INTRODUCTION:

The topic of Cloud Computing is gaining more and more attention in the service research community. The main idea is to make applications available on flexible execution environments primarily located in the Internet. Several flavours are known, and three important ones are depicted in the figure below.

Infrastructure as a service refers to the sharing of hardware resources for executing services, typically using virtualization technology. With this so-called Infrastructure as a Service (IaaS) approach, potentially multiple users use existing resources. The resources can easily be scaled up when demand increases, and are typically charged for on a per-pay-use basis. In the Platform as a Service (PaaS) approach, the offering also includes a software execution environment, such as an application server. In the Software as a Service approach (SaaS), complete applications are hosted on the Internet so that e.g. your word processing software isn’t installed locally on your PC anymore but runs on a server in the network and is accessed through a web browser.

At the same time, the networking research community is working on exploring the benefits arising from the new paradigm of content-centric or information-centric networking. Traditional networking architectures for the PSTN and the Internet solve the problem of connecting terminals or hosts in order to support a specific application such as telephony or WWW. To this end, traditional naming and addressing schemes employ box- or domain-oriented identities such as E.164 numbers for telephony, or IP addresses and URLs for the Internet. However, the end user is typically interested in reaching a destination object that sits behind or in the host, such as a human being or a file, rather than communicating with the host itself. As the destination objects move to new hosts, the host- or network dependent identities of these objects must be updated. Information-centric networking provides a solution to these issues by directly addressing the information objects instead of using the host-dependent or domain-dependent addressing schemes.While URLs are also used to identify information objects, there is an important difference to how NetInf names information objects. URLs include the domain name or locator of the host at which the target object is stored and are therefore based on the traditional location-oriented communication paradigm. Consequently, links based on such URLs break when the host of a target object moves to a new location, or when the address of the host of the object changes. In the current Internet, there are several fixes to circumvent this problem, such as HTTP redirects and dynamic DNS. The location-independent object naming scheme of NetInf avoids this problem altogether, as the NetInf object names remain the same independent of any topology events, including location updates.

Figure 1 : Cloud Computing Over View

II. SCHEDULING:

Job scheduling (JS) system is one of the core and challenging issues in a Cloud Computing system. Traditional job scheduling systems in Cloud (or Grid) computing only consider how to meet the QoS requirements for the resources users, they seldom consider how to make the maximum profits for the resource providers. Actually, a job scheduling system plays a very important role in how to meet Cloud computing users’ job QoS requirements and use the Cloud resources efficiently in an economic way. Usually, from the Cloud computing resources users’ sides (we use CCU stands for Cloud computing user), users always think which Cloud computing resource can meet their job QoS requirements for computing (such as the due time of job finishing, the computing capacity etc.), how much money they must pay for the Cloud Computing resources. While, from the Cloud Computing service providers (we use CCSP stand for Cloud Computing Service Provider) side, the CCSP always think how they can gain the maximum profits by offering Cloud Computing resources, apart from meeting the CCU’s job QoS requirements. To make these two ends meet, the job scheduling system must take efficient and economic strategies for CCU’s differentiated service QoS requirements. Focus on this issue, this paper put forward an optimistic differentiated service job scheduling system for CCSP and CCU.

III. RELATED WORKS:

Job scheduling and resource planning system is a hot and one of core research areas in Cloud and Grid Computing. It plays a similar role in Cloud and Grid Computing. Job scheduling system is responsible to select the best suitable resources in a Cloud or Grid for CCU’s jobs, by taking some static and dynamic parameters restrictions of CCU’s jobs into consideration. Most research work in Grid Computing can be used directly in Clouding Computing environment. Today, we can find many research work have done on Job scheduling in Grid computing. References [1-7] provided a board view for the roles of job scheduling in a Grid computing environment. The presented topologies of job scheduling system in Cloud or Grid are classified into centralized and decentralized schedulers [1]. Due to the implementation complexity of decentralized schedulers, most related works are on centralized schedulers. Reference [2] gave a brief description of a modeling and performance evaluation of hierarchical job scheduling, [3] showed an iterative scheduling algorithms on the grids. [4] presented a novel stochastic algorithm for QoS-constrained workflows job scheduling in a web service-oriented grid. [5] put forward a definition, modeling and simulation for a Cloud Computing scheduling system to get high throughput of computing etc.. In recent years, more and more academic researchers began to study the QoS of job scheduling system; we can see that references [6-10] put forward the approach of QoS performance analysis for Cloud Computing services with dynamic scheduling system. However, most research papers rarely mention the differential service-oriented QoS guaranteed job scheduling system in a Cloud computing environment.

