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Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various reports within the issue on topics including strategies to improve data locality in heterogeneous clusters, dynamic data replication strategy, and grid performance enhancement through processor allocation.

Grid scheduling is one of the most known NP-complete problems. Heterogeneity of machines causes mapping of tasks to be a challenging problem. Several meta-heuristic algorithms have been designed to reach optimality as possible. Sort-Mid is a recent efficient scheduler of excellent resource utilization. Its strategy was based on computing the mean of two consecutive middle values in the sorted list of completion time. Thereafter, it maps the task with the maximum mean to the machine having the minimum completion time. However, Range-Suffrage scheduler obtains a better makespan. It was built on searching for the maximum average value of completion times among certain tasks. These tasks were selected according to their suffrage values under specified constraint. This paper proposes RSSM as highly efficient scheduler. It combines Sort-Mid and Range-Suffrage algorithms to achieve the maximum resource utilization and the minimum makespan. RSSM methodology is based on sorting only the completion time corresponding to tasks satisfying Range-Suffrage constraint. Experimental tests manifest the superiority of proposed algorithm over the two original meta- heuristics and other promising algorithms such as Min-Min. [ABSTRACT FROM AUTHOR]

Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research. [ABSTRACT FROM AUTHOR]

I/O- and data-intensive workloads, as represented by the Grand Challenge problems, multimedia applications, cosmology simulations, climate modeling, and large collaborative visualizations, to name a few, entail innovative approaches to alleviate the I/O (both bandwidth and data access) performance bottlenecks. The advent of low-cost hardware platforms, such as the Beowulf clusters, has opened up numerous possibilities in mass data storage, scalable architectures, and large-scale simulations. The objective of this Special Issue is to discuss problems and solutions, to identify new issues, and to help shape future research and development directions in these areas. From these perspectives, the Special Issue addresses the problems encountered at the hardware, middleware, and application levels, providing conceptual as well as empirical treatments. [ABSTRACT FROM AUTHOR]

Wireless computing has revolutionized our life with the technological advancement from the traditional networking into a new epoch for communication in ad hoc decorum. An energy efficient network of sensors based on wireless communication and networking principles can enhance the effectiveness of computing during unpredictable circumstances. If a computable required resource is readily available within the reachable region and it is identified to be idle and it is ready to share the corresponding information from its end without affecting the normal behavior of the device or the node, then there exists an opportunity to utilize those resources for computing. Opportunistic computing has a great potential of growth in the field of wireless ad hoc network computing. In traditional network computing technology, mobile computing, grid computing, distributed computing, ubiquitous computing and cloud computing, formerly ensues the communication with minimal resources currently available at the terminal point. This paper proposes a novel framework for an effective utilization of sharable resources, which are available within the reachable region, by creating an opportunity to frame an opportunistic computing while preserving the user's privacy. The proposed framework adopts a bio-inspired technique for identifying and collecting resources information, and thereby recognizes which resource is ready to participate in the opportunistic computing. Experimental results of a system that implements the bio-inspired technique along with the natural behavior of the bee colony approach was analyzed and found that the proposed system shows comparatively high performance in terms of computation resource searching, identifying, emergency data transfer, and participative node privacy preserving. [ABSTRACT FROM AUTHOR]

Nowadays, the adoption of Cloud Computing platforms and Service Computing technologies are almost natural for the different e-Science communities. Cost benefits for data-intensive applications, ease of access, rich and varied offers for services are examples of positive returns by users. However, beyond this favorable welcome for the technology, some research problems remain and are still challenging. In this paper, we focus on the problems of automatically deploying IaaS for computing and for data management, using the SlapOS Cloud. The core of the system is a distributed protocol for orchestrating data and compute nodes. Using this interaction scheme, users are able to deploy, without any system administrator intervention, a PaaS inside the IaaS basically a Desktop Grid middleware. The aim of this paper is to demonstrate that the Desktop Grid and Cloud paradigms may merge and may be widely used by non-experts in the different areas of e-Science. We propose a fully self-organized volunteer Cloud for researchers where they can carry out e-Science experiments and process large amounts of data in a coherent way. [ABSTRACT FROM AUTHOR]

The Software as a Service (SaaS) methodology is a key paradigm of Cloud computing. In this paper, we focus on an interesting topic—to dynamically host services on existing production Grid infrastructures. In general, production Grids normally employ a Job-Submission-Execution (JSE) model with rigid access interfaces. In this paper, we implement the Cyberaide onServe, a lightweight middleware with a virtual appliance. The Cyberaide onServe implements the SaaS model on production Grids by translating the SaaS model to the JSE model. The Cyberaide onServe can be deployed on demand in a virtual appliance, host users’ software as a Web service, accept Web service invocations; finally, the Cyberaide onServe can execute them on production Grids. We have deployed the Cyberaide onServe on the TeraGrid and the test results show that the Cyberaide onServe can provide SaaS functionalities with a good performance. [ABSTRACT FROM AUTHOR]

