Multiple query scheduling for distributed semantic caches

In distributed query processing systems, load balancing plays an important role in maximizing system throughput. When queries can leverage cached intermediate results, improving the cache hit ratio becomes as important as load balancing in query scheduling, especially when dealing with computationally expensive queries. The scheduling policies must be designed to take into consideration the dynamic contents of the distributed caching infrastructure. In this paper, we propose and discuss several distributed query scheduling policies that directly consider the available cache contents by employing distributed multidimensional indexing structures and an exponential moving average approach to predicting cache contents. These approaches are shown to produce better query plans and faster query response times than traditional scheduling policies that do not predict dynamic contents in distributed caches. We experimentally demonstrate the utility of the scheduling policies using MQO, which is a distributed, Grid-enabled, multiple query processing middleware system we developed to optimize query processing for data analysis and visualization applications.     

Preemptive Online Scheduling: Optimal Algorithms for All Speeds

Our main result is an optimal online algorithm for preemptive scheduling on uniformly related machines with the objective to minimize makespan. The algorithm is deterministic, yet it is optimal even among all randomized algorithms. In addition, it is optimal for any fixed combination of speeds of the machines, and thus our results subsume all the previous work on various special cases. Together with a new lower bound it follows that the overall competitive ratio of this optimal algorithm is between 2.054 and e≈2.718. We also give a complete analysis of the competitive ratio for three machines. T. Ebenlendr and J. Sgall partially supported by Institutional Research Plan No. AV0Z10190503, by Inst. for Theor. Comp. Sci., Prague (project 1M0545 of MŠMT ČR), grant 201/05/0124 of GA ČR, and grant IAA1019401 of GA AV ČR. W. Jawor supported by NSF grants CCF-0208856 and OISE-0340752.     

Communication infrastructure in distributed scheduling

The emergence of distributed artificial intelligent (DAI) introduced a new approach to solve scheduling problems by a set of scheduling systems that interact with each other in the problem-solving process. In this paper, we describe a communication infrastructure to handle connection and communication between distributed Internet scheduling systems for distributed applications. First, we present an agent model of distributed scheduling systems where agents can communicate and coordinate activities with each other via an agent communication language. Then, we define the syntax and semantics for the agent communication languages, and negotiation mechanism. Following that, we discuss the design and development of the prototype for the multi-agent scheduling systems. We conclude with a discussion of communication issues for heterogeneous agent-based scheduling systems to solve distributed scheduling problems.     

Solving distributed FMS scheduling problems subject to maintenance: Genetic algorithms approach

In general, distributed scheduling problem focuses on simultaneously solving two issues: (i) allocation of jobs to suitable factories and (ii) determination of the corresponding production scheduling in each factory. The objective of this approach is to maximize the system efficiency by finding an optimal planning for a better collaboration among various processes. This makes distributed scheduling problems more complicated than classical production scheduling ones. With the addition of alternative production routing, the problems are even more complicated. Conventionally, machines are usually assumed to be available without interruption during the production scheduling. Maintenance is not considered. However, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it influences the production scheduling. In this connection, maintenance should be considered in distributed scheduling. The objective of this paper is to propose a genetic algorithm with dominant genes (GADG) approach to deal with distributed flexible manufacturing system (FMS) scheduling problems subject to machine maintenance constraint. The optimization performance of the proposed GADG will be compared with other existing approaches, such as simple genetic algorithms to demonstrate its reliability. The significance and benefits of considering maintenance in distributed scheduling will also be demonstrated by simulation runs on a sample problem.     

Scheduling in a dynamic heterogeneous distributed system using estimation error

In real-world dynamic heterogeneous distributed systems, allocating tasks to processors can be an inefficient process, due to the dynamic nature of the resources, and the tasks to be processed. The information about these tasks and resources is not known a priori, and thus must be estimated online. We utilize the accuracy of these estimates, and when combined with different objectives, such as minimizing makespan and evenly distributing load, naturally gives rise to a family of four different scheduling algorithms. The algorithms have been implemented on a real-world heterogeneous distributed system with up to 90 processors. A set of real-world problems from the areas of cryptography, bioinformatics, and biomedical engineering were used as a test-set to measure the effectiveness of the scheduling algorithms. We have found that considering estimation error when allocating tasks to processors can provide more efficient solutions, than when estimation error is not considered. We have found that using a simple heuristic, combined with estimation error, can in some cases provide solutions approaching the efficiency of complicated well-known evolutionary algorithms.     

