomic visibility for a read-optimized workload at scale
Modern applications tend to be composed from relationships between smaller applications. Secure modern applications thus need a way to express and enforce security policies that span multiple services. This is the “server-to-server” (S2S) authentication and authorization problem
What do you do when a set of key/value pairs no longer fits in memory, and you want to design a disk-friendly structure to store them all?
Pluggable caching engine to build and scale high performance cache services
This repo contains an implementation of the Homa transport protocol as a Linux kernel module
This work aims to improve Cassandra to support fast general purpose transactions. That is, those that may operate over any set of keys in the database atomically, modifying their contents at-once, with any action conditional on the existing contents of any key.
Find bottlenecks in your code and fix performance issues
Git-cliff can generate changelog files from the Git history by utilizing conventional commits as well as regex-powered custom parsers. The changelog template can be customized with a configuration file to match the desired format.
The PAM Duress is a module designed to allow users to generate 'duress' passwords that when used in place of their normal password will execute abritrary scripts.
Bloom filters are used to quickly check whether an element is part of a set. Xor filters and binary fuse filters are faster and more concise alternatives to Bloom filters. They are also smaller than cuckoo filters.
Homa is a new transport protocol for datacenter networks. It provides exceptionally low latency, especially for workloads with a high volume of very short messages, and it also supports large messages and high network utilization. Homa uses in-network priority queues to ensure low latency for short messages; priority allocation is managed dynamically by each receiver and integrated with a receiver-driven flow control mechanism. Homa also uses controlled overcommitment of receiver downlinks to ensure efficient bandwidth utilization at high load. Our implementation of Homa delivers 99th percentile round-trip times less than 15{\mu}s for short messages on a 10 Gbps network running at 80% load. These latencies are almost 100x lower than the best published measurements of an implementation. In simulations, Homa's latency is roughly equal to pFabric and significantly better than pHost, PIAS, and NDP for almost all message sizes and workloads. Homa can also sustain higher network loads than pFabric, pHost, or PIAS.
It implements a backend for sql.js (sqlite3 compiled for the web) that treats IndexedDB like a disk and stores data in blocks there. That means your sqlite3 database is persisted. And not in the terrible way of reading and writing the whole image at once -- it reads and writes your db in small chunks.
A fast concurrent persistent key-value store and log, in C# and C++. Latest release v1.9.5
Safe lock-free memory reclamation is a difficult problem. Existing solutions follow three basic methods (or their combinations): epoch based reclamation, hazard pointers, and optimistic reclamation. Epoch-based methods are fast, but do not guarantee lock-freedom. Hazard pointer solutions are lock-free but typically do not provide high performance. Optimistic methods are lock-free and fast, but previous optimistic methods did not go all the way. While reads were executed optimistically, writes were protected by hazard pointers. In this work we present a new reclamation scheme called version based reclamation (VBR), which provides a full optimistic solution to lock-free memory reclamation, obtaining lock-freedom and high efficiency. Speculative execution is known as a fundamental tool for improving performance in various areas of computer science, and indeed evaluation with a lock-free linked-list, hash-table and skip-list shows that VBR outperforms state-of-the-art existing solutions.
Firefox currently ships with a variety of third-party and in-house libraries running sandboxed using a new framework called RLBox. We explore how RLBox uses the C++ type system to simplify retrofitting sandboxing in existing code bases, and consider how better tooling and architecture support can enable a future where library sandboxing is a standard part of how we secure applications.
Lets talk about why automerge is currently slow, and I'll take you through all the steps toward making it super fast.
This paper presents the tracking approach for deriving detectably recoverable (and thus also durable) implementations of many widely-used concurrent data structures. Such data structures, satisfying detectable recovery, are appealing for emerging systems featuring byte-addressable non-volatile main memory (NVRAM), whose persistence allows to efficiently resurrect failed processes after crashes. Detectable recovery ensures that after a crash, every executed operation is able to recover and return a correct response, and that the state of the data structure is not corrupted. Info-Structure Based (ISB)-tracking amends descriptor objects used in existing lock-free helping schemes with additional fields that track an operation's progress towards completion and persists these fields to memory in order to ensure detectable recovery. ISB-tracking avoids full-fledged logging and tracks the progress of concurrent operations in a per-process manner, thus reducing the cost of ensuring detectable recovery. We have applied ISB-tracking to derive detectably recoverable implementations of a queue, a linked list, a binary search tree, and an exchanger. Experimental results show the feasibility of the technique.
Paseto is everything you love about JOSE (JWT, JWE, JWS) without any of the many design deficits that plague the JOSE standards.
Crux is a general purpose database with graph-oriented bitemporal indexes. Datalog, SQL & EQL queries are supported, and Java, HTTP & Clojure APIs are provided.
