ASP: Adaptive online parallel disk scheduling

Lecture Notes in Computer Science, 1998

We address the problem of I O scheduling of read-once reference strings in a multiple-disk parallel I O system. We present a n o vel online algorithm, Red-Black Prefetching RBP, for parallel I O scheduling. In order to perform accurate prefetching RBP uses L-block l o o k ahead. The performance of RBP is analyzed in the standard parallel disk model with D independent disks and a shared I O bu er of size M. W e show that the number of parallel I Os performed by RBP is within a factot maxf p M D = L ; D 1=3 g of the number of I Os done by the optimal oline algorithm. This ratio is within a canstant factor of the best possible when L is L = OM D 1=3 .

Proceedings. Fifth International Conference on High Performance Computing (Cat. No. 98EX238)

We address the problems of prefetching and I/O scheduling for read-once reference strings in a parallel I/O system. Read-once reference strings, in which each block is accessed exactly once, arise naturally in applications like databases and video retrieval. Using the standard parallel disk model with £ disks and a shared I/O buffer of size ¤ , we present a novel algorithm, Red-Black Prefetching (RBP), for parallel I/O scheduling. The number of parallel I/Os performed by RBP is within O(£ ¦ ¥ § ©) of the minimum possible. Algorithm RBP is easy to implement and requires computation time linear in the length of the reference string. Through simulation experiments we validated the benefits of RBP over simple greedy prefetching.

IEEE Transactions on Computers, 2002

We address the problem of prefetching and caching in a parallel I/O system and present a new algorithm for parallel disk scheduling. Traditional buffer management algorithms that minimize the number of block misses are substantially suboptimal in a parallel I/O system where multiple I/Os can proceed simultaneously. We show that in the offline case, where a priori knowledge of all the requests is available, PC-OPT performs the minimum number of I/Os to service the given I/O requests. This is the first parallel I/O scheduling algorithm that is provably offline optimal in the parallel disk model. In the online case, we study the context of global L-block lookahead, which gives the buffer management algorithm a lookahead consisting of L distinct requests. We show that the competitive ratio of PC-OPT, with global L-block lookahead, is ÂðM À L þ DÞ, when L M, and ÂðMD=LÞ, when L > M, where the number of disks is D and buffer size is M.

Journal of Algorithms, 2000

We provide a competitive analysis framework for online prefetching and buffer management algorithms in parallel I/O systems, using a read-once model of block references. This has widespread applicability to key I/O-bound applications such as external merging and concurrent playback of multiple video streams. Two realistic lookahead models, global lookahead and local lookahead, are defined. Algorithms NOM and GREED based on these two forms of lookahead are analyzed for shared buffer and distributed buffer configurations, both of which occur frequently in existing systems. An important aspect of our work is that we show how to implement both the models of lookahead in practice using the simple techniques of forecasting and flushing. Given a ¤-disk parallel I/O system and a globally shared I/O buffer that can hold upto ¥ disk blocks, we derive a lower bound of ¦ § © ¤ on the competitive ratio of any deterministic online prefetching algorithm with § ¥ lookahead. NOM is shown to match the lower bound using global ¥-block lookahead. In contrast, using only local lookahead results in an ¦ § ¤ competitive ratio. When the buffer is distributed into

2004

Parallel disks provide a cost effective way of speeding up I/Os in applications that work with large amounts of data. The main challenge is to achieve as much parallelism as possible, using prefetching to avoid bottlenecks in disk access. Efficient algorithms have been developed for some particular patterns of accessing the disk blocks. In this paper, we consider general request sequences. When the request sequence consists of unique block requests, the problem is called prefetching and is a well-solved problem for arbitrary request sequences. When the reference sequence can have repeated references to the same block, we need to devise an effective caching policy as well. While optimum offline algorithms have been recently designed for the problem, in the online case, no effective algorithm was previously known. Our main contribution is a deterministic online algorithm threshold-LRU which achieves O((M D/L) 2/3 ) competitive ratio and a randomized online algorithm threshold-MARK which achieves O( p (M D/L) log(M D/L)) competitive ratio for the caching/prefetching problem on the parallel disk model (PDM), where D is the number of disks, M is the size of fast memory buffer, and M + L is the amount of lookahead available in the request sequence. The best-known lower bound on the competitive ratio is Ω( p M D/L) for lookahead L ≥ M in both models. We also show that if the deterministic online algorithm is allowed to have twice the memory of the offline then a tight competitive ratio of Θ( p M D/L) can be achieved. This problem generalizes the well-known paging problem on a single disk to the parallel disk model.

