cloud

    For years, organizations have been using On-premises relational database systems as the heart of mission-critical applications. The classic On-premise relational mode as we know might not likely survive in the world of cloud computing. 

    The first aspect which I like to discuss is fate of database model in cloud: Relational vs. NoSQL. RDBMS requires that data is normalized so that it can provide quality results and prevent duplicates and orphan records. Normalizing the data requires creation of more tables, which require table joins, and thus requiring more indexes and keys. The problem becomes more apparent with highly diverse datasets with lots of unstable indexes on them probably a hundred or so tables, and each table having varying indexes. I/O becomes chaotic when indexes of different tables are stored on different parts of persistence storage devices like HDD or SSD and you have concurrent reads/writes. In case of Cloud, the storage represented to the user may be different disks or different kinds of store; Cloud storage comes with abstraction; as databases start to grow into the terabytes or even petabytes, performance starts to fall off significantly.

    Business Intelligence as a Service

    Ovum research predicts that companies will spend over $9.1bn on Business Intelligence (BI) by 2014 which represents a 6% of Compound Annual Growth Rate (CAGR) over the period. Underlining this growth are significant changes to the way in which BI systems are built and deployed. Till now, only large enterprises could afford to build sustainable enterprise analytic capabilities due to Capital expenditures (CapEx) involved in BI systems. Hence gaining competitive edge through BI has been a forbidden fruit for small and medium businesses (SMBs). Cloud computing is a disruptive trend in recent times which uses pay-per-use billing model and brings various characteristics like scalability, elasticity, emphasis in Operating expense (OpEx) (rather than CapEx model) onto the plate. Cloud computing will be the best solution to BI for SMBs.

     

    Compute Grid

    The ever growing demand for computing from industry has lead to excessive resource consumption issues which is not only impacting sustainability of compute and utility grids in terms of cost but also from environment perspective as well. Since Grid computing consists of several High Performance Computing centers under different domain, it makes the problem even more difficult. The process of managing resources and scheduling computations over the Grid is complex as they are heterogeneous in nature, distributed, owned by different organizations/individuals with their own policies, have varying load/availability and different cost models. This introduces a huge number of challenges such as heterogeneous substrate, policy extensibility, site autonomy, online control, resource allocation, transparency, and scalability. Some of these issues are currently being addressed by tool-kits such as Globus, gLite and UNICORE. For effective resource utilization, we need to address the various challenges by effectively distributing parallel applications on grid.

    In Memory Data

    Almost every major database management system product in the market, tries to keep the portion of database resident in the main memory and the rest on the primary Persistent storage device (HDD or SSD). An In-memory database (IMDB) resides completely in the main memory. If you have the required memory, you will benefit from faster access to data. Random access to large datasets with very low latency will simplify development and enable new applications to  access large data more intensively than has ever been possible.

    IMDBs have all the properties of a traditional RDBMS, but are fine tuned for data to reside in main memory. Unlike NoSQL databases, IMDBs can cater for systems which are already written for earlier generation of RDBMS. These technologies are already reshaping the BI and analytics segment and they will significantly impact transactional and operational processing workloads as well.

    Massively parallel computing

    From discovering a cure for a disease to exploring energy to estimating weather, engineering and science today relies heavily on the simulation and modelling performed on high performance computing systems. But to keep ahead of this exploding demand for computing requires innovation, especially with programming issues and power. New and improved Many-core processors are causing changes in the stable computing architecture used in the past 2 decades. To add on top is the increasing importance of cloud computing which is leading to significant changes in computer system architecture.

    Enter Many-core systems

    A Multi-core chip is a processing chip with 2 or more identical cores in one package, whereas a Manycore chip is a processing chip that has 10 or more heterogeneous cores in one package. In Manycore system, all cores will share resources including main memory and hard drives. Industry analysts anticipate the advent of affordable “manycore” systems in the next couple of years.

    1. Database systems with MapReduce
    2. Distributed Space based architecture
    3. Connectome as a template for Network, Cognitive and Cloud systems
    4. Patterns for Cloud Computing

    About Me

    Hi, I'm Anup Hariharan Nair.

     

    Anup's read book montage

    The Start-Up of You: Adapt to the Future, Invest in Yourself, and Transform Your Career
    Paper Promises: Debt, Money, and the New World Order
    The 8th Habit: From Effectiveness to Greatness
    The 7 Habits of Highly Effective People: Powerful Lessons in Personal Change
    The Quest: Energy, Security, and the Remaking of the Modern World
    The Ascent of Money: A Financial History of the World
    Civilization: The West and the Rest
    The Complete Sherlock Holmes
    A Brief History of Time
    The Physics of Star Trek
    Breakout Nations: In Pursuit of the Next Economic Miracles
    The Power of Habit: Why We Do What We Do in Life and Business
    End This Depression Now!
    Micromotives and Macrobehavior
    The Origin of Wealth: Evolution, Complexity, And the Radical Remaking of Economics
    The Wealth of Networks: How Social Production Transforms Markets and Freedom
    That Used to Be Us: How America Fell Behind in the World It Invented and How We Can Come Back
    Code: Version 2.0
    Start Something That Matters
    The Future of Ideas: The Fate of the Commons in a Connected World


    Anup Nair's favorite books »
    © 2024 The opinions and views expressed in the blog are those of mine and don’t always necessarily reflect official positions of organizations and authors who are referenced in the articles.

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