This book proposed a modified artificial bee colony algorithm for job scheduling problem. Results of the proposed algorithm shows that the efficiency of proposed algorithm is better then the original Genetic Algorithm. This algorithm produced better results for those problems that do not generate exact solution.Finally, Overall motive of these type of algorithms are to get more optimized results from the previous one. Silent Features are: More efficient algorithm, Less time complexity, Easily to understand the proposed concept, Simple language, Helpful for generating new ideas.
Swarm Intelligence becomes a crucial importance for the solution of many problems which cannot be easily solved with many classical mathematical techniques. The main concern while searching for new nature based solution is population. The collective behaviour of swarm’s or any individuals inspire us to develop optimization-based algorithm. Artificial Bee Colony Algorithm (ABC) is the most recent advance technique to solve many mathematical problems and engineering problems. The inspiration behind this is Nature, where problems are solved on the basis of behavior of swarms, ants, bees etc. The foraging behavior of bees plays an important role while approaching ABC algorithms.
Ant colony algorithm and Genetic algorithm are considered as the most important and advanced Evolutionary algorithms. These two algorithms have got extensive real world applications and solutions for optimization problems. One such type is the multiple travelling salesmen problem. The research finds a better solution for this problem and further research on these algorithm would find even more better solutions.
Scheduling is the central concept used in operating system. It helps in choosing the processes for execution. There are many scheduling algorithms available for a multi-programmed operating system like FCFS, SJF, Priority, Round Robin etc. We mainly focused on Round Robin scheduling algorithm. We proposed a new algorithm as titled “A NOVEL APPROACH FOR SCHEDULING”. It is the combination of Round Robin scheduling algorithm and Dynamic Time Quantum. We get better result in terms of Waiting Time, Turnaround Time and number of Context Switch than the Round Robin scheduling algorithm using static time quantum, Average Mid Max Round Robin scheduling algorithm and Min-Max Round Robin scheduling algorithm.
In this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput-optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs. Table of Contents: Introduction / Overview / Scheduling in Wireless Networks / Utility Maximization in Wirele...
To carryout an operational design of Cellular Manufacturing System (CMS), a two stage methodology has been developed in this research. First, the problem of Machine-Part grouping (CMS design) is solved, and then sequencing and scheduling of parts on machines is carried out. Since each cell is like a Job-Shop, therefore the scheduling part of the problem is solved using a similar approach as in case of a Job-Shop scheduling problem (JSSP). The effectiveness of both the tools developed during this research has been verified separately before combining them together.
Multiprocessors have become powerful computing means for running real-time applications and their high performance depends greatly on parallel and distributed network environment system. Consequently, several methods have been developed to optimally tackle the multiprocessor task scheduling problem which is called NP-hard problem. To address this issue, this research presents two approaches. The first approach is Modified List Scheduling Heuristic (MLSH). The second approach is hybrid approach which is composed of Genetic Algorithm (GA) and MLSH for task scheduling in multiprocessor system.
Grid computing is a high performance computing environment to solve large-scale computational demands. Computational grids has emerged as a next generation computing platform which is a collection of heterogeneous computing resources connected by a network across dynamic and geographically dispersed organizations, to form a distributed high performance computing infrastructure. Our work is mainly based on job-grouping approach for fine-grained job scheduling in computational grids. Resources in computational grid are heterogeneous in nature, owned and managed by different organizations with different allocation policies. In our scheduling algorithm jobs are scheduled based on resources computational and communication capabilities. Independent fine-grained jobs are grouped together based on the chosen resources characteristics, to maximize resource utilization and minimize processing time and cost. The performance of the algorithm is evaluated based on above mentioned performance parameters and compared with other existing fine-grained job scheduling strategies using GridSim toolkit.
