Towards Simulation of Parallel File System Scheduling Algorithms with pfssim



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Towards Simulation of Parallel File System Scheduling Algorithms with PFSsim



Yonggang Liu, Renato Figueiredo

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL

{yonggang,renato}@acis.ufl.edu
Dulcardo Clavijo, Yiqi Xu, Ming Zhao

School of Computing and Information Sciences, Florida International University, Miami, FL

{darte003,yxu006,ming}@cis.fiu.edu




Abstract
Many high-end computing (HEC) centers and commercial data centers adopt parallel file systems (PFSs) as their storage solutions. With the concurrent applications in PFS grow in both quantity and variety, it is expected that scheduling algorithms for data access will play an increasingly important role in PFS service quality. However, it is costly and disruptive to thoroughly research different scheduling mechanisms in the peta- or exascale systems; and the complexity in scheduling policy implementation and experimental data gathering even makes the tests harder. While a few parallel file system simulation frameworks have been proposed (e.g., [15,16]), their goals have not been in the scheduling algorithm evaluation. In this paper, we propose PFSsim, a simulator designed for the purpose of evaluating I/O scheduling algorithms in PFS. PFSsim is a trace-driven simulator based on the network simulation framework OMNeT++ and the disk device simulator DiskSim. A proxy-based scheduler module is implemented for scheduling algorithm deployment, and the system parameters are highly configurable. We have simulated PVFS2 on PFSsim, and the experimental results show that PFSsim is capable of simulating the system characteristics and capturing the effects of the scheduling algorithms.

  1. Introduction



Recent years, Parallel file systems (PFS) such as Lustre[1], PVFS2[2], Ceph[3], and PanFS[4] are increasingly popular in high-end computing (HEC) centers and commercial data centers — for instance, as of April 2009, half of the world’s top 30 supercomputers use Lustre[5] as their storage solutions. PFSs outperform traditional distributed file systems such as NFS[6] in many application domains, and among the reasons, an important one is that they adopt the object-based storage model[7] and stripe large data accesses into smaller storage objects distributed in the storage system for high-throughput parallel access and load balancing.

In HEC and data center systems, there are often large numbers of applications that access data from a distributed storage system with a variety of Quality-of-Service requirements[8]. As such systems are predicted to continue to grow in terms of amount of resources and concurrent applications, I/O scheduling strategies that allow performance isolation are expected to become increasingly important. Unfortunately, most PFSs are not able to manage I/O flows on a per-data-flow basis. The scheduling modules that come with the PFSs are typically configured to fulfill an overall performance goal, instead of the quality of service for each application.

There is a considerable amount of existing work[9,10,11,12,13,24] on the problem of achieving differentiated service on a centralized management point. Nevertheless, the applicability of these algorithms in the context of parallel file systems has not been thoroughly studied. Challenges in HEC environments include the facts that applications have data flows issued from potentially large number of clients and parallel checkpointing of the applications becomes increasingly important to achieve desired levels of reliability; in such environment, centralized scheduling algorithms can be limiting from scalability and availability standpoints. To the best of our knowledge, there are not many existing decentralized I/O scheduling algorithms for distributed storage systems, while the proposed decentralized algorithms (e.g., [14]) may still need verification in terms of their suitability for PFSs, which are a subset of distributed storage systems.

While PFSs are widely adopted in the HEC field, research on corresponding scheduling algorithms is not easy. The two key factors that prevent the testing on real systems are: 1) the cost of scheduler testing on a peta- or exascale file system requires complex deployment and experimental data gathering; 2) experiments with the storage resources used in HEC systems can be very disruptive, as deployed production systems typically are expected to have high utilization. Under this context, a simulator that allows developers to test and evaluate different scheduler designs for HEC systems is very valuable. It extricates the developers from complicated deployment headaches in the real systems and cuts their cost in the algorithm development. Even though simulation results are bound to have discrepancies compared to the real performance, simulation results can offer very useful insights in the performance trends and allow the pruning of the design space before implementation and evaluation on a real testbed or a deployed system.

