Ayaz Isazadeh

PHFS: A dynamic replication method, to decrease access latency in the multi-tier data grid

Abstract Data replication is a method to improve the performance of data access in distributed systems. Dynamic replication is a kind of replication that adapts replication configuration with the change of users’ behavior during the time to ensure the benefits of replication. In this paper, we propose a new dynamic replication method in a multi-tier data grid called predictive hierarchical fast spread (PHFS) which is an extended version of fast spread (a dynamic replication method in the data grid). Considering spatial locality, PHFS tries to predict future needs and pre-replicates them in hierarchal manner to increase locality in accesses and consequently improves performance. In this paper, we compare PHFS and CFS (common fast spread) with an example from the perspective of access latency. The results show that PHFS causes lower latency and better performance in comparison with CFS.

Imperialist competitive algorithm for solving systems of nonlinear equations

Solving systems of nonlinear equations is a relatively complicated problem in which arise a diverse range of sciences. There are a number of different approaches that have been proposed. In this paper, we employ the imperialist competitive algorithm (ICA) for solving systems of nonlinear equations. Some well-known problems are presented to demonstrate the efficiency of this new robust optimization method in comparison to other known methods.

QoS-aware service composition in cloud computing using data mining techniques and genetic algorithm

One of the requirements of QoS-aware service composition in cloud computing environment is that it should be executed on-the-fly. It requires a trade-off between optimality and the execution speed of service composition. In line with this purpose, many researchers used combinatorial methods in previous works to achieve optimality within the shortest possible time. However, due to the ever-increasing number of services which leads to the enlargement of the search space of the problem, previous methods do not have adequate efficiency in composing the required services within reasonable time. In this paper, genetic algorithm was used to achieve global optimization with regard to service level agreement. Moreover, service clustering was used for reducing the search space of the problem, and association rules were used for a composite service based on their histories to enhance service composition efficiency. The conducted experiments acknowledged the higher efficiency of the proposed method in comparison with similar related works.

Traffic distribution for end-to-end QoS routing with multicast multichannel services

With the development of multimedia group applications and multicasting demands, the construction of multicast routing tree satisfying Quality of Service (QoS) is more important. A multicast tree, which is constructed by existing multicast algorithms, suffers three major weaknesses: (1) it cannot be constructed by multichannel routing, transmitting a message using all available links, thus the data traffic cannot be preferably distributed; (2) it does not formulate duplication capacity; consequently, duplication capacity in each node cannot be optimally distributed; (3) it cannot change the number of links and nodes used optimally. In fact, it cannot employ and cover unused backup multichannel paths optimally. To overcome these weaknesses, this paper presents a polynomial time algorithm for distributed optimal multicast routing and Quality of Service (QoS) guarantees in networks with multichannel paths which is called Distributed Optimal Multicast Multichannel Routing Algorithm (DOMMR). The aim of this algorithm is: (1) to minimize End-to-End delay across the multichannel paths, (2) to minimize consumption of bandwidth by using all available links, and (3) to maximize data rate by formulating network resources. DOMMR is based on the Linear Programming Formulation (LPF) and presents an iterative optimal solution to obtain the best distributed routes for traffic demands between all edge nodes. Computational experiments and numerical simulation results will show that the proposed algorithm is more efficient than the existing methods. The simulation results are obtained by applying network simulation tools such as QSB, OpNet and MATLB to some samples of network. We then introduce a generalized problem, called the delay-constrained multicast multichannel routing problem, and show that this generalized problem can be solved in polynomial time.

Optimal multicast multichannel routing in computer networks

This paper presents a polynomial time algorithm for optimal multicast routing and quality of service (QoS) guarantees in networks with multichannel paths. It minimizes end-to-end delay and consumption of bandwidth across channels using linear programming formulation (LPF). At first, we study some existing unicast and multicast algorithms. The existing unicast algorithms transfer messages from a source node to a destination node and existing multicast algorithms transfer messages from a source node to a group of destination nodes using single paths, multichannel paths or minimum spanning trees in a minimum time. Our survey shows that there are some problems and weaknesses in applying these algorithms to the network. Then we will remove these weaknesses by constructing a new algorithm, called optimal multicast multichannel routing algorithm, which is more efficient than existing algorithms. The OMMR algorithm formulates capacities and resources of the network in terms of a linear programming system of equalities or inequalities. It also, transfers data from a sender node to the group of receivers using multichannel paths in minimal time. In fact, the key innovative aspect of this work lies in the ability to allocating resources to traffic flows in multicast environments optimally. Computer examples will show that the new algorithm is more efficient than the existing algorithms.

