The power of grid technology in aggregating autonomous resources owned by several organizations into a single virtual system has made it popular in compute-intensive and data-intensive applications. Complex and dynamic nature of grid makes failure of users’ jobs fairly probable. Furthermore, traditional methods for job failure recovery have proven costly and thus a need to shift toward proactive and predictive management strategies is necessary in such systems. In this paper, an innovative effort is made to predict the futurity of jobs submitted to a production grid environment (AuverGrid). By analyzing grid workload traces and extracting patterns describing common failure characteristics, the success or failure status of jobs during 6 months of AuverGrid activity was predicted with around 96% accuracy. The quality of services on grid can be improved by integrating the result of this work into management services like scheduling and monitoring.

Grid is an environment that makes it possible to share resources that are managed by diverse, independent and administrative geographically distributed organizations. The main objective of grid is to enable users to solve problems using the available geographically distributed resources. Grid resource discovery is a challenging issue because characteristics of resources are heterogeneous, dynamic, various and autonomous. In this paper we propose a mobile agent approach based on peer to peer model for the resource discovery problem that presents essential characteristics for efficient, self-configuring and fault-tolerant resource discovery and is able to handle range queries. For this reason, we encode resources’ range attributes to bitmap index and compare with query attributes in order to know whether resource satisfies query or not. Moreover, we employ a Distributed Ant Colony System (ACS) algorithm to route requests into Grid and locate the required resources. The innovation in this paper is to support range queries and to eliminate centralized control and provide node autonomy.

Wireless networks are poised to support a myriad of existing as well as emerging multimedia streaming applications. With increase in use of wireless local area networks (WLANs), ranging from simple data transfer to bandwidth-intense, delay-sensitive, and loss-tolerant multimedia applications, issues regarding quality of service (QoS) have become a major research endeavor. Several challenges posed by robust video streaming over 802.11 WLANs include coping with packet losses caused by network buffer overflow, link erasures, or deadline misses. A major observation is that the retry limit settings of the MAC layer can be optimized in a way such that the overall packet loss caused by link erasure, buffer overflow, or deadline miss is minimized. In this paper, we propose a novel solution for minimizing packet loss in multimedia applications over WLANs. Simulation results justify the improved quality of the received video based on our proposed approach.

The evolution of optical technology has resulted in the emergence of several promising paradigms so as to realize the next generation Internet backbone infrastructure. Among all the existing switching techniques, optical burst switching (OBS) has shown to support the bursty nature of diverse IP traffic classes more efficiently. Nevertheless, with increase in realtime applications over the Internet, devising mechanisms that guarantee quality of service (QoS) and efficient bandwidth utilization within the OBS network has become a major endeavor. In the literature, resource allocation-based QoS management has been investigated as a possible solution to overcome this necessity. However, to the best of our knowledge, the resource-based technique has not been analytically studied for any arbitrary number of traffic classes taking both, wavelengths and fiber delay lines (FDLs) into account. In this paper, we present a novel mathematical model for the resource-based approach with two service classes namely, real-time (H) and non real-time (L) classes. We then extend the model to cover any number of classes. The effectiveness of the model is justified through simulation experiments.

Locating sensor nodes is essential for many applications and protocols in wireless sensor network. Large numbers of localization algorithms have been proposed to do this. Distributed algorithms decrease communication cost while having lower accuracy. In this paper we propose a distributed method to localize sensor node using only neighboring information. Our focus is on location estimation although some ideas are also presented for implementation. Our algorithm employs an error analysis scheme and calculates expected error as well as estimated location for each sensor node. Simulation results confirm effectiveness of our scheme and prove capability of using connectivity approach.

Multiprocessor scheduling belongs to a special category of NP-complete computational problems. The purpose of scheduling is to scatter tasks among the processors in such a way that the precedence constraints between tasks are kept, and the total execution time is minimized. Cellular automata (CA) can be used for multiprocessor scheduling, but one of the difficulties in using CA is the exponentially increasing number of rules with increasing number of processor and neighborhood radius. Here, we propose a combined use of ant colony and evolutionary meta-heuristics to search the rule’s feasible space in order to find optimal rule base. Also we introduce a two dimensional cellular automata structure based on the important task attributes in the precedence task graph. The proposed scheduler that uses evolving cellular automata based on ant colony can find optimal response time for some of well known precedence task graph in the multiprocessor scheduling area.

