Empirical Study Grid Computing Simulator

A group of physically interconnected computers that are aimed to execute certain tasks for solving a complex problem is called grid computing. This grid system is constructed as a “super virtual computer” to crack specific applications. In this, the size of the grid system varies from small to big enterprises network. Also, it is built and functioned based on the gird middleware software for data transmission. This middleware is programmed with the instruction to translate specific node data into another identifiable form. There are two forms of communication is established between the infrastructure as Peer to Peer communication or Distributed Communication. 

Through this article, both the current PhD / MS research scholars and final year students can find whole development information about Grid Computing Simulator for their grid computing project!!!

Compare to cluster computing, “grid computing” is completely different from each other. On the one hand, every node of grid computing has heterogeneous characteristics which are geographically distributed like WAN. Further, the resource manager executes various tasks which are formed by loosely and low-speed networks. On the other hand, every node and resource of cluster computing is handled in only one location (i.e. LAN).

Latest Grid Computing Technologies

• Fog Computing
• Edge Computing
• Cloud Computing
• Pervasive Computing
• Parallel Computing
• Utility Computing
• Distributed Computing
• Ubiquitous Computing

In recent days, cloud computing has gained peak popularity in both the academic and research sectors. Since it focuses on all future research areas including in different dimensions. When the researchers are not flexible to attempt real cloud testbeds for scheduling algorithm inspection, the cloud computing field provides cloud simulation tools. These simulators replicate the real-world cloud behaviour at a lower cost than the original deployment cost. Further, it not only tests the scheduling methods but also tests the other techniques/application services.

Description of Grid Computing Simulation

Now, we can see what exactly grid computing simulator is. The main objective of grid simulation is to design and assess real-world problems particularly large-scale computer networks. Due to technological advancements, computer network design has to turn out to be an important discipline in which uses application-specific tools and technologies.  

• Requirements for Simulation
  • Application Specific Tools (For example: OMNet++)
  • Libraries (For example: SimJava)
  • Languages (For Example: Simscript)
  • Environs (For example: Parsec)

In specific, the grid computing field is furnished with only a few tools and technologies for application scheduling simulation. Some of the prominent grid computing simulation tools that used extensively are given as follows, 

Grid Computing Simulators

• GridSim Toolkit
• MicroGrid Toolkit
• Simgrid Toolkit

Furthermore, our developers have given you other fundamental grid simulators. Different tools support to design, develop and simulation of grid computing projects. So, one should be aware of the functional purpose and system requirements of each tool. Based on your project requirements, you can choose your best simulators. Our programmers are proficient to address the efficiency of all possible grid computing simulators. So, we are accurate in identifying the best-fitting tool for our handhold scholars/students code development phase. 

Best Grid Computing Simulators

• Alea Simulator
  • Programming Language – Java
  • Intention – Resource / Job Scheduling in Grid Computing
  • Operating System – Linux / Windows
• GridSim
  • Programming Language – Java
  • Intention – Resource / Task Scheduling in Grid Computing
  • Operating System – Linux / Windows
• Balls Simulator
  • Programming Language – Java
  • Intention – Peer-to-Peer Construction
  • Operating System – Linux / Windows
• Sim-G Batch Grid Simulator
  • Programming Language – C++
  • Intention – Security and Energy Simulation
  • Operating System – Linux – Ubuntu 10.10

For instance: we can see two primary simulation tools such as SimJava and GridSim in detail

SimJava

SimJava is a discrete-event-based simulation package that is used for general-purpose. In this, simulation is performed through a various number of entities that execute in parallel using their thread. It uses the body () function to encode the connected entity behavior using java code. Further, these entities have access to a small number of simulation primitives. These characteristics help to perform network construction where the entities are flexible to communicate with event objects. 

