Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
Market-oriented reverse auction is an efficient and cost-effective method for resource allocation in cloud workflow systems since\nit can dynamically allocate resources depending on the supply-demand relationship of the cloud market. However, during the\nauction the price of cloud resource is usually fixed, and the current resource allocation mechanisms cannot adapt to the changeable\nmarket properly which results in the low efficiency of resource utilization. To address such a problem, a dynamic pricing reverse\nauction-based resource allocation mechanism is proposed. During the auction, resource providers can change prices according to\nthe trading situation so that our novel mechanism can increase the chances of making a deal and improve efficiency of resource\nutilization. In addition, resource providers can improve their competitiveness in the market by lowering prices, and thus users\ncan obtain cheaper resources in shorter time which would decrease monetary cost and completion time for workflow execution.\nExperiments with different situations and problem sizes are conducted for dynamic pricing-based allocation mechanism (DPAM)\non resource utilization and the measurement of TimeâË?â??Cost (TC).The results show that our DPAM can out per form its representative\nin resource utilization, monetary cost, and completion time and also obtain the optimal price reduction rates....
A fundamental key for enterprise users is a cloud-based parameter-driven statistical service\nand it has become a substantial impact on companies worldwide. In this paper, we demonstrate the\nstatistical analysis for some certain criteria that are related to data and applied to the cloud server\nfor a comparison of results. In addition, we present a statistical analysis and cloud-based resource\nallocation method for a heterogeneous platform environment by performing a data and information\nanalysis with consideration of the application workload and the server capacity, and subsequently\npropose a service prediction model using a polynomial regression model. In particular, our aim is to\nprovide stable service in a given large-scale enterprise cloud computing environment. The virtual\nmachines (VMs) for cloud-based services are assigned to each server with a special methodology\nto satisfy the uniform utilization distribution model. It is also implemented between users and\nthe platform, which is a main idea of our cloud computing system. Based on the experimental\nresults, we confirm that our prediction model can provide sufficient resources for statistical services\nto large-scale users while satisfying the uniform utilization distribution....
VCC (Vehicular Cloud Computing) is an emerging and promising paradigm, due to its significance in traffic management and\nroad safety. However, it is difficult to maintain both data security and system efficiency in Vehicular Cloud, because the traffic and\nvehicular related data is large and complicated. In this paper, we propose a conditional ciphertext-policy attribute-based encryption\n(C-CP-ABE) scheme to solve this problem. Comparing with CP-ABE, this scheme enables data owner to add extra access trees and\nthe corresponding conditions. Experimental analysis shows that our system brings a trivial amount of storage overhead and a lower\namount of computation compared with CP-ABE....
Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations\nto outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in\ncloud users� demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial\namounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this\npaper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically\nassigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy\nconsumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate\nthe VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to\nfinding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based\napproach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more\nthan 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach....
Cloud computing enables scalable computation based on virtualization technology.However, current resource reallocation solution\nseldom considers the stability of virtual machine (VM) placement pattern. Varied workloads of applications would lead to frequent\nresource reconfiguration requirements due to repeated appearance of hot nodes. In this paper, several algorithms for VM placement\n(multiobjective genetic algorithm(MOGA), power-aware multiobjective genetic algorithm(pMOGA), and enhanced power-aware\nmultiobjective genetic algorithm (EpMOGA)) are presented to improve stability of VM placement pattern with less migration\noverhead. The energy consumption is also considered. A type-matching controller is designed to improve evolution process.\nNondominated sorting genetic algorithm II (NSGAII) is used to select new generations during evolution process. Our simulation\nresults demonstrate that these algorithms all provide resource reallocation solutions with long stabilization time of nodes. pMOGA\nand EpMOGA also better balance the relationship of stabilization and energy efficiency by adding number of active nodes as one\nof optimal objectives. Type-matching controller makes EpMOGA superior to pMOGA....
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