Optimal tasks allocation is one of the most important problems in multiprocessing. Optimal assignment of tasks to a multiprocessor is an NPhard problem in general cases, and precedence task graph makes it more complex. Many factors affect optimal tasks allocation. One of them is cache reload time in multiprocessor systems. These problems exist in real-time systems, too. Due to high sensitivity of ‘time’ in real-time systems, scheduling with respect to time constraints becomes very important. This paper proposes a suboptimal scheduler for hard realtime heterogeneous multiprocessor systems considering time constraints and cache reload time simultaneously, using multiobjective genetic algorithm. In addition, it tries to propose a generalized method for real-time multiobjective scheduling in multiprocessor systems using genetic algorithms.

Grid computing is a promising technology for future computing platforms and is expected to provide easier access to remote computational resources that are usually locally limited. Scheduling is one of the active research topics in grid environments. The goal of grid task scheduling is to achieve high system throughput and to allocate various computing resources to applications. The Complexity of scheduling problem increases with the size of the grid and becomes highly difficult to solve effectively. Many different methods have been proposed to solve this problem. Some of these methods are based on heuristic techniques that provide an optimal or near optimal solution for large grids. In this paper we introduce a new task scheduling algorithm based on Ant Colony Optimization (ACO). According to the experimental results, the proposed algorithm confidently demonstrates its competitiveness with previously proposed algorithms.

Grid environment is being a service oriented infrastructure in which many heterogeneous resources participate for providing the high performance computation. On of bug issue in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. In this work we propose a solution for the vagueness and uncertainty problems based on rough set theory. Here you can see how the rough set theory is developed to deal with the problem. We also report the result of the solution obtained from the simulation in Gridsim simulator. The comparison has been made between the proposed method and UDDI and OWL-S combined method. Rough set theory shows much better precision for the cases with vagueness and uncertainty.

the concept of multi-hop CDMA cellular networks has received increasing attention now. It is a widely accepted assumption that using multi-hopping in cellular networks will increase the cellular throughput. There are several advantages of MCN such as the improved signal quality and higher coverage. Finding an available relaying path is a critical prerequisite for the success of the multi-hop cellular networks and has a great impact on the network throughput. Most works use signal strength, distance and power consumption as routing criteria, but the effect of interference specially intercell interference does not take into account in routing algorithms. For this reason, in this paper interference is calculated at any receiver during the uplink, and a novel routing scheme based on minimum interference and best link gain in each hop, is proposed. Simulation results indicate that the routing algorithm based on our proposed metric results in higher throughput than the other metrics under a certain constraint.

In this paper, we study the selection of active sensors in wireless sensor networks (WSN) subject to a limited energy consumption and a given estimation error. A cost function based on spatial correlation is derived for active sensor selection. Accordingly, a new adaptive algorithm is proposed in which the number of active sensors is adaptively determined and the best topology of active set is selected based on the add one sensor node at a time method. Simulation results show that the distortion of the optimum defined cost function is less than suboptimum one. Also, the performance of the proposed algorithm for sensor selection in computational burden is better than the other algorithm.

In recent years many researches have focused on ad-hoc networks, mainly because of their independence to any specific structure. These networks suffers from frequent and rapid topology changes that cause many challenges in their routing. Most of the routing protocols try to find a path between source and destination nodes because any path will expire, offer a short period, the path reconstruction may cause the network inefficiency. The proposed protocol build two paths between source and destination and create backup paths during the route reply process, route maintenance process and local recovery process in order to improve the data transfer and the fault tolerance. The protocol performance is demonstrated by using the simulation results obtain from the global mobile simulation software(Glomosim). The experimental results show that this protocol can decrease the packet loss ratio rather than DSR and SMR and it is useful for the applications that need a high level of reliability.

The dynamic nature of mobile nodes in mobile adhoc networks (MANETs), causes their association and disassociation to and from clusters perturb the stability of network and problem becomes worse if nodes are clusterheads (CH). Therefore cluster maintenance schemes are needed to handle new admissions and releases of node in the clusters. In this paper, we introduce a novel cluster maintenance algorithm which selects a new clusterhead from a trusty area that is defined previously based on some maintenance optimization rules. The election process is done before the current clusterhead leaves the cluster. So the routes which include this clusterhead as a middle node are less probable to break and will be more stable. Number of nodes belonging to a cluster is restricted in the proposed algorithm. In order to prevent of overusing of clusterheads' battery power, the CH selection process is invoked whenever the battery power of current CH goes below some threshold.

