Wide-area Grid Research Articles

Energy Efficient Security Algorithm for Power Grid Wide Area Monitoring System

Modern power grid is the most complex human-made system, which is monitored by wide-area monitoring system (WAMS). Providing time-synchronized data of power system operating states, WAMS will play a crucial role in next generation smart grid protection and control. WAMS helps secure efficient energy transmission as well as reliable and optimal grid management. As the key enabler of a smart grid, numerous sensors such as PMU and current sensors transmit real-time dynamic data, which is usually protected by encryption algorithm from malicious attacks, over wide-area-network (WAN) to power system control centers so that monitoring and control of the whole system is possible. Security algorithms for power grid need to consider both performance and energy efficiency through code optimization techniques on encryption and decryption. In this paper, we take power nodes (sites) as platforms to experimentally study ways of energy consumptions in different security algorithms. First, we measure energy consumptions of various security algorithms on CrossBow and Ember sensor nodes. Second, we propose an array of novel code optimization methods to increase energy consumption efficiency of different security algorithms. Finally, based on careful analysis of measurement results, we propose a set of principles on using security algorithms in WAMS nodes, such as cryptography selections, parameter configuration, and the like. Such principles can be used widely in other computing systems with energy constraints.

An Autonomous Agent Approach to Query Optimization in Stream Grids

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

Flexible session management in a distributed environment

Many secure communication libraries used by distributed systems, such as SSL, TLS, and Kerberos, fail to make a clear distinction between the authentication, session, and communication layers. In this paper we introduce CEDAR, the secure communication library used by the Condor High Throughput Computing software, and present the advantages to a distributed computing system resulting from CEDAR's separation of these layers. Regardless of the authentication method used, CEDAR establishes a secure session key, which has the flexibility to be used for multiple capabilities. We demonstrate how a layered approach to security sessions can avoid round-trips and latency inherent in network authentication. The creation of a distinct session management layer allows for optimizations to improve scalability by way of delegating sessions to other components in the system. This session delegation creates a chain of trust that reduces the overhead of establishing secure connections and enables centralized enforcement of system-wide security policies. Additionally, secure channels based upon UDP datagrams are often overlooked by existing libraries; we show how CEDAR's structure accommodates this as well. As an example of the utility of this work, we show how the use of delegated security sessions and other techniques inherent in CEDAR's architecture enables US CMS to meet their scalability requirements in deploying Condor over large-scale, wide-area grid systems.

Overhead Analysis of Scientific Workflows in Grid Environments

Scientific workflows are a topic of great interest in the grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area grid infrastructures. Traditionally, the grid workflow execution is approached as a pure best effort scheduling problem that maps the activities onto the grid processors based on appropriate optimization or local matchmaking heuristics such that the overall execution time is minimized. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable grid environments is prone to severe performance losses that must be understood for minimizing the completion time or for the efficient use of high-performance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured grid execution time based on a hierarchy of performance overheads for grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of grid computing, including speedup and efficiency. We present a distributed online tool for computing and analyzing the performance overheads in real time based on event correlation techniques and introduce several performance contracts as quality-of-service parameters to be enforced during the workflow execution beyond traditional best effort practices. We illustrate our method through postmortem and online performance analysis of two real-world workflow applications executed in the Austrian grid environment.

Chirp: a practical global filesystem for cluster and Grid computing

Traditional distributed filesystem technologies designed for local and campus area networks do not adapt well to wide area Grid computing environments. To address this problem, we have designed the Chirp distributed filesystem, which is designed from the ground up to meet the needs of Grid computing. Chirp is easily deployed without special privileges, provides strong and flexible security mechanisms, tunable consistency semantics, and clustering to increase capacity and throughput. We demonstrate that many of these features also provide order-of-magnitude performance increases over wide area networks. We describe three applications in bioinformatics, biometrics, and gamma ray physics that each employ Chirp to attack large scale data intensive problems.