In this paper, we propose a Parallel File System simulator, PFSsim. Our design objectives for this simulator are: 1) Easy-to-use: Scheduling algorithms, PFS characteristics and network topologies can be easily configured at compile-time; 2) fidelity: It can accurately model the effect of HEC workloads and scheduling algorithms; 3) Ubiquity: the simulator should have good flexibility to simulate large variety of storage and network characteristics. 4) Scalable: It should be able to simulate up to thousands of machines in a medium-scale scheduling algorithm study.

The rest of the paper is organized as follows. In section 2, we introduce the related work on PFS simulation. In section 3, we talk about the PFS and scheduler abstractions. In section 4, we illustrate the implementation details of PFSsim. In section 5, we show the validation results. In the last section, we conclude our work and discuss the future work.

  1. Related Work

To the best of our knowledge, there are two parallel file system simulators presented in the literature, one is the IMPIOUS simulator proposed by E. Molina-Estolano, et. al.[15], and the other one is the simulator developed by P. Carns et. al.[16].

The IMPIOUS simulator is developed for fast evaluation of PFS designs. It simulates the parallel file system abstraction with user-provided file system specifications, which include data placement strategies, replication strategies, locking disciplines and cache strategies. In this simulator, the client modules read the I/O traces and the PFS specifications, and then directly issue them to the Object Storage Device (OSD) modules according to the configurations. The OSD modules can be simulated with the DiskSim simulator[17] or the “simple disk model”, while the former one provides higher accuracy and the later one provides higher efficiency. For the goal of fast and efficient simulation, IMPIOUS simplifies the PFS model by omitting the metadata server modules and corresponding communications, and since the focus is not on the scheduling strategies, it does not support explicit deployment of scheduling policies.

The other PFS simulator is described in the paper written by P. H. Carns et. al., which is about the PVFS2 server-to-server communication mechanisms [16]. This simulator is used for testing the overhead of metadata communications, specifically in PVFS2. Thus, a detailed TCP/IP based network model is implemented. The authors employed the INET extension[18] of the OMNeT++ discrete event simulation framework[19] to simulate the network. The simulator also uses a “bottom-up” technique to simulate the underlying systems (PVFS2 and Linux), which achieves high fidelity but compromises on flexibility.

We take inspiration from these related systems and develop an expandable, modularized design where the emphasis is on the scheduler. Based on this goal, we use DiskSim to simulate physical disks in detail, and we use the extensible OMNeT++ framework for the network simulation and the handling of simulated events. While we currently use a simple networking model, as pointed out above OMNeT++ supports INET extensions that can be incorporated to enable precise network simulations in our simulator, at the expense of longer simulation times.

  1. System Modeling




    1. Abstraction of Parallel File Systems

In this subsection, we will first describe the similarities in Parallel File Systems (PFSs), and then we are going to discuss the differences exist among different PFSs.

Considering all the commonly used PFSs, we find that the majority of them share the same basic architecture:

1. There is one or more data servers, which are built based on the local file systems (e.g., Lustre, PVFS2) or the block devices (e.g., Ceph). The application data are stored in the form of fixed-size PFS objects, whose IDs are unique in a global name space. File clocking feature is enabled in some PFSs (e.g., Lustre, Ceph);

2. There is one or more metadata servers, which typically manage the mappings from PFS file name space to PFS storage object name space, PFS object placement, as well as the metadata operations;

3. The PFS clients are run on the system users’ machines; they provide the interface (e.g., POSIX) for users/user applications to access the PFS.

For a general PFS, a file access request (read/write operation) goes through the following steps:

1. Receiving the file I/O request: By calling an API, the system user sends its request {operation, offset, file_path, size} to the PFS client running on the user’s machine.

2. Object mapping: The client tries to map the tuple {offset, file_path, size} to a serial of objects, which contain the file data. This information is either available locally or requires the client to query the metadata server.

3. Locating the object: The client locates the object to the data servers. Typically each data server stores a static set of object IDs, and this mapping information is often available on the client.

4. Data transmission: The client sends out data I/O requests to the designated data servers with the information {operation, object_ID}. The data servers reply the requests, and the data I/O starts.

Note that we have omitted the access permission check (often conducted on the metadata server) locking schemes (conducted on either the metadata server or the data server).

Although different PFSs follow the same architecture, they differ from each other in many ways, such as data distribution methodology, metadata storage pattern, user API, etc. Nevertheless, there are four aspects that we consider to have significant effects on the I/O performance: metadata management, data placement strategy, data replication model and data caching policy. Thus, to construct a scheduling policy test simulator for various PFSs, we should have the above specifications faithfully simulated.