SimJava Functions

• sim_schedule() – Use ports to transfer event objects to other entities
• sim_hold() – Stand idle for few simulation duration
• sim_wait() – Pauses for receiving event objects from sender

GridSim

GridSim provides a widespread framework for enabling users to simulate a grid computing model. Majorly, it also exhibits the grid network/resource characteristics at various configurations. As well, it aims to serve both scholars and final year students to investigate and assess proposed algorithms in controlled infrastructure. And also, it enables the system to perform various experiments at real-world dynamic scenarios in grid computing. For your information, a few of the main characteristics of GridSim is listed below,

GridSim Characteristics

• Permit to design different regional GIS entities
• Capable to reserve or auction resource distribution techniques
• Allow to assign jobs by means of time-shared / space-shared mode
• Able to design various resource features with its failure belongings
• Enable to simulate different kinds of jobs from actual supercomputers
• Scalable to accept large request and allocate data-intensive jobs accordingly
• Possible to simulate data-intensive jobs even in public network traffic using probabilistic distribution
• Empower to apply different resource allocation techniques using sophisticated interface

Simulation Environment Settings for Grid Computing

Next, we can see how to set the simulation environment for the grid computing field. This environment is required to abstract all the entities with their time-dependent communications. Moreover, it also enables the creation of the method by Time-Dependent Response for User-Defined to establish communication between entities. In specific, this response method supports all three states of entities such as current, past, and both. Most importantly, it comprises entities for resources, users, brokers, input/output design, and information services. Let’s see those Gridsim entities in detail.

• Grid Resources
  • Grid resource is nothing but every instance of resource entity
  • All the resources are different with other based on following characteristics,
    • Time zone
    • Processing speed
    • Processing cost 
    • Factor of Local load
    • Total count of processors
    • Scheduling strategies of internal process
  • Based on the rate of SPEC and MIPS standards, job completion duration and resource speed are described. Further, it also uses machine standard
  • Broker can query the resources to know their fixed and varying properties from grid information service
• Grid User
  • Grid user is nothing but every user entity
  • All the users are different with other based on following characteristics,
    • Rate of Activity, For instance: Frequent creation of new jobs
    • Creation of different job, For instance: number of parametric replications, job execution time, etc.
    • Optimization of scheduling policies, For instance: cost / time / both reduction
    • B and D factors, For instance: budget and deadline metrics which ranges between 0 to 1 user affordability. Also, it represents the application needs and resource accessibility
    • Time zone, For instance: · Total budget / deadline 
• Grid Broker
  • Gird Broker enables you connect with large volume of grid users through broker entity
  • Here, broker collect user jobs and use scheduling policies to schedule parametric tasks
  • For scheduling, broker analyse the existing resources from global directory entity
  • Majorly, broker looking forward to optimize the scheduling policy to manage the multiple resource access requests
• Input and Outputs
  • Input and Output entities enables you to know the data flow over GridSim entities
  • All the entities have their respective I/O ports or channel to create connection among all I/O entities
  • It uses body () function to manage events via own threads in their I/O threaded entities
  • Allow connected entities to design full duplex as well as multiple-user parallel transmission
  • For establishing communication over entities, it uses buffered input and output channels and also obviously create required communication latency
• Grid Information Services
  • Grid information service enables you to hold the available grid resource information
  • Through this, the broker can collect data regarding resource configuration, status and contact details

In addition, we have given you the list of simulation parameters for grid computing. All these parameters are very significant for enhancing system performance. Our developers are adept to analyse the proposed research work to identify suitable simulation parameters. Further, we also know to adjust the performance parameters in the designing phase of grid computing models. As a result, it improves the system performance based on custom requirements. Moreover, we also suggest you some other simulation and performance parameters depending on your project objectives.

Simulation Parameters for Grid computing

• Gridlets Count
• Machine Count
• PE ratings
• Resource Count
• PEs Count / machine
• Job Input Size
• Bandwidth
• Job Output Size
• Cost / Job

When we debate on the development phase, the discussion will be incomplete without highlighting the implementation tools. Here, we have given you some important grid computing simulators/frameworks. All these development technologies have sophisticated modules, toolboxes, libraries, modules, and functions to support all dimensions of grid computing projects. Our developers are smart to handle all these technologies to create the best project every time. 

