Building and Protecting vSphere Data Centers Using Site Recovery Manager (SRM)

With the evolution of cloud computing technology, companies like Amazon, Microsoft, Google, Softlayer, and Rackspace have started providing Infrastructure as a Service, Software as a Service, and Platform as a Service offering to their customers. For these companies, providing a high degree of availability is as important as providing an overall great hosting service. Disaster is always being unpredictable, the destruction caused by it is always worse than expected. Sometimes it ends up with the loose of information, data and records. Disaster can also make services inaccessible for very long time if disaster recovery was not planned properly. This paper focuses on protecting a vSphere virtual datacenter using Site Recovery Manager (SRM). A study says 23% of companies close within one year after the disaster struck. This paper also discusses on how SRM can be a cost effective disaster recovery solution compared to all the recovery solutions available. It will also cover Recovery Point Objective and Recovery Time Objective. The SRM works on two different replication methodologies that is vSphere replication and Array based replications. These technologies used by Site Recovery Manager to protect Tier-1, 2, and 3 applications. The recent study explains that Traditional DR solutions often fail to meet business requirements because they are too expensive, complex and unreliable. Organizations using Site Recovery Manager ensure highly predictable RTOs at a much lower cost and level of complexity. Lower cost for DR. Site Recovery Manager can reduce the operating overhead by 50% by replacing complex manual run books with simple, automated recovery plans that can be tested without disruption. For organizations with an RPO of 15 minutes or higher, vSphere Replication can eliminate up to $10,000 per TB of protected data with storage-based technologies. The combined solution can save over USD$1,100 per protected virtual machine per year. These calculations were validated by a third-party global research firm. Integration with Virtual SAN reduces the DR footprint through hyper-converged, software-defined storage that runs on any standard x86 platform. Virtual SAN can decrease the total cost of ownership for recovery storage by 50 percent

Data management in cloud environments: NoSQL and NewSQL data stores

: Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages

Integrating legacy mainframe systems: architectural issues and solutions

For more than 30 years, mainframe computers have been the backbone of computing systems throughout the world. Even today it is estimated that some 80% of the worlds' data is held on such machines. However, new business requirements and pressure from evolving technologies, such as the Internet is pushing these existing systems to their limits and they are reaching breaking point. The Banking and Financial Sectors in particular have been relying on mainframes for the longest time to do their business and as a result it is they that feel these pressures the most. In recent years there have been various solutions for enabling a re-engineering of these legacy systems. It quickly became clear that to completely rewrite them was not possible so various integration strategies emerged. Out of these new integration strategies, the CORBA standard by the Object Management Group emerged as the strongest, providing a standards based solution that enabled the mainframe applications become a peer in a distributed computing environment. However, the requirements did not stop there. The mainframe systems were reliable, secure, scalable and fast, so any integration strategy had to ensure that the new distributed systems did not lose any of these benefits. Various patterns or general solutions to the problem of meeting these requirements have arisen and this research looks at applying some of these patterns to mainframe based CORBA applications. The purpose of this research is to examine some of the issues involved with making mainframebased legacy applications inter-operate with newer Object Oriented Technologies

Performance Comparison of Message Queue Methods

Message queues are queues of messages that facilitate communication between applications. A queue is a line of messages or events waiting to be handled in a sequential manner. A message queue is a queue of messages sent between applications. It includes a sequence of work objects that are waiting to be processed. For a distributed system to work, it needs to pass information between various machines. No single machine is responsible for the entire system, but all information is interrelated. Hence a major concern of distributed systems is this transfer of data. Which also proves to be one of the most significant challenges. Message Queues provide this asynchronous communication between applications. Major factors behind the success of an application is the ability to decouple and scale it. In this thesis, we focus on analyzing and comparing the performance of three most widely used open source message brokers namely Apache ActiveMQ, RabbitMQ and Apache Kafka which help in creating a distributed system. An end to end message queuing model is setup for each of the brokers to mimic real world application models. The producers, consumers and brokers that make up the message queuing system are then put through rigorous benchmarking tests to analyze their performance. The performance is evaluated based on major factors like throughput, latency and total time taken by the transaction. Based on the benchmarking results, it was observed that Apache Kafka which was initially developed to be a message queue but later enhanced to be a streaming platform outdid RabbitMQ and Apache ActiveMQ in almost all the performance factors. It was also observed that the larger the message size, more constant is the performance of all message brokers. Hence, for gauging the performance in hard times, the message sizes considered for the experiments is very small. This gives us a glimpse of the actual performance capabilities of the message queuing brokers