This paper illustrates various content sharing systems that take advantage of cloud's storage and computational resources as well as their supporting conventional technologies. First, basic technology concepts supporting cloud-based systems from a client-server to cloud computing as well as their relationships and functional linkages are shown. Second, the taxonomy of cloud-based system models from the aspect of multiple clouds' interoperability is explained. Interoperability can be categorized into provider-centric and client-centric scenarios. Each can be further divided into federated clouds, hybrid clouds, multi-clouds and aggregated service by broker. Third, practical cloud-based systems related to contents sharing are reported and their characteristics are discussed. Finally, future direction of cloud-based content sharing is suggested.

Scheduling plays a key role in MapReduce systems. In this paper, we explore the efficiency of an MapReduce cluster running lots of independent and continuously arriving MapReduce jobs. Data locality and load balancing are two important factors to improve computation efficiency in MapReduce systems for data-intensive computations. Traditional cluster scheduling technologies are not well suitable for MapReduce environment, there are some in-used schedulers for the popular open-source Hadoop MapReduce implementation, however, they can not well optimize both factors. Our main objective is to minimize total flowtime of all jobs, given it's a strong NP-hard problem, we adopt some effective heuristics to seek satisfied solution. In this paper, we formalize the scheduling problem as job selection problem, a load balance aware job selection algorithm is proposed, in task level we design a strict data locality tasks scheduling algorithm for map tasks on map machines and a load balance aware scheduling algorithm for reduce tasks on reduce machines. Comprehensive experiments have been conducted to compare our scheduling strategy with well-known Hadoop scheduling strategies. The experimental results validate the efficiency of our proposed scheduling strategy.

In this paper we report on an approach inspired by Ant Colony Optimization (ACO) to provide a fault tolerant and efficient means of transferring data in dynamic environments. We investigate the problem of distributing data between a client and server by using pheromone equations. Ants choose the best source of food by selecting the strongest pheromone trail leaving the nest. The pheromone decays over-time and needs to be continually reinforced to define the optimum route in a dynamic environment. This resembles the dynamic environment for the distribution of data between clients and servers. Our approach uses readily available network and server information to construct a pheromone that determines the best server from which to download data. We demonstrate that the approach is self-optimizing and capable of adapting to dynamic changes in the environment.

In large-scale collaborative computing, users and resource providers organize various Virtual Organizations (VOs) to share resources and services. A VO organizes other sub-VOs for the purpose of achieving the VO goal, which forms hierarchical VO environments. VO participants agree upon a certain policies, such as resource sharing amount or user accesses. In this letter, we provide an optimal resource sharing mechanism in hierarchical VO environments under resource sharing agreements. The proposed algorithm enhances resource utilization and reduces mean response time of each user.

In parallel computing systems using the master/worker model for distributed grid computing, as the size of handling data grows, the increase in the data transmission time degrades the performance. For divisible workload applications, therefore, multiple-round scheduling algorithms have been being developed to mitigate the adverse effect of longer data transmission time by dividing the data into chunks to be sent out in multiple rounds, thus overlapping the times required for computation and transmission. However, a standard multiple-round scheduling algorithm, Uniform Multi-Round (UMR), adopts a sequential transmission model where the master communicates with one worker at a time, thus the transmission capacity of the link attached to the master cannot be fully utilized due to the limits of worker-side capacity. In the present study, a Parallel Transferable Uniform Multi-Round algorithm (PTUMR) is proposed. It efficiently utilizes the data transmission capacity of network links by allowing chunks to be transmitted in parallel to workers. This algorithm divides workers into groups in a way that fully uses the link bandwidth of the master under some constraints and considers each group of workers as one virtual worker. In particular, introducing a Grouping Threshold effectively deals with very heterogeneous workers in both data transmission and computation capacities. Then, the master schedules sequential data transmissions to the virtual workers in an optimal way like in UMR. The performance evaluations show that the proposed algorithm achieves significantly shorter turnaround times (i.e., makespan) compared with UMR regardless of heterogeneity of workers, which are close to the theoretical lower limits.

Recently, Trust has been recognized as an important factor for Grid computing security. In this paper, we propose a trust model in Grid system. It consists of Application Domain (AD), Client Domain (CD), Resource Domain (RD), and Trust Manager (TM). TM controls the relationship between RD and CD depending on the trust level value of each client and classification of each resource. Performance criteria are makespan and utilization. We evaluated our trust model in six scheduling algorithms in nine scenarios. The simulation results show that the proposed trust model improves the performance in all scheduling algorithms.

