MongoDB Performance In The Cloud

Web applications are growing at a staggering rate every day. As web applications keep getting more complex, their data storage requirements tend to grow exponentially. Databases play an important role in the way web applications store their information. Mongodb is a document store database that does not have strict schemas that RDBMs require and can grow horizontally without performance degradation. MongoDB brings possibilities for different storage scenarios and allow the programmers to use the database as a storage that fits their needs, not the other way around. Scaling MongoDB horizontally requires tens to hundreds of servers, making it very difficult to afford this kind of setup on dedicated hardware. By moving the database into the cloud, this opens up a possibility for low cost virtual machine instances at reasonable prices. There are many cloud services to choose from and without testing performance on each one, there is very little information out there. This paper provides benchmarks on the performance of MongoDB in the cloud

METHOD AND DEVICE FOR AUTOMATED JOB FAILOVER

In an embodiment of the present disclosure, there is provided a method of managing multi data cluster failure. The method comprises scheduling one or more applications on a primary cluster and a secondary cluster, monitoring a status of the one or more applications scheduled in one cluster by the other data cluster, identifying the status of the applications of one cluster by the other cluster, updating the status of the one or more applications associated with each cluster on the distributed file system (DFS) of respective cluster, and generating output reports of the scheduled one or more applications. The method automatically converts the secondary data cluster to the primary cluster and vice-versa upon identifying the status of primary cluster as under failure or under maintenance during monitoring

Redundancy schemes for high availability computer clusters

The primary goal of computer clusters is to improve computing performances by taking advantage of the parallelism they intrinsically provide. Moreover, their use of redundant hardware components enables them to offer high availability services. In this paper, we present an analytical model for analyzing redundancy schemes and their impact on the cluster’s overall performance. Furthermore, several cluster redundancy techniques are analyzed with an emphasis on hardware and data redundancy, from which we derive an applicable redundancy scheme design. Also, our solution provides a disaster recovery mechanism that improves the cluster’s availability. In the case of data redundancy, we present improvements to the replication and parity data replication techniques for which we investigate the availability of the cluster under several scenarios that take into account, among other things, the number of replicated nodes, the number of CPUs that hold parity data and the relation between primary and replicated data. For this purpose, we developed a simulator that analyzes the impact of a redundancy scheme on the processing rate of the cluster. We also studied the performance of two well-known schemes according to the usage rate of the CPUs. We found that two important aspects influencing the performance of a transaction-oriented cluster were the cluster’s failover and data redundancy schemes. We simulated several data redundancy schemes and found that data replication offered higher cluster availability than the parity model

MDDPro: Model-Driven Dependability Provisioning in Enterprise Distributed Real-Time and Embedded Systems

Abstract Service oriented architecture (SOA) design principles are increasingly being adopted to develop distributed real-time and embedded (DRE) systems, such as avionics mission computing, due to the availability of real-time component middleware platforms. Traditional approaches to fault tolerance that rely on replication and recovery of a single server or a single host do not work in this paradigm since the fault management schemes must now account for the timely and simultaneous failover of groups of entities while improving system availability by minimizing the risk of simultaneous failures of replicated entities. This paper describes MDDPro, a model-driven dependability provisioning tool for DRE systems. MDDPro provides intuitive modeling abstractions to specify failover requirements of DRE systems at different granularities. MDDPro enables plugging in different replica placement algorithms to improve system availability. Finally, its generative capabilities automate the deployment and configuration of the DRE system on the underlying platforms

EdgeBOX high-availability

Estágio realizado na Critical Links, S. A. e orientado por Nuno FerreiraTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200