Despite many algorithms for embedding graphs on unbounded grids, only a few results on embedding graphs on restricted grids have been published. In this paper, we study the problem of embedding paths and cycles on solid grid graphs. We show that a cycle of length k is unit-length embeddable on a solid grid graph G if k is an even integer between four and the length of the longest cycle of G. In addition, our result shows that a path of length k is unit-length embeddable on G, between its two given vertices s and t, if $$k\le L$$ and $$k\equiv L (\mathrm{mod}\ 2)$$ , in which L is the length of the longest path between s and t. Our presented two algorithms show that such embeddings can be found in linear time for cycles and quadratic time for paths, with respect to the size of graph G. In the case of rectangular grid graphs, the running time of the algorithms can be improved to O( k) and O $$(k^2)$$ , respectively. In addition, we extend our results to $$m\times n\times o$$ 3D grids. A application of our result is in the interconnection network mapping in parallel processing. [ABSTRACT FROM AUTHOR]

Grid performance are usually measured by the average turnaround time of all jobs in the system. A job's turnaround time consists of two parts: queue waiting time and actual execution time, which in a heterogeneous grid environment, are severely affected by the resource fragmentation and speed heterogeneity factors. Most existing processor allocation methods focus on one of these two factors only. This paper proposes processor allocation methods, which consider both resource fragmentation and speed heterogeneity, to improve system performance of heterogeneous grids. Extensive simulation studies have been conducted to show that the proposed methods can effectively deliver better performance under most resource and workload conditions. [ABSTRACT FROM AUTHOR]

Grid architecture integrates geographically distributed nodes to manage and provide resources to execute scientific applications. For data locality, applications with different computational phases require data redistribution for realignment. The tradeoff between high efficiency computation and communication cost of data redistribution accompanies. This paper introduces a research model and two methods to derive new lists of processor logical id according to the characteristics of heterogeneous network. Both methods provide choices of more low-cost communication schedules in grid. The simulations show both proposed methods yield outstanding performance in grid. [ABSTRACT FROM AUTHOR]

How to guarantee user's QoS (Quality of Service) demands become increasingly important in a service-oriented grid environment. Current research on grid resource advance reservation, a well known and effective mechanism to guarantee QoS, fails to adapt to dynamic behavior of grid, and cannot solve imprecise deny of reservation request problem efficiently. For this, enabling system architecture for advance reservation is proposed. Virtual resource container (VRC) is adopted to alleviate a negative effect resulted from dynamic behavior of grid and QoS deviation distance (QDD) based logical resource selection algorithm is put forward to decrease imprecise reject ratio of reservation. At last, this new architecture is deployed to campus grid, and two illustrative experiments are conducted. Experimental results show that system architecture for advance reservation proposed in this paper can alleviate negative influence of grid resource dynamic fluctuation and avoid imprecise reject of advance reservation request effectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Cloud computing has been developed as a means to allocate resources efficiently while maintaining service-level agreements by providing on-demand resource allocation. As reactive strategies cause delays in the allocation of resources, proactive approaches that use predictions are necessary. However, due to high variance of cloud host load compared to that of grid computing, providing accurate predictions is still a challenge. Thus, in this paper we have proposed a prediction method based on Long Short-Term Memory Encoder–Decoder (LSTM-ED) to predict both mean load over consecutive intervals and actual load multi-step ahead. Our LSTM-ED-based approach improves the memory capability of LSTM, which is used in the recent previous work, by building an internal representation of time series data. In order to evaluate our approach, we have conducted experiments using a 1-month trace of a Google data centre with more than twelve thousand machines. Our experimental results show that while multi-layer LSTM causes overfitting and decrease in accuracy compared to single-layer LSTM, which was used in the previous work, our LSTM-ED-based approach successfully achieves higher accuracy than other previous models, including the recent LSTM one. [ABSTRACT FROM AUTHOR]

The effectiveness of loop self-scheduling schemes has been shown on traditional multiprocessors in the past and computing clusters in the recent years. However, parallel loop scheduling has not been widely applied to computing grids, which are characterized by heterogeneous resources and dynamic environments. In this paper, a performance-based approach, taking the two characteristics above into consideration, is proposed to schedule parallel loop iterations on grid environments. Furthermore, we use a parameter, SWR, to estimate the proportion of the workload which can be scheduled statically, thus alleviating the effect of irregular workloads. Experimental results on a grid testbed show that the proposed approach can reduce the completion time for applications with regular or irregular workloads. Consequently, we claim that parallel loop scheduling can benefit applications on grid environments. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical model to evaluate the performance of grid resources when availability of the resources is taken into account. The proposed model uses continuous time Markov chains (CTMCs) to model the failure-repair behavior of a grid resource. In grid computing environment, a resource not only may fail during task execution, but also it can cancel its membership at any time. Hence, the proposed CTMC considers the availability of a grid resource to a grid user in both failure and membership refusal situations. After modeling the availability of the resource, the mean sojourn time of grid tasks in each of the availability states is estimated. Assigning the mean sojourn times of the tasks as performance levels to each of the CTMC's states, a Markov reward model (MRM) representing the combined performance and availability measures is obtained. Computing the cumulative state probability of the CTMC and multiplying reward rates of the MRM's states to each of the corresponding state probabilities, the expected accumulated sojourn time of grid tasks in each of the grid resources is achieved. An illustrative example is presented and the results obtained from the proposed model are reported in cases where various scheduling disciplines are considered inside the grid resource to simultaneously service grid and local tasks. [ABSTRACT FROM AUTHOR]