Apart from this, very a few papers care about for the how to make the maximum profits for CCSP. For, the conditions of existence for a Cloud Computing environment are that it must make profits for the CCSP with the lowest system costs. To meet the CCU’s job QoS requirement, job scheduling system should use the Cloud computing resource as little as possible.

IV. RESOURCE MANAGEMENT:

Cloud computing is becoming one of the most explosively expanding technologies in the computing industry today. It enables users to migrate their data and computation to a remote location with minimal impact on system performance [7].

Typically this provides a number of benefits which could not otherwise be realized. These benefits include:

· Scalable - Clouds are designed to deliver as much computing power as any user wants. While in practice the underlying infrastructure is not infinite, the cloud resources are projected to ease the developer’s dependence on any specific hardware.

· Quality of Service (QoS) - Unlike standard data centers and advanced computing resources, a well designed Cloud can project a much higher QoS than typically possible. This is due to the lack of dependence on specific hardware, so any physical machine failures can be mitigated without the user’s knowledge.

· Specialized Environment - Within a Cloud, the user can utilize custom tools and services to meet their needs. This can be to use the latest library, toolkit, or to support legacy code within new infrastructure.

· Cost Effective - Users finds only the hardware required for each project. This greatly reduces the risk for institutions who may be looking to build a scalable system. Thus providing greater flexibility since the user is only paying for needed infrastructure while maintaining the option to increase services as needed in the future.

· Simplified Interface - Whether using a specific application, a set of tools or Web services, Clouds provide access to a potentially vast amount of computing resources in an easy and user-centric way. We have investigated such an interface within Grid systems through the use of the Cyberaide project [8], [9].

There are a number of underlying technologies, services, and infrastructure-level configurations that make Cloud computing possible. One of the most important technologies is the use of virtualization [10], [11]. Virtualization is a way to abstract the hardware and system resources from a operating system. This is typically performed within a Cloud environment across a large set of servers using a Hypervisor or Virtual Machine Monitor (VMM) which lies in between the hardware and the Operating System (OS). From here, one or more virtualized OSs can be started concurrently as seen in Figure 1, leading to one of the key advantages of Cloud computing. This, along with the advent of multi-core processing capabilities, allows for a consolidation of resources within any data center. It is the Cloud’s job to exploit this capability to its maximum potential while still maintaining a given QoS.

Virtualization is not specific to Cloud computing. IBM originally pioneered the concept in the 1960’s with the M44/44X systems. It has only recently been reintroduced for general use on x86 platforms. Today there are a number of Clouds that offer Infrastructure as a Service (IaaS). The Amazon Elastic Compute Cloud (EC2) [12], is probably the most popular of which and is used extensively in the IT industry. Eucalyptus [13] is becoming popular in both the scientific and industry communities. It provides the same interface as EC2 and allows users to build an EC2-like cloud using their own internal resources. Other scientific Cloud specific projects exist such as OpenNebula [14], In-VIGO [15], and Cluster-on-Demand . They provide their own interpretation of private Cloud services within a data center. Using a Cloud deployment overlaid on a Grid computing system has been explored by the Nimbus project with the Globus Toolkit All of these clouds leverage the power of virtualization (typically using the Xen hypervisor) to create an enhanced data center.

