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\title{The Contemporary Review of Notable Cloud Resource Scheduling Strategies}
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             \author[1]{  P.Sowjanya}

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\date{\small \em Received: 8 December 2015 Accepted: 3 January 2016 Published: 15 January 2016}

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\begin{abstract}
        


Cloud computing has become a revolutionary development that has changed the dynamics of business for the organizations and in IT infrastructure management. While in one dimension, it has improved the scope of access, reliability, performance and operational efficiency, in the other dimension, it has created a paradigm shift in the way IT systems are managed in an organizational environment. However, with the increasing demand for cloud based solutions, there is significant need for improving the operational efficiency of the systems and cloud based services that are offered to the customers. As cloud based solutions offer finite pool of virtualized on-demand resources, there is imperative need for the service providers to focus on effective and optimal resource scheduling systems that could support them in offering reliable and timely service, workload balancing, optimal power efficiency and performance excellence. There are numerous models of resource scheduling algorithms that has been proposed in the earlier studies, and in this study the focus is upon reviewing varied range of resource scheduling algorithms that could support in improving the process efficiency. In this manuscript, the focus is upon evaluating various methods that could be adapted in terms of improving the resource scheduling solutions.

\end{abstract}


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\let\tabcellsep& 	 	 		 
\section[{TheContemporaryReviewofNotableCloudResourceSchedulingStrategies}]{TheContemporaryReviewofNotableCloudResourceSchedulingStrategies}\par
Strictly as per the compliance and regulations of: -I. Introduction nformation and cloud computing technology solutions has become an integral factor of today's communication systems and there are phenomenal developments in terms of how the companies are depending upon vivid range of information systems network for handling the business operations. Also, the volume of data that is generated by the companies is turning out to be a potential solution for the organizations. The volume of internet access and the big data solutions that are being implemented is creating a significant need for the organizations to focus upon varied kinds of data.\par
Such developments have eventually led to the rising demands of cloud computing solutions  {\ref [1]} [2]. The scope of computing resource capabilities and easy access to the data with increased mobility are the key solutions offered by cloud computing [3] and there are varied ways in which the cloud based solutions are offered to the users by the service providers. [4]  {\ref [5]}.\par
Despite the fact that the cloud computing is similar to the process of grid computing and cluster computing models, in terms of characteristics that are common to parallel computing, but the usage of virtualization for resource management is a significant development  {\ref [6]} which facilitates in effective services as a utility model  {\ref [7]}.\par
The scope of computing and accessibility to information has become much easier and the cost of managing IT systems to has come down significantly, which is not feasible in the traditional computing environment  {\ref [2]}.\par
With the emerging trends of cloud computing solutions, there are numerous research studies that are being carried out in varied dimensions of cloud computing adaptation like focusing on increasing the operational scope of cloud computing, and predominantly the factor of virtualization. In the market oriented utility service stream like cloud computing, it is very important to focus upon optimally scheduling resources to reap potential benefits from the implementation \hyperref[b0]{[8]}.\par
In the SLA's between the service providers and end users, the emphasis is more on optimal scheduling of resources as the key deliverable of cloud computing services offered. \hyperref[b1]{[9]}. Underestimating or unrealistic planning and provision of resources are leading to complexities \hyperref[b2]{[10]}. In the dimension of optimum outcome, addressing the power efficient requirements, focusing on improving operational efficiency of the systems are becoming a major challenge for offering QoS requirements of the service. \hyperref[b3]{[11]}.\par