Distributed constraint optimization with MULBS: A case study on collaborative meeting scheduling

This paper introduces MULBS, a new DCOP (distributed constraint optimization problem) algorithm and also presents a DCOP formulation for scheduling of distributed meetings in collaborative environments. Scheduling in CSCWD can be seen as a DCOP where variables represent time slots and values are resources of a production system (machines, raw-materials, hardware components, etc.) or management system (meetings, project tasks, human resources, money, etc). Therefore, a DCOP algorithm must find a set of variable assignments that maximize an objective function taking constraints into account. However, it is well known that such problems are NP-complete and that more research must be done to obtain feasible and reliable computational approaches. Thus, DCOP emerges as a very promising technique: the search space is decomposed into smaller spaces and agents solve local problems, collaborating in order to achieve a global solution. We show with empirical experiments that MULBS outperforms some of the state-of-the-art algorithms for DCOP, guaranteeing high quality solutions using less computational resources for the distributed meeting scheduling task.     

SIG框架基于多线程技术分布式系统的任务调度

空间信息网格是利用网格技术实现空间信息资源的共享、管理和提供空间信息服务的系统和各种的空问信息服务的基础设施,任务调度是分布式系统和网格系统最具有挑战的问题之一,Java语言里的多线程机制很好地解决了这一问题。该文主要研究了SIG中的任务调度技术,通过实验确定分布式系统里的线程数目,使系统性能获得局部最优。     

Distributed computing with advice: information sensitivity of graph coloring

We study the problem of the amount of information (advice) about a graph that must be given to its nodes in order to achieve fast distributed computations. The required size of the advice enables to measure the information sensitivity of a network problem. A problem is information sensitive if little advice is enough to solve the problem rapidly (i.e., much faster than in the absence of any advice), whereas it is information insensitive if it requires giving a lot of information to the nodes in order to ensure fast computation of the solution. In this paper, we study the information sensitivity of distributed graph coloring. A preliminary version of this paper appeared in the proceedings of the 34th International Colloquium on Automata, Languages and Programming (ICALP), July 2007. A part of this work was done during the stay of David Ilcinkas at the Research Chair in Distributed Computing of the Université du Québec en Outaouais, as a postdoctoral fellow. P. Fraigniaud received additional support from the ANR project ALADDIN. A. Pelc was supported in part by NSERC discovery grant and by the Research Chair in Distributed Computing of the Université du Québec en Outaouais.     

Performance evaluation of two new disk scheduling algorithms for real-time systems

In this paper, we present two new disk scheduling algorithms for real-time systems. The two algorithms, called SSEDO (Shortest Seek and Earliest Deadline by Ordering) and SSEDV (Shortest Seek and Earliest Deadline by Value), combine deadline information and disk service time information in different ways. The basic idea behind these new algorithms is to give the disk I/O request with the earliest deadline a high priority, but if a request with a larger deadline is very close to the current disk arm position, then it may be assigned the highest priority. The performance of the SSEDO and SSEDV algorithms is compared with three other proposed real-time disk scheduling algorithms ED, P-SCAN, and FD-SCAN, as well as four conventional algorithms SSTF, SCAN, C-SCAN, and FCFS. An important aspect of the performance study is that the evaluation is not done in isolation with respect to the disk, but as part of an integrated collection of protocols necessary to support a real-time transaction system. The transaction system model is validated on an actual real-time transaction system testbed, called RT-CARAT. The performance results show that SSEDV outperforms SSEDO; that both of these new algorithms can improve performance of up to 38% over previously-known real-time disk scheduling algorithms; and that all of these real-time scheduling algorithms are significantly better than nonreal-time algorithms in the sense of minimizing the transaction loss ratio.This work is supported, in part, by the Office of Naval Research under contract N00014-87-K-796, by NSF under contract IRI-8908693, and by an NSF equipment grant CERDCR 8500332.     

CAMPS: a constraint-based architecturefor multiagent planning and scheduling

A new integrated architecture for distributed planning and scheduling is proposed that exploits constraints for problem decomposition and coordination. The goal is to develop an efficient method to solve densely constrained planning/scheduling problems in a distributed manner without sacrificing solution quality. A prototype system (CAMPS) was implemented, in which a set of intelligent agents try to coordinate their actions for satisfying planning/scheduling results by handling several intra- and inter-agent constraints. The repair-based methodology for distributed planning/scheduling is described, together with the constraint-based mechanism of dynamic coalition formation among agents.     

