Error-Tolerant Resource Allocation and Payment Minimization for Cloud System

ABSTRACT:

With virtual machine (VM) technology being increasingly mature, compute resources in cloud systems can be partitioned in fine granularity and allocated on demand. We make three contributions in this paper: 1) we formulate a deadline-driven resource allocation problem based on the cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users’ payment in terms of their expected deadlines. 2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task’s completion within its deadline. 3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task execution length can always be limited within its deadline in the sufficient-supply situation; the mean execution length still keeps 70 percent as high as user specified deadline under the severe competition. Under the original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as high as their deadline,

 which still conforms to the deadline-guaranteed requirement. Only 20 percent of tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of deadlines.

 EXISTING SYSTEM:

In literatures, traditional optimization problems are often subject to the precise prediction of task’s characteristic (or execution property), which is nontrivial to realize in practice.

 Traditional job scheduling is often formulated as a kind of combinatorial optimization problem (or queue-based multiprocessor scheduling problem, due to the nonguaranteed performance isolation for multiple tasks running on the same machines. That is, most of the existing deadline-driven task scheduling solutions (from single cluster environment confined in LAN to the Grid computing environment suitable for WAN are also strictly subject to the queuing model under which a single machine’s multiple resources cannot be further split to smaller fractions at will. This will eventually cause the raw-grained resource allocation, relatively low resource utilization and suboptimal task execution efficiency

DISADVANTAGES OF EXISTING SYSTEM:

Users may wish to minimize their payments when guaranteeing their service level such that their tasks can be finished before deadlines. Such a deadline-guaranteed resource allocation with minimized payment is rarely studied in literatures. Moreover, inevitable errors in predicting task workloads will definitely make the problem harder.

 PROPOSED SYSTEM:

We make three contributions in this paper:

1) We formulate a deadline-driven resource allocation problem based on the cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users’ payment in terms of their expected deadlines.

2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task’s completion within its deadline.

3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition.

ADVANTAGES OF PROPOSED SYSTEM:

All the theoretical conclusions are confirmed with our experiments. Specifically, in the situation with relatively sufficient resources, the worst case tasks under the stricter deadline-based allocation only take as about 0.75 times as their deadlines to complete, as compared to the 1.2 times of the deadlines under the original user-predefined deadline based allocation. We also observe that in the competitive environment, the latter algorithm performs much more stable than the former instead, which means that the latter tolerates the resource competition better. We also confirm the effectiveness of our solution via the distribution of the number of tasks with respect to execution times and user payments: in the competitive situation, majority of tasks can be guaranteed to be completed within deadlines.

MODULES:

1.     Task Assignment

2.     Physical Node

3.     Virtual Machine

4.     Cloud server

5.     Allocated and Available resource

MODULES DESCRIPTION:

   Task Assignment:

In cloud systems, the cloud proxy (a.k.a., server) continually receives and responds to user requests (or tasks) with customized requirements (or virtual machines). All tasks will be handled based on their priorities (like Google task scheduler) or in terms of First-Come-First-Serve (FCFS) policy when the tasks are of the same priorities. Each task’s execution may involve multidimensional resources, such as CPU and disk I/O. A data mining task, for example, usually needs to load a large set of data from disk before or in the middle of its computation. Eventually, such a task may store its computation results onto the local disk or a public server through network.

  Virtual Machines:

In our cloud model, any task will be executed on one or more virtual machines with user-reserved resources and the payment is calculated based on the customized resource (a.k.a., pay-by-reserve policy). Adopting such a pricing policy is driven by three reasons. First, the efficiencies of many applications usually rely on multiple resources but it is nontrivial to precisely evaluate the exact amount of their consumption separately on individual resources. Second, quite a few users prefer to reserving resources for tolerating usage burst and guaranteeing their service levels. Lastly, the alternative pricing policy, pay-as-you-consume, is rather simple because its payment is always fixed regardless of the resource allocation.

  Cloud server:

In this module, we create a local Cloud and provide priced abundant storage services. The users can upload their data in the cloud. We develop this module, where the cloud storage can be made secure. However, the cloud is not fully trusted by users since the CSPs are very likely to be outside of the cloud users’ trusted domain. Similar to we assume that the cloud server is honest but curious. That is, the cloud server will not maliciously delete or modify user data due to the protection of data auditing schemes, but will try to learn the content of the stored data and the identities of cloud users.

  Allocated and Available resource:

In this module, proved optimal for minimizing the payment cost within user-defined deadline for his/her task, the deadline still may not be guaranteed due to two factors, either bounded available resources or inaccurate workload vector information about the task. We propose effective technique, which provides a necessary and sufficient condition of guaranteeing the task’s deadline given accurate prediction and relatively sufficient re-sources.

SYSTEM CONFIGURATION:-

HARDWARE CONFIGURATION:-

Processor      -  Pentium –IV

Speed           -  1.1 Ghz

RAM            -  256 MB(min)

Hard Disk     -  20 GB

Key Board    -  Standard Windows Keyboard

Mouse           -  Two or Three Button Mouse

Monitor         -  SVGA

SOFTWARE CONFIGURATION:-

ü Operating System                    : Windows XP

ü Programming Language           : JAVA/J2EE

ü Java Version                           : JDK 1.6 & above.

ü IDE                                          : NETBEANS 7.2.1


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