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Hive: Fault Containment for Shared-Memory Multiprocessors

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Hive: Fault Containment for Shared-Memory Multiprocessors Hive: Fault Containment for Shared-Memory Multiprocessors John Chapin, Mendel Rosenblum, Scott Devine, Tirthankar Lahiri, Dan Teodosiu, and Anoop Gupta Computer Systems Laboratory Stanford University, Stanford CA 94305 htkp: //www-flash. skanforcl. edu Abstract In this paper we describe Hive, an operahng system designed for large-scale shared-memory multiprocessors. Hive is Reliability and scalability are major concerns when designing fundamentally different from previous monolithic and microkernel operating systems for large-scale shared-memory multiprocessors. SMP OS implementations: it IS structured as an internal distributed In this paper we describe Hive, an operating system with a novel system of independent kernels called ce/ls. This multicellular kernel architecture that addresses these issues Hive is structured kernel architecture has two main advantages: as an internal distributed system of independent kernels called ● Re[mbiltry: In SMP OS implementations, any significant cells. This improves reliabihty because a hardwme or software hardware or software fault causes the entire system to crash. fault damages only one cell rather than the whole system, and For large-scale machines this can result in an unacceptably low improves scalability because few kernel resources are shared by mean time to failure. In Hive, only the cell where the fault processes running on different cells. The Hive prototype is a occurred crashes, so only the processes using the resources of complete implementation of UNIX SVR4 and is targeted to run on that cell are affected. This is especially beneficial for compute the Stanford FLASH multiprocessor. server workloads where there are multiple independent This paper focuses on Hive’s solutlon to the following key processes. the predominant situation today. In addition, challenges: ( 1) fault containment, i.e. confining the effects of scheduled hardware maintenance and kernel software upgrades hardware or software faults to the cell where they occur, and (2) can proceed transparently to applications, one cell at a time. memory sharing among cells, which is requmed to achieve ● Scdabi/i@: SMP OS implementations are difficult to scale to application performance competitive with other multiprocessor large ma~hines because ;11 processors directly share all kernel operating systems. Fault containment in a shared-memory resources. Improving parallelism in a ‘<shared-everything” multiprocessor requmes defending each cell against erroneous architecture 1s an iterative trial-and-error process of identifying writes caused by faults in other cells. Hive prevents such damage and fixing bottlenecks. In contrast, Hive offers a systematic by using the FLASH jirewzdl, a write permission bit-vector approach to scalablhty. Few kernel resources are shared by associated with each page of memory, and by discarding processes running on different cells, so parallelism can be potentially corrupt pages when a fault is detected. Memory sharing Improved by increasing the number of cells. is provided through a unified file and virtual memory page cache However, the multicellular architecture of Hive also creates new across the cells, and through a umfied free page frame pool. implementation challenges. These include: We report early experience with the system, including the results of fault injection and performance experiments using ● Fault comirrrrwrr,’ The effects of faults must be confined to the SimOS, an accurate simulator of FLASH, The effects of faults cell in which they occur, This M difficult since a shared-memory were contained to the cell in which they occurred m all 49 tests multiprocessor allows a faulty cell to issue wild writes which where we injected fail-stop hardware faults, and in all 20 tests can corrupt the memory of other cells. where we injected kernel data corruption. The Hive prototype ● Resource sharing: Processors, memory, and other system executes test workloads on a four-processor four-cell system with resources must be shared flexibly across cell boundaries, to between 0’%. and 11YOslowdown as compared to SGI IRIX 5.2 (the preserve the execution efficiency that justifies investing in a version of UNIX on which it is based). shared-memory multiprocessor. ● Smg/e-sy.s tent image.. The cells must cooperate to present a 1 Introduction standard SMP OS interface to applications and users. In this paper, we focus on Hive’s solution to the fault containment Shared-memory multiprocessors are becoming an increasingly problem and on Its solution to a key resource sharing problem, common server platform because of their excellent performance sharing memory across cell boundaries, The solutions rely on under dynamic multlprogrammed workloads, However, the hardware as well as software mechamsms: we have designed Hive symmetric multiprocessor operating systems (SMP OS) in conjunction with the Stanford FLASH multiprocessor [ 10], commonly used for small-scale machines are difficult to scale to which has enabled us to add