This document discusses process migration and allocation in distributed systems. It covers:
1) Process allocation is easier in multiprocessor systems where all processors share memory and resources, compared to multicomputer systems without shared memory.
2) Processes can either be non-migratory and run on one system, or migratory and move between systems to improve resource utilization. Ensuring transparency is important for migratory processes.
3) Different strategies for process migration include moving state, keeping state on the original system and using RPC, or ignoring state. Centralized, hierarchical, and distributed algorithms can be used to determine optimal or suboptimal migration.
Allocation of processors to processes in Distributed Systems. Strategies or algorithms for processor allocation. Design and Implementation Issues of Strategies.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
Allocation of processors to processes in Distributed Systems. Strategies or algorithms for processor allocation. Design and Implementation Issues of Strategies.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
Chorus - Distributed Operating System [ case study ]Akhil Nadh PC
ChorusOS is a microkernel real-time operating system designed as a message-based computational model. ChorusOS started as the Chorus distributed real-time operating system research project at Institut National de Recherche en Informatique et Automatique (INRIA) in France in 1979. During the 1980s, Chorus was one of two earliest microkernels (the other being Mach) and was developed commercially by Chorus Systèmes. Over time, development effort shifted away from distribution aspects to real-time for embedded systems.
Open Book Management presented by Ted Maziejka of the Zweig Group. Presented at the 2014 Hot Firm and A/E Industry Awards Conference in Beverly Hills, CA.
INTRODUCTIONTO OPERATING SYSTEM
What is an Operating System?
Mainframe Systems
Desktop Systems
Multiprocessor Systems
Distributed Systems
Clustered System
Real -Time Systems
Handheld Systems
Computing Environments
Threads,
system model,
processor allocation,
scheduling in distributed systems
Load balancing and
sharing approach,
fault tolerance,
Real time distributed systems,
Process migration and related issues
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
Chorus - Distributed Operating System [ case study ]Akhil Nadh PC
ChorusOS is a microkernel real-time operating system designed as a message-based computational model. ChorusOS started as the Chorus distributed real-time operating system research project at Institut National de Recherche en Informatique et Automatique (INRIA) in France in 1979. During the 1980s, Chorus was one of two earliest microkernels (the other being Mach) and was developed commercially by Chorus Systèmes. Over time, development effort shifted away from distribution aspects to real-time for embedded systems.
Open Book Management presented by Ted Maziejka of the Zweig Group. Presented at the 2014 Hot Firm and A/E Industry Awards Conference in Beverly Hills, CA.
This is the twelfth set of slightly updated slides from a Perl programming course that I held some years ago.
I want to share it with everyone looking for intransitive Perl-knowledge.
A table of content for all presentations can be found at i-can.eu.
The source code for the examples and the presentations in ODP format are on https://github.com/kberov/PerlProgrammingCourse
A distributed operating system is an extension of the network operating system that supports higher levels of communication and integration of the machines on the network. This system looks to its users like an ordinary centralized operating system but runs on multiple, independent central processing units (CPUs).
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
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• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Connector Corner: Automate dynamic content and events by pushing a button
Processor Allocation (Distributed computing)
1. Process Migration & Allocation Paul Krzyzanowski [email_address] [email_address] Distributed Systems Except as otherwise noted, the content of this presentation is licensed under the Creative Commons Attribution 2.5 License.
Processor allocation was not a serious problem when we examined multiprocessor systems (shared memory). In those systems, all processors had access to the same image of the operating system and grabbed jobs from a common job queue. When a quantum expired or a process blocked, it could be restarted by any available processor. In multicomputer systems, things get more complex. We may not be able to use shared memory segments or message queues to communicate with other processes. The file system may look different on different machines. The overhead of dispatching a process on another system may be high compared to the run time of the process.
Most of today’s environments have a nonmigratory model of processor allocation. A processor is chosen by the user (e.g. by the workstation being used or by an rsh command) or else the system makes an initial decision on a system on which the process will execute. Once it starts, it will continue running on that processor. An alternative is to support process migration , where processes can move dynamically during their lifetime. The hope in such a system is that it will allow for better system-wide utilization of resources (e.g. as one computer becomes too heavily loaded, some of the processes can migrate to a less loaded system). When we discuss implementing processor allocation, we are talking about one of two types of processes: nonmigratory processes remain on the processor on which they were created (the decision is where to create them); migratory processes can be moved after creation, which allows for better load balancing but is more complex.
If we are to run a process on an arbitrary system, it is important that all systems present the same execution environment. Certainly system binaries must be capable of executing on a different machine (unless we use interpreted pseudocode such as Java). Processes typically do not run in a vacuum but read input and write output. Even if a process will never migrate to another machine during execution it should have predictable access to a file system name space (it would be hard to debug a program that opens a different file or fails to open a file depending on what system it was assigned to). To accomplish this, any of the files that a program will read/write should be on a distributed file system that is set up to provide a uniform name space across all participating machines. Moreover, the process may have to forward operations on the standard input and standard output file descriptors to the originating machine. This may be done during the creation of those file descriptors on the remote machine using a mechanism such as sockets (this is what rsh does). With migratory processes, things get more complicated. If a running process is to continue execution on a different system, any existing descriptors to open files must continue to operate on those files (this includes stdin, stdout, stderr as well as other files). If a process expects to catch signals, the signal mask for the process should be migrated. If there are any pending signals for the process, they also must be migrated. Shared memory should continue to work if it was in use (this will most likely necessitate a DSM system). Any existing network connections should also continue to be active. Since a process may rely on a service such as system time (to time latencies, for example), clocks should be synchronized.