The Bip-Buffer is like a circular buffer, but slightly different. Instead of keeping one head and tail pointer to the data in the buffer, it maintains two revolving regions, allowing for fast data access without having to worry about wrapping at the end of the buffer. Buffer allocations are always maintained as contiguous blocks, allowing the buffer to be used in a highly efficient manner with API calls, and also reducing the amount of copying which needs to be performed to put data into the buffer. Finally, a two-phase allocation system allows the user to pessimistically reserve an area of buffer space, and then trim back the buffer to commit to only the space which was used..
Prototypes give rise to a broad spectrum of interesting technical, conceptual and philosophical issues. In this paper we take a rather unusual, non-technical approach and investigate object-oriented programming and the prototype-based programming field from a purely philosophical viewpoint. Some historical facts and observations pertaining to objects and prototypes are presented, and conclusions based on those observations are derived
E propose a simple yet expressive storage model, termed Parametric I/O Model (PIO) that captures contemporary devices by parameterizing read/write asymmetry(α)and access concurrency(k). PIO enables device-specific decisions at algorithm design time, rather than as an optimization during deployment and testing, thus ensuring optimal algorithm design by taking into account the properties of each device.
The Ribbon filter is a new data structure that is more space-efficient than the popular Bloom filters that are widely used for optimizing data retrieval.
Cyclomatic Complexity was initially formulated as a measurement of the “testability and maintainability” of the control flow of a module.While it excels at measuring the former , its underlying mathematical model is unsatisfactory at producing a value that measures the latter. This white paper describes a new metric that breaks from the use of mathematical models to evaluate code in order to remedy Cyclomatic Complexity’s shortcomings and produce a measurement that more accurately reflects the relative difficulty of understanding,and therefore of maintaining methods, classes, and applications
This paper makes an eccentric proposal: clients can write without waiting, then wait for durability before reading. This doesn’t prevent data loss, but it does guarantee that clients never read data that is lost subsequently. Add a further guarantee that the system replies to each query with data at least as fresh as its previous reply, no matter which client is querying it, and you get a quirky new consistency property: “cross-client monotonic reads”.
AIP stands for API Improvement Proposal, which is a design document providing high-level, concise documentation for API development. They are to serve as the source of truth for API-related documentation at Google and the means by which API teams discuss and come to consensus on API guidance
We propose a distributed protocol for a queue, called \textsc{Skueue}, which spreads its data fairly onto multiple processes, avoiding bottlenecks in high throughput scenarios. \textsc{Skueue} can be used in highly dynamic environments, through the addition of join and leave requests to the standard queue operations enqueue and dequeue. Furthermore \textsc{Skueue} satisfies sequential consistency in the asynchronous message passing model. Scalability is achieved by aggregating multiple requests to a batch, which can then be processed in a distributed fashion without hurting the queue semantics. Operations in \textsc{Skueue} need a logarithmic number of rounds w.h.p. until they are processed, even under a high rate of incoming requests.
We introduce PathQuery, a graph query language developed to scale with Google's query and data volumes as well as its internal developer community. PathQuery supports flexible and declarative semantics. We have found that this enables query developers to think in a naturally "graphy" design space and to avoid the additional cognitive effort of coordinating numerous joins and subqueries often required to express an equivalent query in a relational space. Despite its traversal-oriented syntactic style, PathQuery has a foundation on a custom variant of relational algebra -- the exposition of which we presently defer -- allowing for the application of both common and novel optimizations. We believe that PathQuery has withstood a "test of time" at Google, under both large scale and low latency requirements. We thus share herein a language design that admits a rigorous declarative semantics, has scaled well in practice, and provides a natural syntax for graph traversals while also admitting complex graph patterns
Serverless computing has emerged as an attractive deployment option for cloud applications in recent times. The unique features of this computing model include, rapid auto-scaling, strong isolation, fine-grained billing options and access to a massive service ecosystem which autonomously handles resource management decisions. This model is increasingly being explored for deployments in geographically distributed edge and fog computing networks as well, due to these characteristics. Effective management of computing resources has always gained a lot of attention among researchers. The need to automate the entire process of resource provisioning, allocation, scheduling, monitoring and scaling, has resulted in the need for specialized focus on resource management under the serverless model. In this article, we identify the major aspects covering the broader concept of resource management in serverless environments and propose a taxonomy of elements which influence these aspects, encompassing characteristics of system design, workload attributes and stakeholder expectations. We take a holistic view on serverless environments deployed across edge, fog and cloud computing networks. We also analyse existing works discussing aspects of serverless resource management using this taxonomy. This article further identifies gaps in literature and highlights future research directions for improving capabilities of this computing model.
