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description=An introduction to the Mesos cluster management framework;
author=Peter Goldsborough;
keywords=distributed-systems;

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apr, 2019,

Text of the page (random words):
ameworks like hadoop are the processes nodes in the cluster or more precisely their cpus ram and disk space are the shared resources and the mesos library is the operating system one noteworthy difference between mesos and an os is that an os has more of a pull model for its resources while mesos uses a push model processes request resources and the os fulfills those requests if possible mesos by contrast offers resources to frameworks which then have the chance to accept them there are some nuances to this but i think the distinction between push and pull is an important one to mention architecture at this point you should know that the goal of mesos is to allow multiple frameworks to run on the same cluster simultaneously while overseeing the sharing of computational resources among them the big question of course is how mesos does this to answer this let me describe the architecture of mesos that is its high level building blocks and system design the next section will then discuss the behavior of mesos i e how the components of this architecture interact mesos splits its architecture into two parts the core mesos library and the interfaces into which frameworks plug in the figure below shows a birds eye view of a typical mesos cluster core mesos the core mesos architecture is the set of components that mesos needs to manage a cluster and broker resources it consists of two kinds of actors a master node and one or more agents a k a slaves there is one active master for the whole cluster and one agent per machine host master the master is naturally the brain of the system its primary tasks include aggregating resources from individual machines in the cluster which it gets by communicating with agents offering resources to frameworks as resources become available hosting a web ui for humans to monitor and manage the system to achieve resiliency mesos maintains hot standby replicas of the master and keeps exclusively soft state on each replica soft state here means state that is replicated elsewhere and can easily be re materialized if the master were to go down if the master does go down a hot standby can immediately take its place to elect a new master mesos uses zookeeper to get quorum agent a mesos agent sits on one node in the cluster and acts as the point of contact for this node to the mesos master it supervises the execution of tasks scheduled by frameworks reports which local resources are available monitors the health of tasks running on the node and manages checkpointing of tasks for fault tolerance note that in this context a task is the smallest unit of execution scheduled by a framework for example this would be one map or one reduce task in hadoop these tasks would themselves be part of a job in the framework one mapreduce job and there could be many jobs running simultaneously on the cluster however mesos itself doesn t have a notion of a job it only cares about frameworks and individual tasks that those frameworks want to schedule another important detail is how mesos achieves task isolation and enforces resource constraints as in how does mesos ensure a task only uses the resources it was allowed to use and how does it keep tasks from interfering with each other the answer to both of these questions is linux containers when the original paper was published this meant plain lxc there is also mention of solaris projects for the solaris operating system soon after the release of mesos it started supporting the fancier version of plain lxc docker containers still today mesos uses docker containers to isolate tasks from each other and monitor as well as contrain their resource usage rkt is also supported note that mesos agents used to be called slaves slave is now outdated terminology and deprecated in favor of agent framework interfaces the second part of the mesos architecture is the stubs it leaves for frameworks to plug in remember that mesos leaves the scheduling as well as execution launch of tasks to frameworks themselves as such in order to run a framework on mesos you need to implement a scheduler and an executor scheduler the scheduler is the component that receives resource offers from mesos more on this in a bit and decides how many tasks to launch using those resources this would effectively be a wrapper around the yarn scheduler for hadoop for example the scheduler is also responsible for responding to failures of any tasks that it launched hadoop might want to re schedule such a failed task executor a framework s executor is responsible for launching a task and updating its state this could literally just be a thin shell script that starts a binary but it could just as well be a complex application that starts a thread for a new task rather than a new process the executor must also communicate the status and health of the task to the mesos agent so that the agent can in turn notify the mesos master and framework scheduler of task completion or failure as far as i understand there can be executors from multiple frameworks running on the same machine but there will only be one mesos agent per host behavior having described the components in a typical mesos cluster we need to discuss in more detail how those components behave and interact first we ll go over the api that mesos exposes on masters and agents schedulers and executors then we ll dive deeper into what a resource offer actually is lastly i want to touch upon the allocation algorithm that mesos uses to decide which framework gets which resources apis i care to discuss the api mesos provides because it gives a good overview of the way mesos interacts with its agents as well as indidvidual frameworks the api here refers to remote procedure calls rpcs frameworks can make to the mesos master and agents and vice versa more precisely the api is split into four parts the methods a framework s scheduler must implement so that the mesos master can call them callbacks the methods a framework s scheduler can call on the mesos master actions the methods a framework s executor must implement so that the mesos agent can call them callbacks the methods a framework s executor can call on the mesos agent actions table i from the paper summarizes these api calls let s touch upon them individually scheduler callbacks these are methods a framework must implement to respond to the mesos master which makes these api calls resourceoffer offerid offers called by the mesos master to offer the framework a collection of resources offerrescinded offerid called by the mesos master to indicate that a previous offer it made via resourceoffer is no longer valid this could be because the framework scheduler took too long to respond so the mesos master wants to offer the resources to another framework statusupdate taskid status called by the mesos master when it receives an updated status for one of the framework s tasks from a mesos agent for example it could indicate that a task failed slavelost slaveid called by the mesos master when a whole machine appears to have gone down in the cluster it is up to frameworks to decide what to do with the information conveyed by these api calls scheduler actions these are methods already implemented on the mesos master side that a framework can invoke replytooffer offerid tasks called by a framework to give the mesos master tasks