Deo Prakash Vidyarthi

Maximizing reliability of distributed computing system with task allocation using simple genetic algorithm

Reliability is one of the very important characteristics of the distributed computing system (DCS), and articles on task allocation (an NP-Hard problem) to maximize the reliability of DCS have appeared in the past [S. Kartik, C.S. Ram Murthy, IEEE Trans. Comput. 46 (6) (1997) 719; S.M. Shatz, Wang, Goto, IEEE Trans. Comput. 41 (90) (1992) 1156]. Genetic Algorithm (GA) has emerged as a successful tool for optimization purposes. We, in this work, have used a simple GA to optimize the reliability of DCS with task allocation.

Improved genetic algorithm for channel allocation with channel borrowing in mobile computing

This paper exploits the potential of the Genetic Algorithm to solve the cellular resource allocation problem. When a blocked host is to be allocated to a borrowable channel, a crucial decision is which neighboring cell to choose to borrow a channel. It is an optimization problem and the genetic algorithm is efficiently applied to handle this. The Genetic Algorithm, for this particular problem, is improved by introducing a new genetic operator, named pluck, that incorporates a problem-specific knowledge in population generation and leads to a better channel utilization by reducing the average blocked hosts. The pluck operator makes the crucial decision of when and which cell to borrow with the future consideration that the borrowing should not lead the network to chaos. It makes a channel borrowing decision that minimizes the number of blocked hosts and improves the long-term performance of the network. Efficacy of the proposed method has been evaluated by experimentation.

A GA-Based Effective Fault-Tolerant Model for Channel Allocation in Mobile Computing

Efficient channel allocation to mobile hosts is of utmost importance in a cellular network. A genetic algorithm (GA), which is a useful tool in solving optimization problems, is explored to design a fault-tolerant cellular channel allocation model that allows a cell to continue communicating with its mobile hosts, even if there are insufficient channels available in the cell. Sometimes, the load over a cell may increase to the extent that it needs more channels than it actually has in order to handle the traffic. On the other hand, it is quite possible that the load in some other cell is less than its channel capacity, resulting in underutilization of the channels. This problem is solved by temporarily taking unutilized channels from cells that have lesser load and allocating them to the cells that are overloaded. We propose a model that reuses available channels more efficiently. The model also considers the handoff by using the reserved channel technique. A reserved pool of channels makes the model fault tolerant. Thus, the proposed work uses GA for fault-tolerant dynamic channel allocation to minimize the average number of blocked hosts and handoff failures in the mobile computing network. Simulation experiments evaluate the performance of the proposed model. Comparison of the results with the two recent earlier models reveals that the proposed model works better in serving mobile hosts.

A GA BASED MULTIPLE TASK ALLOCATION CONSIDERING LOAD

A Distributed Computing System (DCS) comprising networked heterogeneous processors requires ecient tasks to processor allocation to achieve minimum turnaround time and highest possible throughput. Task allocation in DCS remains an important and relevant problem attracting the attention of researchers in the discipline. A good number of task allocation algorithms have been proposed in the literature [3{9]. This algorithm considered allocation of the modules of a single task to various processing nodes and aim to minimize the turnaround time of the given task. But they did not consider execution of modules belonging to various dierent tasks (i.e. multiple tasks). In this work we have considered the number of modules that can be accepted by individual processing nodes along with their memory capacities and arrival of multiple disjoint tasks to the DCS from time to time. In this paper, a method based on genetic algorithm is developed which is memory ecient and give an optimal solution of the problem. The given simulation results also show signicant achievement in this regard.

A GENETIC TASK ALLOCATION ALGORITHM FOR DISTRIBUTED COMPUTING SYSTEMS INCORPORATING PROBLEM SPECIFIC KNOWLEDGE

Distributed Computing Systems (DCS) promise a convenient platform for parallel processing and consequently can be expected to provide highly improved throughput and turnaround characteristics for all types of computing jobs. Task allocation in DCS remains to be an important and relevant problem attracting the attention of researchers in the discipline. Genetic Algorithms (GA) have successfully been used to solve various optimization problems. A GA based task allocation model for multiprocessors has been proposed by Hou, Ansari & Ren [3]. We present a Genetic Task Allocation Algorithm for DCS, wherein we have considered the underlying interconnection network of the processors, communication requirements among modules of the tasks apart from the precedence relation of the task graph that has been considered in [3] also. We have also considered multiprogramming at every processing nodes with related characteristic values. We have, intentionally, made use of the finding [4] that the incorporation of the problem specific knowledge in construction of GAs improves the initial population structures. The model and algorithm proposed by us is sufficiently simple and adequately usable for the purpose of simulation experiments and its possible incorporation in future operating systems of DCS.

