A maximal chain approach for scheduling tasks in a multiprocessor system.

The scheduling problem has been an interesting problem for quite some time. But recently, in the era of parallel and distributed computing it has seen increased activity and many researchers have focused their attention on the scheduling problem once again. Task scheduling is one of the most challenging problems in parallel and distributed computing. It is known to be NP-complete in its general form as well as several restricted cases. Researchers have studied restricted forms of the problem by constraining either the task graph representing the parallel tasks or the computer model. For example, the 2- processor problem has a polynomial-time algorithm. In an attempt to solve the problem in the general case, a number of heuristics have been developed. These heuristics do not guarantee an optimal solution to the problem, but they attempt to find near-optimal solutions most of the time. In this thesis, we study the scheduling problem for a fixed number of processors m. In the proposed work, we are suggesting generating the maximal chain and reducing the problem to (m-1) processors until we apply the two-processor scheduling algorithm on the remaining tasks. This way we can reduce an m-processor problem to a (m-1 )­processor problem. We are trying a new heuristic approach, which tries to reduce the problem to a 2-processor problem; from then on it is just a matter of merging the maximal chain and the derived (m-1) processor schedule. The motivation for reducing it to a 2-processor problem is because there are well known polynomial algorithms to solve this problem. The two-processor algorithm that we will be using is one of the famous algorithms by Coffman and Graham, Sethi, and Gabow. The proposed heuristic will be compared with other well-known heuristics such as List scheduling heuristics. A user-friendly Graphical User Interface will be developed to simplify the use of the developed algorithm. The GUI will allow the user to create a task graph by plotting the nodes and the edges and then there will be various menu items, which will help in generating the labels, maximal chains and schedules for the plotted graph. The User will be able to save the plotted graph and functionality will be provided to copy, cut and paste entire graphs and portions of the graph

On the Tradeoff between Speedup and Energy Consumption in High Performance Computing – A Bioinformatics Case Study

High Performance Computing has been very useful to researchers in the Bioinformatics, Medical and related fields. The bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Automated techniques such as DNA sequencers, DNA microarrays & others are continually growing the dataset that is stored in large public databases such as GenBank and Protein DataBank. Most methods used for analyzing genetic/protein data have been found to be extremely computationally intensive, providing motivation for the use of powerful computers or systems with high throughput characteristics. In this paper, we provide a case study for one such bioinformatics application called BLAT running in a high performance computing environment. We use sequences gathered from researchers and parallelize the runs to study the performance characteristics under three different query and data partitioning models. This research highlights the need to carefully develop a parallel model with energy awareness in mind, based on our understanding of the application and then appropriately designing a parallel model that works well for the specific application and domain. We found that the BLAT program is highly parallelizable and a high degree of speedup is achievable. The experiments suggest that the speed up depends on model used for query and database segmentation

Dynamic Energy Aware Task Scheduling for Periodic Tasks using Expected Execution Time Feedback

Scheduling dependent tasks is one of the most challenging problems in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. An interesting application of scheduling is in the area of energy awareness for mobile battery operated devices where minimizing the energy utilized is the most important scheduling policy consideration. A number of heuristics have been developed for this consideration. In this paper, we study the scheduling problem for a particular battery model. In the proposed work, we show how to enhance a well know approach of accounting for the slack generated at runtime due to the difference between WCET (Worst Case Execution Time) and AET (Actual Execution Time). Our solution exploits the knowledge gained about the AET of the tasks after the first period, to come up with EET (Expected Execution Time). We then use the EET as an input for the next period to use as much slack as possible and to eliminate wastage of slack generated. This happens because WCET is used to determine if a task should be executed at runtime. Dynamically adjusting the run-queue to use EET as a feedback, which is based on the previous period’s AET eliminates wastage of the slack generated. Based on the outcome of the conducted experiments, the proposed algorithm outperformed or matched the performance of the 2-Phase dynamic task scheduling algorithm and the run-queue peek algorithm all the time

A Dynamic Run-Profile Energy-Aware Approach for Scheduling Computationally Intensive Bioinformatics Applications

High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In the Offline Phase, we use a run profile based approach to generate the initial schedule. In the Online Phase a feedback mechanism is incorporated between the EAS Engine and the master scheduling process. As scheduled tasks are completed, actual execution times are used to adjust the resources required for scheduling remaining tasks using the least number of nodes while meeting a given deadline. In this paper we study the impact of the quality of initial schedule using different run profiles which is the starting point for the EAS algorithm on the number of adjustments which is critical to the overall energy optimization as every adjustment made has an overhead. The conducted experiments show that the proposed approach succeeded in meeting preset deadlines while minimizing the number of nodes; thus reducing overall energy utilized and that choosing the right profile in the Offline phase has an impact on the energy optimization achieved by the EAS algorithm

Dynamic Energy Aware Task Scheduling using Run-Queue Peek

Scheduling dependent tasks is one of the most challenging problems in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. An interesting application of scheduling is in the area of energy awareness for mobile battery operated devices where minimizing the energy utilized is the most important scheduling policy consideration. A number of heuristics have been developed for this consideration. In this paper, we study the scheduling problem for a particular battery model. In the proposed work, we show how to enhance a well know approach of accounting for the slack generated at runtime due to the difference between WCET (Worst Case Execution Time) and AET (Actual Execution Time). Our solution exploits the fact that even though some tasks become available based on the actual periodicity of a task they are not executed because the run queue is determined by the schedule generated in the offline phase I of the algorithm using the conservative EDF (Earliest Deadline First) algorithm. We peek at the task run-queue to find such tasks to eliminate wastage of the slack generated. Based on the outcome of the conducted experiments, the proposed algorithm outperformed or matched the performance of the 2-Phase dynamic task scheduling algorithm all the time