It is proved that at least in some cases, metadata operation takes a big proportion of file system workloads[27], and also because it lies in the critical path, the metadata management can be very important to the overall I/O performance. Different PFSs use different techniques to manage metadata to achieve different levels of metadata access speed, consistency and reliability. For example, Ceph adopts the dynamic subtree partitioning technique[30] to distribute the metadata onto multiple metadata servers for high access locality and cache efficiency. Lustre deploys two metadata servers, which includes one “active” server and one “standby” server for failover. In PVFS2, metadata are distributed onto data servers to prevent single point of failure and the performance bottleneck. By tuning the metadata server module and the network topology in our simulator, system users are able to set up the metadata storage, caching and access patterns.

Data placement strategies are designed with the basic goal of achieving high I/O parallelism and server utilization/load balancing. But different PFS still vary with each other significantly, by reason of different usage contexts. Ceph is aiming at the large-scale storage systems that potentially have big metadata communication overhead. Thus, Ceph uses the local hashing function and CRUSH (Controlled Replication Under Scalable Hashing) technique[26] to map the object IDs to the corresponding OSDs in a distributed manner, which avoids metadata communication during data location lookup, and reduces the update frequency of the system map. In contrast, aiming to serve the users with higher trust and skills, PVFS2 provides flexible data placement options to the users, and it even delegates the users the ability to store the data on user-specified data servers. In our simulation, data placement strategies are set up on the client modules, and the reason is that, this approach gives the clients the capability to simulate both the distributed-style and centralized-style data location lookup.

Data replication and failover models also affect the I/O performance, because for systems with data replication setup, data are written to multiple locations, which may prolong the writing process. For example, with data replication enabled in Ceph, every write operation is committed to both the primary OSD and the replica OSDs inside a placement group. Though Ceph maintains parallelism when forwarding the data to the replica OSDs, the costs of data forwarding and synchronization are still non-negligible. Lustre and PVFS2 do not implement explicit data replication models assuming that the replication is done by the underlay hardware.

Data caching on the server side and client side may promote the PFS I/O performance. But it is importance that, the client-side caching coherency also needs to be managed. PanFS data servers implement write-data caching that aggregates multiple writes for efficient transmission and data layout at the OSDs, which may increase the disk I/O rate. Ceph implements the O_LAZY flag for open operation at client-side that allows applications to explicitly relax the usual coherency requirements for a shared-write file, which facilitates those HPC applications that often have concurrent accesses to different parts of the files. Some PFSs do not implement client caching by default, such as PVFS. We have not implemented the data caching components in PFSsim, and we plan to implement it in the near future.

    1. Abstraction of PFS Scheduler

Among the many Figure 1. The architecture of PFSsim. The two big dash-line boxes mean the entities inside are simulated by OMNeT++ platform and DiskSim platform.


proposed centralized or decentralized scheduling algorithms in distributed storage systems, large varieties of network fabric and deployment locations are chosen. For instance, in [14], the schedulers are deployed on the Coordinators, which reside between the system clients and the storage Bricks. In [24], the scheduler is implemented on a centralized broker, which captures all the system I/O and dispatches them to the disks. In [30], the scheduling policies are deployed on the network gateways which serve as the storage system portals to the clients. And in [29], the scheduling policies are deployed on the per-server proxies, which intercept I/O and virtualize the data servers to the system clients.

In our simulator, the system network is simulated with high flexibility, which means the users are able to deploy their own network fabric with the basic or user-defined devices. The schedulers can also be created and positioned to any part of the network. For more advanced designs, inter-scheduler communications can also be enabled. The scheduling algorithms are to be defined by the PFSsim users, and abstractive APIs are exposed to enable the schedulers to keep track of the data server status.



  1. Simulator Implementation




    1. Parallel File System Scheduling Simulator

Based on the abstractions we mentioned, we have developed the Parallel File System scheduling simulator (PFSsim) based on the discrete event simulation framework OMNeT++4.0 and the disk model simulator DiskSim4.0.

