Dharmendra Prasad Mahato

Adaptability in transaction oriented grid service

Adaptability is the ability of a system to adapt itself efficiently to changed circumstances. Adaptability in transaction oriented grid service is a challenge due to the extreme complexity and the occurrence of failures in the grid system. This paper presents the design, implementation, and evaluation of an adaptive model, ATOGS, using adaptive fault-tolerance (i.e., checkpointing and replication) during the execution of services. We evaluate our adaptive model experimentally comparing with the Dynasa and the experimental results have demonstrated that ATOGS enables the application itself to handle the failure problems efficiently and it achieves better performance in terms of execution time, network bandwidth, load, resulting in up to a lower overhead. The results indicate that the performance of transaction oriented grid service is better than the performance of general grid service when replication technique is used. This model is based on a modeling and simulation tool, Coloured petri nets (CPNs).

Balanced task allocation in the on‐demand computing‐based transaction processing system using social spider optimization

Balanced task allocation is one of the methods that can be used to maximize the performance and reliability in the on‐demand computing‐based transaction processing system. On‐demand computing is an increasingly popular enterprise model. It provides computing resources to the user as needed, which may be maintained within the users enterprise, or made available by a service provider. The balanced task allocation in such environment is known to be an NP hard. The reliability is a measure of trustworthiness of the system while executing the task. So we derive the reliability formula for the on‐demand computing‐based transaction processing system considering resource availability. We propose the balanced task allocation based on social spider optimization (LBTA_SSO) method for this problem. The LBTA_SSO is based on the cooperative behavior of social‐spiders to find a collection of task allocation solutions. We modified five existing algorithms to obtain the task allocation algorithms; Honey Bee Optimization (HBO), Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized Algorithm respectively. Then, we compared the proposed algorithm with these modified algorithms. The results show that our algorithm works better than the modified existing algorithms.

Dynamic and adaptive load balancing in transaction oriented grid service

Dynamic and decentralized load balancing in transaction oriented grid service is a challenge due to its heterogeneous, real-time, autonomous and adaptive nature. The execution of these services increases the loads on the processing nodes or the required resources at the time of task recovery from failures. The task recovery may be of two types: local level and replicated level. In both the cases, the job queues at global nodes and local nodes are crowded with incoming new and older failed tasks. This paper presents a sender-initiated dynamic and adaptive load balancing approach (SI-DALB) based model using hypercube topology. The proposed model is based on Coloured Petri Nets (CPNs) and uses decentralized approach to balance and manage the load distributions among resources. Experimental results are validated and compared with the model consisting NoLB (No load balancing) approach. The experimental results show that the proposed algorithm is better and effective in distributing and balancing the loads of transaction oriented grid service.

On maximizing reliability of grid transaction processing system considering balanced task allocation using social spider optimization

Abstract This paper deals with the problem of task allocation in the grid transaction processing system. There has been quite some research on the development of tools and techniques for grid computing systems, yet some important issues, e.g., service reliability with load balanced transaction allocation in grid computing system, have not been sufficiently studied. Load balanced transaction allocation becomes a challenging job in such a complex and dynamic environment as both the application and computational resources are heterogeneous. The problem is further complicated by the fact that these resources may fail at any point of time. The problem of finding an optimal task allocation solution is known to be an NP-hard. We propose grid transaction allocation based on social spider optimization (LBGTA_SSO) method for this problem. The LBGTA_SSO is based on the cooperative behavior of social spiders to find a collection of task allocation solutions. We also derive reliability formulae for grid transactions considering resource availability. For comparison we modify some existing algorithms to obtain the task allocation algorithms. The results show that our algorithm works better than the modified existing algorithms.