Accurate estimation of workflow Quality of Service (QoS) enhances the efficiency of scheduling algorithms. The availability and performance variations of Grid computing resources have made this estimation a great challenge. Most workflow QoS estimation algorithms are based on static performance of resources. In this paper, based on resources availability prediction, we propose an algorithm called WQE for estimating the QoS of a Grid workflow. WQE consists of two phases: resource monitoring and analysis and workflow QoS computation. In the first phase, two prediction algorithms are proposed to stochastically predict the availability state of resources. In the second phase, the QoS of each activity is estimated based on the host availability prediction result. The QoS of basic structures is computed by aggregating the QoS of their operands. Using a tree structure corresponding to the workflow, the QoS of basic structures is used to compute the total QoS of the workflow. The simulation results on Notre Dame University trace showed that the proposed method has higher estimation accuracy in comparison with HEFT. [ABSTRACT FROM AUTHOR]

A computational grid ensures the on-demand delivery of computing resources, in a security-aware, shared, scalable, and standards-based computing environment. A major concern is how to evolve a general and an encompassing framework that guarantees users’ satisfaction measured as Quality of Services (QoS). To obtain a higher QoS, effective QoS perceived by subscribers (users) must conform to specified QoS agreements in the Service Level Agreements (SLAs) document—a legal contract between the Grid Services Provider (GSP) and users. Sometimes the effective user QoS does not conform to the specifications in the SLA because of the vagueness in linguistic definitions in the SLA. Existing approaches overcommitted resources to meet QoS. In this paper, we propose a fuzzy logic framework for calibrating a grid resources user-QoS that addresses the vagueness in linguistic definitions of the SLA document without overcommitting grid resources. [ABSTRACT FROM AUTHOR]

Graphical workflow modeling tools, such as UML and DAG, can facilitate users to express workflow process logic, but lack of abilities to carry out simulation and correctness checking. In this paper, based on Petri net, we propose a service composition oriented Grid workflow model and its related six elementary workflow patterns: sequence, condition, iteration, concurrency, synchronization, and triggering. In addition, we present our Grid workflow analysis approaches on three aspects: workflow reachability verification, workflow deadlock verification, and workflow optimization. The experimental results show that our workflow verification and optimization mechanisms are feasible and efficient. [ABSTRACT FROM AUTHOR]

As Grid computing has emerged as a technology for providing the computational resources to industries and scientific projects, new requirements arise. Nowadays, resource management has become an important research area in the Grid computing environment. To provision the appropriate resource to a corresponding application is a tedious task. So, it is important to check and verify the provisioning of the resource before the application's execution. In this paper, a resource provisioning framework has been presented that offers a resource provisioning policy, which caters to provisioned resource allocation and resource scheduling. The framework has been formally specified and verified. Formal specification and verification of the framework helps in predicting possible errors before the scheduling process itself, and thus results in efficient resource provisioning and scheduling of Grid resources. [ABSTRACT FROM AUTHOR]

The Computational Grid (CG) provides a wide distributed platform for high end computing intensive applications. Scheduling on Computational grid is known to be NP-Hard problem and requires an efficient solution. Recently, quantum inspired computing has been introduced in the literature to solve such a complex combinatorial optimization problem efficiently. Combination of Genetic Algorithm (GA) and quantum concept evolves a new meta-heuristic technique known as Quantum Genetic Algorithms (QGA). QGA is a search procedure based on evolutionary computation and Quantum Computing (QC). This paper proposes a novel technique of scheduling in computational grid using QGA. The work simulates the model to study its performance. It also makes a comparative study with a GA-based scheduling model. Simulation results reveal the effectiveness of the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated load management protocol for Peer-to-Peer (P2P) coupled federated Grid systems. The participants in the system, such as the resource providers and the consumers who belong to multiple control domains, work together to enable a coordinated federation. The coordinated load management protocol embeds a logical spatial index over a Distributed Hash Table (DHT) space for efficient management of the coordination objects; the DHT-based space serves as a kind of decentralized blackboard system. We show that our coordination protocol has a message complexity that is logarithmic to the number of nodes in the system, which is significantly better than existing broadcast based coordination protocols. The proposed load management protocol can be applied for efficiently coordinating resource brokering services of distributed computing systems such as grids and PlanetLab. Resource brokering services are the main components that control the way applications are scheduled, managed and allocated in a distributed, heterogeneous, and dynamic Grid computing environments. Existing Grid resource brokers, e-Science application work-flow schedulers, operate in tandem but still lack a coordination mechanism that can lead to efficient application schedules across distributed resources. Further, lack of coordination exacerbates the utilization of various resources (such as computing cycles and network bandwidth). The feasibility of the proposed coordinated load management protocol is studied through extensive simulations. [ABSTRACT FROM AUTHOR]

