Quelles sont les fonctionnalités principales de apache/mesos ?

Les fonctionnalités principales de apache/mesos sont : Distributed Computing Frameworks, Two-Level Scheduling, Offer-Based Allocation, Multi-Framework Orchestration, Distributed Cluster Management, Distributed Infrastructure Orchestration, Workload Isolation, Resource Isolation.

Quelles sont les alternatives open-source à apache/mesos ?

Les alternatives open-source à apache/mesos incluent : dask/dask — Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows… apache/storm — Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to… baidu/braft — braft is an embeddable C++ library that implements the Raft consensus algorithm, providing a distributed consensus… ltsopensource/light-task-scheduler — Light Task Scheduler is a distributed job scheduling and workflow orchestration platform designed for managing… hashicorp/raft — This is a Raft consensus library and distributed consensus engine implemented in Go. It provides the primitives… linkedin/school-of-sre — This project is a comprehensive educational resource and curriculum focused on site reliability engineering,…

Mesos

Apache Mesos est un noyau de systèmes distribués et un gestionnaire de ressources de cluster qui abstrait le CPU, la mémoire et le stockage à travers un pool de nœuds. Il fonctionne comme un orchestrateur d'infrastructure distribuée, fournissant une couche pour exécuter plusieurs frameworks d'orchestration sur un ensemble partagé de machines physiques ou virtuelles.

Le système agit comme un moteur d'isolation de ressources, divisant un cluster partagé en conteneurs isolés pour exécuter diverses charges de travail simultanément. Il permet l'orchestration multi-framework, permettant à différents frameworks d'applications distribuées de partager une infrastructure unique pour maximiser l'utilisation du matériel.

Le projet couvre la distribution de calcul à grande échelle et la gestion de cluster distribué. Ses capacités incluent la gestion des ressources distribuées et l'isolation de la puissance de calcul à travers plusieurs applications pour éviter les interférences et assurer des performances stables sur des serveurs partagés.

Features

Distributed Computing Frameworks - Provides a distributed infrastructure for running multiple computing frameworks across networked machines.
Two-Level Scheduling - Implements a two-level scheduling mechanism that delegates task launching to individual frameworks.
Offer-Based Allocation - Distributes cluster resources by offering available CPU and memory to frameworks for acceptance or rejection.
Multi-Framework Orchestration - Supports running several different orchestration frameworks on a single shared infrastructure.
Distributed Cluster Management - Manages a pool of shared computing resources across multiple nodes to ensure efficient hardware utilization.
Distributed Infrastructure Orchestration - Provides a layer for running multiple orchestration frameworks on a shared set of physical or virtual machines.
Workload Isolation - Divides a shared cluster into isolated containers to run diverse workloads concurrently without interference.
Resource Isolation - Partitions system resources across applications to prevent interference and maintain stable performance.
Heartbeat Monitors - Monitors worker node availability and cluster health via periodic heartbeat signals between master and agents.
Distributed Systems Kernels - Abstracts CPU, memory, and storage across a pool of nodes to function as a distributed systems kernel.
Namespace-Based Isolation - Implements resource isolation using Linux kernel namespaces and control groups to prevent application interference.
Cluster Resource Managers - Acts as a cluster resource manager that provisions and scales compute power across diverse infrastructure.
Cluster Resource Isolation - Provides strict isolation of compute resources to allow multiple frameworks to run concurrently on shared hardware.
Multi-Framework Orchestration - Enables multiple orchestration frameworks to share a single physical infrastructure to maximize hardware utilization.
Replicated State Machines - Maintains consistent cluster state across multiple master nodes using a distributed consensus algorithm.
Framework Registrations - Allows different orchestration engines to connect to the cluster runtime dynamically without restarting the master.
Distributed Compute Environments - Provides a distributed compute environment for executing workloads across a vast network of servers.
Container Orchestration - Distributed systems kernel for resource management.
Workload Orchestration and Scheduling - Distributed systems kernel for resource abstraction.

