Kalim Qureshi

Adaptive checkpointing strategy to tolerate faults in economy based grid

In this paper, we develop a fault tolerant job scheduling strategy in order to tolerate faults gracefully in an economy based grid environment. We propose a novel adaptive task checkpointing based fault tolerant job scheduling strategy for an economy based grid. The proposed strategy maintains a fault index of grid resources. It dynamically updates the fault index based on successful or unsuccessful completion of an assigned task. Whenever a grid resource broker has tasks to schedule on grid resources, it makes use of the fault index from the fault tolerant schedule manager in addition to using a time optimization heuristic. While scheduling a grid job on a grid resource, the resource broker uses fault index to apply different intensity of task checkpointing (inserting checkpoints in a task at different intervals).To simulate and evaluate the performance of the proposed strategy, this paper enhances the GridSim Toolkit-4.0 to exhibit fault tolerance related behavior. We also compare “checkpointing fault tolerant job scheduling strategy” with the well-known time optimization heuristic in an economy based grid environment. From the measured results, we conclude that even in the presence of faults, the proposed strategy effectively schedules grid jobs tolerating faults gracefully and executes more jobs successfully within the specified deadline and allotted budget. It also improves the overall execution time and minimizes the execution cost of grid jobs.

Performance evaluation of fault tolerance techniques in grid computing system

As fault tolerance is the ability of a system to perform its function correctly even in the presence of faults. Therefore, different fault tolerance techniques (FTTs) are critical for improving the efficient utilization of expensive resources in high performance grid computing systems, and an important component of grid workflow management system. This paper presents a performance evaluation of most commonly used FTTs in grid computing system. In this study, we considered different system centric parameters, such as throughput, turnaround time, waiting time and network delay for the evaluation of these FTTs. For comprehensive evaluation we setup various conditions in which we vary the average percentage of faults in a system, along with different workloads in order to find out the behavior of FTTs under these conditions. The empirical evaluation shows that the workflow level alternative task techniques have performance priority on task level checkpointing techniques. This comparative study will help to grid computing researchers in order to understand the behavior and performance of different FTTs in detail.

A hybrid fault tolerance technique in grid computing system

In order to achieve high level of reliability and availability, the grid infrastructure should be a foolproof fault tolerant. Fault tolerance plays a key role in order to assert availability and reliability of a grid system. Since the failure of resources affects job execution fatally, fault tolerance service is essential to satisfy QoS requirement in grid computing.In this paper we proposed two hybrid fault tolerance techniques (FTTs) that are called alternate task with checkpoint and alternate task with retry. These proposed hybrid FTTs inherit the good features and overcome the limitations of workflow level FTT and task level FTT. We evaluate the performance of our proposed FTTs under different experimental environments. Finally, we conclude that alternate task with checkpoint improves the reliability of a grid system more significantly than alternate task with retry.

A Hybrid Approach of Using Symmetry Technique for Brain Tumor Segmentation

Tumor and related abnormalities are a major cause of disability and death worldwide. Magnetic resonance imaging (MRI) is a superior modality due to its noninvasiveness and high quality images of both the soft tissues and bones. In this paper we present two hybrid segmentation techniques and their results are compared with well-recognized techniques in this area. The first technique is based on symmetry and we call it a hybrid algorithm using symmetry and active contour (HASA). In HASA, we take refection image, calculate the difference image, and then apply the active contour on the difference image to segment the tumor. To avoid unimportant segmented regions, we improve the results by proposing an enhancement in the form of the second technique, EHASA. In EHASA, we also take reflection of the original image, calculate the difference image, and then change this image into a binary image. This binary image is mapped onto the original image followed by the application of active contouring to segment the tumor region.

Performance Comparison of ICE, HORB, CORBA and Dot NET Remoting Middleware Technologies

Distributed computing systems are designed to solve computationally intensive problems with the help of convergence of computing resources scattered across the network. Distributed computing object middleware technologies have bring revolutionary concepts in the world of distributed computing and also made the building of distributed computing applications more efficient and nearer to real world. But the selection of most efficient distributed computing object middleware technology on the basis of different performance metrics is an important research issue. In this paper we are presenting the performance evaluation and comparison of distributed computing object middleware technologies which include Common Object Request Broker Architecture (CORBA), Internet Communication Engine (ICE), HORB, and TCP based Dot NET Remoting. Because these distributed computing object middleware technologies have not been evaluated and compared collectively on the basis of performance metrics which include overhead generation and round trip latency. The results that we have gathered showed that ICE is showing better performance in terms of overhead generation. And HORB has showed reduced round trip latency as compared to other middleware’s.

