Parallel computing is always capable to integrate with distributed computing systems for fast computation processing. There is one main difference between parallel and distributed computing. Usually, parallel computing makes one computer uses multiple processors to perform more tasks at the same time. On contrary, distributed computing makes multiple computing devices perform more tasks.
This page completely explores the latest developments of Parallel and Distributed Simulation Systems to meet PhD / MS scholars’ requirements!!!
What is parallel and distributed processing explains with example?
As mentioned earlier, parallel computing concurrently performs multiple tasks using multiple processors. So, it eventually reduces the processing time and cost. In the case of distributed computing, it performs multiple tasks at multiple individual computers. As well, these systems collectively give a single system look to the user
Fundamentals of Parallel and Distributed Simulation
Now, we can see the parallel and distributed simulation systems. Generally, the simulation method is introduced to analyze the behaviour and performance of a real system before the direct development. Especially, it helps the larger system to investigate the complexity.
Overall, simulation is a cost-effective method to examine real systems. Particularly, the parallel and distributed system fields have several simulation tools. So, it is crucial to choose the enriched simulator which satisfies all your project requirements. Here, we have given you the basic design and simulation models.
Parallel and Distributed Systems Models
- Design Model
- Visual-based
- Library-based
- Simulation Model
- Hybrid Event-based
- Discrete Event-based
- Continuous Event-based
Why do we use parallel and distributed computing simulation?
When the need for fast processing power increases, ultimately the usage of parallel computing is increasing. For instance: Supercomputers. Since parallel computing makes the system use multiple processors for completing tasks at high-speed. Similarly, when the systems are placed at distant locations, distributed computing is used. Since it efficiently supports the communication between remote devices. Overall, these technologies are best to acquire the following benefits in the simulation.
Benefits of parallel and distributed computing Simulation
- Create a virtualized network to understand the efficiency of the algorithm
- Accurately assess the performance of new solutions
In the end, simulation has to turn out to be the best testing and evaluation method for all the research areas of parallel and distributed simulation systems. In recent days, different simulation packages are growing fast due to their flexibility in replicating real-world systems. Although it is beneficial to use, it also has some limitations while dealing with larger complex systems. For instance: Smart grid systems and control systems, requires high computation effort to perform efficiently in the distributed environment.
Now, we can see the emerging research challenges of parallel and distributed computing. All these challenges are collected from our recent research issues list. To present you with up-to-date research issues, we always conduct an in-depth study on research gaps.
For that, we analyze the recent parallel and distributed computing research papers and look for the issues that are not addressed properly so far and issues that are not solved effectively. In this way, we updated our recent research issues list with new research challenges. Here, we have given you only a few important research challenges to share current research directions with you.
Research Challenges of Parallel and Distributed Simulation Systems
- Resource Virtualization
- Require more accuracy in resource virtualization
- Simulation Types – network communication and Computing (Disk, Host, CPU)
- Simulation
- Make sure that proposed applications need to perceive virtual resources. Also, physical resources need not be dependent
- Global Synchronization
- Require reliability in heterogeneous resources simulation from a global perspective. Also, need to synchronize with dynamic resource quantity changes
To solve the recent challenges, you need to choose the best-fitting research solutions. Each solution has individual capabilities to solve a specific set of problems. Also, these technologies are getting more advanced in recent days. Once we finalized the solution for the given problem which must be satisfied once the experiments are completed and compared with the previous works. To implement such ideas, we need to perform the simulation.
Therefore, your handpicked simulation tool is required to support all sorts of modern technologies. We are here to help you to choose a suitable tool that is effective to perform domain-specific simulation.
How to choose the best simulation tool for parallel and distributed systems?
A good simulator is required to support the following things in developing parallel and distributed systems.
- Need to support fast prototyping and various algorithms evaluation
- Need to produce precise experimental outcomes
- Need to support different complicated scenarios
- Need to give real simulation than existing research works
- Need to include IP protection and scalability characteristics
- Need to be efficient to process the computation at high-speed
- Need to serve entities in geographically distributed locations
- Need to provide apt abstraction level and right model based on project needs
- Need to incorporate interoperability and composability features of simulation models
Next, our developers like to share important elements that are essential for developing and simulating parallel and distributed systems. When you have handpicked the appropriate simulation tool for your project, the next step is to collect the requirements of simulation models. In the earlier section, we have already seen the two types of simulation models. Here, we have addressed the key elements of the simulation model. Then, we have also included the requirements that you need to focus on while designing and developing models. If you are connected with us, we support you in all these project phases.
What are the requirements for Parallel and Distributed Simulation?
- Simulation Model
- Behavior
- Probabilistic
- Deterministic
- Supported Model
- Network
- User Apps
- Host
- Middleware
- Scope and Validation
- Model Design and Development
- Input Data
- Dataset Collection
- Data Generators
- Simulation Engine
- Design Factors
- Mechanics (Time / Event or Trace-driven)
- Simulation (Distributed / Centralized)
- Model Requirement
- Libraries
- Visual Entities
- Supportive Languages
In addition, we have also given you some important development tools that are flexible to design and develop both simple and complex parallel and distributed simulation systems. All these tools are listed based on our developing experts’ suggestions. Since, these tools are well-equipped with the latest libraries, packages, modules, and toolboxes. Therefore, these tools have abilities to support any sort of futuristic technology. Our development team has sufficient practice not only in the below tools but also in other emerging tools. So, we give the best guidance to select the best implementation tool for your matlab project.