Three strategies for migration can be adopted. The most thorough, and most complicated, is to move the entire system state. This means that open file descriptors have to be reconstructed on the remote system and the state of kernel objects such as signals, message queues and semaphores has to be propagated. Mechanisms should also exist for shared memory (if the os supports it) and sending signals/messages across different machines. To implement this requires a kernel that is capable of migrating this information as well as a global process ID space.
A somewhat easier design, still requiring operating system kernel modifications, is to maintain a concept of a “home” system. This is the approach taken by the Berkeley Sprite operating system (which is built from Berkeley Unix). The system on which a process is created is considered its “home”. The operating system supports the invocation of system calls through an operating-system-level remote procedure call mechanism. When a process that has migrated issues a system call (e.g. read, write, ioctl, get time of day ), the operating system checks whether this machine is the process’ home system or whether it has migrated here. If it’s the home system, the call is processed locally. If the process migrated from another system, any system call that needs kernel state (such as file system operations) is forwarded to the home system (which maintains state on behalf of that process). The system call is processed on the home machine and results are returned to the requestor via the remote procedure call.
Finally, the easiest design is to assume that there is little or no state that deserves to be preserved. This is an approach taken by Condor , a software package that provides process migration for Unix systems without kernel changes. The assumption here is that there is no need for any inter-process communication mechanism: processes know they are running on a foreign system.
There are a number of different issues in constructing processes migration algorithms: deterministic vs. heuristic if we know all the resource usage up front, we can create a deterministic algorithm. This data is usually unknown and heuristic techniques often have to be employed. Centralized, hierarchical, or distributed a centralized algorithm allows all the information necessary for making scheduling decisions to reside in one place but it can also put a heavy load on the central machine. With a hierarchical system, we can have a number of load managers, organized in a hierarchy. Managers make process allocation decisions as far down the tree as possible, but may transfer processes from one to another via a common ancester. optimal vs. suboptimal do we really want the best allocation or simply an acceptable one? If we want the best allocation, we'll have to pay a price in the computation and data needed to make that decision. Quite often it's not worth it. local or global? Does a machine decide whether a process stays on the local machine using local information (its system load, for example) or does it rely on global system state information? This is known as the transfer policy . location policy Does the machine send requests asking for help or does it send requests for work to perform?
The up-down algorithm (Mutka and Livny, 1987) relies on a centralized coordinator which maintains a usage table . This table contains one entry per workstation. Workstations send messages containing updates to this coordinator. All allocation decisions are based on the data in this table. The goal of the up-down algorithm is to give each workstation owner a fair share of the available compute power (and not allow the user to monopolize the environment). When a system has to create a process, it first decides whether it should run it locally or seek help. This is generally done in most migration algorithms as an optimization (why seek help when you don't need it?). If it decides to ask for help, it sends a message to the coordinator asking for a processor. The coordinator's table keeps points per workstation. If you run a process on another machine, you get penalty points which are added ( n /second) to your entry in the usage table. If you have unsatisfied requests pending, then points are subtracted from your entry. If no requests are pending and no processors are used, your entry gradually erodes to zero. Looking at the points for a given workstation, a positive amount indicates that the workstation is a net user of resources and a negative amount indicates that the workstation needs resources. The coordinator simply chooses to process the request from the workstation with the lowest score.
The centralized algorithm has the same pitfall that all centralized algorithms share: scalability. A hierarchical processor allocation algorithm attempts to overcome scalability while still maintaining efficiency. In this algorithm, every group of k workers gets a "manager" - a coordinator responsible for processor allocation to machines within its group. Each manager keeps track of the approximate number of workers below it that are available for work. In this case, it behaves like a centralized algorithm. If, for some job, the manager does not have enough workers (worker CPU cycles), it then passes the request to its manager (up the hierarchy). The upper manager checks with its subordinates (the pool of up to k managers under it) for available workers. If the request can be satisfied, it is parceled among the managers and, ultimitely, among the workers. If it cannot be satisfied, the second-level manager may contact a third-level manager. The hierarchy can be extended ad infinitum.
Sender initiated distributed heuristic This algorithm requires no coordinator whatsoever. If a machine decides that it should not run its job locally, it picks a machine at random and sends it a probe message ( "can you run my job?" ). If the randomly selected machine cannot run the job, another machine is picked at random and a probe sent to it. The process is repeated until a willing machine is located or after n tries. This algorithm has been shown to behave well and is stable. Its failing is when the overall system load gets heavy. At those times, many machines in the network are looping n times, sending requests to machines too busy to service them. Receiver initiated distributed heuristic To overcome the problem of traffic in loaded systems, we can do the opposite of a sender initiated algorithm and have machines advertise themselves as being available for work. In this algorithm, when a processor is done with a process, it picks some random machine and sends it a message: "do you have any work for me?” If the machine responds in the affirmative, the sender gets a job. If the machine has no work, the sender picks another machine and tries again, doing this n times. Eventually, the sender will go to sleep and then start the whole process again until it gets work. While this creates a lot of messages, there is no extra load on the system during critical times.