the framework wants to schedule using the resources provided in a previous resourceoffer the framework itself decides how many tasks to schedule based on a group of resources setneedsoffers bool called by a framework to indicate it urgently needs resource offers to schedule more tasks setfilters filters called by a framework to provide the mesos resource offer allocator boolean filters it can use to pre filter offers for that framework this allows a framework to provide scheduling constraints for example a framework may want to filter out any offers with less than 4 cpus or 1gb of ram getguaranteedshare this one is a bit mysterious there s no mention of it in the paper and mesos github repository shows no reference to it either i think it was part of a mechanism where a framework could be guaranteed a fixed static share of resources maybe the minimum number of resources it needs to run at all to run its own master and auxiliary services killtask taskid called by a framework to kill a particular running task executor callbacks these are api calls the mesos agent will make on a framework s executor a framework must implement these methods in its executor component launchtask taskdescriptor called by a mesos agent for each task that was scheduled by a framework s scheduler to run on this node the task descriptor would be whatever data the executor needs to launch a task killtask taskid called by a mesos agent to kill a particular task for example it could be on behalf of the framework scheduler itself when it calls killtask on the mesos master executor actions these are methods a framework s executor can call on the mesos agent running on its machine sendstatus taskid status updates the mesos agent and in turn the rest of the system about the status of a particular task resource offers one of the scheduler callbacks we touched upon above was resourceoffer offerid offers which the mesos master calls on a framework s scheduler to offer it resources but what exactly is that second parameter offers what exactly is a resource offer as the age old idiom goes a snippet of code is worth a thousand words so let s take a look at the code from mesos proto describes some resources available on a slave an offer only contains resources from a single slave message offer required offerid id 1 required frameworkid framework_id 2 required slaveid slave_id 3 repeated resource resources 5 besides information to identify the offer the agent slave on which resources are available and the target framework to which resources are offered we see repeated resource resources 5 a list of resources the resource proto is a bit large but essentially it describes a quantity of some resource for example 4gb of ram or 8 cpus a framework s scheduler gets offered this list of resources and then decides how many tasks to schedule using those resources note that a resource offer always corresponds to a single host a single agent resource allocation the last bit of detail i want to talk about is how the mesos master decides which resources to offer to which framework after all this seems like it would be one of the most critical decisions the paper does not delve too deeply into this topic and takes it somewhat for granted i did some additional research and want to outline the algorithm mesos uses by default note that you can swap out this algorithm for a different one if you need or want to let s take a step back what problem are we talking about here the problem we are talking about is that there are many frameworks competing for resources in our cluster of course each framework would ideally like to have the whole cluster to itself but that is not quite the point of mesos the point of mesos is to share resources among frameworks in a way that keeps utilization of the cluster high and gives each framework a fair share of resources the algorithm mesos uses for this is called dominant resource fairness or drf for short it was developed by many of the same authors as the mesos paper the drf paper is itself worth a sweep as it delves into interesting topics such as game theory and how to fairly divide resources among contestants in short the way drf works is that it tries maximize the smallest dominant share among all frameworks the dominant share is the share a framework has of the resource it demands the most this resource is termed the dominant resource example take a cluster with 10 cpus and 10 gb of ram available in total among all machines two frameworks are running on this cluster using mesos their resource shares are currently distributed as follows framework number of tasks cpus ram a 2 4 2 b 3 1 3 let s first determine the dominant resources and shares framework a s dominant resource is cpu since that is the resource for which its share is the largest framework b s dominant resource is ram framework a s dominant share is 40 because its share of its dominant resource is 40 framework b s dominant share is 30 because it has 3 out of 10 gb of ram say mesos now wants to offer 2 cpus and 2 gb of ram as part of a single resource offer if it gave it to a it would have 6 cpus and 4 gb of ram and its dominant share would be 60 if it instead supplied it to framework b it would have 3 cpus and 5 gb of ram and its dominant share would be 50 since the other framework s share would stay as shown in the table above the drf algorithm would choose to offer the resources to framework b this is because dominant shares of 40 50 have a larger minimum 40 than 60 30 as would have been the case if framework a had gotten the offer see this article for more in depth information on drf in the context of mesos apache aurora i hope that by now you have a reasonably well founded understanding of how mesos works before we conclude this sweeping tour of mesos i want to talk about one project built on top of mesos that deserves mention apache aurora in the project s own words aurora is a mesos framework for long running services and cron jobs there are two important parts to this sentence the first is that aurora is meant for long running services a typical example of this are microservices as they are used to power much of modern backend infrastructure this is relevant because mesos itself was more targeted towards cluster frameworks that launch many small short lived tasks such as hadoop however there is also nothing that fundamentally stands in the way of long running jobs running mesos where aurora does improve on bare mesos for this is that it has better support for managing monitoring and updating long running jobs the second important part of that summary is that aurora is a mesos framework that is it s integrated into mesos with exactly the interfaces and hooks we have described so far so what does aurora exactly provide if you ve ever heard of google s borg facebook s tupperware or the kubernetes project you can think of aurora on mesos as an alternative to these it is a service oriented cluster management framework used by twitter and uber for all of their service orchestration it does things such as managing resource quotas for different services supporting updating services new configuration or binary releases providing service discovery to enable services to talk to each other aurora also has a fancy domain specific language dsl to describe job configuration as well as complex scheduling constraints such as requiring two tasks to be co located on the same machine one interesting thing to note here is that often times aurora will be the only framework running on a mesos cluster in some sense this is peculiar since mesos was really conceived to allow m...