A Maximal Chain Approach for Scheduling Tasks in a Multiprocessor Systems

Scheduling dependent tasks is one of the most challenging versions of the scheduling problem in parallel and distributed systems. It is known to be computationally intractable in its general form as well as several restricted cases. As a result, researchers have studied restricted forms of the problem by constraining either the task graph representing the parallel tasks or the computer model. Also, in an attempt to solve the problem in the general case, a number of heuristics have been developed. In this paper, we study the scheduling problem for a fixed number of processors m. In the proposed work, we approach the problem by recursively reducing the m-processor scheduling to (m-1)-processor scheduling until we apply the optimal two-processor scheduling algorithm when m equals two. This is accomplished by identifying a maximal chain C in the task graph G and merging the (m-1) processor scheduling of (G-C) and the 1-processor scheduling of C. A number of experiments were conducted to compare the suggested approach with the standard list-scheduling algorithm. Based on the outcome of the conducted experiments, the proposed algorithms outperformed or matched the performance of the list heuristic almost all the time

On the Tradeoff between Speedup and Energy Consumption in High Performance Computing – A Bioinformatics Case Study

High Performance Computing has been very useful to researchers in the Bioinformatics, Medical and related fields. The bioinformatics domain is rich in applications that require extracting useful information from very large and continuously growing sequence of databases. Automated techniques such as DNA sequencers, DNA microarrays & others are continually growing the dataset that is stored in large public databases such as GenBank and Protein DataBank. Most methods used for analyzing genetic/protein data have been found to be extremely computationally intensive, providing motivation for the use of powerful computers or systems with high throughput characteristics. In this paper, we provide a case study for one such bioinformatics application called BLAT running in a high performance computing environment. We use sequences gathered from researchers and parallelize the runs to study the performance characteristics under three different query and data partitioning models. This research highlights the need to carefully develop a parallel model with energy awareness in mind, based on our understanding of the application and then appropriately designing a parallel model that works well for the specific application and domain. We found that the BLAT program is highly parallelizable and a high degree of speedup is achievable. The experiments suggest that the speed up depends on model used for query and database segmentation

Optimizing Campus Mobility with a focus on Sustainability: A Graph Theory Approach to Intra-Campus Transportation Networks

The idea of public transportation is supported by most in theory but often heavily criticized by users when put into application. There are common tensions that are related to public transportation, as described by frequent users: unreliable, too crowded, and slow. The University of Nebraska-Omaha (UNO) is a growing metropolitan institution that uses a shuttle system to transport students among their three campuses daily. As of 2015, the current total student enrollment is approximately 16,000; UNO plans to enroll 20,000 students by 2020. The expected student growth is also reflected by the current construction of new buildings and expansion of UNO\u27s campus. Like most metropolitan universities, space and parking on a college campus is a limited resource, and UNO\u27s shuttle transportation system plays a vital role in ensuring student mobility between campuses. With growing pressure from the UNO community to improve kinesis there is a need to optimize intra-campus transportation in an environmentally sustainable manner. To alleviate the tensions involved with the UNO shuttle system, we have created an algorithm to model shuttle routes using graph theory. Once modeled, our program chooses an optimized route based on various conditions: time, volume of students anticipated to use the shuttle, and fuel cost. The algorithm created can be used to optimize transportation routes, alleviate user tension, and decrease the carbon footprint of transportation networks. Our project thus charts the future by improving student transportation methods and people movement between urban campuses in an environmentally friendly and efficient way

An Energy-Aware Bioinformatics Application for Assembling Short Reads in High Performance Computing Systems

Current biomedical technologies are producing massive amounts of data on an unprecedented scale. The increasing complexity and growth rate of biological data has made bioinformatics data processing and analysis a key and computationally intensive task. High performance computing (HPC) has been successfully applied to major bioinformatics applications to reduce computational burden. However, a naïve approach for developing parallel bioinformatics applications may achieve a high degree of parallelism while unnecessarily expending computational resources and consuming high levels of energy. As the wealth of biological data and associated computational burden continues to increase, there has become a need for the development of energy efficient computational approaches in the bioinformatics domain. To address this issue, we have developed an energy-aware scheduling (EAS) model to run computationally intensive applications that takes both deadline requirements and energy factors into consideration. An example of a computationally demanding process that would benefit from our scheduling model is the assembly of short sequencing reads produced by next generation sequencing technologies. Next generation sequencing produces a very large number of short DNA reads from a biological sample. Multiple overlapping fragments must be aligned and merged into long stretches of contiguous sequence before any useful information can be gathered. The assembly problem is extremely difficult due to the complex nature of underlying genome structure and inherent biological error present in current sequencing technologies. We apply our EAS model to a newly proposed assembly algorithm called Merge and Traverse, giving us the ability to generate speed up profiles. Our EAS model was also able to dynamically adjust the number of nodes needed to meet given deadlines for different sets of reads

On-line Banking Systems: Are they sustainable?

Although the trend for on-line banking has increased in recent years, the customers have not shown enthusiastic participation in the past and in present. Since the sustainability of a bank supporting on-line-banking service depends on what capacity it can attract new customers, retain already existing customers and how well can it extend its services to the current and future customer base. This investigation is focused on examining if there is any significant difference among the factors namely the transactional security, information design, navigational design, visual design, web site trust, web site satisfaction and e-loyalty over sustainability of on-line banking for different banks in USA and in India. An already available questionnaire with 23 questions classified under seven different factors mentioned above was circulated among 91 and 93 participants from USA and India who had a good experience on on-line banking. The results of the survey were analyzed using MANOVA and ANOVA. Results from the survey indicated that transaction security in on-line banking system was a concern among on-line bankers in India. But none of the factors were significant for on-line bankers in USA