Scheduling with Setup Costs and Monotone Penalties

We consider single processor preemptive scheduling with job-dependent setup times. In this model, a job-dependent setup time is incurred when a job is started for the first time, and each time it is restarted after preemption. This model is a common generalization of preemptive scheduling, and actually of non-preemptive scheduling as well. The objective is to minimize the sum of any general non-negative, non-decreasing cost functions of the completion times of the jobs -- this generalizes objectives of minimizing weighted flow time, flow-time squared, tardiness or the number of tardy jobs among many others. Our main result is a randomized polynomial time O(1)-speed O(1)-approximation algorithm for this problem. Without speedup, no polynomial time finite multiplicative approximation is possible unless P=NP. We extend the approach of Bansal et al. (FOCS 2007) of rounding a linear programming relaxation which accounts for costs incurred due to the non-preemptive nature of the schedule. A key new idea used in the rounding is that a point in the intersection polytope of two matroids can be decomposed as a convex combination of incidence vectors of sets that are independent in both matroids. In fact, we use this for the intersection of a partition matroid and a laminar matroid, in which case the decomposition can be found efficiently using network flows. Our approach gives a randomized polynomial time offline O(1)-speed O(1)-approximation algorithm for the broadcast scheduling problem with general cost functions as well

Storage and Search in Dynamic Peer-to-Peer Networks

We study robust and efficient distributed algorithms for searching, storing, and maintaining data in dynamic Peer-to-Peer (P2P) networks. P2P networks are highly dynamic networks that experience heavy node churn (i.e., nodes join and leave the network continuously over time). Our goal is to guarantee, despite high node churn rate, that a large number of nodes in the network can store, retrieve, and maintain a large number of data items. Our main contributions are fast randomized distributed algorithms that guarantee the above with high probability (whp) even under high adversarial churn: 1. A randomized distributed search algorithm that (whp) guarantees that searches from as many as $n - o(n)$ nodes ($n$ is the stable network size) succeed in ${O}(\log n)$-rounds despite ${O}(n/\log^{1+\delta} n)$ churn, for any small constant $\delta > 0$, per round. We assume that the churn is controlled by an oblivious adversary (that has complete knowledge and control of what nodes join and leave and at what time, but is oblivious to the random choices made by the algorithm). 2. A storage and maintenance algorithm that guarantees (whp) data items can be efficiently stored (with only $\Theta(\log{n})$ copies of each data item) and maintained in a dynamic P2P network with churn rate up to ${O}(n/\log^{1+\delta} n)$ per round. Our search algorithm together with our storage and maintenance algorithm guarantees that as many as $n - o(n)$ nodes can efficiently store, maintain, and search even under ${O}(n/\log^{1+\delta} n)$ churn per round. Our algorithms require only polylogarithmic in $n$ bits to be processed and sent (per round) by each node. To the best of our knowledge, our algorithms are the first-known, fully-distributed storage and search algorithms that provably work under highly dynamic settings (i.e., high churn rates per step).Comment: to appear at SPAA 201

Finding Nearby Objects in Peer-to-Peer Networks

Finding Nearby Objects in Peer-to-Peer Networks by Kirsten Weale Hildrum Doctor of Philosophy in Computer Science University of California, Berkeley Professor John Kubiatowicz, co-Chair Professor Satish Rao, co-Chair A peer-to-peer object location system is an evolving set of computers cooperating to store objects. A reasonable system should easily adapt when computers join or leave the network (self-organization), reliably find objects (completeness), and ensure that no computer works too hard (load balance). Searches in this network should find nearby copies of objects when possible: a searcher in Berkeley looking for an object on the Berkeley subnetwork should find the object without ever sending a message outside of Berkeley

Asymptotically Efficient Approaches to Fault-Tolerance in Peer-to-Peer Networks

In this paper, we show that two peer-to-peer systems, Pastry [13] and Tapestry [17] can be made tolerant to certain classes of failures and a limited class of attacks. These systems are said to operate properly if they can find the closest node matching a requested ID

Optimizations for Locality-Aware Structured Peer-to-Peer Overlays

We present several optimizations aimed at improving the object location performance of locality-aware structured peer-to-peer overlays. We present simulation results that demonstrate the effectiveness of these optimizations in Tapestry, and discuss their usage of the overall storage resources of the system

General Terms Algorithms,Theory

ABSTRACT Modern networking applications replicate data and services widely, leadingto a need for location-independent routing- the ability to route queries directly to objects using names independent of the objects ' physical locations.Two important properties of a routing infrastructure are routing locality and rapid adaptation to arriving and departing nodes. We show how these twoproperties can be efficiently achieved for certain network topologies. To do this, we present a new distributed algorithm that can solve the nearest-neighbor problem for these networks. We describe our solution in the context of Tapestry, an overlay network infrastructure that employs techniquesproposed by Plaxton, Rajaraman, and Richa [14]