hardware support m a few critical the large shared-memory multiprocessors that can now be built areas. (Stanford DASH [11 ], MIT Alewife [3], Convex Exemplar [5]). Hive’s fault containment strategy has three mam components. Permission to make digitalrhard copy of part or all of this work for personal Each cell uses firewrll hardware rxovided by FLASH to defend or classroom use is granted without fee provided that copies are not made most of its memory pages against ;ild writes. Any pages writable or distributed for profit or commercial advantage, the copyright notic8, the by a failed cell are preemptively discarded when the failure is title of the publication and its date appear, and notice is given that copying is by permission of ACM, Inc. To oopy otherwise, to republish, to detected, which prevents any corrupt data from being read post on servers, or to redistribute to lists, requires prior specific permission subsequently by applications or written to disk. Finally, aggressive and/or a fee. fadure detection reduces the delay until preemptive discard occurs. Cell failures are detected initially using heuristic checks, then SIGOPS ’95 12/95 CO, USA 0 1995 ACM 0-89791 -715-419510012 ...$3.50 12 confirmed with a distributed agreement protocol that minimizes redefine fault containment to include this resource management the probability of concluding that a functioning cell has failed. requirement: Hive prowdes two types of memory sharing among cells. First, A system provides fcadtcontainment f the probability thut the file system and the virtual memory system cooperate so an application falls is proportional to the amount of processes on multiple cells can use the same memory page for resources used by that application. not to the total amount shared data. Second, the page allocation modules on different cells of resources in the system. cooperate so a free page belonging to one cell can be loaned to another cell that is under memory pressure. Either type of sharing One important consequence of choosing this as the reliability goal would cause fault containment problems on current is that large applications which use resources from the whole multiprocessors, since a hardware fault in memory or in a system receive no reliability benefits. For example, some compute processor caching the data could halt some other processor that server workloads contain parallel applications that run with as tries to access that memory FLASH makes memory sharing safe many threads as there are processors in the system. However, these by providing timeouts and checks on memory accesses. large applications have previously used checkpointing to provide The current prototype of Hive is based on and remains binary their own reliability, so we assume they can continue to do so. compatible with IRIX 5.2 (a version of UNIX SVR4 from Silicon The fault containment strategy can be used in both distributed Graphics, Inc. ). Because FLASH is not available yet, we used the systems and multiprocessors. However, the problems that arise in SlmOS hardware simulator [ 18] to develop and test Hive. Our implementing fault containment are different in the two early experiments using SimOS demonstrate that: environments. In addition to all the problems that arise in distributed systems, the shared-memory hardware of Hive can survive the halt of a processor or the failure of a range multiprocessors increases vulnerability to both hardware faults and of memory. In all of 49 experiments where we injected a fail- software faults. We describe the problems caused by each of these stop hardware fault, the effects were confined to the cell where in turn. the fault occurred. Hardware faults: Consider the architecture of the Stanford Hive can surwve kernel software faults. In all of 20 FLASH, which is a typical large-scale shared-memory experiments where we randomly corrupted internal operating multiprocessor (Figure 2.1 ). FLASH consists of multiple nodes, system data structures, the effects were confined to the cell each with a processor and its caches, a local portion of main where the fault occurred. memory, and local 1/0 devices. The nodes commumcate through a Hive can offer reasonable performance while providing fault high-speed low-latency mesh network. Cache coherence is containment. A four-cell Hive executed three test workloads provided by a coherence controller on each node. A machine like with between 0% and 1170 slowdown as compared to IRIX 5.2 this M called a CC-NUMA multiprocessor (cache-coherent with on a four-processor machine. non-uniform memory access time) since accesses to local memory These results indicate that a multicellular kernel architecture can are faster than accesses to the memory of other nodes. provide fault containment in a shared-memory multiprocessor. The In a CC-NUMA machine, an important unit of failure is the performance results are also promising, but significant further node, A node failure halts a processor and has two direct effects on work is required on resource sharing and the single-system image the memory of the machine: the portion of main memory assigned before we can make definitive conclusions about performance.
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