In our simulator, the client modules, metadata server modules, scheduler modules and the local file system modules are simulated by OMNeT++. OMNeT++ also simulates the network for communications between client – metadata server, client – data server, and scheduler – scheduler. DiskSim is employed for the detailed simulation of disk models; one DiskSim process is deployed for each independent disk module being simulated. For details, please refer to Figure 1.

The simulation input is provided in the form of trace files which contain the I/O requests of the system users. Upon reading one I/O request from the input file, the client creates a QUERY object, and sends it to the metadata server through the simulated network. On the metadata server, the corresponding data server IDs and the stripping information are added to the QUERY object, which is sent back to the client. The client stripes the I/O request according to the stripping information, and for each stripe the client issues a JOB object to the designated data server through the simulated network.

The JOB object is received by the scheduler module on the data server (the detailed design of the scheduler is covered in following subsection). When a JOB object is dispatched by the scheduler, the local file system module maps the logical address of the job to the physical block number. Finally, the job information is sent to the DiskSim simulator via inter-process communication over a network connection (currently, TCP).

When a job is finished on DiskSim, its ID and finish time are sent back to the data server module on OMNeT++. Then, the corresponding JOB object is found from the local record. After writing the “finish time” into the JOB object, it is sent to the client. Finally, the client writes the job information into the output file, and the JOB object is destroyed.



    1. Scheduler Implementation

To provide an interface for the users to implement their own scheduling algorithms, we provide a base class for all algorithms, so that scheduling algorithms can be implemented by inheriting this class. The base class mainly contains the following functions:



void jobArrival(JOB * job);

void jobFinish(int ID);

void getSchInfo(Message *msg);

JOB means the JOB object referred in the above subsection. Message is defined by the schedulers for exchanging the scheduling information. The jobArrival function is called by the data server when a new job arrives at the scheduler. The jobFinish function is called when a job just finishes the serving phase. The getSchInfo function is called when the data server receives a scheduler-to-scheduler message. The simulator users can overwrite these functions to specify the corresponding behaviors regarding to their algorithms.

The data server module also exports interfaces to the schedulers. Two important functions exported by the DataServer class are:

bool dispatchJob(JOB * job);

bool sendInfo(int ID, Message * msg);

The dispatchJob function is called for dispatching jobs to the resources. The sendInfo function is called for sending scheduling information to other schedulers.



    1. TCP Connection between OMNET++ and DiskSim

One challenge of building a system with multiple integrated simulators is the virtual time synchronization. Each simulator instance runs its own virtual time, and each one has a large amount of events emerging every second. The synchronization, if performed inefficiently, can inevitably become a bottleneck on the simulation speed.

Since DiskSim has the functionality of getting the time stamp for the next event, OMNeT++ can always proactively synchronize with every DiskSim instance at the provided time stamp.

Currently we have implemented TCP connections between the OMNeT++ simulator and the DiskSim instances. Even though optimizations are done in improving the synchronization efficiency, we found the TCP connection cost is still a bottleneck of the simulation speed. In the future work, we plan to introduce more efficient synchronization mechanisms, such as shared memory.



    1. Local File System Simulation

In our simulator, the local file system is a simple file system that provides a static mapping between file and disk blocks. This model is inaccurate, but the reason why we did not implement a fully simulated local file system is the local file system block allocation heavily depends on the context of storage usage (e.g., EXT4 [22]), which is out of our control. This simple file system model guarantees the data that are adjacent in the logical space are also adjacent on the physical storage. In the future, we plan to implement a general file system with cache functionality enabled.



  1. Validation and Evaluation

In this section, we validate the PFSsim simulation results against the real system performance. In the real system, we deployed a set of virtual machines; each virtual machine is configured with 2.4GHz AMD CPU and 1 GB memory. EXT3 is used as the local file system. We deployed the PVFS2 parallel file system, containing 1 metadata server and 4 data servers on both the real system and the simulator. On each data server node, we also deploy a proxy-based scheduler that intercepts all the I/O requests going through the local machine.

In the experiments, we use the Interleaved or Random (IOR) benchmark [23] developed by the Scalable I/O Project (SIOP) at Lawrence Livermore National Laboratory. This benchmark facilitates us by allowing the specification of the trace file patterns.

