Companies today are finding that the size and growth of stored data is becoming overwhelming. As the databases grow, the challenge is to create value by discovering insights and connections in the big databases in as close to real time as possible. In the recently published whitepaper, “Achieving Real-Time Business Solutions Using Graph Database Technology and High Performance Networks“ we describe a combination of high performance networking and graph base and analytics technologies which offers a solution to this need.
Each of the examples in the paper is based on an element of a typical analysis solution. In the first example, involving Vertex Ingest Rate shows the value of using high performance equipment to enhance real-time data availability. Vertex objects represent nodes in a graph, such as Customers, so this test is representative of the most basic operation: loading new customer data into the graph. In the second example, Vertex Query Rate highlights the improvement in the time needed to receive results, such as finding a particular customer record or a group of customers.
The third example, Distributed graph navigation processing starts at a Vertex and explores its connections to other Vertices. This is representative of traversing social networks, finding optimal transportation or communications routes and similar problems. The final example, Task Ingest Rate shows the performance improvement when loading the data connecting each of the vertices. This is similar to entering orders for products, transit times over a communications path and so on.
Each of these elements is an important part of a Big Data analysis solution. Taken together, they show that InfiniteGraph can be made significantly more effective when combined with Mellanox interconnect technology.
The University of Edinburgh’s entry into the ISC 2014 Student Cluster Competition, EPCC, has been awarded first place in the LINPACK test. The EPCC team harnessed Boston’s HPC cluster to smash the 10Tflop mark for the first time – shattering the previous record of 9.27Tflops set by students at ASC14 earlier this month. The team recorded a score of 10.14Tflops producing 3.38 Tflops/kW which would achieve a rank of #4 in the Green500, a list of the most energy efficient supercomputers in the world.
This achievement was made possible thanks to the provisioning of a high performance, liquid cooled GPU cluster by Boston. The system consisted on four 1U Supermicro servers, each comprising of two Intel® Xeon™ ‘Ivy Bridge’ processors and two NVIDIA® K40 Tesla GPUs, and Mellanox FDR 56Gb/s InfiniBand adapters, switches and cables.
Hadoop has become a leading programming framework in the big data space. Organizations are replacing several traditional architectures with Hadoop and use it as a storage, data base, business intelligence and data warehouse solution. Enabling a single file system for Hadoop and other programming frameworks benefits users who need dynamic scalability of compute and or storage capabilities.
Virtualization has already proven itself to be the best way to improve data center efficiency and to simplify management tasks. However, getting those benefits requires using the various services that the Hypervisor provides. This introduces delay and results in longer execution time, compared to running over a non-virtualized data center (native infrastructure). This drawback hasn’t been hidden from the eyes of the high-tech R&D community seeking ways to enjoy the advantages of virtualization with a minimal effect on performance.
One of the most popular solutions today to enable native performance is to use the SR-IOV (Single Root IO Virtualization) mechanism which bypasses the Hypervisor and enables a direct link between the VM to the IO adapter. However, although the VM gets the native performance, it loses all of the Hypervisor services. Important features like high availability (HA) or VM migration can’t be done easily. Using SR-IOV requires that the VM must have the specific NIC driver (that he communicates with) which results in more complicated management since IT managers can’t use the common driver that runs between the VM to the Hypervisor.
As virtualization becomes a standard technology, the industry continues to find ways to improve performance without losing benefits, and organizations have started to invest more in the deployment of RDMA enabled interconnects in virtualized data centers. In one my previous blogs, I discussed the proven deployment of RoCE (RDMA over Converged Ethernet) in Azure using SMB Direct (SMB 3.0 over RDMA) enabling faster access to storage.
Today’s data centers demand that the underlying interconnect provide the utmost bandwidth and extremely low latency. While high bandwidth is important, it is not worth much without low latency. Moving large amounts of data through a network can be achieved with TCP/IP, but only RDMA can produce the low latency that avoids costly transmission delays.
The speedy transfer of data is critical to it being used efficiently. Interconnect based on Remote Direct Memory Access (RDMA) offers the ideal option for boosting data center efficiency, reducing overall complexity, and increasing data delivery performance. Mellanox RDMA enables sub-microsecond latency and up to 56Gb/s bandwidth, translating to screamingly fast application performance, better storage and data center utilization, and simplified network management.
