Sharif Digital Repository

In this work, studies of underground gas storage (UGS) were performed on a partially depleted, naturally fractured gas reservoir through compositional simulation. Reservoir dynamic model was calibrated by history matching of about 20 years of researvoir production. Effects of fracture parameters, i.e. fracture shape factor, fracture permeability and porosity were studied. Results showed that distribution of fracture density affects flow and production of water, but not that of gas, through porous medium. However, due to high mobility of gas, the gas production and reservoir average pressure are insensitive to fracture shape factor. Also, it was found that uniform fracture permeability... 

This paper addresses scheduling problem of independent tasks in the market-based grid environment. In market-based grids, resource providers can charge users based on the amount of resource requested by them. In this case, scheduling algorithms should consider users' willingness to execute their applications in most economical manner. As a solution to this problem, a hybrid genetic algorithm and variable neighborhood search is presented to reduce overall cost of task executions without noticeable increment in system makespan. Simulation results show that our algorithm performs much better than other algorithms in terms of cost of task executions. Considering the negative correlation between... 

In cloud computing services, high availability is one of the quality of service requirements which is necessary to maintain customer confidence. High availability systems can be built by applying redundant nodes and multiple clusters in order to cope with software and hardware failures. Due to cloud computing complexity, dependability analysis of the cloud may require combining state-based and nonstate-based modeling techniques. This article proposes a hierarchical model combining reliability block diagrams and continuous time Markov chains to evaluate the availability of OpenStack private clouds, by considering different scenarios. The steady-state availability, downtime, and cost are used... 

This paper investigates the performance of epidemic routing, in mobile social networks (MSNs), which makes use of the store-carry-forward paradigm for communication. Real-life mobility traces show that people have skewed location visiting preferences, with some places visited frequently and some others infrequently. In order to model epidemic routing in MSNs, we first analyze the time taken for a node to meet the first node belonging to a set of nodes restricted to move in a specific subarea. Afterwards, a monolithic stochastic reward net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting... 

This paper investigates the performance of epidemic routing in mobile social networks considering several communities which are frequently visited by nodes. To this end, a monolithic Stochastic Reward Net (SRN) is proposed to evaluate the delivery delay and the average number of transmissions under epidemic routing by considering skewed location visiting preferences. This model is not scalable enough, in terms of the number of nodes and frequently visited locations. In order to achieve higher scalability, the folding technique is applied to the monolithic model, and an approximate folded SRN is proposed to evaluate performance of epidemic routing. Discrete-event simulation is used to... 

This paper presents a probabilistic task scheduling method to minimize the overall mean response time of the tasks submitted to the grid computing environments. Minimum mean response time of a given task can be obtained by finding a subset of appropriate computational resources to service the task. To achieve this, a discrete time Markov chain (DTMC) representing the task scheduling process within the grid environment is constructed. The connection probabilities between the nodes representing the grid managers and resources can be considered as transition probabilities of the obtained DTMC. Knowing the mean response times of the managers and resources, and finding fundamental matrix of the... 

In this paper, the availability of the Resource Management System (RMS) and computational resources distributed within grid computing environments is studied. Since the RMS acts as a heart of the grid environments, the unavailability of this system can render the entire environment to the inoperable phase. Furthermore, the unavailability of the grid resources may result in degradation of the performance of the grid. Therefore, considering the great importance of the availability issue in grid computing environments, the Stochastic Activity Networks (SANs) are exploited to model and evaluate the availability of grid environments. The proposed SAN models the failure of the resource management... 

Task scheduling algorithms in grid environments strive to maximize the overall throughput of the grid. In order to maximize the throughput of the grid environments, the makespan of the grid tasks should be minimized. In this paper, a new task scheduling algorithm is proposed to assign tasks to the grid resources with goal of minimizing the total makespan of the tasks. The algorithm uses the genetic approach to find the suitable assignment within grid resources. The experimental results obtained from applying the proposed algorithm to schedule independent tasks within grid environments demonstrate the applicability of the algorithm in achieving schedules with comparatively lower makespan in... 

In this article, a fully analytical solution of the generalized coupled thermoelasticity problem in a rotating disk subjected to thermal and mechanical shock loads, based on Lord–Shulman model, is presented. The general forms of axisymmetric thermal and mechanical boundary conditions as arbitrary time-dependent heat transfer and traction, respectively, are considered at the inner and outer radii of the disk. The governing equations are solved analytically using the principle of superposition and the Fourier–Bessel transform. The general closed form solutions are presented for temperature and displacement fields. To validate the solutions, the results of this study are compared with the... 

Recent years witnessed a steep rise in data generation and, consequently, the widespread adoption of software solutions able to support data-intensive applications. Many companies currently engage in data-intensive processes, however, fully embracing a data-driven paradigm is still cumbersome, and establishing a production-ready and fine-tuned deployment is time-consuming. This situation calls for innovative models and techniques to streamline the process of deployment configuration for Big Data applications. Moreover, many companies are using Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. Accurate and fast prediction of the execution time... 

