CHAPTER 6 ENERGY AWARE SCHEDULING ALGORITHMS IN CLOUD ENVIRONMENT

CHAPTER 6 ENERGY AWARE SCHEDULING ALGORITHMS IN CLOUD ENVIRONMENT This chapter discusses software based scheduling and testing. DVFS (Dynamic Voltage and Frequency Scaling) [42] based experiments have been conducted for minimizing the processing cost, makespan time in Energy Aware environment so as to achieve energy savings without compromising QoS. Simulations have been carried out using CloudSim [165] with combination of various QoS parameters and energy aware VM allocation policies. A comparison of these algorithms is shown with the commonly used algorithms based on the processing cost, makespan time and energy utilization parameters. Scheduling is a software technique to gain higher performance for applications in cloud computing. But unfortunately, to the best of our knowledge, existing energy-aware scheduling algorithms at large do not consider real-time task allocation on heterogeneous systems and energy saving simultaneously in clouds. Normally, workflow scheduling is carried out separately for achieving deadline constraints, cost minimization and energy minimization. To overcome this gap and come up with a strategy which jointly addresses above issues. Our work will provide a novel solution that will jointly address VM allocations in cloud environment with minimum energy consumption, processing cost and makespan time. 6.1 INTRODUCTION Energy consumption is a major issue in Cloud Computing environments. Its efficient use can benefit in many ways such as cost saving, efficient utilization of resources [148] and also saving the environment, as energy consumption, cost and time are important and decision making factors for both user as well as cloud service providers. QoS conscious job scheduling along with energy awareness is very important, especially in cloud environment, where large datacenters are to be maintained and at the same time massive computations are involved. Optimal resource usage and price reduction are direct and operational benefits for both users as well as service providers. Moreover scheduling can be improvised on many fronts such as energy efficiency, cost minimization, maximization of resource utilization, etc. Hence, energy 115

aware computations and scheduling is a big future concern that may heavily contribute to maintain the nature s environmental systems, ecological balances and may avoid direct and indirect health hazards to all living beings. Omni-directional benefits are the outcome of using energy aware scheduling techniques for Cloud environments and that too without compromising the Quality of Service. 6.2 SOME CONTEMPORARY ENERGY SAVING ALGORITHMS Amount of energy consumed, cost incurred to provide services over the cloud, amount of execution time; are major causes of concern and improvising the scheduling of tasks helps in minimizing these. Multi-objective workflow analysis framework proposed by Luo L., et al. [89] puts forward an algorithm which gives an optimal solution in the case when users require task scheduling to meet various objectives at the same time such as cost minimization along with reduction of makespan. Users expect the makespan of tasks to be as small as possible. The key to the algorithm is that a task is enveloped by a VM to enable transferring of the task to another resource while the task is in progress. Li, B., et al. [141] proposed this strategy to save energy wasted due to servers sitting idle during the placement of applications with constantly varying requirements. This helps in reducing the number of active servers. During insertion the task in hand replaces a smaller task, if it can and the smaller one is now inserted in turn. The principle behind this strategy is that smaller tasks can be easily accommodated into already active servers as compared to bigger tasks. When a task is to be deleted it releases the resources and tasks on that server go through insertion process. Vinh, T. et al [151] presented a Green scheduling algorithm based on the decision made by a neural network predictor. It was observed that powering off the servers while they were not in use saved a lot more energy as compared to lowering the voltage. The problem encountered was to assess the future demand and power off the extra servers as per the assessment because in case of a wrong prediction the drop rate of user requests increases and service level agreement is not assured. Gao, Y. et al. [176] proposed a strategy to reduce the occurrence of soft errors along with energy efficiency consideration. It includes two schedulers static and dynamic. Soft errors are caused due to noise, high energy cosmic particles and hardware fatigue. Soft errors may lead to a corrupted output or a system crash decreasing the user s QoS. To increase the QoS for the users these soft errors have to be hidden from them. This can be done by 116