The book describes the research work done by author during his PhD. It is suitable as reference book in the advance course of real-time system as well as for the researchers in the same field. It discusses the scheduling problem for real-time operating systems. Dynamic scheduling algorithms can not perform well during overloaded condition. The objective of the book is to get optimum performance in underloaded condition and to improve the performance of real-time systems in overloaded conditions. Initially, new algorithms are proposed with modifications in the conventional EDF algorithm. Then, ACO based dynamic scheduling algorithm for real-time operating systems has been proposed.An adaptive scheduling algorithm is also proposed as pure ACO based algorithm takes more time for execution. All algorithms are tested on single processor system, tightly coupled multiprocessor system and loosely coupled multiprocessor real-time systems. The book covers an important application of ACO in real-time scheduling.
In this literature, the author evaluates some traditional contentious and non-contentious channel access scheduling algorithms and exposes their inadequacy when minimizing lengthy delays in a multimedia cellular environment. Addressing these inadequacies, a novel approach based on a modified operating systems process scheduling algorithm is proposed. The resulting Delay Weighted Priority Scheduling (DWPS) algorithm is evaluated in comparison to the traditional round-robin and random access scheduling algorithms in a simulated multimedia cellular environment and demonstrates a marked improvement in performance.
This work develops an adaptive scheduling algorithm for real-time energy harvesting embedded systems. The algorithm considers both energy and timing constraint of the energy harvesting systems unlike most of the scheduling algorithms. An AFP prediction algorithm was also proposed for a better energy prediction for each slot. Based on this information the initial scheduling, which was designed using the information given by EWMA prediction algorithm, was rescheduled. The purpose was successfully achieved by compensating the extra/less energy harvested from the environment in such a way so that system wide efficiency can be achieved. Using adaptive scheduling we were successfully able to decrease the deadline miss rate of the tasks up to 15-30% in addition to the results accomplished by initial scheduling depending on the amount of energy harvested.
This book investigates the problem of user selection and scheduling in MIMO-BC. A low-complexity user selection algorithm is proposed when the BS has perfect channel-state information and the performance of the proposed algorithm with linear and non-linear precoding techniques is evaluated. A signalling scheme for the MIMO-BC systems in the absence of perfect CSIT is presented. A novel transmit-antenna selection scheme is proposed. The performance of the proposed scheme with different user selection algorithms and linear receivers is evaluated. The book considers a cross-layer scheduling approach in order to provide QoS guarantees to the users. A scheduling algorithm, multi-user ?-Rule scheduling, is proposed with the capability of maximizing the system throughput and providing QoS to the users. The effect of rate estimation on the performance of the scheduling algorithm is analyzed along with the effect of the variability in the allocated rates on the mean queue lengths of the users. It is shown that by increasing the fairness, the variability in rate allocation decreases, which results in smaller queue sizes for the users with marginal reduction in the sum-capacity of the system.
A scheduling algorithm schedules a set of tasks in such way that the tasks are completed before their deadlines reached. There are varieties of algorithms for scheduling of periodic tasks on multiprocessor under partitioning scheme or global scheduling scheme. The most common scheduling algorithms are: Rate Monotonic (RM), Deadline Monotonic (DM), Earliest Deadline First (EDF) and Least Laxity First (LLF). In this book, we have proposed a new algorithm titled as D_EDF which is modified conventional EDF algorithm. The proposed algorithm along with EDF, LLF, RM, DM algorithms are simulated and tested for independent, preemptive, periodic tasks on tightly coupled real-time multiprocessor system under global scheduling. From experiments and result analysis it concludes that the proposed algorithm is very efficient in both underloaded and overloaded conditions. The algorithm proposed in this book; perform quite well during overloaded conditions. The algorithm is truly dynamic as it can work with available number of processors.
Update Vehicle Traffic Routing Using Ant Colony Optimization Algorithm is to implement the solution of combinatorial problem, based on the heuristic behavior of ant. This paper focuses on a highly developed solution procedure using ACO algorithm. This helps to solve routing problems easily. It also reflects the method considering flow, distance, cost, and emergency etc. Here, a new algorithm named UVTR (Update Vehicle Traffic Routing) is represented to overcome the complexity of the previous algorithm. It yields the typical process for removing traffic problems in case of flow, distance, cost etc. This formulation is represented with systematic rules based case study for the Dhaka City.