A modified genetic algorithm for distributed scheduling problems

Genetic algorithms (GAs) have been widely applied to the scheduling and sequencing problems due to its applicability to different domains and the capability in obtaining near-optimal results. Many investigated GAs are mainly concentrated on the traditional single factory or single job-shop scheduling problems. However, with the increasing popularity of distributed, or globalized production, the previously used GAs are required to be further explored in order to deal with the newly emerged distributed scheduling problems. In this paper, a modified GA is presented, which is capable of solving traditional scheduling problems as well as distributed scheduling problems. Various scheduling objectives can be achieved including minimizing makespan, cost and weighted multiple criteria. The proposed algorithm has been evaluated with satisfactory results through several classical scheduling benchmarks. Furthermore, the capability of the modified GA was also tested for handling the distributed scheduling problems.     

Optimal semi-online algorithms for preemptive scheduling problems with inexact partial information

In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some application. This paper considers semi-online problems on identical machines with inexact partial information. Three problems are considered, where we know in advance that the optimal value, or the largest job size are in given intervals, respectively, while their exact values are unknown. We give both lower bounds of the problems and competitive ratios of algorithms as functions of a so-called disturbance parameter r ∈[1, ∞). We establish for which r the inexact partial information is useful to improve the performance of a semi-online algorithm with respect to its pure online problem. Optimal preemptive semi-online algorithms are then obtained. Research supported by Natural Science Foundation of China (10671177) and Natural Science Foundation of Zhejiang Province (Y605316) and its preliminary version appeared in proceedings of ISAAC’05.     

Optimal Algorithms for <Emphasis Type="Italic">k</Emphasis>-Search with Application in Option Pricing

In the k-search problem, a player is searching for the k highest (respectively, lowest) prices in a sequence, which is revealed to her sequentially. At each quotation, the player has to decide immediately whether to accept the price or not. Using the competitive ratio as a performance measure, we give optimal deterministic and randomized algorithms for both the maximization and minimization problems, and discover that the problems behave substantially different in the worst-case. As an application of our results, we use these algorithms to price “lookback options”, a particular class of financial derivatives. We derive bounds for the price of these securities under a no-arbitrage assumption, and compare this to classical option pricing. J. Lorenz is partially supported by UBS AG. K. Panagiotou is partially supported by the SNF, grant number: 200021-107880/1.     

An intelligent query processing for distributed ontologies

In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time.     

Efficient detection of a class of stable properties

Summary We present a general protocol for detecting whether a property holds in a distributed system, where the property is a member of a class of stable properties we call thelocally stable properties. Our protocol is based on a decentralized method for constructing a maximal subset of the local states that are mutually consistent, which in turn is based on a weakened version of vector time stamps. The structure of our protocol lends itself to refinement, and we demonstrate its utility by deriving some specialized property-detection protocols, including two previously-known protocols that are known to be efficient. Laura Sabel received the BSE degree from Princeton University in 1989 and the MS degree in Computer Science from Cornell University in 1992. She is currently a PhD student in the Department of Computer Science at Cornell University. Her research interests include fault-tolerance and distributed systems. she is the recipient of an AT&T PhD Scholarship. Keith Marzullo received his Ph.D. degree in electrical engineering from Stanford University in 1984. He is an associate professor in the Computer Science and Engineering Department at the University of California, San Diego. His research interests are in the area of fault-tolerance in both asynchronous and real-time distributed systems. He has consulted on several projects including the IBM Air Traffic Control System, and is an associate editor for IEEE Transactions on Software Engineering.This work was supported by the Defense Advanced Research Projects Agency (DoD) under NASA Ames grant number NAG 2-593, and by grants from IBM and Siemens. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official Department of Defense position, policy, or decision. An earlier version of this paper appears in theProceedings of the 5th International Workshop on Distributed Systems, October 1991, Springer-Verlag LNCS Vol. 579This author is also supported by an AT&T PhD Scholarship     

分布式任务调度的智能化及其实验系统 KZ2／FRD

在一个大型分布式系统中,传统的调度方法存在着知识的不确定性、不完整性、调度不稳定性和策略单一性等问题.为了解决这些问题,本文提出一个智能分布式任务调度的新方法,并介绍了实验系统KZ2/FRD的实现和性能分析.     