A Launch-time Scheduling Heuristics for Parallel Applications on Wide Area Grids

Large and dynamic computational Grids, generally known as wide-area Grids, are characterized by a large availability, heterogene- ity on computational resources, and high vari- ability on their status during the time. Such Grid infrastructures require appropriate schedule mechanisms in order to satisfy the application performance requirements (QoS). In this paper we propose a launch-time heuristics to schedule component-based parallel applications on such kind of Grid. The goal of the proposed heuristics is threefold: to meet the minimal task computation- al requirement, to maximize the throughput between communicating tasks, and to evaluate on-the-fly the resource availability to minimize the aging effect on the resources state. We evaluate the proposed heuristics by simulations applying it to a suite of task graphs and Grid platforms randomly generated. Moreover, a further test was conducted to schedule a real application on a real Grid. Experimental results shown that the proposed solution can be a viable one.

Complete and fragmented replica selection and retrieval in Data Grids

Data Grids support data-intensive applications in wide area Grid systems. They utilize local storage systems as distributed data stores by replicating datasets. Replication is a commonly used technique in a distributed environment. The motivation of replication is that replication can improve data availability, data access performance, and load balancing. Usually a complete file is copied to many Grid sites for local access. However, a site may only need parts of a replica. Therefore, to use the storage systems efficiently, it is necessary for a Grid site to store only parts of a replica. In this paper, we propose a concept called fragmented replicas. That is, when doing replication, a site can store only some partial contents needed locally. It can greatly save the storage space wasted in storing unused data. We also propose a block mapping procedure to determine the distribution of blocks in every available server for later replica retrieval. According to this procedure, a server can provide its available partial replica contents for other members in the Grid system to access. On the other hand, a client can retrieve a fragmented replica directly by using the block mapping procedure. After the block mapping procedure, some co-allocation schemes can be used to retrieve data sets from the available servers. The simulation shows that the co-allocation schemes also improve download performance in a fragmented replication system.

Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling

In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment.

Strategic Power Infrastructure Defense

This paper provides a comprehensive state-of-the-art overview on power infrastructure defense systems. A review of the literature on the subjects of critical infrastructures, threats to the power grids, defense system concepts, and the special protection systems is reported. The proposed Strategic Power Infrastructure Defense (SPID) system methodology is a real-time, wide-area, adaptive protection and control system involving the power, communication, and computer infrastructures. The SPID system performs the failure analysis, vulnerability assessment, and adaptive control actions to avoid catastrophic power outages. This paper also includes a new concept for bargaining by multiagents to identify the decision options to reduce the system vulnerability. The concept of a flexible configuration of the wide-area grid is substantiated with an area-partitioning algorithm. A 179-bus system is used to illustrate the area partitioning method that is intended to minimize the total amount of load shedding.

Optical code label and its applications to photonic networkings: An approach to optical grid

In the future photonic network, network applications such as grid computing and wide-area distributed SANs (storage area networks) will be realized on a new network platform called the “optical grid.” In this paper, an optical grid is studied, which would provide a network platform to networked applications such as grid computings in the future photonic network. The optical code (OC) label, an identifier for data flow, is an enabler due to its ultrafast processing capability. First, the variable-capacity OC-labeled path and optical code division multiplexed (OCDM) path are studied, which allow one to simultaneously set up a number of connections on a single wavelength path. Next, optical path setup using OC-labeled control packet, which bears the signaling message, is presented and experimentally demonstrated. This enables one to ultimately reduce the overhead time of the optical path setup. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 88(12): 1–10, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20241

Rule-based visualization in the Discover computational steering collaboratory

In this paper, we introduce the concept of rule-based visualization for a computational steering collaboratory and show how these rules can be used to steer the behaviors of the visualization subsystem. Rules define high-level policies and are used to autonomically select and tune the visualization routines based on application requirements and available computing/network resources. Such an autonomic management of the visualization subsystem can significantly improve the effectiveness of computational steering collaboratories in wide-area Grid environments.

