Broadcasting multiple messages in the multiport model

We consider the problem of broadcasting multiple messages from one processor to many processors in the k-port model for message-passing systems. In such systems, processors communicate in rounds, where in every round, each processor can send k messages to k processors and receive k messages from k processors In this paper, we first present a simple and practical algorithm based on variations of k complete k-ary trees. We then present an optimal algorithm up to an additive term of one for this problem for any number of processors, any number of messages, and any value for k     

Material response at hypervelocity impact conditions using laser induced shock waves

Dynamic fracture at hypervelocity impact conditions was investigated in different materials using short pulsed laser induced shock waves. All stages of damage evolution were identified for one dimensional or spherical shock wave impact geometry. A new experimental method is presented to estimate the shock pressure decay in materials. In the theoretical section we obtain the damage induced in the target, as follows: The shock wave is modeled by an expanding stress front, which creates micro-damage in the laser impacted layer and extrudes a bulge at the far surface. The calculated bulge geometry compares well with that observed by us for metal-adhesive-metal sandwiches. The micro-defects coalesce into macro- damage or fracture by a mechanism which is described by percolation theory.     

Computing global combine operations in the multiport postal model

Consider a message-passing system of n processors, in which each processor holds one piece of data initially. The goal is to compute an associative and commutative reduction function on the n pieces of data and to make the result known to all the n processors. This operation is frequently used in many message-passing systems and is typically referred to as global combine, census computation, or gossiping. This paper explores the problem of global combine in the multiport postal model. This model is characterized by three parameters: n-the number of processors, k-the number of ports per processor, and λ-the communication latency. In this model, in every round r, each processor can send k distinct messages to k other processors, and it can receive k messages that were sent from k other processors λ-1 rounds earlier. This paper provides an optimal algorithm for the global combine problem that requires the least number of communication rounds and minimizes the time spent by any processor in sending and receiving messages     

Windows scheduling of arbitrary-length jobs on multiple machines

The generalized windows scheduling problem for n jobs on multiple machines is defined as follows: Given is a sequence, I=〈(w ₁,ℓ ₁),(w ₂,ℓ ₂),…,(w _n ,ℓ _n )〉 of n pairs of positive integers that are associated with the jobs 1,2,…,n, respectively. The processing length of job i is ℓ _i slots where a slot is the processing time of one unit of length. The goal is to repeatedly and non-preemptively schedule all the jobs on the fewest possible machines such that the gap (window) between two consecutive beginnings of executions of job i is at most w _i slots. This problem arises in push broadcast systems in which data are transmitted on multiple channels. The problem is NP-hard even for unit-length jobs and a (1+ε)-approximation algorithm is known for this case by approximating the natural lower bound W(I)=?_i=1ⁿ(1/w_i)W(I)=\sum_{i=1}^{n}(1/w_{i}). The techniques used for approximating unit-length jobs cannot be extended for arbitrary-length jobs mainly because the optimal number of machines might be arbitrarily larger than the generalized lower bound W(I)=?_i=1ⁿ(l_i/w_i)W(I)=\sum_{i=1}^{n}(\ell_{i}/w_{i}). The main result of this paper is an 8-approximation algorithm for the WS problem with arbitrary lengths using new methods, different from those used for the unit-length case. The paper also presents another algorithm that uses 2(1+ε)W(I)+logw _max machines and a greedy algorithm that is based on a new tree representation of schedules. The greedy algorithm is optimal for some special cases, and computational experiments show that it performs very well in practice.     

On Chromatic Sums and Distributed Resource Allocation

This paper studies an optimization problem that arises in the context of distributed resource allocation: Given a conflict graph that represents the competition of processors over resources, we seek an allocation under which no two jobs with conflicting requirements are executed simultaneously. Our objective is to minimize theaverage response timeof the system. In alternative formulation this is known as theMinimum Color Sum (MCS)problem (E. Kubicka and A. J. Schwenk, 1989. An introduction to chromatic sums,in“Proceedings of the ACM Computer Science Conference,” pp. 39–45.). We show that the algorithm based on finding iteratively a maximum independent set (MaxIS) is a 4-approximation to the MCS. This bound is tight to within a factor of 2. We give improved ratios for the classes of bipartite, bounded-degree, and line graphs. The bound generalizes to a 4ρ-approximation of MCS for classes of graphs for which the maximum independent set problem can be approximated within a factor ofρ. On the other hand, we show that ann^1−ε-approximation is NP-hard, for someε>0. For some instances of the resource allocation problem, such as theDining Philosophers, an efficient solution requiresedgecoloring of the conflict graph. We introduce theMinimum Edge Color Sum (MECS)problem which is shown to be NP-hard. We show that a 2-approximation to MECS(G) can be obtained distributively usingcompactcoloring withinO(log² n) communication rounds     

