Anindya Jyoti Pal

Performance enhancement and energy reduction using hybrid cryogenic distillation networks for purification of natural gas with high CO2 content

A novel concept of hybrid cryogenic distillation network has been explored which maximizes the benefits of both desublimation or solid-vapor based separation as well as distillation or vapor-liquid equilibrium based separation during the separation of carbon dioxide from methane or natural gas. Process network synthesis has been performed for four case studies with high carbon dioxide (72 mole%) and medium carbon dioxide (50 mole%) natural gas feed streams. The benefits of optimal locations for cryogenic packed beds were investigated. A conventional cryogenic network consisting of multiple distillation columns with butane as additive for extractive distillation was also studied and presented in this paper. Process modeling of cryogenic distillation network with MESH equations was attempted using an integrated dual loop (C+3) convergence and the results were compared with Aspen Plus simulator for benchmarking. The prediction of solidification region was employed using experimental data from literature to avoid solidification regions in the column. The proposed hybrid cryogenic distillation network showed promising potential for energy and size reduction.

Agent Based Priority Heuristic for Job Scheduling on Computational Grids

Abstract A grid is an infrastructure for resource sharing. At present, many scientific applications require high computing power in processing, which can only be achieved by using the computational grid. For the selection and allocation of grid resources to current and future applications, grid job scheduling is playing a very vital role for computational grids. They constitute the building blocks for making grids available to the society. The efficient and effective scheduling policies, when assigning different jobs to specific resources, are very important for a grid to process high computing intensive applications. This paper presents an agent based job scheduling algorithm for efficient and effective execution of user jobs. This paper also includes the comparative performance analysis of our proposed job scheduling algorithm along with other well known job scheduling algorithms considering the quality of service parameters like waiting time, turnaround time, response time, total completion time, bounded slowdown time and stretch time. We also conducted the QoS based evaluation of the scheduling algorithms on an experimental computational grid using real workload traces. Experimental evaluation confirmed that the proposed grid scheduling algorithms posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs.

Comparative Performance of Modified Simulated Annealing with Simple Simulated Annealing for Graph Coloring Problem

The problems which are NP-complete in nature are always attracting the computer scientists to develop some heuristic algorithms, generating optimal solution in time-space efficient manner compared to the existing ones. Coloring of the vertices of a graph with minimum number of colours belongs to the same category, where the algorithm designers are trying to propose some new algorithms for better result. Here, we have designed modified Simulated Annealing (MSA) for optimal vertex coloring of a simple, symmetric and connected graph (GCP). The algorithm has been tested upon a series of benchmarks including large scale test case and has shown better output than the simple or non-modified version of the same algorithm. This paper describes the advancement of performance of simple SA applied upon the problem of graph coloring using a specially designed operator called random change operator instead of the general change operator. Our work is still going on for designing better algorithms generating optimal solutions.

Performance comparison of least slack time based heuristics for job scheduling on computational grid

In recent years, increasing demand for computing has led to the development of computational grid. Typically scheduling challenges tend to be NP-hard problems where there is no optimal solution. The research reported here therefore is focused on the development of hybrids scheduling algorithms based on deadline and slack time parameters and its variations, using the concept of optimization techniques. An extensive performance comparison has been presented using real workload traces as benchmark on a grid computational environment. The results were compared with some baseline scheduling approaches in extant literature. The results have shown that the performances of grid scheduling algorithms developed and reported in this paper give good results in most of the cases and also support true scalability, when in the scenario of increasing workload and number of processors on a computational grid environment.

An Optimized Tuning of Genetic Algorithm Parameters in Compiler Flag Selection Based on Compilation and Execution Duration

Compiler flags exist to provide option for the software developer to dictate certain parameter to the compiler. Such parameters provide hints to the compiler on how to handle certain portion of the source code. In the realm of optimization, compiler flags provide the fastest way to speed up a program. The right combination of flags will provide significant enhancement in speed without compromising the integrity of the output. However, the main challenge is choosing that particular right set of flags. Many a times, developers work around this issue by dictating the optimization level. In that way, the compiler imposes a package of flags. This process may lead to degradation of performance in terms of execution speed and also significant increase in program size. In this work, we are studying the usage of Genetic Algorithm as a way to select the optimization flags that could produce codes which compile and execute fast.