The process of improving the resource scheduling in cloud services has to focus upon identifying the suitable resources that are essential for scheduling an appropriate workload within time and also in terms of increasing the effective resource utilization process. In the other dimension, the quantum of resources availed for service offering also has to be minimal in terms of workload, ensuring required levels of service quality. To generate effective resource schedu-ling, best ways of mapping the resource workload is very important.\par
The second objective which plays a significant role in the resource scheduling process is to focus upon identifying the appropriate and suitable workload which can facilitate in scheduling multiple workloads which can address the QoS requirements like the CPU utilization, every distinct workload and also predominantly for improving the overall performance which is depending upon the kind of workload like either the heterogeneous workloads or the homogenous workloads. \hyperref[b5]{[13]}.\par
There is extensive research and development in the process of cloud resource scheduling, and from the review of literature it is imperative that resource scheduling is one of the significant challenges facing the development of cloud based solutions.  {\ref [14] [15]}. The issue of Nondeterministic Polynomial Optimization Problem  {\ref [16] [17]} is considered to be one of the key challenges in the process of cloud resource scheduling, as NP-hard issues rise the usage by number of variables that are using the deterministic algorithm for exhaustive search. There are significant chances that there is dimensionality breakdown envisaged when the algorithms that solve relatively routine cloud scheduling problem. The issues are compounded when there is some kind of proliferation, ambition and complexity in terms of cloud computing.\par
Adapting an Evolutionary Computation Algorithm for tackling the cloud resource scheduling has grabbed the attention of researchers, as such a method can offer an effective solution to hard problems in the scheduling \hyperref[b10]{[18]}, \hyperref[b11]{[19]}, \hyperref[b12]{[20]}, \hyperref[b13]{[21]}, \hyperref[b14]{[22]}. The efficacy of such systems in resource scheduling at grid computing levels \hyperref[b15]{[23]}, such algorithms have gained momentum for application in the cloud resource scheduling too  {\ref [24] [25]}.\par
The surveys that are conducted on effective scheduling in cloud computing \hyperref[b18]{[26]} [27] \hyperref[b20]{[28]} [29] \hyperref[b18]{[26]} and some of the methods like the "interconnected cloud computing" \hyperref[b22]{[30]}, not much of algorithms have been discussed in the process. In this study, the focus is on assessing the issues and systematic review of the existing models of cloud resource scheduling solutions that are being adapted in the cloud computing environment. The study focus on following key aspects.\par
? Taxonomic structure evaluation using the scheduling levels of hierarchy ? The clarity on the scope of cloud scheduling complications ? Survey of state-of-art approaches for handling cloud scheduling by evaluating the pros and cons ? Suggestions pertaining to how the various approach for varied levels of hierarchy of cloud resource scheduling is taking place. ? Analyzing the challenges pertaining to potential future research directions and real-time, adaptive dynamic, and distributed scheduling models. 
\section[{a) Resource Scheduling}]{a) Resource Scheduling}\par
In cloud resource scheduling among the key challenges that are envisaged in the process, dispersion, heterogeneity of resources and uncertainties are some of the key issues that are not addressed using the traditional resource scheduling algorithms (RSAs) \hyperref[b23]{[31]} and it is imperative that there is need for making cloud oriented solutions that are more efficient in terms of caring the cloud environment properties. In the process of resource scheduling, the key stages are Resource Mapping, Resource Execution and Resource Monitoring.\par
Initially, the cloud consumer focus on the workload that is pending for execution, and followed by the process of mapping the workloads for right resources are carried out, using the QoS requirements agreed upon in the SLAs section of optimizing QoS parameters. Some of the important parameters like CPU utilization, throughput, memory utilization and other such factors are usually considered in the resource scheduling for all the cloud users in the network.\par
The prerogative of resource execution is to focus on allocating appropriate resources for suitable workloads on time, as the applications can focus on utilizing the resources effectively. Whilst executing a specific workload, the monitoring agent has to check the current workload. If the value of Required Resources (RRs) are higher than the value of Provided Resources (PRs) then it demands more resources. In such instances, the reserve resource pool maintained has to be used for providing the required resources using the rescheduling process, to achieve successful execution of the workload. Once the workload is completed, the resources that are free are released to the resource pool and the scheduler can focus on allocation the resources to execute new workloads. If efficient monitoring and utilization of computing resources are in place, it can help in improving the performance optimization. Thus it is imperative that there is need for effective and comprehensive intelligent monitoring agent for analyzing the performance of resource execution.\par
