Syed Nasir Shah

Hybrid Scheduling and Dual Queue Scheduling

Multiprogramming computer systems execute multiple programs concurrently. An objective of multiprogramming is to optimize resource utilization. Efficient resource utilization is achieved by sharing system resources amongst multiple users and system processes. Optimum resource sharing depends on the efficient scheduling of competing users and system processes for the processor, which renders process scheduling an important aspect of a multiprogramming operating system. As the processor is the most important resource, process scheduling, which is called CPU scheduling, becomes all the more important in achieving the above mentioned objectives. Many algorithms have been developed for the CPU scheduling of a modern multiprogramming operating system. Our research work involves the design and development of new CPU scheduling algorithms (the Hybrid Scheduling Algorithm and the Dual Queue Scheduling Algorithm) with a view to optimization. This work involves a software tool which produces a comprehensive simulation of a number of CPU scheduling algorithms. The tools results are in the form of scheduling performance metrics.

Analysis and evaluation of grid scheduling algorithms using real workload traces

Computational grid has the potential for solving large-scale scientific problems using distributed resources. Grid scheduling is a vital component of a Computational Grid infrastructure. In this paper, we evaluate our proposed Grid scheduling algorithms (the Multilevel Hybrid Scheduling Algorithm and the Multilevel Dual Queue Scheduling Algorithm) 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. To facilitate the research, a software tool has been developed which produces a comprehensive simulation of a number of Grid scheduling algorithms. The tools output is in the form of scheduling performance metrics. The experimental results, based on performance metrics, demonstrate that the performances of our Grid scheduling algorithms give good results. Our proposed scheduling algorithms also support true scalability, that is, they maintain an efficient approach when increasing the number of CPUs or nodes. This paper also includes a statistical analysis of workload traces to present the nature and behavior of jobs.

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.

Dynamic Multilevel Hybrid Scheduling Algorithms for Grid Computing

Abstract A ‘Grid’ is an infrastructure for resource sharing. It is used for large-scale data processing, many of the applications being scientific ones. Grid scheduling is a vital component of a Grid infrastructure. Reliability, efficiency (in terms of time consumption) and effectiveness in resource utilization are the desired characteristics of Grid scheduling systems. Many algorithms have been developed for Grid scheduling. In our previous work, we proposed two scheduling algorithms (the Multilevel Hybrid Scheduling Algorithm and the Multilevel Dual Queue Scheduling Algorithm) for optimum utilization of processors in a Grid computing environment. In this paper, we propose two more flavours of Multilevel Hybrid scheduling algorithms; i.e. the Dynamic Multilevel Hybrid Scheduling Algorithm using Median and the Dynamic Multilevel Hybrid Scheduling Algorithm using Square root. We evaluate our proposed Grid scheduling using real workload traces, taken from leading computational centers. The main idea of the proposed algorithms is to execute jobs optimally, i.e. with minimum average waiting, turnaround and response times. An extensive performance comparison is presented using real workload traces to evaluate the efficiency of scheduling algorithms. To facilitate the research, a software tool has been developed which produces a comprehensive simulation of a number of Grid scheduling algorithms. The tools output is in the form of scheduling performance metrics. The experimental results, based on performance metrics, demonstrate that the performances of our Grid scheduling algorithms give good results. Our proposed scheduling algorithms also support true scalability, that is, they maintain an efficient approach when increasing the number of processors or nodes. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs. Our proposed scheduling algorithms are unique. They have three key features. First, they favor the shortest job for execution. Second, they execute the job on the basis of a dynamic time quantum, to fairly distribute processor time among Grid jobs. A third feature is that they always execute the longest job, thus avoiding starvation.

Extraction and Recovery of Naphthenic Acid from Acidic Oil Using Supported Ionic Liquid Phases (SILPs)

Abstract In this study, functionalized silica supported ionic liquids phases (SILPs) were synthesized and characterized using Fourier Transform infrared (FTIR) spectroscopy, CHNS elemental analysis, thermo gravimetric analysis (TGA) and scanning electron microscopy (SEM). The adsorption of model naphthenic acid such as hexanoic acid, benzoic acid and commercial naphthenic acid from dodecane/kerosene was also investigated. The silica supported ionic liquids are good adsorbent for the removal of naphthenic acid from highly acidic model oil. The regeneration of supported ionic liquid phases as well as recovery of naphthenic acids from the supported ionic liquid phases (SILPs) was investigated.

Development and Performance Analysis of Grid Scheduling Algorithms

Grid scheduling is a vital component of a Grid infrastructure. Reliability, efficiency (in terms of time consumption), effectiveness in resource utilization, and robustness are the desired characteristics of Grid scheduling systems. Many algorithms have been developed for Grid scheduling. In this paper, we propose two new scheduling algorithms (the Multilevel Hybrid Scheduling Algorithm and the Multilevel Dual Queue Scheduling Algorithm) for optimum utilization of CPUs in a master/slave environment. The main idea of the proposed algorithms is to allocate jobs to cluster processors in a circular fashion and execute jobs optimally, i.e. with minimum average waiting, turnaround and response times. To facilitate the research, a software tool has been developed which produces a comprehensive simulation of a number of CPU scheduling algorithms for a clustered system. The tool’s output is in the form of scheduling performance metrics.

Separation of Naphthenic Acid Using Hydroxide Based Ionic Liquids

Isolation of naphthenic acid from model oil using hydroxide based ionic liquid was investigated. A detailed de-acidification study at different temperatures and different ionic liquid/model oil was performed and the content of naphthenic acid before and after extraction was calculated. The ILs containing hydroxide anions has the potential to completely deacidify model oil having high TAN with extremely low ionic liquid /oil ratio.

Dynamic Multilevel Dual Queue Scheduling Algorithms for Grid Computing

Grid computing is the enabling technology for high performance computing in scientific and large scale applications. Grid computing introduces a number of fascinating issues to resource management. Grid scheduling is a vital component of a Grid infrastructure. Reliability, efficiency (in terms of time consumption) and effectiveness in resource utilization are the desired quality attributes of Grid scheduling systems. Many algorithms have been developed for Grid scheduling. In our previous work, we proposed two scheduling algorithms (the Multilevel Hybrid Scheduling Algorithm and the Multilevel Dual Queue Scheduling Algorithm) for optimum utilization of CPUs in a Grid computing environment. In this paper, we propose two more flavours of Multilevel Dual Queue scheduling algorithms, i.e. the Dynamic Multilevel Dual Queue Scheduling Algorithm using Median and the Dynamic Multilevel Dual Queue Scheduling Algorithm using Square root. We evaluate our proposed Grid scheduling, in comparison to other well known scheduling algorithms, on an SGI super computer using parts of the ‘AuverGrid’ workload trace.

SYEDWSIM: A Web Based Simulator for Grid Workload Analysis

Grid computing is becoming the most demanding platform for solving large-scale scientific problems. Grid scheduling is the core component of a Grid infrastructure. Grid scheduling plays a key role in the efficient and effective execution of Grid jobs. In this context, understanding the characteristics of real Grid workloads is a critical step for improving the quality of an existing Grid scheduler, and in guiding the design of new scheduling solutions. Towards this goal, in this paper we present our developed web based simulator for the statistical analysis of Grid workload traces. Our web based simulator provides a comprehensive characterization of the real workload traces. Metrics that we characterize include system utilization, job arrival rate and inter-arrival time, job size (degree of parallelism), job runtime, data correlation and Fourier analysis. Our paper provides a realistic basis for experiments in resource management and evaluations of different job scheduling algorithms in Grid computing.