Simulation Tools for Parallel and Distributed Systems
- Minimum intrusion Grid
- It is developed in the python language
- It is fully used as Grid middleware
- Ganga
- It is introduced by LHCb and ATLAS at CERN
- It acts as an interface to the Grid applications
- Simgrid
- It supports varied distributed infrastructure
- It is flexible to develop various kinds of distributed applications
- PEG
- It is expanded as Python Extensions for the Grid
- It supports any kind of complex grid applications
- MicroGrid
- It is a scalable simulator to support any size of the network
- It is mainly used for simulating grid systems/applications
- It is efficient to use a cluster of resources for computational grid study
- GridSim
- It is easy to design and simulate components of parallel and distributed systems
- It is effective to create, monitor, allocate various resources efficiently
- StarCluster
- It is a cluster-based computing toolkit which is designed for AWS cloud
- It simplifies all the processes related to clusters like creation, configuration, and management
- For instance: Amazon EC2 Cloud
- It enables you to insert or remove cluster/cluster nodes at run-time
- It is easy to create new EBS volumes and AMIs
- It also allows writing python-based plugins for personalized cluster configuration
- SimJava
- It is a discrete event-based tool that works based on process
- It presents the entities communication in animation effect
- PiCloud
- It is specifically for cloud-based applications and services
- It can integrate python code and support AWS computing power
- It is not necessary to fully configure or manage their virtual servers
- It also includes a custom library for python-based cloud application creations
- It has a cloud library to balance the load by auto-scaling cluster. For instance:
- Function call cloud.call(foo) executes in foo()
- Function call cloud.map(foo, range(10)) executes in 10 functions like foo(0), foo(1), foo(2), etc.
- Bricks
- It is used for assessing the performance of the model in grid environ
- It is effective to relate the efficiency of different scheduling policies
- PyCOMPSs
- It supports parallel processing in a distributed environment
- For instance: cloud, clusters, etc.
- It is a programming model which works based on tasks
- It provides a sequential interface, but it performs parallelism over applications while tasks execution
As a summary, now we are going to discuss the importance of the simulation tool. Already, we have mentioned to you the vital simulation tool in the above list. A good simulation tool will be effective to prove your research thought in practical execution. Also, it is truthful to deliver real-world system performance with expectation.
In specific, modeling and development of realistic systems in the field of parallel and distributed computing environments is very important for high computing power. Also, it is reliable to imitate the communication of deployed entities in both parallel and distributed environ. In the following, we have given you some significant simulation technologies and algorithms that are extensively preferred in current research areas.
Algorithms for Parallel and Distributed Systems
- Parallel and Distributed Models
- Embedded Simulation
- High-Performance Computing
- Designing Methodologies
- Distributed Virtual Infrastructure
- Network and Cloud Simulation
- Agent-based Simulation
- Verification, Validation, and Accreditation
- Smart System Virtualization
- Simbiotic Simulation
- Simulators and Virtualization
- Simulation Tools and Languages
So far, we have seen the research challenges, simulation models, technologies, tools, and algorithms. Now, we can see the performance assessment of the parallel and distributed systems using Quality of Service (QoS) metrics. For any simulation technology, it is essential to evaluate the system’s efficiency.
QoS of Parallel and Distributed Simulation
In that case, QoS has a key player role to manage and control the network resources through data priority on the network. Further, it also minimizes the data traffic in terms of
- Jitter
- Packet Loss
- Delay
For your reference, here we have given you other QoS attributes that are essential to enhance the system efficiency.
- Scalability
- Reliability
- Efficiency
- Availability
- Performance
- Interoperability
- Heterogeneity
- And much more
Moreover, we also have the potential to solve major research issues in other service-based fashions. Our ultimate goal is to make the system adaptive to continuously changing applications demands. Also, we focus on increasing the robustness of the system to face unpredicted system failures. In this manner, we keenly work on all parallel and distributed simulation systems.
We assure to focus on every aspect of project development that ranges from tools selection to performance metrics selection. For instance: “Cassandra” is distributed framework used for multimedia analysis and streaming applications. It has a huge volume of loosely-coupled services which are more useful for creating the best-distributed applications/systems.
For your information, here have given you some current research directions of parallel and distributed computing systems. Beyond this, we also have a huge collection of other growing research technologies details. To know more about our service, just connect with us either through online or offline mode. We will give our quick response with a detailed explanation of the latest research updates.
Future Research Directions of Parallel and Distributed Systems
- Simulations Configuration for Heterogeneous Networks
- Assessment of Resource Usage in Datacenters and Mobile Frameworks
- Simulation of GPUs using Hardware in Cloud Infrastructure
- System Optimization using Predictive Web-based Simulation
- Modern Solutions for Huge-scale Complex Network Simulation Problems
On the whole, we are here to serve you in all the possible research areas of parallel and distributed simulation systems. In your desired area, we are ready to share significant research ideas and project topics. Once you confirm your topic with us, we accurately develop your proposed research work which same as in the implementation plan. Then, we deliver your project on time with expected results along with some supplementary materials. The materials include system requirements, project execution video, running procedure, project screenshots with the software installation procedure. Further, we also provide manuscript writing support.