Profoundly, the SLAs should comprise information on varied deviations and scope defined from achieving the appropriate quality attributes. Cloud provider's SLA shall provide indication of the deviation of service if any, scope of feasibility for change and the factors to be considered whilst compensating any kind of unexpected outages \hyperref[b24]{[32]}. To denote the CPU and Memory utilization, the resource monitoring agent is engaged, which collects the resource usage by evaluating performance metrics. It is very essential that the cloud provider have to focus on retaining adequate number of resources for delivering the continuous service to cloud consumer while addressing peak load.\par
of how the provider plans to execute the workload with minimum resources.\par
Resource Monitoring is essential for analyzing performances both in terms of physical and virtual performance, as the resource utilization evaluation can lead to more effective ways of handling the processes. Also, the resource monitoring process can be adapted for handling varied factors like the security, reliability, approach and effectiveness, and confidentiality.\par
In the other way, the scope of achieving the process where the resources to be used for the process has to be minimum for a workload while adhering to the quality metrics of workload can be achieved only when the resource monitoring is effectively carried out. The process of deciding upon acquiring or releasing resources for workload, computing activities that are essential to be mapped for the cloud resources for improving the performance, as one of the key deliverables from the service provider is to focus on adhering to the compliance to SLA conditions pertaining to resource scheduling. \hyperref[b24]{[32]}.\par
From the above factors, it is imperative that there is need for effective resource scheduling algorithm that can support in managing the fluctuations for requirements in the workload and also towards maximizing utilization. To ensure that the resource scheduling requirements are effective, appropriate number of resources are to be deployed for executing the current load by addressing the challenges of underloading or over-loading conditions.. 
\section[{b) Hierarchy of Cloud Resources Scheduling}]{b) Hierarchy of Cloud Resources Scheduling}\par
In the process of resource management in a cloud computing environment, scheduling can be developed at varied levels of service stacks hierarchy. The architecture models of IaaS, PaaS, and SaaS stacks shall be adapted for classifying cloud scheduling problems in to the process of scheduling the application level, scheduling in virtualization layer and scheduling in the deployment layers. Table  {\ref 1} indicates the hierarchy of cloud resources scheduling and the scope of scheduling process at varied hierarchy levels.\par
In terms of preceding the categories on the basis of high-level framework and taxonomy fro the cloud resource scheduling program, the low-level taxonomy can be achieved from varied range of scheduling objectives. Considering the implications like the deadlines and the budget constraints of the cloud users and also the resource needs that are to be balanced at a maximal rate by the service providers, the category of application layer resource scheduling is categorized in to further set of sub categories ? -"scheduling for user," ? -"scheduling for provider efficiency," and Numerous scheduling algorithms are available for routine challenges in the cloud computing \hyperref[b26]{[34]} [35] \hyperref[b28]{[36]}. The algorithms that are offered in the models are little exhaustive in nature but can be very resourceful if the scheduling problem is manageable by converting to a combinational optimization problem like the Linear Programming \hyperref[b29]{[37]}  \hyperref[b30]{[38]}.\par
Integer Programming (IP) \hyperref[b31]{[39]}, and the Integer Linear Programming (ILP) \hyperref[b32]{[40]} and the constraint satisfaction problem \hyperref[b33]{[41]} are very effective models, but considering the NP-hard problem, cloud scheduling has to be addressed with enumerative approaches that can focus on increased dimensionality in terms of number of variables that are to be optimized. Resource scheduling models has been developed by many of the earlier studies. Radu et.al \hyperref[b34]{[42]} proposed CDA (Continuous Double Auction) model for distributed environment, which can support in executing scientific application where the negotiation between the place between resources and the scheduler, by focusing on self-limitation and aggressiveness.\par