Data replication is the creation and maintenance of multiple copies of the same data. Replication is used in Data Grid to enhance data availability and fault tolerance. One of the main objectives of replication strategies is reducing response time and bandwidth consumption. In this paper, a dynamic replication strategy that is based on Fast Spread but superior to it in terms of total response time and total bandwidth consumption is proposed. This is achieved by storing only the important replicas on the storage of the node. The main idea of this strategy is using a threshold to determine if the requested replica needs to be copied to the node. The simulation results show that the proposed strategy achieved better performance compared with Fast Spread with Least Recently Used (LRU), and Fast Spread with Least Frequently Used (LFU). [ABSTRACT FROM AUTHOR]

Job scheduling plays a critical role in resource utilisation in a grid computing environment. The heterogeneity of grid resources adds some challenges to the work of job scheduling especially when jobs have dependencies which can be represented as Direct Acyclic Graphs (DAGs). Heuristics have been developed for job scheduling optimisation. This paper presents six heuristic enhancements-MMSTFT for minimising both makespan and task finish time, levelU for upward DAG levelling, TMWD for matching tasks with data, Slack for prioritising task scheduling based on slack time, LSlack for levelling the Slack heuristic, and NLPETS for non-levelling of performance effective task scheduling (PETS). The performance of LSlack is amongst the best heuristics evaluated (with BL and LMT). Additionally, heuristic enhancements MMSTS and TMWD can significantly improve the makespan of generated schedules. To facilitate performance evaluation, a DAG simulator is implemented which provides a set of tools for DAG job configuration, execution and monitoring. The components of the DAG simulator are also presented in this paper. [ABSTRACT FROM AUTHOR]

In this paper, a mathematical solution for reducing the mean number of the tasks waiting to be processed in a grid environment is proposed. The approach uses queuing theory and models the grid environment in the form of an open queuing network (QN). Applying the steady state analysis to the proposed QN and minimizing the mean number of waiting tasks within the grid environment, an equality and inequality system is obtained. Solving the equality and inequality system, subtasks arrival rates at each of the resources within the grid environment can be estimated. Applying the obtained subtasks arrival rates at each of the grid resources, the mean number of the waiting tasks in the grid environment could be minimized. To evaluate the results obtained from the proposed QN and provide a graphical representation of the grid environment a formal description of the environment in terms of generalized stochastic Petri nets (GSPNs) is presented. Steady state analyzing of the GSPN model and finding the subtask dispatching weights at each of the grid resources, subtask arrival rates can be estimated. Comparing the results obtained from two proposed approaches shows that the subtasks arrival rates achieved from QNs and GSPNs are the same. [ABSTRACT FROM AUTHOR]

Grid is a network of computational resources that may potentially span many continents. Maximization of the resource utilization hinges on the implementation of an efficient load balancing scheme, which provides (i) minimization of idle time, (ii) minimization of overloading, and (iii) minimization of control overhead. In this paper, we propose a dynamic and distributed load balancing scheme for grid networks. The distributed nature of the proposed scheme not only reduces the communication overhead of grid resources but also cuts down the idle time of the resources during the process of load balancing. We apply the proposed load balancing approach on Enhanced GridSim in order to gauge the effectiveness in terms of communication overhead and response time reduction. We show that significant savings are delivered by the proposed technique compared to other approaches such as centralized load balancing and no load balancing. [ABSTRACT FROM AUTHOR]

Computation grids and computational clouds are becoming increasingly popular in the organizations which require massive computational capabilities. Building such infrastructures makes a lucrative business case, thanks to availability of cheap hardware components and affordable software. Maintaining computational grids or cloud, however, require careful planning as in such dedicated environments, round-the-clock availability of workstations is very crucial. Ensuring uninterrupted availability, not only demands mechanism for failover redundancy but also results in constant power drainage. The tradeoff between the cost and the performance is the constant dilemma that the operations of the data centers face today. In this paper, we propose various heuristics for power-aware scheduling algorithms for scheduling jobs with dependent tasks onto the computational grid. We formulate the problem as a multi-objective function which results in various cost-performance tradeoffs each lying within the solution boundary. [ABSTRACT FROM AUTHOR]