predicting and preventing these errors using methods to detect errors and make the system fault tolerant. Some such methods are Virtual Machine Replication [52] or Idempotent Task Try [53]. The drawback of these methods is that they cost a lot in terms of energy. This work advances an integrated algorithm to make the system fault tolerant and energy efficient at the same time. A strategy is proposed wherein the Cloud Service Provider balances between increase in systems fault tolerance and decrease in the energy consumption. Tests proved the approach to be effective as it showed a 50% reduction in failure rate at an overhead of 76% in terms of energy. Energy Aware Computation in Cloud environment has become a major cause of concern for researchers and Cloud Service Providers. Lots of works are going on in this direction to address the issue at software and hardware levels. Some heuristic algorithms [32] were suggested to manage allocations of resources. An efficient architectural framework is proposed where numerous Green Compute and system resource utilization under type of loads and amount of loads on the data centers are considered. Task Consolidation based [33] on energy consumed by each of the tasks were also suggested to reduce energy usage where scheduling of VMs were based on current CPU usage of task in hand. Static and Dynamic VM migration techniques [100] in which some strategically selected VMs that use more energy onto the currently allocated machines were forced to migrate onto another physical machine. These migrations were based on loads on current machine, computational load of the individual VMs and system capacity. Dynamic scheduling algorithms [104] for reducing the energy consumption were also suggested for the data centers. These algorithms were based on the overall runtime and levels of imbalances between various underlying system resources, viz, CPU, memory and network bandwidth consumed by physical machines as well as the Virtual Machines running on those machines. Some Hotspot and Coldspot based migration techniques [35] were also suggested to reduce the energy consumption. These techniques were based on the upper and lower temperature thresholds of the CPU. CPU temperature proportionally increases when the computational load on it increases. This temperature further increases if it is extremely used and reaches a threshold known as Hotspot, beyond which the system performance degrades inspite of more CPU usage. On the other hand, if the CPU temperature of a Consolidating the Virtual Machines onto the optimally running physical 117

machines and switching off the remaining physical machines based on work-load awareness were also suggested to save energy [36][37][66][177]. Scheduling techniques based on either alone or a combination of workflows based on cost constraints, deadline awareness and other energy awareness parameters for homogeneous and heterogeneous processors were also implemented [38][39][40][91]. All above mentioned algorithms and techniques significantly contributed to the energy savings but the Quality of Service parameters were neglected. For example, the lower the energy consumption, lower is the frequency of usage, which further lowers the voltage of the machine. This reduction in voltage may slow down the computational speed and consequently the Quality of Service may get affected. 118

Table 6.1: Critical Analysis of the existing energy aware cloud algorithms. 119

6.3 ANALYSIS OF ENERGY EFFICIENNT CLOUD ALGORITHMS Most existing algorithms [38][39][40][81][91] address one or two of the parameters on the basis of which scheduling is improved. Some target cost optimization whereas others address shortening the make span whereas some work at minimizing the overall energy consumption. At the same time these algorithms struggle to meet the Service Level Agreement and satisfy the users by maintaining the Quality of Service. Different Cloud Structures demand different scheduling strategies. The details regarding various testing parameters and the criterion for testing of algorithms surveyed are summarized in Table 6.1. Context Aware job scheduling algorithm works for scheduling in mobile environments. It reduces resources wasted due to computation of unnecessary requests, those which are no longer useful to the user or the ones which can t be completed in the window of opportunity. The algorithm was tested in three conditions normal, peaky and flat. Normal symbolized a normal working day wherein the maximum number of requests is concentrated in the working hours, peaky is for scenarios where in the working hours number of requests keep on increasing at a high rate and dropping after sometime to rise yet again. Flat is to set a baseline wherein number of requests remain constant throughout the day. This algorithm works best for peak, cases. On increasing the amount of resources the utilization of resources increases to a certain extent in the presence of context after which on further increase in resources utilization follows the same trend as in the absence of context. It works better as compared to Triple Modulated Redundant [178] System in terms of energy overhead. To save energy is the need of the hour. Thus, many algorithms aim at minimizing the amount of energy consumed without compromising the quality of service. Enacloud [141], Green Predict [179], Energy Optimization Framework [151] all aim at increasing the energy efficiency. Enacloud proposes an algorithm which helps in scheduling the tasks on the minimum number of servers such that number of active servers is minimal. Green Predict helps in predicting the number of servers which will be needed at a particular time depending upon previous data. It has four modes normal, optimal, predict and predict with additional servers. Predict with an additional 20% servers proved to be the best mode for energy saving. Energy Optimization framework works well in case hardware is not completely utilized by a task and helps in avoiding allocation of unnecessary resources to a task. Also Li, B. et al [141] proved that prioritizing servers with 120

