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A Survey of Adaptive Optimization in Virtual Machines
 PROCEEDINGS OF THE IEEE, 93(2), 2005. SPECIAL ISSUE ON PROGRAM GENERATION, OPTIMIZATION, AND ADAPTATION
, 2004
"... Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimiza ..."
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Cited by 56 (6 self)
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Virtual machines face significant performance challenges beyond those confronted by traditional static optimizers. First, portable program representations and dynamic language features, such as dynamic class loading, force the deferral of most optimizations until runtime, inducing runtime optimization overhead. Second, modular
Multiprocessor Scheduling with Rejection
, 1996
"... We consider a version of multiprocessor scheduling with the special feature that jobs may be rejected at a certain penalty. An instance of the problem is given by m identical parallel machines and a set of n jobs, each job characterized by a processing time and a penalty. In the online version t ..."
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Cited by 48 (3 self)
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We consider a version of multiprocessor scheduling with the special feature that jobs may be rejected at a certain penalty. An instance of the problem is given by m identical parallel machines and a set of n jobs, each job characterized by a processing time and a penalty. In the online version the jobs arrive one by one and we have to schedule or reject a job before we have any information about future jobs. The objective is to minimize the makespan of the schedule for accepted jobs plus the sum of the penalties of rejected jobs. The main result is a 1 + OE 2:618 competitive algorithm for the online version of the problem, where OE is the golden ratio. A matching lower bound shows that this is the best possible algorithm working for all m. For fixed m we give improved bounds, in particular for m = 2 we give an optimal OE 1:618 competitive algorithm. For the offline problem we present a fully polynomial approximation scheme for fixed m and a polynomial approximation sche...
On Page Migration and Other Relaxed Task Systems
, 1997
"... This paper is concerned with the page migration (or file migration) problem [BS89] as part of a large class of online problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is ..."
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Cited by 32 (4 self)
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This paper is concerned with the page migration (or file migration) problem [BS89] as part of a large class of online problems. The page migration problem deals with the management of pages residing in a network of processors. In the classical problem there is only one copy of each page which is accessed by different processors over time. The page is allowed to be migrated between processors. However a migration incurs higher communication cost than an access (proportionally to the page size). The problem is that of deciding when and where to migrate the page in order to lower access costs. A more general setting is the kpage migration where we wish to maintain k copies of the page. The page migration problems are concerned with a dilemma common to many online problems: determining when is it beneficial to make configuration changes. We deal with the relaxed task systems model which captures a large class of problems of this type, that can be described as the generalizati...
Online Resource Management with Application to Routing and Scheduling
, 1996
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On comparing the power of robots
 International Journal of Robotics Research. Under review
"... Robots must complete their tasks in spite of unreliable actuators and limited, noisy sensing. In this paper, we consider the information requirements of such tasks. What sensing and actuation abilities are needed to complete a given task? Are some robot systems provably “more powerful, ” in terms of ..."
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Cited by 17 (6 self)
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Robots must complete their tasks in spite of unreliable actuators and limited, noisy sensing. In this paper, we consider the information requirements of such tasks. What sensing and actuation abilities are needed to complete a given task? Are some robot systems provably “more powerful, ” in terms of the tasks they can complete, than others? Can we find meaningful equivalence classes of robot systems? This line of research is inspired by the theory of computation, which has produced similar results for abstract computing machines. The basic idea is a dominance relation over robot systems that formalizes the idea that some robots are stronger than others. This comparison, which is based on the how the robots progress through their information spaces, induces a partial order over the set of robot systems. We prove some basic properties of this partial order and show that it is directly related to the robots’ ability to complete tasks. We give examples to demonstrate the theory, including a detailed analysis of a limitedsensing global localization problem. 1
AverageCase Competitive Analyses for SkiRental Problems
, 2002
"... Abstract. Let s be the ratio of the cost for purchasing skis over the cost for renting them. Then the famous result for the skirental problem shows that skiers should buy their skis after renting them (s − 1). In practice, however, it appears that many skiers buy their skis before this optimal poin ..."