Competitive On-Line Switching Policies

Abstract. Consider the following problem. A switch connecting n input channels to a single output channel must deliver all incoming messages through this channel. Messages are composed of packets , and in each time slot the switch can deliver a single packet from one of the input queues to the output channel. In order to prevent packet loss, a buffer is maintained for each input channel. The goal of a switching policy is to minimize the maximum buffer size. The setting is on-line; decisions must be made based on the current state without knowledge of future events. This general scenario models multiplexing tasks in various systems such as communication networks, cable modem systems, and traffic control. Traditionally, researchers analyzed the performance of a given policy assuming some distribution on the arrival rates of messages at the input queues, or assuming that the service rate is at least the aggregate of all the input rates. We use competitive analysis, avoiding any prior assumptions on the input. We show O(log n )-competitive switching policies for the problem and demonstrate matching lower bounds.     

Proactive data dissemination to mission sites

In many situations and domains it is important to deliver information to personnel as they work in the field. We consider a specialized content distribution application in wireless mesh networks. When a new mission arrives??for example, when a fire alarm sounds??data is pushed to storage nodes at the mission site where it may be retrieved locally by responding personnel (e.g., police, firefighters, paramedics, government officials, and the media). It is important that information is available at low latency, when requested or pulled by the personnel. The total latency experienced will be a combination of the push delay (if the personnel arrive at the mission site before all the data can be pushed), and the pull delay. Each delay component will in turn be a function of (1) the hop distance traveled by the data when pushed or pulled and (2) any congestion on the links. In this paper, we define algorithms and protocols that trade-off the push and pull latencies depending on the type of application. Our goal is to choose a storage node assignment minimizing the total latency-based cost. We start with a simple model in which cost is a function of distance, and then extend the model, explicitly taking congestion into account. Since the problem is NP-hard even to approximate, our focus is on developing efficient algorithms and distributed protocols that can be easily deployed in wireless mesh networks. In NS2 simulations, we find that our heuristic algorithms achieve, on average, a cost within at most 15% of the optimum.     

Competitive On-Line Switching Policies

Consider the following problem. A switch connecting n input channels to a single output channel must deliver all incoming messages through this channel. Messages are composed of packets , and in each time slot the switch can deliver a single packet from one of the input queues to the output channel. In order to prevent packet loss, a buffer is maintained for each input channel. The goal of a switching policy is to minimize the maximum buffer size. The setting is on-line; decisions must be made based on the current state without knowledge of future events. This general scenario models multiplexing tasks in various systems such as communication networks, cable modem systems, and traffic control. Traditionally, researchers analyzed the performance of a given policy assuming some distribution on the arrival rates of messages at the input queues, or assuming that the service rate is at least the aggregate of all the input rates. We use competitive analysis, avoiding any prior assumptions on the input. We show O(log n )-competitive switching policies for the problem and demonstrate matching lower bounds.     

Online Dynamic Programming Speedups

Consider the dynamic program h(n)=min _1≤j≤n a(n,j), where a(n,j) is some formula that may (online) or may not (offline) depend on the previously computed h(i), for i<n. The goal is to compute all h(n), for 1≤n≤N. It is well known that, if a(n,j) satisfy the Monge property, then the SMAWK algorithm (Aggarwal et al., Algorithmica 2(1):195–208, 1987) can solve the offline problem in O(N) time; a Θ(N) speedup over the naive algorithm. In this paper we extend this speedup to the online case, that is, to compute h(n) in the order n=1,2,…,N when (i) we do not know the values of a(n′,j) for n′>n before h(n) has been computed and (ii) do not know the problem size N in advance. We show that if a(n,j) satisfy a stronger, but sometimes still natural, property than the Monge one, then each h(n) can be computed in online fashion in O(1) amortized time. This maintains the speedup online, in the sense that the total time to compute all h(n) is O(N). We also show how to compute each h(n) in the worst case O(log N) time, while maintaining the amortized time bound. For a(n,j) satisfying our stronger property, our algorithm is also simpler than the standard SMAWK algorithm for solving the offline case. We illustrate our technique on two examples from the literature; the first is the D-median problem on a line, and the second comes from mobile wireless paging. The research of the first author was partially supported by the NSF program award CNS-0626606; the research of the second and third authors was partially supported by Hong Kong RGC CERG grant HKUST6312/04E.