larger capacity is beneficial to save energy in case of medium loads whereas for small load allocating resources from server having least capacity first is more beneficial. 6.4 PROBLEM STATEMENT To find a VM allocation policy that can detect host overloading and schedule the algorithms in such a way that jointly results in minimum execution cost, time and energy consumption at the same time. 6.5 SOLUTION APPROACH To come up with a strategy that jointly addresses minimization of cost, time and energy consumption using DVFS on CloudSim and WorkflowSim. Our work will provide a novel solution using VM allocations techniques and combinations of scheduling algorithms in cloud environment for minimum energy consumption, processing cost and makespan time. 6.6 SIMULATION STUDY This work is done using Dynamic Voltage Frequency Scaling Technique (DVFS) technique in CloudSim. An introduction about the tools, techniques and algorithms used and implemented are presented below: CloudSim CloudSim was developed in CLOUDS Laboratory at University of Melbourne [165]. It is an open source flexible simulation toolkit. It enables the simulation and modeling of Cloud environment, CloudSim facilitates researchers and industry-based developers to simulate, analyze and develop specific environments and system style problems without bothering about low level details associated with Cloud-based infrastructures and services. Functionalities of CloudSim include support for simulation and modeling of large scale Data Centers, virtualization of Hosts which can be customized for providing host resources to Virtual Machines (VMs) and computation of energy-aware resources. Multiple Virtual Machines are configured to test the performance based on various resource provisioning algorithms and policies that eventually affect system performance. Switching between time-sharing and space-sharing is facilitated to test the performance of the cloud system algorithms for 121

particular application types and job properties for evaluating the optimum conditions that give better results. WorkflowSim WorkflowSim [180] is an extension of CloudSim developed by Weiwei Chen. It provides simulation of tasks at workflow level. It takes workflows as input in the form of Directed Acyclic Graph (DAG). It supports an elaborate model of node failures and delays that occurs at different levels of Workflow Management Systems (WMS) stacks. It consists of Workflow Mapper, Workflow Engine, Clustering Engine and Workflow Scheduler. A Cost-based workflow [181] for scheduling the executions is provisioned based on the users requirements that are agreed upon between users and service providers. These techniques are facilitated using the Partial Critical Path Methods with minimum execution cost within task execution deadlines. Adaptive and heuristic based VM consolidation algorithms [182] were proposed that used historical data with computational usages for future VM provisioning and allocations. It highly reduces the energy consumption by avoiding the computational overheads required as the resource requirements are known a priori. DVFS in CloudSim This is based on Dynamic Voltage and Frequency Scaling (DVFS) simulation technique [42] for supporting energy aware experiments. Power VM module of CloudSim is extended to support DVFS. It facilitated to develop power based module that can dynamically vary the voltage and frequency of a Host CPU based on its load by inculcating various upper and lower frequency based thresholds. Based on these thresholds, logical decisions to increase or decrease the frequencies can be taken. In addition, decision to migrate VMs can also be taken based on these thresholds. Various modes of arranging the computations can be implemented that are based on the variations in the frequencies and change in the voltage. For example, a low voltage reduces the power consumed by CPU but at the same time it also reduces computational power. First Come First Serve (FCFS) It is a simple rule in which tasks are arranged on the basis of their arrival time. The task that arrives first is served first. While, this technique helps in fast execution of tasks, it has one 122

major disadvantage, that is, if a job that requires large execution time arrives first then the jobs with less execution time have to wait in the queue for its completion. Therefore, increasing the waiting time for the whole workflow and thereby decreasing the performance. Min-min Here, we maintain the two separate lists for mapped tasks and unmapped tasks. Algorithm starts by taking all the unmapped tasks and sorting them on the basis of their completion time. Then, task with least time is scheduled first and moved to the mapped list in order to decrease their waiting time. This is repeated until all the tasks are mapped or are scheduled. Max-min Here also, we maintain the two separate lists for mapped tasks and unmapped tasks. Algorithm starts by taking all the unmapped tasks and sorting them on the basis of their completion time. Then, task with maximum completion time is scheduled first and moved to the mapped list. This is repeated until all the tasks are mapped or are scheduled. This technique is similar to Min-min technique but here we execute the larger tasks first instead of the smaller ones. Minimum Completion Time (MCT) Task with minimum completion time is scheduled at random. This technique is slightly different from Min-min in a way that it does not arrange unmapped tasks rather it assigns them randomly in order to get minimum completion time. Data Aware In this technique, task with minimum data transfer time, that is, the virtual machine that is closest is assigned to the task. Round-Robin Task is assigned to the virtual machine which can handle it and is found to be idle in first attempt. 6.7ALLOCATION POLICIES AND SCHEDULING TECNHIQUES Power model used for scheduling of VMs works on linear model in DVFS and is given by equation (6.1): P Total = (1- )P CPUIdle + P CPUFull --- (6.1) 123