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Cited by 17 (2 self)
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Abstract. Let s be the ratio of the cost for purchasing skis over the cost for renting them. Then the famous result for the skirental problem shows that skiers should buy their skis after renting them (s − 1). In practice, however, it appears that many skiers buy their skis before this optimal point of time and also many skiers keep renting them forever. In this paper we show that these behaviors of skiers are quite reasonable by using an averagecase competitive ratio. For an exponential input distribution times, which gives us an optimal competitive ratio of 2 − 1 s ≤ s, then skiers should rent their skis forever and (ii) otherwise should purchase them after renting approximately s 2 λ (< s) times. Thus averagecase competitive analyses give us the result which differs from the worstcase competitive analysis and also differs from the traditional average cost analysis. Other distributions and related problems are also discussed. f(t) = λe −λt, optimal strategies are (i) if 1 λ 1
On Capital Investment
, 1996
"... We deal with the problem of making capital investments in machines for manufacturing a product. Opportunities for investment occur over time, every such option consists of a capital cost for a new machine and a resulting productivity gain, i.e., a lower production cost for one unit of product. T ..."
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Cited by 13 (1 self)
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We deal with the problem of making capital investments in machines for manufacturing a product. Opportunities for investment occur over time, every such option consists of a capital cost for a new machine and a resulting productivity gain, i.e., a lower production cost for one unit of product. The goal is that of minimizing the total production costs and capital costs when future demand for the product being produced and investment opportunities are unknown. This can be viewed as a generalization of the skirental problem and related to the mortgage problem [3].
On the Computational Complexity and Effectiveness of Nhub ShortestPath Routing
"... In this paper we study the computational complexity and effectiveness of a concept we term “Nhub ShortestPath Routing ” in IP networks. Nhub ShortestPath Routing allows the ingress node of a routing domain to determine up to N intermediate nodes (“hubs”) through which a packet will pass before r ..."
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Cited by 11 (1 self)
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In this paper we study the computational complexity and effectiveness of a concept we term “Nhub ShortestPath Routing ” in IP networks. Nhub ShortestPath Routing allows the ingress node of a routing domain to determine up to N intermediate nodes (“hubs”) through which a packet will pass before reaching its final destination. This facilitates better utilization of the network resources, while allowing the network routers to continue to employ the simple and wellknown shortestpath routing paradigm. Although this concept has been proposed in the past, this paper is the first to investigate it in depth. We apply Nhub ShortestPath Routing to the problem of minimizing the maximum load in the network. We show that the resulting routing problem is NPcomplete and hard to approximate. However, we propose efficient algorithms for solving it both in the online and the offline contexts. Our results show that Nhub ShortestPath Routing can increase network utilization significantly even for. Hence, this routing paradigm should be considered as a powerful mechanism for future datagram routing in the Internet.
A Lower Bound For OnLine File Transfer Routing And Scheduling
 IN PROCEEDINGS OF THE 1997 CONFERENCE ON INFORMATION SCIENCES AND SYSTEMS
, 1997
"... In this paper, we study the OnLine File Transfer Routing and Scheduling problem. Given a sequence of file transfer requests and a graph that represents a network, the problem is to determine both a route and schedule for each file transfer in the sequence so as to minimize a suitable objective func ..."
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Cited by 7 (1 self)
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In this paper, we study the OnLine File Transfer Routing and Scheduling problem. Given a sequence of file transfer requests and a graph that represents a network, the problem is to determine both a route and schedule for each file transfer in the sequence so as to minimize a suitable objective function. We require that an algorithm be online in the sense that it must respond to each request in the order given and before future requests are known. We show that there is no online algorithm which produces solutions with network congestion smaller than blog kc + 1 times that of the optimal solution or makespan smaller than blog kc 2 + 1 times that of the optimal solution, where k is the number of file transfer requests. We explain that this bound also holds for randomized online algorithms versus an adaptive online adversary. We discuss briefly the performance of several greedy online algorithms both theoretically and in practice.