Sharif Digital Repository

Computational grids consist of hardware and software infrastructures which can provide end users with the power of computational networks in a ubiquitous and cheaper manner. In fact, computational grids provide the ability to use the computational power of geographically distributed and heterogeneous resources. One of the new challenges facing researchers in this area involves security in grid environment which is somehow hard to achieve, because of the distributed nature of grid resources and fast changes in resource availabilities. Since availability, integrity and confidentiality are key factors of security and system’s fault tolerance capability is directly related to resource... 

Advances in semiconductor manufacturing technologies have enabled us to build billions of transistors on a single die. However, the increasing amount of process variation in nanometer technologies has made it inevitable to move toward statistical analysis methods, instead of deterministic worst-case-based techniques, at all design levels. In this project, we studied the problem of variation-aware task scheduling for MPSoCs. To this end, we first proposed a variability analysis framework to analyze the effect of process variation on the main parameters of MPSoCs. Then, to solve the MPSoC task scheduling problem, we proposed two metaheuristic variation-aware task scheduling method based on... 

One of the main operations of computational grid is job scheduling and resource assignment. Scheduling is a traditional problem in parallel and distributed system. However, due to special issues and goals of Grid, traditional approach is not effective in this environment any more.
Therefore, it is necessary to propose methods specialized for this kind of parallel and distributed system. So far, proposed methods are not publicly accepted and are mostly effective in special conditions.In this thesis, at the first we introduce computational grid and its challenges. Scheduling algorithms and policies in the other parallel and distributed platform is presented and it is shown that variability... 

Emerging technologies enable a set of distributed resources across a network tobe linked together and used in a coordinated fashion to solve a particular parallel applicationat the same time. Such applications are often abstracted as directed acyclicgraphs (DAGs), in which vertices represent application tasks and edges represent datadependencies between tasks.The problem of scheduling in a distributed environment is that ofdeciding where to place tasks, and when to start execution of the tasks. Effective scheduling mechanisms for DAG applicationsare essential to exploit the potential of computational resources. Fault tolerance is an important requirement in distributed computing systems when... 

Cloud computing is a service-driven business model that connects distributed resources and allows multiple service demaders to submit requests simultaneously to a cloud platform via the Internet. An important issue in cloud environments is how to allocate tasks to optimize the performance of a cloud manufacturing system. Allocation of tasks in cloud manufacturing refers to the process of selecting and assigning service providers to manufacturing resources such as design, engineering, machining, testing, and packing to achieve the satisfaction level in terms of time, cost, service availability, and other quality service criteria. Task allocation is an intrinsic part of the cloud manufacturing... 

In this thesis, three different mathematical models named Markov Reward Model (MRM), Stochastic Reward Net (SRN) and Stochastic Activity Network (SAN) are used to model and evaluate the performability of grid computing environments consisting of many grid resources. The proposed models consider the arriving and servicing process of grid tasks inside a resource together with the failure-repair behavior of processors of the resource. Since the proposed MRM cannot be extended to model a grid environment with some realistic assumptions, we switch to use SRNs in modeling a single grid resource with more number of processors. Although the proposed SRN models for a single grid resource can... 

With the rapid growth of technology in recent decades, a revolution has been started, known as Industry 4.0, in current manufacturing systems. In this revolution, cloud manufacturing systems play an important role. Cloud manufacturing is a new service-driven business model developed from existing advanced manufacturing models with the aid of innovative technologies, such as cloud computing, virtualization, Internet of Things (IoT), service-oriented technologies, semantic web, and so forth. This manufacturing paradigm allows service providers to share their manufacturing resources in a cloud platform, converts these distributed manufacturing resources into manufacturing services, and arranges... 

In the cloud computing environment, although many processing resources for application software with the volume of calculations The above is available, but it may not work for software that is sensitive to latency. In fact, due to the large distance between users and end devices to cloud servers, sending requests And the required data to the data centers requires a high bandwidth and therefore the data transfer time is also significant. In addition, cloud computing in platforms such as cloud-based IoT architecture faces other challenges such as connectivity, resource limitations of end devices, and security. In order to solve the challenges in the cloud computing environment and respond to... 

With the increasing development of Internet of Things (IoT) devices, issues such as establishing effective communication, optimizing energy consumption, ensuring reliability and improving the quality of services provided by these devices become more and more complex and critical challenges. With the introduction of fog computing model by Cisco in 2012, some of these challenges were successfully managed. Fog computing is a distributed computing paradigm that acts as a middle layer between cloud data centers and IoT-based devices/sensors. By distributing computing resources closer to edge devices, fog computing enables real-time data processing and analysis. However, one of the key aspects in...