Active rule learning using decision tree for resource management in Grid computing

Grid computing is becoming a mainstream technology for large-scale resource sharing and distributed system integration. One underlying challenge in Grid computing is the resource management. In this paper, active rule learning is considered for resource management in Grid computing. Rule learning is very important for updating rules in an active database system. However, it is also very difficult because of a lack of methodology and support. A decision tree can be used in rule learning to cope with the problems arising in active semantic extraction, termination analysis of the rule set and rule updates. Also our aim in rule learning is to learn new attributes in rules, such as time and load balancing, in regard to instances of a real Grid environment that a decision tree can provide. In our work, a set of decision trees is built in parallel on training data sets based on the original rule set. Each learned decision tree can be reduced to a set of rules and thence conflicting rules can be resolved. Results from cross validation experiments on a data set suggest this approach may be effectively applied for rule learning.

MFlexDT: multi flexible fuzzy decision tree for data stream classification

In many real-world applications, instances (data) arrive sequentially in the form of streams. Processing such data poses challenges to machine learning. While adhering to on-line learning strategies, in this paper we extend the Flexible Fuzzy Decision Tree (FlexDT) algorithm with multiple partitioning that makes it possible to carry out automatic on-line fuzzy data classification. The proposed method is aimed to balance accuracy and tree size in data stream mining. The objective of the classification problem is to predict the true class of each incoming instances in real time. In terms of evaluation of the method, accuracy, tree depth, and the learning time are significant factors influencing the performance. A series of experiments demonstrate that the proposed method produces optimal trees for both numeric and nominal features (variables).

Software reliability assessment based on a formal requirements specification

Assessment of reliability using characteristics of software development process phases is one of the discussions which has been attracting more and more attentions during the recent three decades. Most of the techniques and models use the result of design, implementation and test phases; there are only a few models that are employed at the early phase of software development. Assessment of software reliability in the early phases of software development process, however, is very important for better prognosis and management of risks. In this paper we propose an approach for early software reliability assessment, based on software behavioral requirements. The major difference between our approach and those of others is the fact that we use a formal method, called Viewcharts, to specify the behavior of software systems.

Processor Allocation in Mesh Multiprocessors Using a Hybrid Method

Mesh-connected systems have become popular because of their simple structure. Most of the allocation strategies in mesh systems are contiguous or noncontiguous. We propose a new hybrid processor allocation algorithm for mesh-connected systems. This method starts by processor allocation contiguously; when contiguous allocation is not possible, the request is decomposed into smaller sub-meshes, such that for each sub-mesh a region can be allocated. Regions formed in this method have no regular forms and as a result all the free processors in a mesh are useful in allocating process and number of rejected requests have become minimum. Compared to the other schemes, the proposed algorithm minimizes the communication delay among the selected processors. Our method combines the advantages of both contiguous and non-contiguous allocation schemes. We will show that it achieves minimum job response time and waiting time compared to the other strategies as well as improving the system utilization by using all idle processors in the system

ECA rule learning in dynamic environments

Through the development of management and intelligent control systems, we can make useful decision by using incoming data. These systems are used commonly in dynamic environments that some of which are been rule-based architectures. Event-Condition-Action (ECA) rule is one of the types that are used in dynamic environments. ECA rules have been designed for the systems that need automatic response to certain conditions or events. Changes of environmental conditions during the time are important factors impacting a reduction of the effectiveness of these rules which are implied by changing users demands of the systems that vary over time. Also, the rate of the changes in the rules are not known which means we are faced with the lack of information about rate of occurrence of new unknown conditions as a result of dynamics environments. Therefore, an intelligent rule learning is required for ECA rules to maintain the efficiency of the system. To the best knowledge of the authors, ECA rule learning has not been investigated. An intelligent rule learning for ECA rules are studied in this paper and a method is presented by using a combination of multi flexible fuzzy tree (MFlexDT) algorithm and neural network. Hence data loss could be avoided by considering the uncertainty aspect. Owing to runtime, speed, and also stream data in dynamic environments, a hierarchical learning model is proposed. We evaluate the performance of the proposed method for resource management in the Grid and e-commerce as case studies by modeling and simulating. A case study is presented to show the applicability of the proposed method.