In this paper we proposed a new approach for improving distribution and coverage of mobile sensors in a target filed. The goal is to deploy sensors such that a target area is optimally covered by them. Proposed method works based on number of neighbors. The nodes which have the minimum overlaps with its neighbors start to move away according to summation of forces from their neighbors, boundaries and obstacles. In order to achieve optimum coverage, the effect of field corners is also calculated. For evaluating the effectiveness of our proposed algorithm, we compared the performance of our algorithm with TRI and VEC which are well known algorithms in field of mobile sensors coverage. Simulation results show that our algorithm surely surpasses TRI and VEC in coverage.

In WDM all-optical networks, physical impairments degrade optical signal to noise ratio (OSNR). Therefore, the quality of a lightpath must be checked during connection setup. Since during a lightpath setup, it may inject crosstalk on all previously established lightpaths with which it shares links or nodes, OSNR for those lightpaths should be checked as well as the OSNR of the tentative lightpath. If the number of hops for a lightpath increases, the amount of OSNR degradation increases due to crosstalk. Hence, longer paths are subject to higher QoT degradation due to crosstalk. In this paper, we enhance OSNR degradation for different lightpaths with different hop numbers in such a way that OSNR degradation for long-hop lightpaths becomes closer to short-hop lightpaths, i.e., a fair OSNR degradation. We provide a mechanism to estimate crosstalk risk at the routing process by which we can find the nodes with positive crosstalk risk. Then, rerouting procedure is used to reduce the crosstalk risks of these nodes by migrating some of lightpaths that pass through the nodes.

Wireless sensor networks are limited in energy. Any routing protocol used in wireless sensor networks should take into consideration the time sensitive nature of the traffic in such networks, along with the amount of energy left for each sensor. In this paper we present an energy aware packet delivery mechanism for probabilistic Quality of Service (QoS) guarantee in wireless sensor networks. Each node takes routing decisions based on geographic progress towards the destination sink, required end-to-end total reaching probability, delay at the candidate forwarding node and residual energy. The simulation results demonstrate that the proposed protocol effectively improves the energy usage efficiency of the sensor nodes, maximizing the lifetime of the entire sensor network, while keeping guaranteed QoS.

The discovery of suitable web services for a given task is one of the major operations in SOA architecture, and researches are being done to automate this step. For the large amount of available Web services that can be expected in real-world settings, the computational costs of automated discovery based on semantic matchmaking become important. To make a discovery engine a reliable software component, we must aim at minimizing both the mean and the variance of the duration of the discovery task. For this, we present an extension for discovery engines in SWS environments that exploit structural knowledge and previous discovery results for reducing the search space of consequent discovery operations. Our prototype implementation shows significant improvements when applied to the Stanford SWS Challenge scenario and dataset.

In present study, in order to improve the performance and reduce the amount of power which is dissipated in heterogeneous multicore processors, the ability of detecting the program execution phases is investigated. The program’s execution intervals have been classified in different phases based on their throughput and the utilization of the cores. The results of implementing the phase detection technique are investigated on a single core processor and also on a multi-core processor. To minimize the profiling overhead, an algorithm for the dynamic adjustment of the profiling intervals is presented. It is based on the behavior of the program and reduces the profiling overhead more than three fold. The results are obtained from executing multiprocessor benchmarks on a given processor. In order to show the program phases clearly, throughput and utilization of execution intervals are presented on a scatter plot. The results are presented for both fixed and variable intervals.

This paper proposes an algorithm for the detection of resistive delay faults in deep submicron technology using dynamic strength scaling, which is applicable for 45 nm and below. The approach uses an advanced coding system to build logical functions that are sensitive to strength and able to detect even the slightest voltage changes in the circuit. Such changes are caused by interconnection resistive behavior and result in timing-related defects.

Recent advancements in the field of multimedia and wireless communications have led to a wide array of application and services requiring high data processing rate at minimal power consumption. This new generation of data hungry portable devices requires power efficient hardware solutions where the operational specifications are as important as objective functionality. Conventional processing solutions like MIPS fall short on real time computational intensive operations due to large software overhead. This class of applications demands dedicated hardware units like Application Specific Processors (ASP) working as hardware accelerators for intensive data processing operations. In this paper we describe a novel Register Transfer Level (RTL) synthesis process of a power and throughput optimized ASP for a sample application. The ASP implemented on an FPGA, can serve as a hardware accelerator for system on chip (SOC) or as a standalone Application Specific Integrated Circuit (ASIC) at silicon level.