Scientific applications have the dependent tasks for which the output of one task is highly dependent on the other task. From the test results of implementing CDA model in CloudSim, the reduced time for completion and the reduced scope of relative error is imperative, but the model is more effective for homogenous workloads.\par
Lin et.al \hyperref[b35]{[43]} developed a theoretical dynamic auction mechanism that can be very resourceful for handling the capacity distribution for evaluating the peak and off-peak demands depending on the capacity. Such mechanism supports in addressing the issues of computation capacity, but the model not focus on issues pertaining to any kind of deviation in SLA violation. Zhangjun et.al \hyperref[b36]{[44]} has discussed market oriented based resource scheduling algorithm which contains the service and the task levels for dynamic resources for scheduling and assigning the task to service and the task to VM too. Such a method reduces the scope of operational costs for data centers and towards optimizing the makespan.\par
Mohsen et.al \hyperref[b37]{[45]} has proposed marketoriented adaptive resource scheduling mechanisms for cost and time optimization in addition to addressing the deadlines for execution time. Such mechanism estimated the cost and time depending on completion time for different workloads on the basis of respective policies, but the process is limited only to one single IaaS provider that has uniform price.\par
Tdavid et.al \hyperref[b38]{[46]} has proposed a model of distribution negotiation based resource scheduling model that focus on bargaining and attains higher utility. Such resource scheduling model can be resourceful for heterogeneous environment for improving the resource capabilities, cost and also time for completion. Seokho et.al \hyperref[b39]{[47]} focus on the SLA oriented flexible negotiation that is based on resource scheduling pattern, and considers the crux of tradeoff among utilities for improving the speed and finding an effective service provider for quality performance service. Despite that the method has been resourceful in reducing SLA violations, still in terms of SLAs deviation there is rise in the kind of deviations. 
\section[{b) Compromised Cost and Time based Resource Scheduling}]{b) Compromised Cost and Time based Resource Scheduling}\par
The scheduling models that are proposed based on compromised cost and time has been proposed in some studies. Ganesh et.al \hyperref[b40]{[48]} focused on pricing oriented scheduling algorithms, in which two self-evident bargaining methodologies has been discussed. Raiffa Bargaining Solution and Nash Bargaining solutions that are proposed in the study works on independent workflows.\par
Teng et.al \hyperref[b41]{[49]} has worked an equilibrium resource scheduling technique for forecasting the prospect price even before knowing the competitors bidding information which has shown good results in the implementation on Cloudsim. HCOC (Hybrid Cloud Optimized Cost) model is proposed by Luiz et.al \hyperref[b42]{[50]} which works on resource scheduling mechanism for addressing the problem of resource requirements that executes the workflows on the basis of budget and execution time, by focusing on adequate resource depending on QoS requirements.\par
In another model proposed by Ke et.al \hyperref[b43]{[51]}, the model emphasize on cost time based resource scheduling in which the cost constrained workflows are taken in to account, and execution time and cost for QoS parameters are considered. 
\section[{c) Cost Based Resource Scheduling}]{c) Cost Based Resource Scheduling}\par
Ana et. al \hyperref[b44]{[52]} developed a model using the constraint resource algorithm, in which the First Come First Serve (FCFS) model has been adapted for reducing the cost, time required for completion and also for improving the CPU performance, still the challenge of starvation is a challenge in this model.\par
Ruben et.al \hyperref[b45]{[53]} worked on optimization problem for imposing conditions like execution of job in a multi-provider hybrid cloud environment, depending on the requirements of data transmission, CPU and the memory, for categorization of non-provider and movable workloads.\par
Zhipiao et.al \hyperref[b46]{[54]} has discussed an SLA aware genetic algorithm for resource scheduling mechanism that works on addressing the current requirement of varied applications based on virtual resources offered by third party infrastructure on lease model. Model has been effective in addressing the SLA violations and also in improving resource utilization and profit along with cost. 