Self-Organizing Map approach to determining compromised solutions for multi-objective UAV path planning

The demand for Unmanned Aerial Vehicle (UAV) extends to various civil and military missions. While the use of remotely controlled UAV reduces the rate of human casualties in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. Automated path planning is required and because of the multi-objective nature of UAVs missions, several heuristic approaches to path planning have been proposed in order to automate UAVs navigation. While solving multi-objective problems requires the search for a set of pareto-optimal points, it requires the involvement of the user to select the desired result from the solution space. In this paper, we propose a variant of Self-Organizing Map approach to finding a compromised solution for a multi-objective path planning problem that does not require user involvement. Preliminary tests conducted in virtual environments have shown the immunity of our algorithm to local minima, and its efficiency to respond to multiple objectives.

Quantitative and Qualitative Analysis of Unmanned Aerial Vehicle’s Path Planning Using Master-Slave Parallel Vector-Evaluated Genetic Algorithm

The demand of Unmanned Aerial Vehicle (UAV) to monitor natural disasters extends its use to multiple civil missions. While the use of remotely control UAV reduces the human casualties’ rates in hazardous environments, it is reported that most of UAV accidents are caused by human factor errors. In order to automate UAVs, several approaches to path planning have been proposed. However, none of the proposed paradigms optimally solve the path planning problem with contrasting objectives. We are proposing a Master-Slave Parallel Vector-Evaluated Genetic Algorithm (MSPVEGA) to solve the path planning problem. MSPVEGA takes advantage of the advanced computational capabilities to process multiple GAs concurrently. In our present experimental set-up, the MSPVEGA gives optimal results for UAV.

Performance profile of some hybrid heuristic search techniques using compiler flag selection as a seed example

The availability of different flavor of processor architecture coupled with computer codes of various nature poses a discreet challenge to the programmers in forms of code optimization. Programmers need to contemplate on optimization during pre and post implementation to take advantage of the hardware given for a specific nature of the code. To compliment this requirement, the evolution of compiler technology has resulted in built in optimization functionality called compiler flags. Like a switch the flag turns on or off for a particular optimization behavior. The existence of various flags in turn causes confusion as to which flag or combination of flags to be utilized since misuse has detrimental effect on performance. In this work we are performing a comparative study on the utilization of Genetic Algorithm and Simulated Annealing in finding the best compiler flag combination respectively and finally proposing a hybrid algorithm that produces better flag combination in comparison to the former two.

Performance Comparison of Some Hybrid Deadline Based Scheduling Algorithms for Computational Grid

Grid computing is a form of distributed computing that involves collection of independent computers coordinating and sharing computing, application, data storage or network resources with high speed across dynamic and geographically distributed environment. Grid infrastructure plays a vital role in terms of computation in the performance call center. Moreover, grid scheduling is a vital component of a Computational Grid infrastructure. Typical scheduling challenges tend to be NP-hard problems where there is no optimal solution. In this paper, we proposed and evaluate few hybrid scheduling algorithms (Least Slack Time Round Robin Based Scheduling Algorithm (LSTRR), Shortest Processing Time First Round Robin Based Scheduling Algorithm (SPTFRR), Earliest Deadline First Round Robin Based Scheduling Algorithm (EDFRR) and Firs Come First Served Scheduling Algorithm (FCFS) ) based on deadline, slack time and baseline approaches for a real grid environment using real workload traces, taken from leading computational centers. An extensive performance comparison is presented using real workload traces to evaluate the efficiency of scheduling algorithms. Moreover, experimental results, based on performance metrics, demonstrate that the performances of our grid scheduling algorithms give good results. Our proposed schedule algorithms also support true scalability, that is, they maintain an efficient approach when increasing the number of processors on a real grid environment.