Fig. 2. Virtual Machine Abstraction

B. Green Computing: The past few years has seen an increase in research on developing efficient large computational resources. Supercomputer performance has doubled more than 3000 times in the past 15 to 20 years, the performance per watt has increased 300 fold and performance per square foot has only doubled 65 times [10] in the same period of time. This lag in Moore’s Law over such an extended period of time in computing history has created the need for more efficient management and consolidation of data centers. This can be seen in figure 2 [15]. Much of the recent work in Green computing focuses on Supercomputers and Cluster systems. Currently the fastest Supercomputer in the world is the IBM Roadrunner at Los Alamos National Laboratory [11], [12], which was fundamentally designed for power efficiency. However, Roadrunner consumes several Megawatts of power [13] (not including cooling) and costs millions of dollars to operate every year. The second fastest Supercomputer is Jaguar at Oak Ridge National Laboratory. While Jaguar too has a number of power saving features developed by Sandia, Oak Ridge and Cray [4] such as advanced power metering at the CPU level, 480 volt power supplies, and an advanced cooling system developed by Cray, the system as a whole still consumes almost 7 Megawatts of power.

Fig.3. Performance increases much faster than performance per watt of energy consumed.

One technique being explored is the use of Dynamic Voltage and Frequency Scaling (DVFS) within Clusters and Supercomputers . By using DVFS one can lower the operating frequency and voltage, which results in decreased power consumption of a given computing resource considerably. This technique was originally used in portable and laptop systems to conserve battery power, and has since migrated to the latest server chipsets. Current technologies exist within the CPU market such as Intel’s Speed Step and AMD’s Power Now! technologies. These dynamically raise and lower both frequency and CPU voltage using ACPI P-states [7]. In [8], DVFS techniques are used to scale down the frequency by 400 MHz while sustaining only a 5% performance loss.

A power-aware Cluster supports multiple power and performance modes on processors with frequencies that can be turned up or down. This allows for the creation of an efficient scheduling system that minimizes power consumption of a system while attempting to maximize performance. The scheduler performs the energy-performance trade-off within a cluster. Combining various power efficiency techniques for data centers with the advanced feature set of Clouds could yield drastic results, however currently no such system exists.

V. PROPOSED APPROACH:

T0; scheduler start time

Del T = inter-schedule time

while (true)

T=T+Del T

do until (current time >= T)

collect arriving tasks into

meta-task Ma

end do Ms=Ma

schedule-Meta (Ms, T+Del T)

some tasks in may not have been

scheduled – they are inserted

M;; back Ms into Ma

Endwhile

Function schduleMeta(meta-task Ms,Tn)

Kj = completion time of Tk on Mj

D = deadline of Tk

Vj = availability time of machine mj

Sj = size of task Tk

Rj = no. of resources request by task Tk

For all task Tk in Mj

For all machine mj

Sort the each task Tk in Meta-Task(queue) according to size ,resources and time Tk by ascending order .

Do until(all tasks in Msare scheduled in Meta-Task OR queue is empty)

Mark all machine as available

for each task Tk in Ms

find machine Mj to schedule

select the machine that gives the highest benefit and lock the resources so that is not available for other task

end for

update the vector vbased on the tasks

that were assigned to the machines

update the matrix for the remaining tasks in Ms

recompute avg. slack values sort tasks by avg. slack values

enddo

VI. CONCLUSION:

Such an infrastructure comprises a pool of physical computing resources – i.e. processors, memory, network bandwidth and storage, potentially distributed physically across server and geographical boundaries which can be organized on demand into a dynamic logical entity i.e. “cloud computer”, that can grow or shrink in real-time in order to assure the desired levels of latency sensitivity, performance, scalability, reliability and security to any application that runs in it. We identified some key areas of deficiency with current virtualization and management technologies. In particular we detailed the importance of separating physical resource management from virtual resource management and why current operating systems and hypervisors which were born of the server-computing era, are not designed and hence ill suited to provide this capability for the distributed shared resources typical of cloud deployment. We also highlighted the need for FCAPS-based (Fault, Configuration, Accounting, Performance and Security) service “mediation” to provide global management functionality for all networked physical resources that comprise a cloud – irrespective of their distribution across many physical servers in different geographical locations. We then proposed a reference architecture model for a distributed cloud computing mediation (management) platform which will form the basis for enabling next generation cloud computing infrastructure. We showed how this infrastructure will affect as well as benefit key stakeholders such as the Infrastructure providers, service providers, service developers and end-users.

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Received on 11.11.2011 Accepted on 28.11.2011

Int. J. Tech. 1(2): July-Dec. 2011; Page 121-124