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22
A Taxonomy of Scheduling in GeneralPurpose Distributed Computing Systems
 IEEE Transactions on Software Engineering
, 1988
"... AbstractOne measure of usefulness of a generalpurpose distributed computing system is the system’s ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. Many different approaches and metrics of performance have been proposed in ..."
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Cited by 248 (0 self)
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AbstractOne measure of usefulness of a generalpurpose distributed computing system is the system’s ability to provide a level of performance commensurate to the degree of multiplicity of resources present in the system. Many different approaches and metrics of performance have been proposed in an attempt to achieve this goal in existing systems. In addition, analogous problem formulations exist in other fields such as control theory, operations research, and production management. However, due to the wide variety of approaches to this problem, it is difficult to meaningfully compare different systems since there is no uniform means for qualitatively or quantitatively evaluating them. It is difficult to successfully build upon existing work or identify areas worthy of additional effort without some understanding of the relationships between past efforts. In this paper, a taxonomy of approaches to the resource management problem is presented in an attempt to provide a common terminology and classification mechanism necessary in addressing this problem. The taxonomy, while presented and discussed in terms of distributed scheduling, is also applicable to most types of resource management. As an illustration of the usefulness of the taxonomy an annotated bibliography is given which classifies a large number of distributed scheduling approaches according to the taxonomy. Index TermsDistributed operating systems, distributed resource management, generalpurpose distributed computing systems, scheduling, task allocation, taxonomy. T I.
Process migration
 ACM Computing Surveys
, 2000
"... A process is an operating system abstraction representing an instance of a running computer program. Process migration is the act of transferring a process between two machines during its execution. Several implementations ..."
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Cited by 76 (1 self)
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A process is an operating system abstraction representing an instance of a running computer program. Process migration is the act of transferring a process between two machines during its execution. Several implementations
Applications of parametric maxflow in computer vision
"... The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter λ. In this paper we study vision applications for which it is important to solve the maxflow problem for ..."
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Cited by 39 (7 self)
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The maximum flow algorithm for minimizing energy functions of binary variables has become a standard tool in computer vision. In many cases, unary costs of the energy depend linearly on parameter λ. In this paper we study vision applications for which it is important to solve the maxflow problem for different λ’s. An example is a weighting between data and regularization terms in image segmentation or stereo: it is desirable to vary it both during training (to learn λ from ground truth data) and testing (to select best λ using highknowledge constraints, e.g. user input). We review algorithmic aspects of this parametric maximum flow problem previously unknown in vision, such as the ability to compute all breakpoints of λ and corresponding optimal configurations in finite time. These results allow, in particular, to minimize the ratio of some geometric functionals, such as flux of a vector field over length (or area). Previously, such functionals were tackled with shortest path techniques applicable only in 2D. We give theoretical improvements for “PDE cuts ” [5]. We present experimental results for image segmentation, 3D reconstruction, and the cosegmentation problem. 1.
The Hardness of Perfect Phylogeny, Feasible Register Assignment and Other Problems on Thin Colored Graphs
"... In this paper, we consider the complexity of a number of combinatorial problems; namely, Intervalizing Colored Graphs (DNA physical mapping), Triangulating Colored Graphs (perfect phylogeny), (Directed) (Modified) Colored Cutwidth, Feasible Register Assignment and Module Allocation for graphs of bou ..."
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Cited by 17 (4 self)
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In this paper, we consider the complexity of a number of combinatorial problems; namely, Intervalizing Colored Graphs (DNA physical mapping), Triangulating Colored Graphs (perfect phylogeny), (Directed) (Modified) Colored Cutwidth, Feasible Register Assignment and Module Allocation for graphs of bounded pathwidth. Each of these problems has as a characteristic a uniform upper bound on the tree or path width of the graphs in "yes"instances. For all of these problems with the exceptions of Feasible Register Assignment and Module Allocation, a vertex or edge coloring is given as part of the input. Our main results are that the parameterized variant of each of the considered problems is hard for the complexity classes W [t] for all t 2 N. We also show that Intervalizing Colored Graphs, Triangulating Colored Graphs, and Colored Cutwidth are NPComplete. 1 Introduction This paper focuses on a number of graph decision problems which share the characteristic that all have a uniform upper bo...
Effects of Delayed Communication in Dynamic Group Formation
 IEEE Trans. Syst., Man, Cybern
, 1993
"... We investigate how delayed communication affects the dynamic formation of groups in distributed systems, where all decisionmaking agents join the same group because each expects to improve its own performance. For example, distributed job schedulers may form a group to utilize the idle resources of ..."
