Distributed File System Load Balancing and Cloud Synchronization

IOSR Journal of Computer Engineering, 2014

Cloud Computing is an emerging technology, it is based on demand service in which shared resources, information, software and other devices are provided according to the clients to the requirements at specific time with the availability of internet. Load balancing is one of the challenging issue in cloud computing. An efficient load balancing makes cloud computing more efficient and improves user satisfaction. It includes fault tolerance, high availability, scalability, flexibility, reduced overhead for users, reduced cost of ownership, on demand services etc. Distributed file systems are key building blocks for cloud computing applications based on the Map Reduce programming paradigm. In such file systems, nodes at the same time serve computing and storage functions. Files can be created, deleted, and appended dynamically. This results in load imbalance in a distributed file system; that is, the file chunks are not distributed uniformly as possible among the nodes.

Contemporary Engineering Sciences, 2015

File storage load can be balanced in the storage nodes avail in the cloud system by using totally distributed load rebalancing algorithm. Large level distributed systems such as cloud applications come with rising challenges on how to transfer and where to store compute data. Cloud computing is a distributed computing over a network. Node concurrently serves as a computing and storage task. In cloud computing environment, files can also be dynamically created, deleted and append. The file chunks are not distributed as equally among the nodes. Lead to load inequity in a distributed file system. The existing distributed file system depends on single node to manage almost all operations such as chunk reallocation of every data block in the file system. As a result it can be bottleneck resource and a single point of failure. A new technique Random Linear Network Coding (RLNC) is employed in the proposed system. RLNC is performed at the opening when the file is stored in the cloud. Using this strategy, a file will be split into different parts and send distinct parts to each chunk server. RSA algorithm applied to calculate the response time file size and deadlock detection. RSA algorithm used to detect the anomalies of the system. Dynamic scheduling algorithm present in the proposed system to overcome to load inequity problem. This algorithm is compared against a centralized approach in a production system. The results indicate that our proposal is considerably outperforms the prior distributed in terms of load inequity factor, movement cost, and algorithmic overhead.

International Journal of Computer Applications, 2015

Distributed Systems are useful for computation and storage of large scale data at dispersed location. Distributed File System (DFS) is a subsystem of Distributed System. DFS is a means of sharing of storage space and data. Servers, Storage devices and Clients are on dispersed location in DFS. Fault tolerance and Scalability are two main features of distributed file system. Performance of DFS is measured by response time. Apart from response time there are also other dimensions such as transparency in which performance of DFS is viewed. DFS provides file services with scalability, fault tolerance, availability, minimum response time. The truth behind the minimum response time is good design of load balance algorithm. To improve the minimum response time and utilization of all nodes in DFS cluster it is found static as well as dynamic load balance strategies. In this survey paper Self acting, load balancing for parallel file system, Adaptive loading data migration in distributed file system, Load balancing in distributed multi agent computing systems, Self organizing storage clusters for data intensive applications, User centric data migration in networked storage systems are discussed to study the different load balancing schemes. Adaptive loading data migration is one of the latest solution found in literature survey. Self acting, load balancing (SALB) for parallel file system is for load balancing uses online load prediction methods and is distributed architecture.

2009

Rapid adoption of virtualization technologies has led to increased utilization of physical resources, which are multiplexed among numerous workloads with varying demands and importance. Virtualization has also accelerated the deployment of shared storage systems, which offer many advantages in such environments. Effective resource management for shared storage systems is challenging, even in research systems with complete end-to-end control over all system components. Commercially-available storage arrays typically offer only limited, proprietary support for controlling service rates, which is insufficient for isolating workloads sharing the same storage volume or LUN.