Mobile grid, which combines grid and mobile computing, supports mobile users and resources in a seamless and transparent way. However, mobility, QoS support, energy management, and service provisioning pose challenges to mobile grid. The paper presents a tradeoff policy between energy consumption and QoS in the mobile grid environment. Utility function is used to specify each QoS dimension; we formulate the problem of energy and QoS tradeoff by utility optimization. The work is different from the classical energy aware scheduling, which usually takes the consumed energy as the constraints; our utility model regards consumed energy as one of the components of measure of the utility values, which indicates the tradeoff of application satisfaction and consumed energy. It is a more accurate utility model for abstracting the energy characteristics and QoS requirement for mobile users and resources in mobile grid. The paper also proposes a distributed energy-QoS tradeoff algorithm. The performance evaluation of our energy-QoS tradeoff algorithm is evaluated and compared with other energy and deadline constrained scheduling algorithm. [ABSTRACT FROM AUTHOR]

The currently emerging large-scale complex networks and networks of networks are becoming apparent in the pervasive supply of seamless and transparent access to heterogeneous resources and services such as network domains, applications, services and storage owned by multiple organizations. The dynamics and heterogeneous environments involved, however, pose many challenges for controlling and balancing resource access, composition and deployment across complex grid and network infrastructures. In this paper, a scheme is proposed that gives a distributed load-balancing scheme by generating almost regular resource allocation networks. This network system is self-organized and depends only on local information for load distribution and resource discovery. The in-degree of each node refers to its free resources, and the job assignment and resource discovery processes required for load-balancing are accomplished by using fitted random sampling. Simulation results show that the generated network system provides an effective, scalable, and reliable load-balancing scheme for the distributed resources in grids and networks. The proposed solution is tested with real world data and the performance is tested against a recently reported distributed algorithm for load balancing. [ABSTRACT FROM AUTHOR]

Effective data distribution techniques can significantly reduce the total execution time of a program on grid computing environments, especially for data mining applications. In this paper, we describe a linear programming formulation for the data distribution problem on grids. Furthermore, a heuristic method, named Heuristic Data Distribution Scheme (HDDS), is proposed to solve this problem. We implement two types of data mining applications, Association Rule Mining and Decision Tree Construction, and conduct experiments on grid testbeds. Experimental results show that data mining programs using the proposed HDDS to distribute data could execute more efficiently than traditional schemes could. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive statistical analysis of a variety of workloads collected on production clusters and Grids. The applications are mostly computational-intensive and each task requires single CPU for processing data, which dominate the workloads on current production Grid systems. Trace data obtained on a parallel supercomputer is also included for comparison studies. The statistical properties of workloads are investigated at different levels, including the Virtual Organization (VO) and user behavior. The aggregation procedure and scaling analysis are applied to job arrivals, leading to the identifications of several basic patterns, namely pseudo-periodicity, long range dependence (LRD), and multifractals. It is shown that statistical measures based on interarrivals are of limited usefulness and count based measures should be trusted when it comes to correlations. Other job characteristics like run time and memory consumption are also studied. A “bag-of-tasks” behavior is empirically evidenced, strongly indicating temporal locality. The nature of such dynamics in the Grid workloads is discussed. This study has important implications on workload modeling and performance predictions, and points out the need of comprehensive performance evaluation studies given the workload characteristics. [ABSTRACT FROM AUTHOR]

The grid is a promising infrastructure that can allow scientists and engineers to access resources among geographically distributed environments. Grid computing is a new technology which focuses on aggregating resources (e.g., processor cycles, disk storage, and contents) from a large-scale computing platform. Making grid computing a reality requires a resource broker to manage and monitor available resources. This paper presents a workflow-based resource broker whose main functions are matching available resources with user requests and considering network information statuses during matchmaking in computational grids. The resource broker provides a graphic user interface for accessing available and the appropriate resources via user credentials. This broker uses the Ganglia and NWS tools to monitor resource status and network-related information, respectively. Then we propose a history-based execution time estimation model to predict the execution time of parallel applications, according to previous execution results. The experimental results show that our model can accurately predict the execution time of embarrassingly parallel applications. We also report on using the Globus Toolkit to construct a grid platform called the TIGER project that integrates resources distributed across five universities in Taichung city, Taiwan, where the resource broker was developed. [ABSTRACT FROM AUTHOR]

Resources in the grid context belong to different control organizations with different interest, therefore the economic interest of each grid participant should be considered. The economic mechanism can guarantee the interest of participants in the grid with fairness and efficiency. In this paper, an economic-based resource management framework is put forward for grid computing, and then how to determine the price of resources with the economic mechanism is studied. A general equilibrium method is presented for general resources and a double auction method is proposed for special resources in the grid environment, respectively. Simulations are performed and experimental results indicate that the two methods are effective for corresponding application scenarios. [ABSTRACT FROM AUTHOR]