Efficient collective communication in distributed heterogeneous systems

With recent advances in high-speed networks, distributed heterogeneous computing has emerged as an attractive computational paradigm. Wide-area grid infrastructures will enable distributed applications—such as video conferencing and distributed interactive simulation—to seamlessly integrate collections of heterogeneous workstations, multiprocessors, and mobile nodes. The underlying network is typically a collection of several heterogeneous links, of different networking technologies. Such a heterogeneous network is also typical in local area workstation clusters, which are increasingly being used as alternatives to parallel computing systems. This paper introduces a framework for developing efficient collective communication schedules over such heterogeneous networks. We focus on application-level communication, between processes of a parallel program. Our framework consists of analytical models of the heterogeneous system, scheduling algorithms for the collective communication pattern, and performance evaluation mechanisms. We show that previous models, which considered node heterogeneity but ignored network heterogeneity, can lead to solutions which are worse than the optimal by an unbounded factor. We then introduce an enhanced communication model, and develop three heuristic algorithms for the broadcast and multicast patterns. The completion time of the schedule is chosen as the performance metric. The heuristic algorithms are fastest edge first (FEF), earliest completing edge first (ECEF), and ECEF with look-ahead. For small system sizes, we find the optimal solution using exhaustive search. Our simulation experiments indicate that the performance of our heuristic algorithms is close to optimal. For performance evaluation of larger systems, we have also developed a simple lower bound on the completion time. Our heuristic algorithms achieve significant performance improvements over previous approaches.

PC magazine guide to connectivity

With recent advances in high-speed networks, distributed heterogeneous computing has emerged as an attractive computational paradigm. Wide-area grid infrastructures will enable distributed applications—such as video conferencing and distributed interactive simulation—to seamlessly integrate collections of heterogeneous workstations, multiprocessors, and mobile nodes. The underlying network is typically a collection of several heterogeneous links, of different networking technologies. Such a heterogeneous network is also typical in local area workstation clusters, which are increasingly being used as alternatives to parallel computing systems. This paper introduces a framework for developing efficient collective communication schedules over such heterogeneous networks. We focus on application-level communication, between processes of a parallel program. Our framework consists of analytical models of the heterogeneous system, scheduling algorithms for the collective communication pattern, and performance evaluation mechanisms. We show that previous models, which considered node heterogeneity but ignored network heterogeneity, can lead to solutions which are worse than the optimal by an unbounded factor. We then introduce an enhanced communication model, and develop three heuristic algorithms for the broadcast and multicast patterns. The completion time of the schedule is chosen as the performance metric. The heuristic algorithms are fastest edge first (FEF), earliest completing edge first (ECEF), and ECEF with look-ahead. For small system sizes, we find the optimal solution using exhaustive search. Our simulation experiments indicate that the performance of our heuristic algorithms is close to optimal. For performance evaluation of larger systems, we have also developed a simple lower bound on the completion time. Our heuristic algorithms achieve significant performance improvements over previous approaches.

Directory of computer conferencing in libraries

With recent advances in high-speed networks, distributed heterogeneous computing has emerged as an attractive computational paradigm. Wide-area grid infrastructures will enable distributed applications—such as video conferencing and distributed interactive simulation—to seamlessly integrate collections of heterogeneous workstations, multiprocessors, and mobile nodes. The underlying network is typically a collection of several heterogeneous links, of different networking technologies. Such a heterogeneous network is also typical in local area workstation clusters, which are increasingly being used as alternatives to parallel computing systems. This paper introduces a framework for developing efficient collective communication schedules over such heterogeneous networks. We focus on application-level communication, between processes of a parallel program. Our framework consists of analytical models of the heterogeneous system, scheduling algorithms for the collective communication pattern, and performance evaluation mechanisms. We show that previous models, which considered node heterogeneity but ignored network heterogeneity, can lead to solutions which are worse than the optimal by an unbounded factor. We then introduce an enhanced communication model, and develop three heuristic algorithms for the broadcast and multicast patterns. The completion time of the schedule is chosen as the performance metric. The heuristic algorithms are fastest edge first (FEF), earliest completing edge first (ECEF), and ECEF with look-ahead. For small system sizes, we find the optimal solution using exhaustive search. Our simulation experiments indicate that the performance of our heuristic algorithms is close to optimal. For performance evaluation of larger systems, we have also developed a simple lower bound on the completion time. Our heuristic algorithms achieve significant performance improvements over previous approaches.