\section[{d) Dynamic and Adaptive Approaches forResource}]{d) Dynamic and Adaptive Approaches forResource}\par
Scheduling Ye et.al \hyperref[b49]{[57]} developed a model of community aware resource scheduling technique with intension of reducing waiting time and average job slowdown time even without prior knowledge of real-time processing, for varied nodes being part of decentralized scheduling manner. Gaun et.al \hyperref[b50]{[58]} has worked on queuing theory oriented model for improving the interval time average in an non interactive deadline-bound workload.\par
Altino et.al \hyperref[b51]{[59]} has proposed failure and power aware resource scheduling model which works on reducing power consumption and adhering to SLAs. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVI Issue III Version I  
\section[{B}]{B}\par
The levels of proactive fault-tolerance approaches used for decision making are effective in terms of handling failures for controlling the shared nodes. Jiayin et.al \hyperref[b52]{[60]} has proposed feedback based scheduling model for reducing any kind of resource contention issues using job preemption process. Ayasan et.al \hyperref[b54]{[61]} has worked on Hadoop cluster based resource scheduling technique for calculating job arrival rate and also the execution time for making right decisions towards effective scheduling. The Hadoop system constituting a cluster, and is a combination of linked resources, is organized in to files and based on the file classification method for every job, the decision of whether a reduce task or map task is decided. Algorithm in the model focuses upon satisfaction of minimum share of requirements for all the users and fairness among the users in the system.\par
Zhen et.al \hyperref[b57]{[64]} worked on virtualization oriented dynamic resource allocation mechanism for improving the server utilization. Skewness algorithm is adapted for estimating the disproportion in multi-dimensional utilization of a processor using hotspot mitigation. One of the key limitations in the model is about live migration related developments. 
\section[{e) Energy Based Resource Scheduling}]{e) Energy Based Resource Scheduling}\par
Energy based resource scheduling models are also profound solutions that has been worked in various models. Joseph et. al \hyperref[b58]{[65]} has proposed a SLA aware machine learning based resource scheduling model for map reduction applications. In the proposed model, the exact solver depending on mixed linear programming aims to forecast the resource consumption based on current workload for executing varied tasks and responses time (taskSLA) for a workload and also taking in to account the contention among tasks that are executed on same resource.\par
Moreno et.al \hyperref[b59]{[66]} focused upon the model of EASY (Energy Aware reconfiguration of software Systems) which is a QoS oriented resource scheduling technique towards reducing the power consumption. The model works on on-line algorithm for adjusting the processing speed of individual devices in dynamic manner to ensure that the average system response time is maintained well within the predefined threshold, while minimizing the total power consumption too Yan et al. \hyperref[b60]{[67]} in his study has proposed the model of controlled dependence graph which relies on the energy aware resource scheduling model for executing the HPC applications that are carried out with deadlines and also with scope of minimal energy consumption, in the distributed environment. Approximation of design and also the multiprocessor based scheduling algorithms are devised to address the problem based on the analysis and worst case performance assessment. Also, based on energy consumption, the desired deadline of tasks and pricing scheme is also designed for better execution.\par
In the other models \hyperref[b61]{[68]} [69] [70] \hyperref[b64]{[71]}, there are numerous workload based resource scheduling policies that has been proposed for process improvement and also increasing the energy efficient methods adaptation. However, one of the focus areas in the models are about green revolution and improved performance. 
\section[{f) Hybrid Approaches forResource Scheduling}]{f) Hybrid Approaches forResource Scheduling}\par
Hybrid based resource scheduling solutions are developed considering varied metrics in to account. In many of the hybrid models that are proposed, the emphasis has been more on combination of data transfer, computational costs, and reduced cost factors. In the models that are proposed with Hybrid oriented approach, \hyperref[b65]{[72]} [73] \hyperref[b67]{[74]} the emphasis is on selection of resources from the public cloud for developing effective solutions on the basis of cloud based characteristics for task. 