In a grid computing environment, the network characteristics such as bandwidth and latency affect the task performance. The demands for bandwidth of wide-area networks become large and it reaches more than 100 Gbps. In this article, we focus on parallel routes transmission, such as link aggregation, to realize large bandwidth network. The performance of grid computing with parallel routes transmission is evaluated on the emulated wide-area network.

Due to the dynamic nature and uncertainty of grid computing, system reliability can become very unpredictable. Thus, a well-defined scheduling mechanism that provides high system availability for grid applications is required. In this letter, we propose a SLA-constrained policy-based scheduling mechanism to enhance system performance in grid. Also, we implement the proposed model and show that our policy-based scheduling mechanism can guarantee high system availability as well as support load balancing on an experimental basis.

In this paper, we propose an extension to GridFTP that optimizes its performance by dynamically adjusting the number of parallel TCP connections. GridFTP has been used as a data transfer protocol to effectively transfer a large volume of data in Grid computing. GridFTP supports a feature called parallel data transfer that improves throughput by establishing multiple TCP connections in parallel. However, for achieving high GridFTP throughput, the number of TCP connections should be optimized based on the network status. In this paper, we propose an automatic parallelism tuning mechanism called GridFTP-APT (GridFTP with Automatic Parallelism Tuning) that adjusts the number of parallel TCP connections according to information available to the Grid middleware. Through simulations, we demonstrate that GridFTP-APT significantly improves the performance of GridFTP in various network environments.

This paper investigates migration effects of parallel genetic algorithms (GAs) on the line topology of heterogeneous computing resources. Evolution process of parallel GAs is evaluated experimentally on two types of arrangements of heterogeneous computing resources: the ascending and descending order arrangements. Migration effects are evaluated from the viewpoints of scalability, chromosome diversity, migration frequency and solution quality. The results reveal that the performance of parallel GAs strongly depends on the design of the chromosome migration in which we need to consider the arrangement of heterogeneous computing resources, the migration frequency and so on. The results contribute to provide referential scheme of implementation of parallel GAs on heterogeneous computing resources.

In this paper, we describe a single system image (SSI) architecture for distributed systems. The SSI architecture is constructed through three components: single process space (SPS), process migration, and dynamic load balancing. These components attempt to share all available resources in the cluster among all executing processes, so that the distributed system operates like a single node with much more computing power. To this end, we first resolve broken pipe problems and bind errors on server socket in process migration. Second, we realize SPS based on block process identifier (PID) allocation. Finally, we design and implement a dynamic load balancing scheme. The dynamic load balancing scheme exploits our novel metric, effective tasks, to effectively distribute jobs to a large distributed system. The experimental results show that these three components present scalability, new functionality, and performance improvement in distributed systems.

We present a parallel multilevel fast multipole algorithm aimed at low cost parallel computers such as GRID computer environments and clusters of workstations. The algorithm is a scheduling algorithm where work packets are handled in a certain order to ensure minimal idle time of the processors and to avoid simultaneous bursts of communication between the processors. The algorithm is implemented on a method of moment discretization of a two-dimensional TM electromagnetic scattering problem. Examples of several optical devices with a size up to 28 500 wavelengths are presented.

To make the best use of the resources in a shared grid environment, an application scheduler must make a prediction of available performance on each resource. In this paper, we examine the problem of predicting available CPU performance in time-shared grid system. We present and evaluate a new and innovative method to predict the one-step-ahead CPU load in a grid. Our prediction strategy forecasts the future CPU load based on the variety tendency in several past steps and in previous similar patterns, and uses a polynomial fitting method. Our experimental results on large load traces collected from four different kinds of machines demonstrate that this new prediction strategy achieves average prediction errors which are between 22% and 86% less than those incurred by four previous methods.

Resource performance prediction is known to be useful in resource scheduling in the Grid. The disk I/O workload is another important factor that influences the performance of the CPU and the network which are commonly used in resource scheduling. In the case of disk I/O workload time-series, the adaptation of a prediction algorithm to new time-series should be rapid. Further, the prediction should ensure that the prediction error is minimum in the heterogeneous environment. The storage workload (i.e., the disk I/O load) is a dynamic variable. A prediction parameter based on the characteristics of the current workload must be prepared for prediction purposes. In this paper, we propose and implement the OPHB (On-Line Parameter History Bank). This is a method that stabilizes the incoming disk I/O workload time-series fairly quickly with the help of accurately determined ESM (Exponential Smoothing Method) parameters. The parameters are drawn from a history database. In the case of forecasting with ESM, a smoothing parameter must be specified in advance. If the parameter is statically estimated from observed data found in previous executions, the forecasts would be inaccurate because they do not capture the actual I/O behavior. The smoothing parameter has to be adjusted in accordance with the shape of the new disk I/O workload. The ESM algorithms utilise one of the accumulated parameter histories chronicled by OPHB's Deposit operation. When a new time-series is started, an appropriate parameter value is looked up in the Bank by OPHB's Lookup operation. This is used for the time-series. This process is fully adaptive. We evaluate the proposed method with SES (Single Exponential Smoothing) and ARRSES (Auto-Responsive SES) methods.

