IEEE 2014 JAVA CLOUD COMPUTING PROJECTS Adaptive algorithm for minimizing cloud task length with prediction errors

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Adaptive Algorithm for Minimizing Cloud
Task Length with Prediction Errors
Abstract— Compared to traditional distributed computing like grid
system, it is non-trivial to optimize cloud task’s execution
performance due to its more constraints like user payment budget
and divisible resource demand. In this paper, we analyze in-depth
our proposed optimal algorithm minimizing task execution length
with divisible resources and payment budget: 1) We derive the
upper bound of cloud task length, by taking into account both
workload prediction errors and hostload prediction errors. With
such state-of-the-art bounds, the worst-case task execution
performance is predictable, which can improve the quality of service
in turn. 2) We design a dynamic version for the algorithm to adapt to
the load dynamics over task execution progress, further improving
the resource utilization. 3) We rigorously build a cloud prototype
over a real cluster environment with 56 virtual machines, and
evaluate our algorithm with different levels of resource contention.
Cloud users in our cloud system are able to compose various tasks
based on off-the-shelf web services. Experiments show that task
execution lengths under our algorithm are always close to their
theoretical optimal values, even in a competitive situation with
limited available resources. We also observe a high level of fair
treatment on the resource allocation among all tasks.

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This paper proposes using dynamic workers and an energy saving system to efficiently schedule tasks and reduce idle workers. It introduces using particle swarm optimization and bucket sorting algorithms to assign tasks and identify idle workers that can be closed to save energy. The goal is to balance workload and achieve better energy savings for server nodes by properly allocating computing power.

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Adam bosc-071114fnothaft

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This document compares the performance of two dynamic load balancing algorithms - the Honey Bee algorithm and the Throttled Load Balancing algorithm - in a cloud computing environment. It first describes both algorithms and other related concepts. It then discusses results from simulations run using the CloudAnalyst tool. The simulations show that the Honey Bee algorithm has lower average, minimum, and maximum response times compared to the Throttled algorithm. Additionally, the Honey Bee algorithm results in lower data center processing times and costs. Therefore, the document concludes the Honey Bee algorithm performs better than the Throttled algorithm for load balancing in cloud computing.

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Gc EasyTier1 App

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A rough set-based incremental approach for updating approximations under dyna...Ecway Technologies

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Error tolerant resource allocation and payment minimization for cloud systemJPINFOTECH JAYAPRAKASH

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Error tolerant resource allocation and payment minimization for cloud systemIEEEFINALYEARPROJECTS

JAVA 2013 IEEE CLOUDCOMPUTING PROJECT Error tolerant resource allocation and ...IEEEGLOBALSOFTTECHNOLOGIES

1) The document proposes an error-tolerant resource allocation method for cloud systems that minimizes user payments while guaranteeing task deadlines. 2) It formulates a deadline-driven allocation problem and proposes a polynomial-time solution to minimize costs based on predicted task lengths. 3) It also develops methods to guarantee tasks are completed by their deadlines despite inaccurate workload predictions by analyzing execution length bounds.

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IRJET- Optimization of Completion Time through Efficient Resource Allocation ...IRJET Journal

This document discusses optimizing task completion time in cloud computing through efficient resource allocation using genetic and differential evolutionary algorithms. It aims to reduce makespan (completion time) by combining a genetic algorithm with differential evolutionary algorithms. The genetic algorithm uses selection, crossover and mutation to allocate tasks to resources. The outputs are then input to the differential evolutionary algorithm, which has the same operations in reverse order. This double process refines the allocation to provide the best allocation minimizing completion time. The document outlines the related work in genetic algorithms for resource allocation and task scheduling in cloud computing.

Genetic Algorithm for task scheduling in Cloud Computing EnvironmentSwapnil Shahade

This document proposes a modified genetic algorithm to schedule tasks in cloud computing environments. It begins with an introduction and background on cloud computing and task scheduling. It then describes the standard genetic algorithm approach and introduces the modified genetic algorithm which uses Longest Cloudlet to Fastest Processor and Smallest Cloudlet to Fastest Processor scheduling algorithms to generate the initial population. The implementation and results show that the modified genetic algorithm reduces makespan and cost compared to the standard genetic algorithm.

Multi objective game theoretic scheduling of bag-of-tasks workflows on hybri...Nexgen Technology

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Latest Research Topics on Cloud ComputingThesis Scientist Private Limited

Cloud computing is a type of computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. In cloud computing, the word cloud (also phrased as "the cloud") is used as a metaphor for "the Internet," so the phrase cloud computing means "a type of Internet-based computing," where different services — such as servers, storage and applications — are delivered to an organization's computers and devices through the Internet.

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The document discusses a system that uses virtualization technology to dynamically allocate data center resources based on application demands. It aims to optimize the number of servers in use to support green computing while preventing server overload. The proposed system introduces a concept of "skewness" to measure uneven resource utilization across servers and develops heuristics to minimize skewness and improve overall utilization while avoiding overload and saving energy.

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