Due to the large amounts of data required to be processed by the typical Grid job, it is conceivable that the use of optical transport networks in Grid deployment (hence the term “Lambda Grid”) will increase. The exact topology of the interconnecting network is obtained by solving a dimensioning problem, and the outcome of this strongly depends on both the expected workload characteristics and Grid scheduling policy. Solving this combined scheduling and dimensioning problem using straightforward ILP modelling is cumbersome; however, for steady-state Grid operation, Divisible Load Theory (DLT) can yield scalable formulations of this problem. In this paper, the on-line hierarchical scheduling on a lambda Grid of workload approaching the Grid’s capacity in a two-tier Grid mode of operation is studied. A number of these algorithms are goal-driven, in the sense that target per-resource goals are obtained from the off-line solution to the Divisible Load model. We compare these on-line multiresource scheduling policies for different workloads, Grid interconnection topologies and Grid parameters. We show that these algorithms perform well in the studied scenarios when compared to a fully centralized scheduling algorithm. [ABSTRACT FROM AUTHOR]

Global communication is essential to industry, research and education. The Access Grid (AG) is a suite of hardware, software, and tools to facilitate communication and collaboration over the Internet. These resources are used at over 500 institutions worldwide to support group-to-group interactions across the Grid including collaborative research work sessions, tutorials, lectures, large-scale distributed meetings and training. This paper will provide an overview of the technology to encourage professionals to integrate benefits and tools of the Grid into their instruction and research. Furthermore, this paper will compare this new technology to more traditional videoconferencing and distributed collaborative working environments. Lastly, it will present issues and challenges that must be addressed to incorporate this momentous technology within the classroom and for collaboration throughout the world. [ABSTRACT FROM AUTHOR]

Research on peer-to-peer (P2P) networks has attracted much attention recently. A key factor is their ability to handle dynamics in the system in a distributed fashion, including ad hoc and dynamic join and departure of the peers and dynamic changes in the underlying network environment. As more and more innovating P2P applications appear, the need to support intuitive communication and synchronization among the peers in the P2P system becomes imperative. In this paper, we discuss how to build a tuple space on top of P2P systems and use on-line games as a target application. A simple API is supported for accessing the data stored in the tuple space and communicating between the peers. Through experiments and a demonstration game, we show that our system can facilitate the development of network games in a fully distributed environment. [ABSTRACT FROM AUTHOR]

This paper describes a resource broker whose main function is to match available resources to user needs. The resource broker provides a uniform interface for accessing available and appropriate resources via user credentials. We also focus on providing approximate measurement models for network-related information using NWS for future scheduling and benchmarking. We first propose a network measurement model for gathering network-related information (including bandwidth, latency, forecasting, error rates, etc.) without generating excessive system overhead. Second, we constructed a grid platform using Globus Toolkit that integrates the resources of five schools in Taichung integrated grid environment resources (TIGER). The resource broker runs on top of TIGER. Therefore, it provides security and current information about available resources and serves as a link to the diverse systems available in the Grid. [ABSTRACT FROM AUTHOR]

Irregular array redistribution has been paid attention recently since it can distribute different size of data segment to heterogeneous processors according to their computational ability. It’s also the reason why it has been kept an eye on load balance. High Performance Fortran Version 2 (HPF2) provides GEN_BLOCK distribution format which facilitates generalized block distributions. In this paper, we present a two-phase degree-reduction ( TPDR) method for scheduling HPF2 irregular array redistribution. Using a bipartite communication graph, the first phase of TPDR schedules communication links adjacent to processors that with degree greater than two. A communication step will be scheduled follow each degree-reduction iteration. The second phase of TPDR schedules remaining messages of all processors that with degree-2 and degree-1 using an adjustable coloring mechanism. An extended algorithm based on TPDR is also presented in this paper. Effectiveness of the proposed methods not only avoids node contention but also shortens the overall communication cost. The proposed methods are also practicable due to low algorithmic complexity. To evaluate the performance of our methods, we have implemented both algorithms along with the divide-and-conquer algorithm and two scheduling mechanism. The simulation results show improvement of total communication costs. [ABSTRACT FROM AUTHOR]

Several co-allocation strategies have been coupled and used to exploit rate differences among various client-server links and to address dynamic rate fluctuations by dividing files into multiple blocks of equal sizes. However, a major obstacle, the idle time of faster servers having to wait for the slowest server to deliver the final block, makes it important to reduce differences in finishing time among replica servers. In this paper, we propose a dynamic co-allocation scheme, namely Recursive-Adjustment Co-Allocation scheme, to improve the performance of data transfer in Data Grids. Our approach reduces the idle time spent waiting for the slowest server and decreases data transfer completion time. [ABSTRACT FROM AUTHOR]