In this paper, we present a resource monitoring and selection method for rapid turnaround grid applications (for example, within 10 seconds). The novelty of our method is the distributed evaluation of resources for rapidly selecting the appropriate idle resources. We integrate our method with a widely used resource management system, namely the Monitoring and Discovery System 2 (MDS2), and compare our method with the original MDS2 in terms of the performance and the scalability. The performance is measured using a 64-node cluster of PCs and the scalability is analyzed using a theoretical model and the measured performance. The experimental results show that our method reduces the resource selection time by 82%, as compared with the original MDS2. The scalability analysis also indicates that our method can keep the resource selection time within 1 second, up to 500 nodes in local-area-network (LAN) environments. In addition, some simulation results are presented to estimate the impact of our method for wide-area-network (WAN) environments.

The concept of grid computing emerged with the appearance of high-speed network. Effective grid worker (i.e., computing resource) selection mechanism is important to achieve reliable grid computing system since each worker participate in grid computing is heterogeneous. In this paper, we suggest a credible worker selection mechanism that maximizes grid computing performance by allocating appropriate tasks to each grid worker. Diverse workers can be used efficiently by grid applications through the ranking process of worker's credibility. Initially, the rank of each grid worker's credibility is decided considering static information only such as CPU speed, RAM size, storage capacity and network bandwidth. And then, the rank is refined by using dynamic information such as failure rate, turn around time provided after the task is completed, and correctness of the return value. In the experiments, we find that the proposed mechanism provides improved grid computing performance with high credibility.

Recent improvements in the performance of end-computers and networks have made it feasible to construct a grid system over the Internet. A grid environment consists of many computers, each having a set of components and a distinct performance. These computers are shared among many users and managed in a distributed manner. Thus, it is important to focus on a situation in which the computers are used unevenly due to decentralized management by different task schedulers. In this study, which is a preliminary investigation of the performance of task allocation schemes employed in a decentralized environment, the average execution time of a long-lived task is analytically derived using the M/G/1-PS queue. Furthermore, assuming a more realistic condition, we evaluate the performance of some task allocation schemes adopted in the analysis, and clarify which scheme is applicable to a realistic grid environment.

Grid computing technologies enable large-scale aggregation and sharing of resources via wide-area networks. Grid technologies include elements such as security, job description, information gathering, scheduling, and resource dispatching, among others. In this paper, we address information gathering and focus on providing a domain-based model for network information measurement using Network Weather Service (NWS) on Grid computing environments.

Grid computing presents a new trend to distributed and Internet computing to coordinate large scale resources sharing and problem solving in dynamic, multi-institutional virtual organizations. Due to the diverse failures and error conditions in the grid environments, developing, deploying, and executing applications over the grid is a challenge, thus dependability is a key factor for grid computing. This paper presents a dependable grid computing framework, called DRIC, to provide an adaptive failure detection service and a policy-based failure handling mechanism. The failure detection service in DRIC is adaptive to users' QoS requirements and system conditions, and the failure-handling mechanism can be set optimized based on decision-making method by a policy engine. The performance evaluation results show that this framework is scalable, high efficiency and low overhead.

Efficient task scheduling is critical for achieving high performance in grid computing systems. Existing task scheduling algorithms for grid environments usually assume that the performance prediction for both tasks and resources is perfectly accurate. In practice, however, it is very difficult to achieve such an accurate prediction in a heterogeneous and dynamic grid environment. Therefore, the performance of a task scheduling algorithm may be significantly influenced by prediction inaccuracy. In this paper, we study the influence of inaccurate predictions on task scheduling in the contexts of task selection and processor selection, which are two critical phases in task scheduling algorithms. We develop formulas for the misprediction degree, which is defined as the probability that the predicted values for the performances of tasks and processors reveal different orders from their real values. Based on these formulas, we also investigate the effect of several key parameters on the misprediction degree. Finally, we conduct extensive simulation for the sensitivities of some existing task scheduling algorithms to the prediction errors.