\section[{g) Heuristic and Meta-heuristic Approaches for}]{g) Heuristic and Meta-heuristic Approaches for}\par
Resource Scheduling Heuristic based resource scheduling algorithms are very popular and in some of the models that are developed on heuristic methods, varied levels of heuristic methods were adapted for implementation \hyperref[b68]{[75]} [76] [77] \hyperref[b71]{[78]} for developing contemporary solutions using various kinds of algorithms. Distribution of resources to the workloads on the basis of requirements for reducing the execution time has been the focal point in the aforesaid models.\par
Raju et.al \hyperref[b72]{[79]} proposed ACO and Cuckoo search for hybrid resource scheduling policy for reducing the completion time.\par
Paulin et.al \hyperref[b73]{[80]} proposed firefly oriented resource scheduling technique for improving the load balancing and also for execution time. Also, some of the parameters like load index, access rate, memory usage, processing time are some of the key factors that are taken in to consideration. Some of the other models like GA based \hyperref[b74]{[81]} PSO based \hyperref[b75]{[82]} [83] models discussed the scope of using specific algorithm models for reducing the execution time, and towards improving user's satisfaction.\par
In \hyperref[b77]{[84]} [85] \hyperref[b79]{[86]} [87], also, there were numerous models of GA solutions that are proposed for handling the independent and divisible tasks for cloud computing environment. The models assert the fact that GA approach could be resourceful in handling the user costs together, and also in terms of improving the resourcefulness in a cloud computing environment. Also, in \hyperref[b81]{[88]} [89], the models pertaining to adapting the cloud resources with GA-based approach by focusing on virtual resources for scheduling has been proposed with impressive test outcomes. In \hyperref[b83]{[90]} the model focus on using VM match and execution orders for resource scheduling. Year 2016 ( ) 
\section[{B}]{B}\par
A general framework of using ACO to schedule user tasks is as follows, each ant uses M steps to construct a solution. In the th i step to schedule the th i task i T , the ant uses pheromone and heuristic information to choose the suitable resource j R . After M steps, all the M tasks have been scheduled on different resources.\par
Taking stock of above factors, in \hyperref[b84]{[91]} [92] there are many scheduled M tasks one by one for the cloud resources, using the scheme as at every step for the task can be scheduled on the basis of resources that are set. The heuristic information based on users QoS metrics for user cost, system reliability, response and security are adapted for guiding the ant for selecting optimal resource which is proposed in \hyperref[b85]{[92]}  \hyperref[b86]{[93]}. The tasks were classified in to varied categories are bound to be adapted on cloud resources using ACO optimization.\par
In terms of load balancing for resource scheduling in \hyperref[b96]{[103]}, Nishant et.al proposes an optimized load balancing method using the ACO based algorithm. In extensive of the aforesaid model, in \hyperref[b97]{[104]} the model has been extended with another objective of provider efficiency oriented scheduling of resource utilization.\par
Wen et.al \hyperref[b98]{[105]} has focused on improving the cloud resource utilization ratio by scheduling the cloud resources on the basis of hybrid algorithm comprising ACO and PSO. One of the key objectives of developing provider efficiency for oriented scheduling is to work on energy consumption for the cloud center \hyperref[b99]{[106]}. Such tasks are usually scheduled using modified GA along with local search for optimizing the energy consumption and for reaping potential benefits from the outcome in terms saving energy, by using the combination of ACO and Cuckoo search. \hyperref[b100]{[107]}. The ACO approach that is adapted as main framework and the application of CS rather than heuristic information for finding the next resource for task is an effective outcome. 
\section[{h) Observations}]{h) Observations}\par
From the review of the studies, it is evident that despite of numerous models that are proposed, majority of them are probabilistic rather than deterministic. Also not every scope and parameter of QoS metrics in SLAs are assured in the existing models. In this research review, the focus is upon reviewing the earlier models in the area of cloud resource scheduling and the QoS metrics adapted in the process of effective resource management. It is also evident that by using some of the effective evolutionary models of resource scheduling algorithms, the operational efficiency of resource scheduling can be improved to great extent. 