One of the most challenging problems in cloud datacenters is the degradation of performance and energy efficiency due to the overutilization of hosts and their exposition to excessive workload. Virtual machine (VM) consolidation and migration from one host to another are strategies that have been proven to successfully bring about performance improvements and energy efficiency. These schemes help in energy optimization by moving VMs experiencing difficulty functioning on an overloaded host to another host. Similarly, by migrating VMs from an underloaded host and consolidating them, unnecessary resources have a chance to be shut down. This makes clear why the accurate detection of overloaded... 

In this paper, we have developed a production planning and marketing model in unreliable flexible manufacturing systems with inconstant demand rate that its rate depends on the level of advertisement on that product. The proposed model is more realistic and useful from a practical point of view. The flexible manufacturing system is composed of two machines that produce a single product. Markovian models frequently have been used in modeling a wide variety of real-world systems under uncertainties. Therefore, in this paper, the inventory balance equation is represented by a continuous-time model with Markov jump process to take into account machines breakdown. The objective is to minimize the... 

In this paper, Stochastic Activity Networks (SANs) are exploited to model and evaluate the power consumption and performance of virtualized servers in cloud computing. The proposed SAN models the physical servers in three different power consumption and provisioning delay modes, switching the status of the servers according to the workload of the corresponding cluster if required. The Dynamic Voltage and Frequency Scaling (DVFS) technique is considered in the proposed model for dynamically controlling the supply voltage and clock frequency of CPUs. Thus, Virtual Machines (VMs) on top a physical server can be divided into several power consumption and processing speed groups. According to the... 

The era of personal resources being sufficient for enterprise big data computations has passed. As computations are executed in the cloud, small policy changes of cloud operators may cause considerable changes in operational costs. Carefully choosing the amount of resources for a given application is thus of great importance. This, however, requires a priori knowledge of the application's performance under different configurations. Creating a performance prediction model needs to account for the heterogeneity of resources and the diversity in application workflows. Previous approaches for heterogeneous environments consider a black-box representation of the application which results in... 

Vehicular edge computing is a progressing technology which provides processing resources to the internet of vehicles using the edge servers deployed at roadside units. Vehicles take advantage by offloading their computationintensive tasks to this infrastructure. However, concerning time-sensitive applications and the high mobility of vehicles, cost-efficient task offloading is still a challenge. This paper establishes a computation offloading strategy based on deep Q-learning algorithm for vehicular edge computing networks. To jointly minimize the system cost including offloading failure rate and the total energy consumption of the offloading process, the vehicle tasks offloading problem is... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This paper fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based Big... 

In this paper, performance of grid computing environment is studied in the presence of failure-repair of the resources. To achieve this, in the first step, each of the grid resource is individually modeled using Stochastic Reward Nets (SRNs), and mean response time of the resource for grid tasks is computed as a performance measure. In individual models, three different scheduling schemes called random selection, non-preemptive priority, and preemptive priority are considered to simultaneously schedule local and grid tasks to the processors of a single resource. In the next step, single resource models are combined to shape an entire grid environment. Since the number of the resources in a... 

Companies depend on mining data to grow their business more than ever. To achieve optimal performance of Big Data analytics workloads, a careful configuration of the cluster and the employed software framework is required. The lack of flexible and accurate performance models, however, render this a challenging task. This article fills this gap by presenting accurate performance prediction models based on Stochastic Activity Networks (SANs). In contrast to existing work, the presented models consider multiple work queues, a critical feature to achieve high accuracy in realistic usage scenarios. We first introduce a monolithic analytical model for a multi-queue YARN cluster running DAG-based... 

This paper explores the capabilities of refined finite elements for 3D analysing of thermoelastic waves propagation in disks made of functionally graded materials. Based on the Lord-Shulman generalized theory of thermoelasticity, the field equations are written according to the three-dimensional formalism of the Carrera Unified Formulation (CUF). The system of the coupled equations is solved in the Laplace domain and, then, converted in the time domain by using numerical inverse Laplace transform. For a functionally graded disk exposed to thermal shock load, the time histories of displacement, temperature and stress fields are reported for different gradation laws. Propagation and reflection... 

This paper presents a mathematical model using Stochastic Activity Networks (SANs) to model a grid resource, and compute the throughput of a resource in servicing grid tasks, wherein the failure–repair behavior of the processors inside the resource is taken into account. The proposed SAN models the structural behavior of a grid resource and evaluates the combined performance and availability measure of the resource. Afterwards, the curve fitting technique is used to find a suitable function fitted to the throughput of a resource for grid tasks. Having this function and the size of each grid job based on its tasks, an algorithm is proposed to compute the makespan of each available resource to...