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Cited by 11 (6 self)
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We investigate how delayed communication affects the dynamic formation of groups in distributed systems, where all decisionmaking agents join the same group because each expects to improve its own performance. For example, distributed job schedulers may form a group to utilize the idle resources of other members within the group. Forming a group is a search problem and we examine agents which use the feedback mechanism of stochastic learning automata to carry out this search. Although a group formation may have the potential for synergy, the agents must successfully coordinate their actions within the group relevant to the application. For example, job schedulers who form a group must still balance the load among the shared resources; that is, the collective actions of the schedulers need to be coordinated and greedy schedulers who all pick the same processor may not be successful. Agents may find that working alone is more desirable since their actions need not be coordinated and the r...
Performance Analysis of Load Balancing Algorithms
"... Abstract—Load balancing is the process of improving the performance of a parallel and distributed system through a redistribution of load among the processors [1] [5]. In this paper we present the performance analysis of various load balancing algorithms based on different parameters, considering tw ..."
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Cited by 11 (0 self)
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Abstract—Load balancing is the process of improving the performance of a parallel and distributed system through a redistribution of load among the processors [1] [5]. In this paper we present the performance analysis of various load balancing algorithms based on different parameters, considering two typical load balancing approaches static and dynamic. The analysis indicates that static and dynamic both types of algorithm can have advancements as well as weaknesses over each other. Deciding type of algorithm to be implemented will be based on type of parallel applications to solve. The main purpose of this paper is to help in design of new algorithms in future by studying the behavior of various existing algorithms. Keywords—Load balancing (LB), workload, distributed systems, Static Load balancing, Dynamic Load Balancing I.
Dynamic Scope of Control in Decentralized Job Scheduling
 Proc. 1st International Symposium on Autonomous Decentralized Systems
, 1993
"... Each job scheduling agent in large decentralized load balancing systems generally has a set of remote hosts (i.e. its scope of control) to consider for ofloading when the local load is too high. Typically, each agent’s scope of control includes all the hosts in the system. We investigate the potenti ..."
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Cited by 9 (6 self)
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Each job scheduling agent in large decentralized load balancing systems generally has a set of remote hosts (i.e. its scope of control) to consider for ofloading when the local load is too high. Typically, each agent’s scope of control includes all the hosts in the system. We investigate the potential performance benefits of limiting the size of each agent’s scope of control. The larger the scope of control, the less often state information can be communicated, and old state information can lower the quality of load balancing decisions. The smaller the scope of control, the less opportunity an agent has forfinding a lightly loaded host for ofloading. Agents adaptively modify their scope of control over time based on feedback regarding the success of load balancing decisions and act as a selforganizing system to eficiently share available processing power. 1
New DistanceDirected Algorithms for Maximum Flow and Parametric Maximum Flow Problems
, 1987
"... ..."
Distancedirected augmenting path algorithms for maximum flow and parametric maximum flow problems
 Naval Research Logistics
, 1991
"... Until recently, fast algorithms for the maximum flow problem have typically proceeded by constructing layered networks and establishing blocking flows in these networks. However, in recent years, new distancedirected algorithms have been suggested that do not construct layered networks but instead ..."
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Cited by 4 (0 self)
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Until recently, fast algorithms for the maximum flow problem have typically proceeded by constructing layered networks and establishing blocking flows in these networks. However, in recent years, new distancedirected algorithms have been suggested that do not construct layered networks but instead maintain a distance label with each node. The distance label of a node is a lower bound on the length of the shortest augmenting path from the node to the sink. In this article we develop two distancedirected augmenting path algorithms for the maximum flow problem. Both the algorithms run in O(n 2 m) time on networks with n nodes and m arcs. We also point out the relationship between the distance labels and layered networks. Using a scaling technique, we improve the complexity of our distancedirected algorithms to O(nm log U), where U denotes the largest arc capacity. We also consider applications of these algorithms to unit capacity maximum flow problems and a class of parametric maximum flow problems. t i
Balancing applied to maximum network flow problems
 In Proc. ESA, LNCS 4168
, 2006
"... Abstract. We explore balancing as a definitional and algorithmic tool for finding minimum cuts and maximum flows in ordinary and parametric networks. We show that a standard monotonic parametric maximum flow problem can be formulated as a problem of computing a particular maximum flow that is balanc ..."
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Cited by 4 (2 self)
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Abstract. We explore balancing as a definitional and algorithmic tool for finding minimum cuts and maximum flows in ordinary and parametric networks. We show that a standard monotonic parametric maximum flow problem can be formulated as a problem of computing a particular maximum flow that is balanced in an appropriate sense. We present a divideandconquer algorithm to compute such a balanced flow in a logarithmic number of ordinary maximumflow computations. For the special case of a bipartite network, we present two simple, local algorithms for computing a balanced flow. The local balancing idea becomes even simpler when applied to the ordinary maximum flow problem. For this problem, we present a roundrobin arcbalancing algorithm that computes a maximum flow on an nvertex, marc network with integer arc capacities of at most U in O(n 2 mlog(nU)) time. Although this algorithm is slower by at least a factor of n than other known algorithms, it is extremely simple and wellsuited to parallel and distributed implementation. 1