Journal of Systems and Software, 2014

Because of the rapid growth of the World Wide Web and the popularization of smart phones, tablets and personal computers, the number of web service users is increasing rapidly. As a result, large web services require additional disk space, and the required disk space increases with the number of web service users. Therefore, it is important to design and implement a powerful network file system for large web service providers. In this paper, we present three design issues for scalable network file systems. We use a variable number of objects within a bucket to decrease internal fragmentation in small files. We also propose a free space and access load-balancing mechanism to balance overall loading on the bucket servers. Finally, we propose a mechanism for caching frequently accessed data to lower the total disk I/O. These proposed mechanisms can effectively improve scalable network file system performance for large web services.

ACM Transactions on Computer Systems, 1988

The Andrew File System is a location-transparent distributed tile system that will eventually span more than 5000 workstations at Carnegie Mellon University. Large scale affects performance and complicates system operation. In this paper we present observations of a prototype implementation, motivate changes in the areas of cache validation, server process structure, name translation, and low-level storage representation, and quantitatively demonstrate Andrew's ability to scale gracefully. We establish the importance of whole-file transfer and caching in Andrew by comparing its performance with that of Sun Microsystem's NFS tile system. We also show how the aggregation of files into volumes improves the operability of the system.

ArXiv, 2019

Disaggregated, or non-local, file storage has become a common design pattern in cloud systems, offering benefits of resource pooling and server specialization, where the inherent overhead of separating compute and storage is mostly hidden by storage device latency. We take an alternate approach, motivated by the commercial availability of very low latency non-volatile memory (NVM). By colocating computation and NVM storage, we can provide applications much higher I/O performance, sub-second application failover, and strong consistency. To demonstrate this, we built the Assise distributed file system, based on a persistent, replicated cache coherence protocol for managing a set of colocated NVM storage devices as a layer. Unlike disaggregated file stores, Assise avoids the read and write amplification of page granularity operations. Instead, remote NVM serves as an intermediate, byte-addressable cache between colocated NVM and slower storage, such as SSDs. We compare Assise to Ceph/B...

Proceedings of the 1st USENIX Symposium on Networked Systems Design and Implementation NSDI, 2004

We present the design, implementation, and evaluation of the Batch-Aware Distributed File System (BAD-FS), a system designed to orchestrate large, I/O-intensive batch workloads on remote computing clusters distributed across the wide area. BAD-FS consists of two novel components: a storage layer that exposes control of traditionally fixed policies such as caching, consistency, and replication; and a scheduler that exploits this control as necessary for different workloads. By extracting control from the storage layer and placing it within an external scheduler, BAD-FS manages both storage and computation in a coordinated way while gracefully dealing with cache consistency, fault-tolerance, and space management issues in a workload-specific manner. Using both microbenchmarks and real workloads, we demonstrate the performance benefits of explicit control, delivering excellent end-to-end performance across the wide-area.

IEEE Transactions on Information Theory, 2021

Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems. Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balance in the system should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate load balancing performance of natural storage schemes in which each data object is stored at d different nodes and each node stores the same number of objects. We find that load balance in a system of n nodes improves multiplicatively with d as long as d = o (log(n)), and improves exponentially as soon as d = Θ (log(n)). We show that the load balance in the system improves the same way with d when the service choices are created with XOR's of r objects rather than object replicas, which also reduces the storage overhead multiplicatively by r. However, unlike accessing an object replica, access through a recovery set composed by an XOR'ed object copy requires downloading content from r nodes, which increases the load imbalance in the system additively by r.

2006

File system designers continue to look to new architectures to improve scalability. Object-based storage diverges from server-based (e. g. NFS) and SAN-based storage systems by coupling processors and memory with disk drives, delegating low-level allocation to object storage devices (OSDs) and decoupling I/O (read/write) from metadata (file open/close) operations. Even recent object-based systems inherit decades-old architectural choices going back to early UNIX file systems, however, limiting their ability to effectively scale to hundreds of petabytes. We present Ceph, a distributed file system that provides excellent performance and reliability with unprecedented scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable OSDs. We leverage OSD intelligence to distribute data replication, failure detection and recovery with semi-autonomous OSDs running a specialized local object storage file system (EBOFS). Finally, Ceph is built around a dynamic distributed metadata management cluster that provides extremely efficient metadata management that seamlessly adapts to a wide range of general purpose and scientific computing file system workloads. We present performance measurements under a variety of workloads that show superior I/O performance and scalable metadata management (more than a quarter million metadata ops/sec).

