We study the minimum backlog problem (MBP). This online problem arises, e.g., in the context of sensor networks. We focus on two main variants of MBP. The discrete MBP is a 2-person game played on a graph $G=(V,E)$. The player is initially located at a vertex of the graph. In each time step, the adversary pours a total of one unit of water into cups that are located on the vertices of the graph, arbitrarily distributing the water among the cups. The player then moves from her current vertex to an adjacent vertex and empties the cup at that vertex. The players objective is to minimize the backlog, i.e., the maximum amount of water in any cup at any time. The geometric MBP is a continuous-time version of the MBP: the cups are points in the two-dimensional plane, the adversary pours water continuously at a constant rate, and the player moves in the plane with unit speed. Again, the players objective is to minimize the backlog. We show that the competitive ratio of any algorithm for the MBP has a lower bound of $Omega(D)$, where $D$ is the diameter of the graph (for the discrete MBP) or the diameter of the point set (for the geometric MBP). Therefore we focus on determining a strategy for the player that guarantees a uniform upper bound on the absolute value of the backlog. For the absolute value of the backlog there is a trivial lower bound of $Omega(D)$, and the deamortization analysis of Dietz and Sleator gives an upper bound of $O(Dlog N)$ for $N$ cups. Our main result is a tight upper bound for the geometric MBP: we show that there is a strategy for the player that guarantees a backlog of $O(D)$, independently of the number of cups.
Databases need to allocate and free blocks of storage on disk. Freed blocks introduce holes where no data is stored. Allocation systems attempt to reuse such deallocated regions in order to minimize the footprint on disk. If previously allocated blocks cannot be moved, the problem is called the memory allocation problem, which is known to have a logarithmic overhead in the footprint. This paper defines the storage reallocation problem, where previously allocated blocks can be moved, or reallocated, but at some cost. The algorithms presented here are cost oblivious, in that they work for a broad and reasonable class of cost functions, even when they do not know what the cost function is. The objective is to minimize the storage footprint, that is, the largest memory address containing an allocated object, while simultaneously minimizing the reallocation costs. This paper gives asymptotically optimal algorithms for storage reallocation, in which the storage footprint is at most (1+epsilon) times optimal, and the reallocation cost is at most (1/epsilon) times the original allocation cost, which is also optimal. The algorithms are cost oblivious as long as the allocation/reallocation cost function is subadditive.
Randomized exponential backoff is a widely deployed technique for coordinating access to a shared resource. A good backoff protocol should, arguably, satisfy three natural properties: (i) it should provide constant throughput, wasting as little time as possible; (ii) it should require few failed access attempts, minimizing the amount of wasted effort; and (iii) it should be robust, continuing to work efficiently even if some of the access attempts fail for spurious reasons. Unfortunately, exponential backoff has some well-known limitations in two of these areas: it provides poor (sub-constant) throughput (in the worst case), and is not robust (to resource acquisition failures). The goal of this paper is to fix exponential backoff by making it scalable, particularly focusing on the case where processes arrive in an on-line, worst-case fashion. We present a relatively simple backoff protocol~Re-Backoff~that has, at its heart, a version of exponential backoff. It guarantees expected constant throughput with dynamic process arrivals and requires only an expected polylogarithmic number of access attempts per process. Re-Backoff is also robust to periods where the shared resource is unavailable for a period of time. If it is unavailable for $D$ time slots, Re-Backoff provides the following guarantees. When the number of packets is a finite $n$, the average expected number of access attempts for successfully sending a packet is $O(log^2( n + D))$. In the infinite case, the average expected number of access attempts for successfully sending a packet is $O( log^2(eta) + log^2(D) )$ where $eta$ is the maximum number of processes that are ever in the system concurrently.
A mesh is a graph that divides physical space into regularly-shaped regions. Meshes computations form the basis of many applications, e.g. finite-element methods, image rendering, and collision detection. In one important mesh primitive, called a mesh update, each mesh vertex stores a value and repeatedly updates this value based on the values stored in all neighboring vertices. The performance of a mesh update depends on the layout of the mesh in memory. This paper shows how to find a memory layout that guarantees that the mesh update has asymptotically optimal memory performance for any set of memory parameters. Such a memory layout is called cache-oblivious. Formally, for a $d$-dimensional mesh $G$, block size $B$, and cache size $M$ (where $M=Omega(B^d)$), the mesh update of $G$ uses $O(1+|G|/B)$ memory transfers. The paper also shows how the mesh-update performance degrades for smaller caches, where $M=o(B^d)$. The paper then gives two algorithms for finding cache-oblivious mesh layouts. The first layout algorithm runs in time $O(|G|log^2|G|)$ both in expectation and with high probability on a RAM. It uses $O(1+|G|log^2(|G|/M)/B)$ memory transfers in expectation and $O(1+(|G|/B)(log^2(|G|/M) + log|G|))$ memory transfers with high probability in the cache-oblivious and disk-access machine (DAM) models. The layout is obtained by finding a fully balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree. The second algorithm runs faster by almost a $log|G|/loglog|G|$ factor in all three memory models, both in expectation and with high probability. The layout obtained by finding a relax-balanced decomposition tree of $G$ and then performing an in-order traversal of the leaves of the tree.