Task partitioning, scheduling and load balancing strategy for mixed nature of tasks

Load balancing and task partitioning are important components of distributed computing. The optimum performance from the distributed computing system is achieved by using effective scheduling and load balancing strategy. Researchers have well explored CPU, memory, and I/O-intensive tasks scheduling, and load balancing techniques. But one of the main obstacles of the load balancing technique leads to the ignorance of applications having a mixed nature of tasks. This is because load balancing strategies developed for one kind of job nature are not effective for the other kind of job nature. We have proposed a load balancing scheme in this paper, which is known as Mixed Task Load Balancing (MTLB) for Cluster of Workstation (CW) systems. In our proposed MTLB strategy, pre-tasks are assigned to each worker by the master to eliminate the worker’s idle time. A main feature of MTLB strategy is to eradicate the inevitable selection of workers. Furthermore, the proposed MTLB strategy employs Three Resources Consideration (TRC) for load balancing (CPU, Memory, and I/O). The proposed MTLB strategy has removed the overheads of previously proposed strategies. The measured results show that MTLB strategy has a significant improvement in performance.

Replication based fault tolerant job scheduling strategy for economy driven grid

In this paper, the problem of fault tolerance in grid computing is addressed and a novel adaptive task replication based fault tolerant job scheduling strategy for economy driven grid is proposed. The proposed strategy maintains fault history of the resources termed as resource fault index. Fault index entry for the resource is updated based on successful completion or failure of an assigned task by the grid resource. Grid Resource Broker then replicates the task (submitting the same task to different backup resources) with different intensity, based on vulnerability of resource towards faults suggested by resource fault index. Consequently, in case of possible fault at a resource the results of replicated task(s) on other backup resource(s) can be used. Hence, user job(s) can be completed within specified deadline and assigned budget, even on the event of faults at the grid resource(s).Through extensive simulations, performance of the proposed strategy is evaluated and compared with the Time Optimization and Checkpointing based Strategy in an economy driven grid environment. The experimental results demonstrate that in the presence of faults, proposed fault tolerant strategy improves the number of tasks completed with varied deadline and fixed budget as well as number of tasks completed with varied budget and fixed deadline. Additionally, the proposed strategy used a smaller percentage of deadline time as compare to both Time Optimization and Checkpointing based Strategy. Although the proposed strategy has a percentage of budget spent greater than that of Time Optimization Strategy and Checkpointing based Strategy, it is accepted as a proposed strategy in time optimization where the main objective is to maximize tasks completed within a given deadline. It can be concluded from the experiments that the proposed strategy shows improvement in satisfying the user QoS requirements. It can effectively schedule tasks and tolerate faults gracefully even in the presence of failures, but the costs are slightly higher in terms of budget consumption. Hence, the proposed fault tolerant strategy helps in sustaining user’s faith in the grid, by enabling the grid to deliver reliable and consistent performance in the presence of faults.

A reliable checkpoint storage strategy for grid

Computational grids are composed of heterogeneous autonomously managed resources. In such environment, any resource can join or leave the grid at any time. It makes the grid infrastructure unreliable in nature resulting in delay and failure of executing jobs. Thus, fault tolerance becomes a vital aspect of grid for realizing reliability, availability and quality-of-service. The most common technique, for achieving fault tolerance, used in High Performance Computing is rollback recovery. It relies on the availability of checkpoints and stability of storage media. Thus the checkpoints are replicated on storage media. It increases the job execution time, if replication is not done in proper manner. Furthermore, dedicating powerful resources solely as checkpoint storage results in loss of computation power of these resources. It may results in bottlenecks, when the load on the network is high. To address the problem, in this paper checkpoint replication based fault tolerance strategy named as Reliable Checkpoint Storage Strategy (RCSS) is proposed. In RCSS, the checkpoints are replicated on all checkpoint servers in the grid in distributed manner. It decreases the checkpoint replication time and in turn improves the overall job execution time. Additionally, if a resource fails during execution of a job, the RCSS restarts the job from its last valid checkpoint taken from any checkpoint server in the grid. Furthermore to increase the grid performance, CPU cycles of checkpoint servers are also utilized during high load on network. To evaluate the performance of RCSS simulations are carried out using GridSim. The simulation results show that RCSS outperforms in intra-cluster Checkpoint wave completion time by 12.5 % with varying number of checkpoint servers. RCSS also reduces checkpoint wave completion time by 50 % with varying number of clusters. Additionally RCSS reduces replication time within cluster by 39.5 %.

A COMPARATIVE STUDY OF PARALLELIZATION STRATEGIES FOR FRACTAL IMAGE COMPRESSION ON A CLUSTER OF WORKSTATIONS

In this paper we implement and compare the performance of the Message Passing Interface (MPI) static master-worker and three strategies of MPI task farm implementations for fractal image compression on a Beowulf cluster of workstations, namely Local Predecimation with Range Index Communication (LPRI), Global Predecimation with Range Communication (GPR) and No Predecimation with Range Index Communication (NPRI). Our results show that the MPI task farm implementations balance the load effectively among workers as compared to the MPI static master-worker implementation. The task farm strategies are compared by measuring their speedup and worker idle time cost.

An efficient grid scheduling strategy for data parallel applications

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes’ resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.