Torus network topology offers many advantages such as higher speed, lower latency, better fairness, and lower energy consumption. For these kinds of benefits, nowadays, it is used to construct many parallel and distributed systems like IBM Blue Gene, IBM Sequoia, Mira, and Sugon TC8600. In parallel and distributed computing, multiple nodes act together to carry out large tasks fast. Hence, coordination is indispensable among these nodes to complete such tasks. A possible coordination method could be to elect a leader among the nodes. Along with coordination, the elected leader node also manages other activities such as task allocation, result aggregation, clock synchronization, proficient resource sharing management, and communication among the system's nodes. In this paper, we propose a lower bound Ω (N log 3 N) of message complexity on a comparison-based leader election for a 2D torus network (where N is the number of nodes in the network). Next, we sketch a new leader election algorithm (Lea-TN) considering both the node and link failures for a 2D torus network. This Lea-TN is a deterministic and robust algorithm that elects a leader for a synchronous distributed system. The algorithm chooses a leader, even when there are some link or node failures in the system. We consider the number of non-faulty links and the subsisting nodes' failure rate to elect a reliable leader. We introduce new patterns for sending messages that help reduce the number of exchanged messages and the execution time of the election process. The proposed algorithm (Lea-TN) enables a node to identify its link failures during the election also. Further, we simulate the Lea-TN algorithm and compare its performance with that of the well-known existing algorithms. [ABSTRACT FROM AUTHOR]

It is widely accepted that the asynchronous parallel methods are more suitable than the synchronous ones on a grid architecture. Indeed, they outperform the synchronous methods, because they overlap the communications of the synchronous methods with computations. However, they also usually execute more iterations than the synchronous ones and thus consume more energy. To reduce the energy consumption of the CPUs executing such methods, the Dynamic voltage and frequency scaling technique can be used. It lowers the frequency of a CPU to reduce its energy consumption, but it also decreases its computing power. Therefore, the frequency that gives the best trade-off between energy consumption and performance must be selected. This paper presents a new online frequency selecting algorithm for parallel iterative asynchronous methods running over grids. It selects a vector of frequencies that gives the best trade-off between energy consumption and performance. New energy and performance models were used in this algorithm to predict the execution time and the energy consumption of synchronous, asynchronous, or hybrid iterative applications running over grids. The proposed algorithm was evaluated on the SimGrid simulator. The experiments showed that synchronously applying the proposed algorithm to the asynchronous version of the application reduces on average its energy consumption by 22% and speeds it up by 5.72%. Finally, the proposed algorithm was also compared to a method that uses the well-known energy and delay product and the comparison results showed that it outperforms this method in terms of energy consumption and performance. [ABSTRACT FROM AUTHOR]

Large-scale distributed computing systems like grids and more recently clouds are a platform of choice for many resource-intensive applications. Workflow applications account for the majority of these applications, particularly in science and engineering. A workflow application consists of multiple precedence-constrained tasks with data dependencies. Since resources in those systems are shared by many users and applications deployed there are very diverse, scheduling is complicated. Often, the actual execution of applications differs from the original schedule following delays such as those caused by resource contention and other issues in performance prediction. These delays have further impact when running multiple workflow applications due to inter-task dependencies. In this paper, we investigate the problem of scheduling multiple workflow applications concurrently, explicitly taking into account scheduling robustness. We present a dynamic task rearrangement and rescheduling algorithm that exploits the scheduling flexibility from precedence constraints among tasks. The algorithm optimizes resource allocation among multiple workflows, and it often stops the influence of delayed execution passing to subsequent tasks. The experimental results demonstrate that our approach can significantly improve performance in multiple-workflow scheduling. [ABSTRACT FROM AUTHOR]

Grid computing being immensely based on the concept of resource sharing has always been closely associated with a lot many challenges. Growth of Resource provisioning-based scheduling in large-scale distributed environments like Grid computing brings in new requirement challenges that are not being considered in traditional distributed computing environments. Resources being the backbone of the system, their efficient management plays quite an important role in its execution environment. Many constraints such as heterogeneity and dynamic nature of resources need to be taken care as steps toward managing Grid resources efficiently. The most important challenge in Grids being the job-resource mapping as per the users' requirement in the most secure way. The mapping of the jobs to appropriate resources for execution of the applications in Grid computing is found to be an NP-complete problem. Novel algorithm is required to schedule the jobs on the resources to provide reduced execution time, increased security and reliability. The main aim of this paper is to present an efficient strategy for secure scheduling of jobs on appropriate resources. A novel particle swarm optimization-based hyper-heuristic resource scheduling algorithm has been designed and used to schedule jobs effectively on available resources without violating any of the security norms. Performance of the proposed algorithm has also been evaluated through the GridSim toolkit. We have compared our resource scheduling algorithm with existing common heuristic-based scheduling algorithms experimentally. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving more reduced cost and makespan of user's application being submitted to the Grids. [ABSTRACT FROM AUTHOR]

Grid computing is a framework for large-scale resource sharing and indexing that evolves with the goal of resource provisioning. In this paper, we develop a distributed learning automata (DLA) based on multi-swarm discrete particle swarm optimization (PSO) approach for Grid resource discovery, called distributed optimization grid (DOG) resource discovery algorithm. This algorithm makes use of swarms of particles for different computational resource metrics while a group of DLA is the control unit of each swarm of particles. The algorithm takes advantage of the PSO solution diversity to optimize the quality of delivered resource. Moreover, the recommended algorithm uses DLA as a fully distributed model for imitating the Grid infrastructure topology. Our experimental results show that DOG is fast as well as efficient and accurate. [ABSTRACT FROM AUTHOR]