Historique des stars

apachemesos

Name: apache/mesos
Author: apache

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5,369 stars1,663 forksC++Apache-2.03 vues

Mesos

Features

Distributed Computing Frameworks - Provides a distributed infrastructure for running multiple computing frameworks across networked machines.
Two-Level Scheduling - Implements a two-level scheduling mechanism that delegates task launching to individual frameworks.
Offer-Based Allocation - Distributes cluster resources by offering available CPU and memory to frameworks for acceptance or rejection.
Multi-Framework Orchestration - Supports running several different orchestration frameworks on a single shared infrastructure.
Distributed Cluster Management - Manages a pool of shared computing resources across multiple nodes to ensure efficient hardware utilization.
Distributed Infrastructure Orchestration - Provides a layer for running multiple orchestration frameworks on a shared set of physical or virtual machines.
Workload Isolation - Divides a shared cluster into isolated containers to run diverse workloads concurrently without interference.
Resource Isolation - Partitions system resources across applications to prevent interference and maintain stable performance.
Heartbeat Monitors - Monitors worker node availability and cluster health via periodic heartbeat signals between master and agents.
Distributed Systems Kernels - Abstracts CPU, memory, and storage across a pool of nodes to function as a distributed systems kernel.
Namespace-Based Isolation - Implements resource isolation using Linux kernel namespaces and control groups to prevent application interference.
Cluster Resource Managers - Acts as a cluster resource manager that provisions and scales compute power across diverse infrastructure.
Cluster Resource Isolation - Provides strict isolation of compute resources to allow multiple frameworks to run concurrently on shared hardware.
Multi-Framework Orchestration - Enables multiple orchestration frameworks to share a single physical infrastructure to maximize hardware utilization.
Replicated State Machines - Maintains consistent cluster state across multiple master nodes using a distributed consensus algorithm.
Framework Registrations - Allows different orchestration engines to connect to the cluster runtime dynamically without restarting the master.
Distributed Compute Environments - Provides a distributed compute environment for executing workloads across a vast network of servers.
Container Orchestration - Distributed systems kernel for resource management.
Workload Orchestration and Scheduling - Distributed systems kernel for resource abstraction.

Alternatives open source à Mesos

Projets open source similaires, classés selon le nombre de fonctionnalités partagées avec Mesos.

dask/dask
dask/dask
13,746Voir sur GitHub
Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl
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baidu/braft
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braft is an embeddable C++ library that implements the Raft consensus algorithm, providing a distributed consensus engine for building fault-tolerant, replicated state machines. At its core, it manages leader election, log replication, cluster membership changes, and state machine synchronization across a cluster of nodes, ensuring strong consistency and data durability even in the face of node failures. The library distinguishes itself through a comprehensive set of mechanisms for reliable distributed coordination. It uses a randomized timeout-based leader election process with term manageme
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apache/storm
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6,683Voir sur GitHub
Storm is a distributed stream processing framework designed to execute unbounded computations across a cluster to process real-time data streams. It functions as a data pipeline orchestrator that allows users to define and deploy declarative data flow graphs connecting streaming sources to processing components. The system operates as a multi-tenant distributed compute engine that isolates workloads and limits resource usage across shared clusters using dedicated pools and access control. It is also a secure distributed processing engine that employs encrypted node communication and SSL-secur
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hashicorp/raft
hashicorp/raft
9,037Voir sur GitHub
This is a Raft consensus library and distributed consensus engine implemented in Go. It provides the primitives necessary to build fault-tolerant distributed services by implementing a replicated state machine that ensures a group of servers agree on a shared system state through leader election and log replication. The project distinguishes itself through a pluggable architecture for storage backends and snapshot storage, decoupling the consensus logic from physical persistence. It includes specialized mechanisms for leadership transfer, protocol version management to support rolling upgrade
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Voir les 30 alternatives à Mesos

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