2006

We have developed Ceph, a distributed file system that provides excellent performance, reliability, and scalability. Ceph maximizes the separation between data and metadata management by replacing allocation tables with a pseudo-random data distribution function (CRUSH) designed for heterogeneous and dynamic clusters of unreliable object storage devices (OSDs). We leverage device intelligence by distributing data replication, failure detection and recovery to semi-autonomous OSDs running a specialized local object file system. A dynamic distributed metadata cluster provides extremely efficient metadata management and seamlessly adapts to a wide range of general purpose and scientific computing file system workloads. Performance measurements under a variety of workloads show that Ceph has excellent I/O performance and scalable metadata management, supporting more than 250,000 metadata operations per second.

Proceedings of the 2003 ACM/IEEE conference on Supercomputing - SC '03, 2003

We develop and evaluate a system for load management in shared-disk file systems built on clusters of heterogeneous computers. The system generalizes load balancing and server provisioning. It balances file metadata workload by moving file sets among cluster server nodes. It also responds to changing server resources that arise from failure and recovery and dynamically adding or removing servers. The system is adaptive and self-managing. It operates without any a-priori knowledge of workload properties or the capabilities of the servers. Rather, it continuously tunes load placement using a technique called adaptive, non-uniform (ANU) randomization. ANU randomization realizes the scalability and metadata reduction benefits of hash-based, randomized placement techniques. It also avoids hashing's drawbacks: load skew, inability to cope with heterogeneity, and lack of tunability. Simulation results show that our load-management algorithm performs comparably to a prescient algorithm.

2001

Describes a virtual file system that allows data to be transferred on demand between storage and computational servers for the duration of a computing session. The solution works with unmodified applications (even commercial ones) running on standard operating systems and hardware. The virtual file system employs software proxies to broker transactions between standard NFS (Network File System) clients and servers; the proxies are dynamically configured and controlled by computational grid middleware. The approach has been implemented and extensively exercised in the context of PUNCH (Purdue University Network Computing Hubs), an operational computing portal that has more than 1,500 users across 24 countries. The results show that the virtual file system performs well in comparison to native NFS: performance analyses show that the proxy incurs mean overheads of 1% and 18% with respect to native NFS for a single-client execution of the Andrew benchmark in two representative computing environments, and that the average overhead for eight clients can be reduced to within 1% of native NFS with concurrent proxies

2010

Abstract: Large scale distributed systems such as cloud computing applications are becoming very common. These applications come with increasing challenges on how to transfer and where to store and compute data. The most prevalent distributed file systems to deal with these challenges is the Hadoop File System (HDFS) which is a variant of the Google File System (GFS). However HDFS has two potential problems.

2019 XVI International Symposium "Problems of Redundancy in Information and Control Systems" (REDUNDANCY), 2019

Contention at the storage nodes is the main cause of long and variable data access times in distributed storage systems. Offered load on the system must be balanced across the storage nodes in order to minimize contention, and load balance in the system should be robust against the skews and fluctuations in content popularities. Data objects are replicated across multiple nodes in practice to allow for load balancing. However redundancy increases the storage requirement and should be used efficiently. We evaluate load balancing performance of natural storage schemes in which each data object is stored at d different nodes and each node stores the same number of objects. We find that load balance in a system of n nodes improves multiplicatively with d as long as d = o (log(n)), and improves exponentially as soon as d = Θ (log(n)). We show that the load balance in the system improves the same way with d when the service choices are created with XOR's of r objects rather than object replicas, which also reduces the storage overhead multiplicatively by r. However, unlike accessing an object replica, access through a recovery set composed by an XOR'ed object copy requires downloading content from r nodes, which increases the load imbalance in the system additively by r.