where, is CPU load and P CPUIdle is power consumed by CPU at 0% utilization and P CPUFull is power consumed by CPU at 100% utilization. The energy aware VM allocation policies that we have considered are as follows 6.7.1 Minimum Used Host Firstly, all the list of suitable host is prepared (in terms of MIPS). Thereafter the minimum used host is selected, i.e., the one with maximum available MIPS is chosen for VM. 6.7.2 Watt Per MIPS Host using minimum Watts/MIPS is chosen. Energy usage for any particular host in Watts per MIPS can be calculated as per equation 6.2. ((Pma- Pmi)*S/M)/N --- (6.2) where, Pma= maximum power consumed by host Pmi= minimum power consumed by host S= MIPS sum of all currently running VMs M= maximum MIPS that host can consume N= number of host processors 6.7.3 Dedicated Network Disk Host A dedicated host for fixed number of VMs is chosen based on the available Million Instructions Per Second (MIPS). All the available VMs are scaled down to 100 hosts. So, there are 100 dedicated disks or hosts for the VMs. 6.8 CALCULATIONS Average Bandwidth calculation of average bandwidth of all the VMs in Mb/sec. Computation Cost calculation of computation cost field with time in seconds. Transfer Cost calculation of transfer cost and mapping with time in seconds to transfer all files. Rank calculation of rank for each task to be scheduled. Tasks allocations scheduling of task given in one of the VMs minimizing the Earliest Finish Time. Finish Time finding best time slot available to minimize the finish time of the given task in the VM with the constraint that scheduling should not start before ready time. Maximum Execution Time returning the largest execution time among all tasks. 124

Add Slack adding necessary slack to the task. Minimum Power Consumption returning minimum power consumed by any VM among all VMs. Maximum Power Consumption returning the maximum power consumed by any VM among all VMs. Energy Consumption calculating the energy consumed by particular task on VM 6.9 IMPLEMENTATION DETAILS CloudSim is used to simulate the cloud environment along with WorkflowSim. It supports in deployment, analysis and running the workflow applications. Application Modelling: Workflow Montage model is considered. This workflow covers all the basic components viz., pipeline, data aggregation, data distribution and data redistribution. Experiments were carried out for Montage based [183] two different sizes, i.e., for small (around 25 tasks) and large (100 tasks) sizes. Resource Modelling: Cloud model with single data center offering different types of VMs is considered. Configuration combinations of Host and VM parameters are considered as follows: Results were observed for five different configurations by varying the DVFS state, Host and VM MIPS. Various configurations along with their graph are presented below: 125

6.9.1 Configuration 1: Energy aware with deadline and cost aware scheduling of cloudlets using Montage_25 workflow model [183], 5 hosts and 5 VMs each of same configurations (VM MIPS=1000). Sampling rate=0.1, Host MIPS=2500.DVFS=ON. Figure 6.1: Configuration-1 Cost Graph Figure 6.2: Configuration-1 Time Graph 126

Figure 6.3: Configuration-1 Energy Graph Analysis of Configuration 1: Following observations are made regarding the processing cost, makespan time and energy consumption for Config.1: Processing Cost: Dedicated Network Disk Host VM allocation policy has minimum processing cost as compared to Minimum Used Host and Watts Per MIPS VM allocation policies, which have similar but higher processing cost as compared to Dedicated Network Disk Host allocation policy. Further, Min-min cloudlet scheduling algorithm gives least processing cost as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.1. Makespan time: Dedicated Network Disk Host VM allocation policy has minimum makespan time as compared to Minimum Used Host and Watts Per MIPS VM allocation policies, which have similar but higher makespan time. Further, Min-min cloudlet scheduling algorithm has the least makespan time as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.2. Energy Consumption: Minimum Used Host and Watts Per MIPS VM allocation policy has minimum and similar amount of energy consumption as compared to Dedicated Network Disk Host. Further, Max-min cloudlet scheduling algorithm has the least energy consumption as 127

compared to Data Aware, FCFS, MCT, RR and Min-min cloudlet scheduling algorithms as evident from Figure 6.3. 6.9.2 Configuration 2: Energy aware with deadline and cost aware scheduling of cloudlets using Montage_100 workflow model [183], 5 hosts and 5 VMs (BW & MIPS =1250, 1500, 1750, 2000, 2250) each of same configurations. Sampling rate=0.1, Host MIPS =2500. DVFS=ON. Figure 6.4: Configuration-2 Cost Graph 128