This post describes how we empowered our millions of users at Canva to collaborate at scale by introducing services that support bidirectional streaming using RSocket.
Run a real Postgres database locally on Linux, OSX or Windows as part of another Go application or test.
Locality-sensitive hashing (LSH) is a set of techniques that dramatically speed up search-for-neighbors or near-duplication detection on data. These techniques can be used, for example, to filter out duplicates of scraped web pages at an impressive speed, or to perform near-constant-time lookups of nearby points from a geospatial data set
CurveCP is similar to TCP but uses high-speed high-security elliptic-curve cryptography to protect every packet against espionage, corruption, and sabotage. The cryptographic tools used in CurveCP are the same as the cryptographic tools used in DNSCurve. CurveCP was announced at the 27th Chaos Communication Congress on 28 December 2010.
We describe metastable failures—a failure pattern in distributed systems. Currently, metastable failures manifest themselves as black swan events; they are outliers because nothing in the past points to their possibility, have a severe impact, and are much easier to explain in hindsight than to predict. Although instances of metastable failures can look different at the surface, deeper analysis shows that they can be understood within the same framework.We introduce a framework for thinking about metastable failures, apply it to examples observed during years of operating distributed systems at scale, and survey ad-hoc techniques developed post-factum for making systems resilient to known metastable failures. A systematic approach for building systems that are robust against unknown metastable failures remains an open problem
We observe that widely deployed NICs possess scatter-gather capabilities that can be re-purposed to accelerate serialization’s core task of coalescing and flattening in-memory data structures. It is possible to build a completely zero-copy,zero-allocation serialization library with commodity NICs.Doing so introduces many research challenges, including using the hardware capabilities efficiently for a wide variety of non-uniform data structures, making application memory available for zero-copy I/O, and ensuring memory safety
In this paper, we propose contextual concurrency control(C3), a new paradigm that enables userspace applications to tune concurrency control in the kernel.C3allows developers to change the behavior and parameters of kernel locks, to switch between different lock implementations and to dynamically profile one or multiple locks for a specific scenario of interest.
Kernel-bypass networking, which allows applications to circumvent the kernel and interface directly with NIC hardware,is one of the main tools for improving application network performance. However, allowing applications to circumvent the kernel makes it impossible to use tools (e.g.,tcpdump) or impose policies (e.g., QoS and filters) that need to interpose on traffic sent by different applications running on a host.This makes maintainability and manageability a challenge for kernel-bypass applications. In response, we propose Kernel On-Path Interposition (KOPI), in which traditional kernel data-plane functionality is retained but implemented in a fully programmable SmartNIC. We hypothesize that KOPI can support the same tools and policies as the kernel stack while retaining the performance benefits of kernel bypass
We argue that network virtualization as experienced by many cloud tenants is overly complex and needs to be rethought.We propose that the goal for a new design should be to free cloud tenants entirely from having to build and operate virtual networks. Building on this philosophy, we propose that instead of low-level building blocks (virtual links, routers,firewalls), cloud networking should be exposed to tenants in a declarative and endpoint-centric manner
The Syndicated Actor model is a new model of concurrency, closely related to the actor, tuplespace, and publish/subscribe models.
PatchELF is a simple utility for modifying existing ELF executables and libraries
We present a fully lock-free variant of the recent Montage system for persistent data structures. Our variant, nbMontage, adds persistence to almost any nonblocking concurrent structure without introducing significant overhead or blocking of any kind. Like its predecessor, nbMontage is buffered durably linearizable: it guarantees that the state recovered in the wake of a crash will represent a consistent prefix of pre-crash execution. Unlike its predecessor, nbMontage ensures wait-free progress of the persistence frontier, thereby bounding the number of recent updates that may be lost on a crash, and allowing a thread to force an update of the frontier (i.e., to perform a sync operation) without the risk of blocking. As an extra benefit, the helping mechanism employed by our wait-free sync significantly reduces its latency. Performance results for nonblocking queues, skip lists, trees, and hash tables rival custom data structures in the literature -- dramatically faster than achieved with prior general-purpose systems, and generally within 50% of equivalent non-persistent structures placed in DRAM.
