Shabeera, TP, MadhuKumar SD, Salam SM, Krishnan MK.
2016.
Optimizing VM allocation and data placement for data-intensive applications in cloud using ACO metaheuristic algorithm. Elsevier-Engineering Science and Technology, an International Journal. :-.
AbstractAbstract Nowadays data-intensive applications for processing big data are being hosted in the cloud. Since the cloud environment provides virtualized resources for computation, and data-intensive applications require communication between the computing nodes, the placement of Virtual Machines (VMs) and location of data affect the overall computation time. Majority of the research work reported in the current literature consider the selection of physical nodes for placing data and \{VMs\} as independent problems. This paper proposes an approach which considers \{VM\} placement and data placement hand in hand. The primary objective is to reduce cross network traffic and bandwidth usage, by placing required number of \{VMs\} and data in Physical Machines (PMs) which are physically closer. The \{VM\} and data placement problem (referred as MinDistVMDataPlacement problem) is defined in this paper and has been proved to be NP- Hard. This paper presents and evaluates a metaheuristic algorithm based on Ant Colony Optimization (ACO), which selects a set of adjacent \{PMs\} for placing data and VMs. Data is distributed in the physical storage devices of the selected PMs. According to the processing capacity of each PM, a set of \{VMs\} are placed on these \{PMs\} to process data stored in them. We use simulation to evaluate our algorithm. The results show that the proposed algorithm selects \{PMs\} in close proximity and the jobs executed in the \{VMs\} allocated by the proposed scheme outperforms other allocation schemes.
AnuMary, C, MadhuKumar SD, Munavar F.
2016.
Provenance-Aware NoSQL Databases. International Symposium on Security in Computing and Communications (SSCC'16). , Jaipur: Springer
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