Distributed and Parallel Databases, 1993

Improvements in the processing speed of multiprocessors are outpacing improvements in the speed of disk hardware. Parallel disk I/O subsystems have been proposed as one way to dose the gap between processor and disk speeds. In a previous paper we showed that prefetching and caching have the potentT"al to deliver the performance benefits of parallel file systems to parallel applications. In this paper we describe experiments with practical prefetching policies that base decisions only on on-line reference history, and that can be implemented efficiently. We also test the ability of those policies across a range of architectural parameters.

We consider the natural extension of the well-known single disk caching problem to the parallel disk I/O model (PDM) [17]. The main challenge is to achieve as much par-allelism as possible and avoid I/O bottlenecks. We are given a fast memory (cache) of size M memory blocks along with a request sequence Σ = (b1, b2, ..., bn) where each block bi resides on one of D disks. In each parallel I/O step, at most one block from each disk can be fetched. The task is to serve Σ in the minimum number of parallel I/Os. Thus, each I/O is analogous to a page fault. The difference here is that during each page fault, up to D blocks can be brought into memory, as long as all of the new blocks entering the memory reside on different disks. The problem has a long history [18, 12, 13, 26]. Note that this problem is non-trivial even if all requests in Σ are unique. This restricted version is called read-once. Despite the progress in the offline version [13, 15] and read-once version [12], the general online problem still remained open. Here, we provide comprehensive results with a full general solution for the problem with asymptotically tight competitive ratios. To exploit parallelism, any parallel disk algorithm needs a certain amount of lookahead into future requests. To provide effective caching, an online algorithm must achieve o(D) competitive ratio. We show a lower bound that states, for lookahead L ≤ M , any online algorithm must be Ω(D)-competitive. For lookahead L greater than M (1 + 1//), where is a constant, the tight upper bound of O(p M D/L) on competitive ratio is achieved by our algorithm SKEW. The previous algorithm tLRU [26] was O((M D/L) 2/3) competitive and this was also shown to be tight [26] for an LRU-based strategy. We achieve the tight ratio using a fairly * different strategy than LRU. We also show tight results for randomized algorithms against oblivious adversary and give an algorithm achieving better bounds in the resource augmentation model.

1996

AbstractÐThe I/O performance of applications in multiple-disk systems can be improved by overlapping disk accesses. This requires the use of appropriate prefetching and buffer management algorithms that ensure the most useful blocks are accessed and retained in the buffer. In this paper, we answer several fundamental questions on prefetching and buffer management for distributed-buffer parallel I/O systems. First, we derive and prove the optimality of an algorithm, P-min, that minimizes the number of parallel I/Os. Second, we analyze P-con, an algorithm that always matches its replacement decisions with those of the well-known demand-paged MIN algorithm. We show that P-con can become fully sequential in the worst case. Third, we investigate the behavior of on-line algorithms for multiple-disk prefetching and buffer management. We define and analyze P-lru, a parallel version of the traditional LRU buffer management algorithm. Unexpectedly, we find that the competitive ratio of P-lru ...

Conference on Parallel andDistributed Information Systems, 1991

Improvements in the processing speed of multiprocessorsare outpacing improvements in the speed ofdisk hardware. Parallel disk I/O subsystems have beenproposed as one way to close the gap between processorand disk speeds. In a previous paper we showedthat prefetching and caching have the potential to deliverthe performance benefits of parallel file systems toparallel applications. In this paper we describe experimentswith practical

1999

Parallel I/O systems are an integral component of modern high performance systems, providing large secondary storage capacity, and having the potential to alleviate the I/O bottleneck of data intensive applications. In these systems the I/O buffer can be used for two purposes (a) improve I/O parallelism by buffering prefetched blocks and making the load on disks more uniform, and (b) improve I/O latency by caching blocks to avoid repeated disk accesses for the same block. To make best use of available parallelism and locality in I/O accesses, it is necessary to design and implement prefetching and buffer management algorithms that schedule reads intelligently so that the most useful blocks are prefetched into the buffer and the most valuable blocks are retained in the buffer when the need for evictions arises. This dissertation focuses on prefetching and buffer management algorithms for parallel I/O systems. Our aim is to exploit the high parallelism provided by multiple disks by using appropriate buffer management to reduce the average read latency seen by an application. The thesis is that, prefetching and buffer management in parallel I/O systems is fundamentally different from that in systems with a single disk, thereby necessitating new algorithms to handle it. To this end we first present evidence to show the limitations of intuitive algorithms that generalize those for sequential systems to a parallel I/O system, and then design algorithms that have better performance.