Figure 6.5: Configuration-2 Time Graph Figure 6.6: Configuration-2 Energy Graph 129

Analysis of Configuration 2: Following observations are made regarding the processing cost, makespan time and energy consumption for Configuration 2: Processing Cost: All three VM allocation policies i.e., Minimum Used Host, Watts Per MIPS and Dedicated Network Disk Host have similar processing costs. Further, Min-min cloudlet scheduling algorithm gives least processing cost as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.4. Makespan time: All three VM allocation policies i.e., Minimum Used Host, Watts Per MIPS and Dedicated Network Disk Host have similar makespan time. Further, Min-min cloudlet scheduling algorithm has least makespan time as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.5. Energy Consumption: All three VM allocation policies i.e., Minimum Used Host, Watts Per MIPS and Dedicated Network Disk Host have similar energy consumptions. Further, Max-min cloudlet scheduling algorithm has least energy consumption as compared to Data Aware, FCFS, MCT, RR and Min-min cloudlet scheduling algorithms as evident from Figure 6.6. 6.9.3 Configuration 3: Energy aware with deadline and cost aware scheduling of cloudlets using Montage_100 workflow model, 5 hosts and 5 VMs (BW & MIPS = 1250, 1500, 1750, 2000, 2250) each of same configurations. Sampling rate=0.1, Host MIPS=3800. DVFS=ON. Figure 6.7: Configuration-3 Cost Graph 130

Figure 6.8: Configuration-3 Time Graph Figure 6.9: Configuration-3 Energy Graph Analysis of Configuration 3: Following observations are made regarding the processing cost, makespan time and energy consumption for Config. 3: 131

Processing Cost: Minimum Used Host and Watts Per MIPS VM allocation policies gives minimum processing cost as compared to Dedicated Network Disk Host VM allocation policy which incur higher processing cost. Further, Min-min cloudlet scheduling algorithm gives least processing cost as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.7. Makespan time: Minimum Used Host and Watts Per MIPS VM allocation policies gives minimum makespan time as compared to Dedicated Network Disk Host VM allocation policy which has higher makespan time. Further, Min-min cloudlet scheduling algorithm has the least makespan time as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.8. Energy Consumption: Minimum Used Host and Watts Per MIPS VM allocation policies has minimum energy consumption as compared to Dedicated Network Disk Host VM allocation policy which has higher energy consumption. Further, Max-min cloudlet scheduling algorithm has the least energy consumption as compared to Data Aware, FCFS, MCT, RR and Min-min cloudlet scheduling algorithms as evident from Figure 6.9. 6.9.4 Configuration 4: Energy aware with deadline and cost aware scheduling of cloudlets using Montage_100 workflow model, 5 hosts and 5 VMs (BW & MIPS = 1250, 1500, 1750, 2000, 2250) each of same configurations. Sampling rate=0.1, Host MIPS=3800. DVFS=OFF. Figure 6.10: Configuration-4 Cost Graph 132

Figure 6.11: Configuration-4 Time Graph Figure 6.12: Configuration-4 Energy Graph Analysis of Configuration. 4: Following observations are made regarding the processing cost, makespan time and energy consumption for Config. 4: Processing Cost: Minimum Used Host VM allocation policy has minimum processing cost as compared to Dedicated Network Disk Host and Watts Per MIPS VM allocation policies, which have higher processing cost as compared to Minimum Used Host allocation policy. Further, Min-min cloudlet scheduling algorithm gives least processing cost as compared to 133

Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.10. Makespan time: Minimum Used Host VM allocation policy has minimum makespan time as compared to Dedicated Network Disk Host and Watts Per MIPS VM allocation policies, which have higher makespan time as compared to Minimum Used Host allocation policy. Further, Min-min cloudlet scheduling algorithm has the least makespan time as compared to Data Aware, FCFS, Max-min, MCT and RR cloudlet scheduling algorithms as evident from Figure 6.11. Energy Consumption: Minimum Used Host VM allocation policy has least energy consumption as compared to Dedicated Network Disk Host and Watts Per MIPS VM allocation policies, which have more energy consumption as compared to Minimum Used Host allocation policy. Further, Max-min cloudlet scheduling algorithm has the least energy consumption as compared to Data Aware, FCFS, MCT, RR and Min-min cloudlet scheduling algorithms as evident from Figure 6.12. 6.9.5 Configuration 5: Energy aware with deadline and cost aware scheduling of cloudlets using Montage_100 workflow model, 5 hosts and 5 VMs (BW & MIPS=1000 each) each of same configurations. Sampling rate=0.1, Host MIPS=2500. DVFS=OFF. Figure 6.13: Configuration-5 Cost Graph 134

Figure 6.14: Configuration-5 Time Graph Figure 6.15: Configuration-5 Energy Graph Analysis of Configuration 5: Following observations are made regarding the processing cost, makespan time and energy consumption for Config. 5: 135

Processing Cost: Watts Per MIPS VM allocation policy has minimum processing cost as compared to Minimum Used Host and Dedicated Network Disk Host VM allocation policies, which have higher processing cost as compared to Watts Per MIPS allocation policy. Further, all cloudlet scheduling algorithm i.e., Data Aware, FCFS, Max-min, MCT, Min-min and RR algorithms incur similar processing costs as shown in Figure 6.13. Makespan time: Watts Per MIPS VM allocation policy has minimum makespan time as compared to Minimum Used Host and Dedicated Network Disk Host VM allocation policies, which have higher makespan time as compared to Watts Per MIPS allocation policy. Further, all cloudlet scheduling algorithm i.e., Data Aware, FCFS, Max-min, MCT, Min-min and RR algorithms has similar makespan time as shown in Figure 6.14. Energy Consumption: Minimum Used Host VM allocation policy has minimum amount of energy consumption as compared to Dedicated Network Disk Host and Watts Per MIPS VM allocation policies. Further, Max-min cloudlet scheduling algorithm has the least energy consumption as compared to Data Aware, FCFS, MCT, RR and Min-min cloudlet scheduling algorithms as evident from Figure 6.15. 6.10 RESULTS A combination of Max-Min scheduling algorithm for cloudlet or task scheduling with Minimum Used Host scheduling algorithm gives the most efficient environment in terms of Processing Cost, Makespan Time and Energy Consumption maintain the QoS. It is observed that adoption of modified, conscious and logical scheduling policy in Cloud environments may drastically improve the QoS and save the energy usage as well, which is extremely important for huge Data Centers used in Cloud Environments. Task or Cloudlet Scheduling Algorithm and VM Allocation Policy where we observed Minimum Processing Cost, Minimum Makespan Time and Minimum Energy Consumption in all five configurations are shown in the following table: 136

Table 6.2: Result Summary. Configuration ID Minimum Processing Cost Minimum Makespan Time Minimum Energy Consumption VM Cloudlet VM Allocation Cloudlet VM Cloudlet Allocation Scheduling Policy Scheduling Allocation Scheduling Policy Algorithm. Algorithm. Policy Algorithm. Config-1 Dedicated Min Min Dedicated Min Min Min Used Max Min Network Disk Host Network Disk Host Host &Watt Per MIPS Config-2 All Three Min Min All Three Min Min All Three Max Min Config-3 Min Used Min Min Min Used Host Min-Min Min Used Max Min Host &Watt &Watt Per MIPS Host &Watt Per MIPS Per MIPS Config-4 Min Used Min Min Min Used Host Min Min Min Used Max Min Host Host Config-5 Watt Per All Six Watt Per MIPS All Six Min Used Max Min MIPS Same Same Host 6.11 CONCLUSION By carefully analyzing all the cases, it is found that combination of Min-Min and Min Used Host is the most effective in terms of Cost and Makespan Time but combination of Max-Min and Min Used Host is most energy efficient. We also found that when Min-Min gives least cost and time then Max-Min was second most efficient with a difference of approximately 0.1% and when Max-Min gives most energy efficient environment, Min-Min consumes maximum energy with a difference of approximately 3% as observed from graphs. Therefore, if we use the combination of Max-Min scheduling algorithm for cloudlet or task and Minimum Used Host scheduling algorithm for virtual machine allocation, then we get the most efficient environment in terms of processing cost, makespan time and energy consumption. 137