\section[{IV. Conclusion}]{IV. Conclusion}\par
Information systems management has become an integral part of organizational process and with the kind of fast emerging computational solutions and methods like the cloud computing solutions, the efficacy of the systems has increased standards of accessibility, flexibility and scalability. In the contemporary scenario, organizations are keen on adapting cloud based solutions for the information systems management in the context of various reasons like the ease of access to data, reduced cost of operations. And IT infrastructure management related costs going down, and the flexibility in terms of managing the data in third party services. However, one of the significant factors that make a vital role in the success of adapting the cloud computing process is about the Service Level Agreements with the service provider for offering the reliable services.\par
Despite the fact that there are many factors that impact the service quality, one of the profound factors that impact the cloud solution efficacy is about resource scheduling process in the cloud services. There are numerous models that have been proposed in the earlier models. It is imperative from the review of extensive literature on the varied models of cloud based resource scheduling algorithms, that in the case of majority of models that are proposed, they are probabilistic models rather than being a deterministic models.\par
Considering the fact that the current models have high levels of computational complexities and the rising standards and requirements of cloud based solutions, there is significant need for focusing on more effective solutions that could be adapted for resource scheduling. Using some of the effective methods like CUCKOO search, TABU search and other such contemporary evolutionary algorithms, the process of resource scheduling can be improvised to great extent. If such a contemporary solution could be achieved it can support in improving the efficiency and attaining the optimal resource scheduling process in the cloud services and also in terms of reducing the linear complexity in the systems. \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.7320113314447592\textwidth}P{0.1179886685552408\textwidth}}
\multicolumn{2}{l}{Table1: Nomenclature of the resource scheduling in}\\
\multicolumn{2}{l}{cloud Computing}\\
\multicolumn{2}{l}{Nomenclature of the resource Scheduling}\\
Scheduling Approach\tabcellsep Objectives\\
Scheduling at Software\tabcellsep User QoS,\\
layer\tabcellsep Provider Efficiency,\\
\tabcellsep Negotiation\\
Scheduling at Platform\tabcellsep Load Balance,\\
Layer\tabcellsep Energy Efficiency\\
\tabcellsep Cost Effectiveness\\
Scheduling at\tabcellsep Service Placement,\\
Infrastructure Layer\tabcellsep Partner Federation,\\
\tabcellsep Data Routing\\
\multicolumn{2}{l}{In the further process, at the scheduling levels}\\
\multicolumn{2}{l}{in the virtualization layer, the challenges are}\\
\multicolumn{2}{l}{predominantly about scheduling the Virtual Machines}\\
\multicolumn{2}{l}{(VMs) and the Physical Machines (PMs) that has}\\
\multicolumn{2}{l}{efficient load balance, level of conservation and the term}\\
\multicolumn{2}{l}{of cost effectiveness. Hence the following sub}\\
\multicolumn{2}{l}{categories are aimed in the process}\\
\multicolumn{2}{l}{? Scheduling for cost effectiveness}\\
\multicolumn{2}{l}{? Scheduling to ensure energy conservation}\\
\multicolumn{2}{l}{? Scheduling on basis of load balancing}\\
\multicolumn{2}{l}{And the deployment category of the scheduling}\\
\multicolumn{2}{l}{process are sub categorized as}\\
\multicolumn{2}{l}{? Scheduling for partner federation}\\
\multicolumn{2}{l}{? Schedule for service placement}\\
\multicolumn{2}{l}{? Scheduling for data routing.}\\
\multicolumn{2}{l}{III. Review of Notable Cloud Resource}\\
\multicolumn{2}{l}{Scheduling Strategies of}\\
\multicolumn{2}{l}{Contemporary Solutions}\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2016\\
38\\
( ) B\\
on Industrial Mechatronics and Automation. 240-\\
243.\\
4.\\
© 2016 Global Journals Inc. (US) 1\end{longtable} \par
 
\caption{\label{tab_2}}\end{figure}
 			\footnote{The Contemporary Review of Notable Cloud Resource Scheduling Strategies} 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{bibitemlist}
 			 		 	 
\end{document}