A fair multi-attribute combinatorial double auction model for resource allocation in cloud computing

Abstract Recently, Cloud computing has emerged as a market where computing related resources are treated as a utility and are priced. There is a big competition among the Cloud service providers and therefore, the providers offer the services strategically. Auction, a market based resource allocation strategy, has received the attention among the Cloud researchers recently. The auction principal of resource allocation is based on demand and supply. This work proposes a multi-attribute combinatorial double auction for the allocation of Cloud resources, which not only considers the price but other quality of service parameters also. Auctioneer extends some of the parameters to the offered bids from the bidders in order to provide fairness and robustness. In case of not meeting the assured quality, a penalty is imposed on the provider and customer is compensated. The reputation of the provider also diminishes in the forthcoming rounds. Performance study of the proposed model is done by simulation which reflects the usefulness of the method.

Reliability-Based Channel Allocation Using Genetic Algorithm in Mobile Computing

Mobile computing involves bulk data transmission over the transmission media. To achieve highly reliable data transmission, wireless mobile networks require efficient reliable link connectivity, regardless of terminal mobility and, thus, a reliable traffic performance. Mobile networks consist of mobile hosts, base stations, links, etc. that are often vulnerable to failure. It is desirable to design a reliable network, in terms of services of both the base stations and the communication channels of the network, for the reliable transmission of the data. An attempt is made to employ those channels that offer a reliable communication at any given time. The objective of this study is to design an appropriate reliability-based model for channel allocation that retains the overall system reliability with acceptable system performance. The system may achieve acceptable performance not only during normal operations but also under various component failures. A genetic algorithm, which is a search procedure based on evolutionary computation, is suited to solve a class of complex optimization problems. The potential of the genetic algorithm is used, in this paper, to improve the reliability of the mobile communication system. The proposed model designs a reliable mobile communication system, irrespective of the mobile hosts that change their position due to mobility. A simulation experiment to evaluate the performance of the proposed algorithm is conducted, and results reveal the effectiveness of this model.

Scheduling in Distributed Computing Systems

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A novel scheduling model for computational grid using quantum genetic algorithm

The Computational Grid (CG) provides a wide distributed platform for high end computing intensive applications. Scheduling on Computational grid is known to be NP-Hard problem and requires an efficient solution. Recently, quantum inspired computing has been introduced in the literature to solve such a complex combinatorial optimization problem efficiently. Combination of Genetic Algorithm (GA) and quantum concept evolves a new meta-heuristic technique known as Quantum Genetic Algorithms (QGA). QGA is a search procedure based on evolutionary computation and Quantum Computing (QC). This paper proposes a novel technique of scheduling in computational grid using QGA. The work simulates the model to study its performance. It also makes a comparative study with a GA-based scheduling model. Simulation results reveal the effectiveness of the model.

A novel hybrid PSO---GA meta-heuristic for scheduling of DAG with communication on multiprocessor systems

This work presents a novel hybrid meta-heuristic that combines particle swarm optimization and genetic algorithm (PSO–GA) for the job/tasks in the form of directed acyclic graph (DAG) exhibiting inter-task communication. The proposed meta-heuristic starts with PSO and enters into GA when local best result from PSO is obtained. Thus, the proposed PSO–GA meta-heuristic is different than other such hybrid meta-heuristics as it aims at improving the solution obtained by PSO using GA. In the proposed meta-heuristic, PSO is used to provide diversification while GA is used to provide intensification. The PSO–GA is tested for task scheduling on two standard well-known linear algebra problems: LU decomposition and Gauss–Jordan elimination. It is also compared with other states-of-the-art heuristics for known solutions. Furthermore, its effectiveness is evaluated on few large sizes of random task graphs. Comparative study of the proposed PSO-GA with other heuristics depicts that the PSO–GA performs quite effectively for multiprocessor DAG scheduling problem.