    1. Simulator Validation

To validate the simulator fidelity in different scales, we have performed five independent experiments with 4, 8, 16, 32 and 64 clients. In this subsection, we do not implement the scheduling algorithms on the proxies. We conduct two sets of experiments and measure the performance of the system. In Set 1, each client continuously issues 400 requests to the data servers. Each of these requests is a 1 MB sequential read from one file, and the file content is stripped and evenly distributed onto 4 data servers. Set 2 is has the same configuration as Set 1, except that the clients issue sequential write requests instead of sequential read requests.

We measure the system performance by collecting the average response time for all I/O requests. The term “response time” is the elapsed time between when a I/O request issues its first I/O packet and when it receives the response for its last packet. As shown in Figure 2, the average response time of the real system increases super-linearly as the number of client increases. The same behavior is also observed on the PFSsim simulator. For the read requests from smaller number of clients, the simulated results do not match the real results very well. This is probably because the real local file system EXT3 provides read prefetching, which accelerates the reading speed when recent accessed data are adjacent in locality. This can also be proved by the fact that, as the number of clients accessing the file system increases, the difference between real results and simulated results gets smaller, which means the read prefetching becomes less effective. For both read and write requests, the simulated results follow the same trend of the real system results as the number of client grows, which confirms that while the absolute simulated values may not predict with accuracy for the real system, relative values can still be used to infer performance trade-offs.

    1. Scheduler Validation

In this subsection, we validate the ability of PFSsim in implementing request scheduling algorithms. We deploy 32 clients in both real system and the simulator. The clients are separated in two groups, each with 16 clients, for the purpose of algorithm validation. Each client continuously issues 400 I/O requests. Each of these requests is a 1 MB sequential write to a single file, and the file content is stripped and evenly distributed onto 4 data servers. The Start-time Fair Queuing algorithm with depth D = 4 (SFQ(4)) [10] is deployed on each data server for resource proportional-sharing.

We conducted three sets of tests, and in each set, we assign a static weight ratio for two groups to enforce the proportional-sharing. Every set is done in both real system and the simulator. Set a, b, c, have the weight ratio (Group1:Group2) of 1:1, 1:2 and 1:4, respectively. We measure and analyze the system throughput ratio each group achieves during the first 100 seconds of system runtime.




Figure 2. Average Response Time with Different Number of clients

As shown in Figure 3, we see that the average throughput ratios of the simulated system have the same trend as that of the results from the real system, which reflects the characteristics of the proportional-sharing algorithm. This validation proves that the PFSsim simulator is able to simulate the performance effect of proportional-sharing algorithms under various configurations on the real system.

From the real system results, we can also see the throughput ratio has more oscillation as the difference in two groups’ sharing ratio grows. This behavior also slightly exists in the results from the simulator, but we observe that the results from the simulator have less oscillation. This is because the real system environment is more complex, where many factors can contribute to the variations in the results, such as TCP time out. But in our simulator, we do not have the variations caused by these factors. In order to capture dynamic variations with higher accuracy, future work will investigate which system modeling aspects need to be accounted for.



  1. Conclusion and Future Work

The design objective of PFSsim is to simulate the important effects of workload and scheduling algorithms on parallel file systems. We implement a proxy-based scheduler model for flexible scheduler deployment. The validations on system performance and scheduling effectiveness show the system is capable of simulating system performance trend given specified workload and scheduling algorithms. For scalability, as far as we tested, the system scales for simulations of up to 512 clients and 32 data servers.

In the future, we will work on optimizing the simulator to improve simulation speed and fidelity. For this goal, we plan to implement efficient synchronization scheme between OMNeT++ and DiskSim, such as shared memory. We will refine the system by implementing some important factors that also contribute to the performance, such as TCP timeout and characteristics of the local file system.

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(a1) weight ratio = 1:1, real system (b1) weight ratio =1:2, real system (c1) weight ratio = 1:4, real system



(a2) weight ratio = 1:1, simulated (b2) weight ratio = 1:2,simulated (c2) weight ratio = 1:4, simulated



Figure 3. The throughput percentage each group takes over in the first 100 second of runtime in both real sytem and the simulated sytem. Under the pictures we indicate the weight ratio of Group1:Group2 in SFQ(4) algorithm. The average throughput ratios for Group 2 are: (a1)50.00%; (a2)50.23%; (b1)63.41%; (b2)66.19%; (c1)68.58%; (c2)77.67%.

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