On scheduling transactions in a grid processing system considering load through Ant Colony Optimization

Abstract Load balanced transaction scheduling problem is an important issue in distributed computing environments including grid system. This problem is known to be NP-hard and can be solved by using heuristic as well as any meta-heuristic method. We ponder over the problem of the load balanced transaction scheduling in a grid processing system by using an Ant Colony Optimization for load balancing. The problem that we consider is to achieve good execution characteristics for a given set of transactions that has to be completed within their given deadline. We propose a transaction processing algorithm based on Ant Colony Optimization (ACO) for load balanced transaction scheduling. We modify two meta-heuristic along with ACO and three heuristic scheduling algorithms for the purpose of comparison with our proposed algorithm. The results of the comparison show that the proposed algorithm provides better results for the load balanced transaction scheduling in the grid processing system.

Fault tolerance for hypercube networks via independent spanning trees

Independent spanning trees (ISTs) may be used for message broadcasting in any interconnection networks, which provides preferential performances. It can be used to enhance the fault tolerance, bandwidth, and security. In this way, the designs of ISTs for various types of topologies have been widely analysed. There is a hypothesis on ISTs: any n-connected network has n ISTs rooted at any node. In this paper, we first analysed the algorithm then parallel construct the ISTs in hypercubes. We give an algorithm for ISTs which can be applied to solve any node broadcast problem for hypercubes. Then, simulation results showed that fault tolerance is effective in reliable broadcasting and concedes 11%-23% fault tolerance for successful broadcasting.

Load balanced scheduling and reliability modeling of grid transaction processing system using colored Petri nets

On-demand computing is a popular enterprise model in which the computing resources are made available to the users as needed. On-demand computing based transaction processing system which has grown rapidly in recent years is an information processing system with the stringent requirements of resources to meet the fluctuating demands. Concepts such as grid computing, utility computing, autonomic computing, and adaptive management seem very similar to the concept of on-demand computing. When demands of resources fluctuate, the system needs load balancing for the efficient utilization of the computational resources. Furthermore, scheduling is needed to assign the transactions to the appropriate resources. Thus, modeling of load balanced scheduling along with reliability analysis for this system is a challenging task. This paper presents the load balanced scheduling and reliability modeling in such an environment by using colored Petri nets (CPNs). CPNs which combine Petri nets with programming languages is a powerful modeling technique. The proposed CPN-based modeling pattern formally describes the process of transaction distribution and execution within the on-demand computing environment. Moreover, the CPN-based model uses the hierarchical modeling capability of CPNs, including different levels of abstraction (sub-modules). This helps easily handling and extending the model. Since, on-demand computing based transaction processing system executes a number of concurrent transactions. The CPN-based model is extended to express the concurrency, thus improving the reliability results. This paper takes the example of grid transaction processing (GTP) system with the problem of load balanced scheduling modeling and reliability evaluation.

Maximizing availability for task scheduling in on‐demand computing–based transaction processing system using ant colony optimization

Maximization of availability and minimization of the makespan for transaction scheduling in an on‐demand computing system is an emerging problem. The existing approaches to find the exact solutions for this problem are limited. This paper proposes a task scheduling algorithm using ant colony optimization (MATS_ACO) to solve the mentioned problem. In this method, first, availability of the system is computed, and then, the transactions are scheduled using the foraging behavior of ants to find the optimal solutions. We also modify two known meta‐heuristic algorithms such as genetic algorithm (GA) and extremal optimization (EO) to obtain transaction scheduling algorithms for the purpose of comparison with our proposed algorithm. The compared results show that the proposed algorithm performs better than others.

Load balanced transaction scheduling using Honey Bee Optimization considering performability in on-demand computing system

The scheduling of load balanced transactions is an emerging problem in on‐demand computing system. This problem can be studied considering several parameters such as resource availability, performance, and reliability. The existing approaches to find the exact solutions for this problem are limited. This paper presents a Honey Bee Optimization (HBO)–based method to solve the mentioned problem. In this method, first, the load of the system is balanced and then the transactions are scheduled using foraging behavior of honey bees to find the optimal solutions. We also modify four known scheduling algorithms such as Ant Colony Optimization (ACO), Hierarchical Load Balanced Algorithm (HLBA), Dynamic and Decentralized Load Balancing (DLB), and Randomized to obtain transaction scheduling algorithms for the purpose of comparison with our proposed algorithm. The compared results show that the proposed algorithm performs better than the modified existing algorithms.