The rapid pace of change in data and business requirements is the biggest challenge when deploying a large scale cloud. It is no longer acceptable to spend years designing infrastructure and developing applications capable to cope with data and users at scale. Applications need to be developed in a much more agile manner, but in such a way that allows dynamic reallocation of infrastructure to meet changing requirements.
Choosing an architecture that can scale is critical. Traditional “scale-up” technologies are too expensive and can ultimately limit growth as data volumes grow. Trying to accommodate data growth without proper architectural design, results in un-needed infrastructure complexity and cost.
The most challenging task for the cloud operator in a modern cloud data center supporting thousands or even hundreds-of-thousands of hosts is scaling and automating network services. Fortunately, server virtualization has enabled automation of routine tasks – reducing the cost and time required to deploy a new application from weeks to minutes. Yet, reconfiguring the network for a new or migrated virtual workload can take days and cost thousands of dollars.
To solve these problems, you need to think differently about your data center strategy. Here are three technology innovations that will help data center architects design a more efficient and cost-effective cloud:
1. Overlay Networks
Overlay network technologies such as VXLAN and NVGRE, make the network as agile and dynamic as other parts of the cloud infrastructure. These technologies enable automated network segment provisioning for cloud workloads, resulting in a dramatic increase in cloud resource utilization.
Overlay networks provide for ultimate network flexibility and scalability and the possibility to:
Combine workloads within pods
Move workloads across L2 domains and L3 boundaries easily and seamlessly
Integrate advanced firewall appliances and network security platform seamlessly
As data continues to grow exponentially storing today’s data volumes in an efficient way is a challenge. Many traditional storage solutions neither scale-out nor make it feasible from Capex and Opex perspective, to deploy Peta-Byte or Exa-Byte data stores.
In this newly published whitepaper, we summarize the installation and performance benchmarks of a Ceph storage solution. Ceph is a massively scalable, open source, software-defined storage solution, which uniquely provides object, block and file system services with a single, unified Ceph storage cluster. The testing emphasizes the careful network architecture design necessary to handle users’ data throughput and transaction requirements.
One of the most important value-add solutions that Mellanox provides to its customers and partners is Educational Services. We offer a variety of learning methods to our partners, customers and other technology leaders.
One of the most successful learning platforms to our customers is our open enrollment courses. These 3-4 day instructor led courses are available worldwide: the United Kingdom, Germany, France, Israel, Australia, China and in the US: New York, California, Massachusetts and Washington. Soon we will offer an “After hours, virtual format”, meaning the students will gain the benefit of a blended (remote instructor led along with online training) learning format, allowing participants flexibility to take the course and still not miss many working hours.
This past week in Atlanta, I got the chance to attend the sessions, presented and exhibited at the OpenStack Summit. The Summit was attended by over 4,500 registered participants. Today there are more users than ever! More than 200 companies have joined the project, and the main contributors of current OpenStack release are Red Hat, HP and IBM. The OpenStack Foundation has posted a recap video showing some highlights:
Some themes emerged during the summit. The new concept of bigusers becoming major contributors is really taking off. Big users are becoming major contributors to the project because it means they can move faster as a company. These big users include large banks, manufacturing, retailers, government agencies, entertainment and everything between. Instead of spending time trying to convince vendors to add features, these large organizations have realized that they can work with the OpenStack community directly to add those features and move faster as a business as a result.
Big Data solutions such as Hadoop and NoSQL applications are no longer a sole game for Internet moguls. Today’s retail, transportation and entertainment corporations use Big Data practices such as Hadoop for data storage and data analytics.
IBM BigInsights makes Big Data deployments an easier task for the system architect. BigInsights with IBM’s GPFS-FPO file system support provides enterprise level Big Data solution, eliminating Single Point of Failure structures and increasing ingress and analytics performance.
The inherent RDMA support in IBM’s GPFS takes the performance aspect a notch higher. The testing conducted at Mellanox Big Data Lab with IBM BigInsights 2.1, GPFS-FPO and FDR 56Gbps InfiniBand showed an increased performance for write and read of 35% and 50 %, respectively, comparing to a vanilla HDFS deployment. On the analytics benchmarks, the system provided 35% throughput gain by enabling the RDMA feature.