With the ever-increasing dataset sizes, several file formats like Parquet, ORC, and Avro have been developed to store data efficiently and to save network and interconnect bandwidth at the price of additional CPU utilization. However, with the advent of networks supporting 25-100 Gb/s and storage devices delivering 1, 000, 000 reqs/sec the CPU has become the bottleneck, trying to keep up feeding data in and out of these fast devices. The result is that data access libraries executed on single clients are often CPU-bound and cannot utilize the scale-out benefits of distributed storage systems. One attractive solution to this problem is to offload data-reducing processing and filtering tasks to the storage layer. However, modifying legacy storage systems to support compute offloading is often tedious and requires extensive understanding of the internals. Previous approaches re-implemented functionality of data processing frameworks and access library for a particular storage system, a duplication of effort that might have to be repeated for different storage systems. In this paper, we introduce a new design paradigm that allows extending programmable object storage systems to embed existing, widely used data processing frameworks and access libraries into the storage layer with minimal modifications. In this approach data processing frameworks and access libraries can evolve independently from storage systems while leveraging the scale-out and availability properties of distributed storage systems. We present one example implementation of our design paradigm using Ceph, Apache Arrow, and Parquet. We provide a brief performance evaluation of our implementation and discuss key results.
Modern online services rely on data stores that replicate their data across geographically distributed data centers. Providing strong consistency in such data stores results in high latencies and makes the system vulnerable to network partitions. The alternative of relaxing consistency violates crucial correctness properties. A compromise is to allow multiple consistency levels to coexist in the data store. In this paper we present UniStore, the first fault-tolerant and scalable data store that combines causal and strong consistency. The key challenge we address in UniStore is to maintain liveness despite data center failures: this could be compromised if a strong transaction takes a dependency on a causal transaction that is later lost because of a failure. UniStore ensures that such situations do not arise while paying the cost of durability for causal transactions only when necessary. We evaluate UniStore on Amazon EC2 using both microbenchmarks and a sample application. Our results show that UniStore effectively and scalably combines causal and strong consistency.
Tuning a PID controller can be easily done using a systematic procedure. We just put it in a web application. No more trial and error.
Recently, TiDB 5.0 takes a big step to greatly reduce the latency of transaction commits—introducing the Async Commit feature. This article describes Async Commit's design ideas and its key implementation details.
In-memory key-value stores are critical components that help scale large internet services by providing low-latency access to popular data. Memcached, one of the most popular key-value stores, suffers from performance limitations inherent to the Linux networking stack and fails to achieve high performance when using high-speed network interfaces. While the Linux network stack can be bypassed using DPDK based solutions, such approaches require a complete redesign of the software stack and induce high CPU utilization even when client load is low. To overcome these limitations, we present BMC, an in-kernel cache for Memcached that serves requests before the execution of the standard network stack. Requests to the BMC cache are treated as part of the NIC interrupts, which allows performance to scale with the number of cores serving the NIC queues. To ensure safety, BMC is implemented using eBPF. Despite the safety constraints of eBPF, we show that it is possible to implement a complex cache service. Because BMC runs on commodity hardware and requires modification of neither the Linux kernel nor the Memcached application, it can be widely deployed on existing systems. BMC optimizes the processing time of Facebook-like small-size requests. On this target workload, our evaluations show that BMC improves throughput by up to 18x compared to the vanilla Memcached application and up to 6x compared to an optimized version of Memcached that uses the SO_REUSEPORT socket flag. In addition, our results also show that BMC has negligible overhead and does not deteriorate throughput when treating non-target workloads.
In Function as a Service (FaaS), a serverless computing variant, customers deploy functions instead of complete virtual machines or Linux containers. It is the cloud provider who maintains the runtime environment for these functions. FaaS products are offered by all major cloud providers (e.g. Amazon Lambda, Google Cloud Functions, Azure Functions); as well as standalone open-source software (e.g. Apache OpenWhisk) with their commercial variants (e.g. Adobe I/O Runtime or IBM Cloud Functions). We take the bottom-up perspective of a single node in a FaaS cluster. We assume that all the execution environments for a set of functions assigned to this node have been already installed. Our goal is to schedule individual invocations of functions, passed by a load balancer, to minimize performance metrics related to response time. Deployed functions are usually executed repeatedly in response to multiple invocations made by end-users. Thus, our scheduling decisions are based on the information gathered locally: the recorded call frequencies and execution times. We propose a number of heuristics, and we also adapt some theoretically-grounded ones like SEPT or SERPT. Our simulations use a recently-published Azure Functions Trace. We show that, compared to the baseline FIFO or round-robin, our data-driven scheduling decisions significantly improve the performance.
Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed
Litestream is a standalone streaming replication tool for SQLite. It runs as a background process and safely replicates changes incrementally to another file or S3.
The goal of Landlock is to enable to restrict ambient rights (e.g. global filesystem access) for a set of processes. Because Landlock is a stackable LSM, it makes possible to create safe security sandboxes as new security layers in addition to the existing system-wide access-controls. This kind of sandbox is expected to help mitigate the security impact of bugs or unexpected/malicious behaviors in user space applications. Landlock empowers any process, including unprivileged ones, to securely restrict themselves
A library to do type-safe and low-boilerplate bit level serialization