The High-Performance Computing (HPC) is an infrastructure to support various research fields. The research using HPC requires big storage for large-scale of raw data and the huge computing resources to analyze. The consolidated operations of the computing center for supporting various researches using HPC are a very effective organization. The consolidated operations are able to increase the utilization. The computing center has to be re-organized continuously in order to support the various fields of research effectively and flexibly. Re-organization to reuse the limited resources enforces the update of the information for verification of the computing resources after the re-allocation of computing resources and the re-distribution of trusted connection between computing resources. Consequently, to maintain effective research support we need automated resource re-organization environment. In this paper, we build an automated update of the trusted connection for re-organized computing resource using puppet which is automated system management platform. Finally, we provide a unified verification point and efficient environment for the re-allocation and re-organization of the integrated computing center. [ABSTRACT FROM AUTHOR]

Service-oriented architecture (SOA), workflow, the Semantic Web, and Grid computing are key enabling information technologies in the development of increasingly sophisticated e-Science infrastructures and application platforms. While the emergence of Cloud computing as a new computing paradigm has provided new directions and opportunities for e-Science infrastructure development, it also presents some challenges. Scientific research is increasingly finding that it is difficult to handle 'big data' using traditional data processing techniques. Such challenges demonstrate the need for a comprehensive analysis on using the above-mentioned informatics techniques to develop appropriate e-Science infrastructure and platforms in the context of Cloud computing. This survey paper describes recent research advances in applying informatics techniques to facilitate scientific research particularly from the Cloud computing perspective. Our particular contributions include identifying associated research challenges and opportunities, presenting lessons learned, and describing our future vision for applying Cloud computing to e-Science. We believe our research findings can help indicate the future trend of e-Science, and can inform funding and research directions in how to more appropriately employ computing technologies in scientific research. We point out the open research issues hoping to spark new development and innovation in the e-Science field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Grid computing is mainly helpful for executing high-performance computing applications. However, conventional grid resources sometimes fail to offer a dynamic application execution environment and this increases the rate at which the job requests of users are rejected. Integrating emerging virtualization technologies in grid and cloud computing facilitates the provision of dynamic virtual resources in the required execution environment. Resource brokers play a significant role in managing grid and cloud resources as well as identifying potential resources that satisfy users' application requests. This research paper proposes a semantic-enabled CARE Resource Broker (SeCRB) that provides a common framework to describe grid and cloud resources, and to discover them in an intelligent manner by considering software, hardware and quality of service (QoS) requirements. The proposed semantic resource discovery mechanism classifies the resources into three categories viz., exact, high-similarity subsume and high-similarity plug-in regions. To achieve the necessary user QoS requirements, we have included a service level agreement (SLA) negotiation mechanism that pairs users' QoS requirements with matching resources to guarantee the execution of applications, and to achieve the desired QoS of users. Finally, we have implemented the QoS-based resource scheduling mechanism that selects the resources from the SLA negotiation accepted list in an optimal manner. The proposed work is simulated and evaluated by submitting real-world bio-informatics and image processing application for various test cases. The result of the experiment shows that for jobs submitted to the resource broker, job rejection rate is reduced while job success and scheduling rates are increased, thus making the resource management system more efficient. [ABSTRACT FROM AUTHOR]

With the rapid development of networking technology, grid computing has emerged as a source for satisfying the increasing demand of the computing power of scientific computing community. Mostly, the user applications in scientific and enterprise domains are constructed in the form of workflows in which precedence constraints between tasks are defined. Scheduling of workflow applications belongs to the class of NP-hard problems, so meta-heuristic approaches are preferred options. In this paper, $$\varepsilon $$ -fuzzy dominance sort based discrete particle swarm optimization ( $$\varepsilon $$ -FDPSO) approach is used to solve the workflow scheduling problem in the grid. The $$\varepsilon $$ -FDPSO approach has never been used earlier in grid scheduling. The metric, fuzzy dominance which quantifies the relative fitness of solutions in multi-objective domain is used to generate the Pareto optimal solutions. In addition, the scheme also incorporates a fuzzy based mechanism to determine the best compromised solution. For the workflow applications two scheduling problems are solved. In one of the scheduling problems, we addressed two major conflicting objectives, i.e. makespan (execution time) and cost, under constraints (deadline and budget). While, in other, we optimized makespan, cost and reliability objectives simultaneously in order to incorporate the dynamic characteristics of grid resources. The performance of the approach has been compared with other acknowledged meta-heuristics like non-dominated sort genetic algorithm and multi-objective particle swarm optimization. The simulation analysis substantiates that the solutions obtained with $$\varepsilon $$ -FDPSO deliver better convergence and uniform spacing among the solutions keeping the computation overhead limited. [ABSTRACT FROM AUTHOR]