Utilizing Round Robin Concept for Load Balancing Algorithm at Virtual Machine Level in Cloud Environment

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4 Initialize ready_queue, BT array, TQ, VM_Statelist, round Readyqueue No Yes =1 round %2 TQ= Avg(BT array) = 0 TQ= min (BT array) Allocate VM to request in ready_queue Set VM_State BUSY for that VM_id Execute Request on VM Deallocate VM, set VM_State to AVAILABLE Calculate BT = BT-TQ Round = round+1 Fig 5 Flowchart for Proposed Algorithm 26

5 FairRR() { Initialize ready_queue, BT list, TQ, VM_State list, round; While(ready_queue!= NULL) { if ( round%2 == 0) TQ= min(bt list); else TQ= avg (BT list); Allocate VM to request in ready_queue, set VM_State to BUSY; Execute request; Deallocate VM and set VM_State to AVAILABLE; round++; New BT=BT-TQ; } } Fig 6 Pseudo code for Proposed algorithm Task Or Request Table 1 Example Execution of FairRR algorithm BT At R0 TQ= min(bt)= 4 At R1 TQ= avg(bt)=8.5 At R2 TQ= min(bt)=8 R Complete R2 4 Complete Complete Complete R Complete Complete R4 8 4 Complete Complete R Complete Complete Pseudo code for our proposed algorithm is shown in Fig-6. Proposed algorithm is fair round robin algorithm because it provides enough fair scheduling when the burst time of incoming request load is having great variance. To better explain this, consider the example shown here: Example in Table-1 shows how the request gets quick response as compared to normal round robin algorithm independent of its request size or Burst time. The example here also shows that the overall algorithm execution requires less number of rounds to follow thus the algorithm when applied to large number of request performs faster providing better response time to any request. FairRR(Fair Round Robin) algorithm thus provides fairness to larger requests and smaller one also to the load coming at executing node in the cloud. 6. SIMULATION ENVIRONMENT To analyze result and compare them with existing algorithm, we used CloudAnalyst tool for simulating FairRR algorithm. The cloud environment set up generated was having following configuration. UserBase Settings, Datacenter Settings are shown in fig 7 and fig-8 respectively. Fig 7 UserBase Settings 27

6 Fig 8 Datacenter Settings User grouping factor which indicates number of simultaneous users from a userbase is taken And Request grouping factor indicating simultaneous requests a single application server can support is taken 100. Executable instruction length per request is taken 250 Bytes. There are 5 VMs working for a Datacenter. 7. SIMULATION RESULT AND COMPARISON Fig-9, fig-10, fig-11, fig-12 represents results of round robin algorithm, Throtled algorithm, active monitoring algorithm and proposed algorithm. Comparison of overall response time and Data Centre processing time is shown in the graph shown in fig 13. Fig 9 Round robin algorithm result Fig 10 Throttled Algorithm result Fig 11 Active monitoring Algorithm result 28

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