Different Types of Grid Computing Simulators

• DynamicCloudSim
  • DynamicCloudSim is an extension of CloudSim simulation
  • Comprises models for dealing with uncertainty in VM performance variation, obstacles in tasks execution
• GreenCloud
  • Greencloud enables simulation of energy-aware cloud data centers
  • Allow to access energy consumption details of distributed environ
  • Popularly known as packet-level simulator which also support cloud interaction
  • Capable to design energy-aware components like connections, servers, network switches, datacenters, etc.
• GangSim
  • GangSim is used to perform grid scheduling
  • Allow to perform SLA-based resource distribution in controlled infrastructure
• OptorSim
  • OptorSim is used to design grid computing systems
  • Enable to evaluate run-time simulation strategies which are used in grid optimization
• SimGrid
  • SimGrid is a developing platform to construct simulators that generate over distributed frameworks
  • Primarily, it is used to design, develop and assess model and relate developed model with other system designs, algorithmic methods and system architecture.
  • Moreover, it is enriched with different unique characteristics which are given below,
  • Features of SimGrid
    • Adaptability
      • It supports various distributed platforms through pre-defined APIs and models
      • For example: LAN, WAN, commodity clusters, datacenters, peers over DSL connectivity
      • It turns out to be a fundamental technology for conducting model development of all distributed domains
      • For instance: Fog computing, P2P computing, Grid computing, MapReduce, Volunteer computing
    • Accuracy
      • It allows to assess the developed model in both theoretical and practical aspects
    • Scalability
      • It is rapid to develop simulation models with low-memory feature. So, it can execute on single machine at high-speed
    • Usability
      • It is free to download and install which supports C, C++, Java and Python.
      • It has LGPL license to support Mac OS X, Windows and Linux
    • SimGrid APIs and Models
      • SimGrid-based Grid Simulator – Precise simulation modelling but need to improve speed
      • SimGrid-based P2P Simulator – Support flexible simulation over peer networks
      • SimGrid-based MPI Simulator – Simulate unchanged MPI program like real
      • SimGrid-based Cloud Simulator – Simulate cloud system based on libvirt-like interface
• CloudSim
  • Cloudsim used to design multiple VMs and assign their respective jobs and datacenter
  • Enable to explore VM policies and federation by simulating datacenters for auto-scaling applications
  • Overview of CloudSim in cloud computing
    • Design and simulate federated clouds
    • Design and simulate huge cloud data centers
    • Design and simulate energy-saving resources for computations
    • Design and simulate hosts along with custom policies for resource allocation
    • Design and simulate message-passing apps and network structure
    • Design and simulate customized policies for host and resource allocation for VMs
    • Design and simulate run-time simulation stopping, resuming and element adding
• NetworkCloudSim
  • NetworkCloudSim able to simulate generalized services and real cloud data centers

Furthermore, we have also given you the primary libraries of grid computing simulators. Since libraries have a key player role in simplifying your code in project execution. Our development team has sufficient knowledge to identify and use appropriate libraries/packages to efficiently solve the proposed research problem in grid computing. Similarly, we also suggest other libraries required for your project.

Grid Computing Libraries

• Minimum intrusion Grid (MiG) – It is a middleware for grid systems which is developed by python
• Ganga – It is a grid interface which are introduced by LHCb and ATLAS researches
• pyGlobus -It is a essential project for all other associated software
• PEG – It is specifically designed for grid as python extensions

For illustration purposes, here we have taken “Ganga” as an example. In this, we have given its purpose with installation command for references.

Ganga

It is a user-friendly library for supporting job definition and accessibility in python. It mainly intended to satisfy the requirements of LHCb and ATLAS for the grid user interface. It comprises built-in features for developing and executing applications /services. Also, it depends on Athena / Gaudi framework. Further, it is used to analyze the grid resources switching over huge-scale processing and local batch system. 

Installation Command (using pip tool)

$ pip install ganga

Overall, the best way to test and analyze techniques of huge distributed systems over heterogeneous resources is simulation. By the by, grid computing simulator captures the behavior and performance of realistic systems in a feasible way. So that, it removes unwanted real-time complexity, resource overheads, etc. Moreover, it is more useful to assess the large hypothetical problems which employ more resources and users. As well, it makes the coordination and study of the multiple systems most simple. 

           On the whole, we assure you to give the best code development assistance in the grid computing field. Most importantly, we provide you with keen assistance in selecting an appropriate grid computing simulator. So, connect with us to design and develop high-quality grid computing projects from the latest research area.

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