Combining databases with prioritized information

To solve a problem one may need to combine the knowledge of several different experts. It can happen that some of the claims of one or more experts may be in conflict with the claims of other experts. There may be several such points of conflict and any claim may be involved in several different such points of conflict. In that case, the user of the knowledge of experts may prefer a certain claim to another in one conflict-point without necessarily preferring that statement in another conflict-point.Our work constructs a framework within which the consequences of a set of such preferences (expressed as priorities among sets of statements) can be computed. We give four types of semantics for priorities, three of which are shown to be equivalent to one another. The fourth type of semantics for priorities is shown to be more cautious than the other three. In terms of these semantics for priorities, we give a function for combining knowledge from different sources such that the combined knowledge is conflict-free and satisfies all the priorities.Jack Minker and Shekhar Pradhan were supported in part by the National Science Foundation grant IRI-89-16059 and Air Force Office of Scientific Research grant 91-0350. V.S. Subrahmanian was supported in part by Army Research Office grant DAAL-03-92-G-0225, Air Force Office of Scientific Research Grant F49620-93-1-0065, and NSF grant IRI-9109755.     

A lower bound on the period length of a distributed scheduler

Ad-scheduling of a graphG is a sequence of rounds, each consisting of some of the nodes of the graph, such that the distance between any two nodes participating in the same round is greater thand. Ad-scheduler is a protocol that determines ad-scheduling ofG. A 1-scheduler is applicable to process scheduling in a resource-sharing system, and to proper communication scheduling of the half-duplex model in communication networks. A 2-scheduler can be used as a collision-free protocol for radio networks.In this paper a simpled-scheduler is analyzed. We first discuss basic properties of this scheduling, and give a complete characterization of this scheduling for trees and cycles. We study the period length of this scheduling, and the main result is a worst-case exponential lower bound for this length.The research of Shmuel Zaks was supported by the Fund for Research in Electronics, Computers, and Communications adminstered by the Israeli Academy of Sciences and Humanities.     

Scheduling in distributed systems: A cloud computing perspective

Scheduling is essentially a decision-making process that enables resource sharing among a number of activities by determining their execution order on the set of available resources. The emergence of distributed systems brought new challenges on scheduling in computer systems, including clusters, grids, and more recently clouds. On the other hand, the plethora of research makes it hard for both newcomers researchers to understand the relationship among different scheduling problems and strategies proposed in the literature, which hampers the identification of new and relevant research avenues. In this paper we introduce a classification of the scheduling problem in distributed systems by presenting a taxonomy that incorporates recent developments, especially those in cloud computing. We review the scheduling literature to corroborate the taxonomy and analyze the interest in different branches of the proposed taxonomy. Finally, we identify relevant future directions in scheduling for distributed systems.     

Distributed scheduling for disconnected cooperation

Ability to cooperate on common tasks in a distributed setting is key to solving a broad range of computation problems ranging from distributed search such as SETI to distributed simulation and multi-agent collaboration. In such settings there exists a trade-off between computation and communication: both resources must be managed to decrease redundant computation and to ensure efficient computational progress. This paper deals with scheduling issues for distributed collaboration. Specifically, we examine the extreme situation where initially collaboration must occur without communication. That is, we consider the extent to which efficient collaboration is possible if all resources are directed to computation at the expense of communication. The results summarized here precisely characterize the ability of distributed agents to collaborate on a known collection of independent tasks by means of local scheduling decisions that require no communication and that achieve low redundancy in task executions. Such scheduling solutions exhibit an interesting connection between the distributed collaboration problem and the design theory. The lower bounds presented here along with the randomized and deterministic schedule constructions show the limitations on such low-redundancy cooperation and show that schedules with near-optimal redundancy can be efficiently constructed by processors working in isolation. The work of G. Malewicz was done when he was a Ph.D. student at the University of Connecticut, and in part during a visit to the Specification and Algorithm Research Department, AT&T Shannon Lab, and the Supercomputing Technologies Group, Massachusetts Institute of Technology. Parts of this article appeared in a preliminary form in the Proceedings of the 14th International Symposium on Distributed Computing [24], Springer LNCS Vol. 1914, pp. 119–133, in the Proceedings of the 8th International Colloquium on Structural Information and Communication Complexity [23], and the doctoral thesis of the first author.