Pattern Based Real Time Disk Scheduling Algorithm ...

2013 2013Sixth 6th International InternationalConference Conferenceon onEmerging EmergingTrends TrendsininEngineering Engineeringand andTechnology Technology

Pattern Based Real Time Disk Scheduling Algorithm for Virtualized Environment Urmila Shrawankar Dept. of C.S.E GHRCE, Nagpur [email protected]

Ashwini Meshram Research Scholar GHRCE, Nagpur [email protected]

Reetu Gupta Research Scholar GHRCE, Nagpur [email protected]

Scheduling algorithm dynamically adapts by making use of EDF and ACO algorithm. To manage under loaded and overloaded condition adaptive scheduling algorithm switched from EDF to ACO algorithm [1]. The disk scheduling in virtual machine plays a key role on improving the virtual machine performance. Small improvement in disk speed considerably improves the system performance. HTBS disk scheduling algorithm is used in the virtual environment to improve reduce access latency [2] [3].

Abstract—Achieving deadline in real time environment is a real practical difficulty. Real time scheduling algorithms consume more time in making scheduling decisions which leads to increase in deadline misses. The problem is further complicated in virtual environment. This motivates the idea for designing an efficient algorithm for future prediction of block access requests in virtual environment. Disk block access requests made by real time applications in virtualized environment are analyzed. Future disk access request are predicted by non-work conserving disk scheduling algorithm in offline mode. These pre-fetched disk blocks are moved to buffer cache. Pattern based detection technique is applied for predicting the future access of buffer cache blocks. Executing processes access large amount of data and require disk accesses. In real time virtualized environment the requesting processes are scheduled using an adaptive real time scheduling algorithm which reduces the deadline misses. Thus, due to non-work conserving algorithm seek time is reduced. Pattern based technique improves the hit ratio and reduces I/O time. An adaptive real time scheduling algorithm helps to schedule the processes for achieving their deadlines. Thus the performance of applications is enhanced in virtual environment and therefore non-work conserving pattern based adaptive real time scheduling algorithm is found very useful for hard real time applications.

Buffer caches are created in main memory and managed by the operating system to avoid the latencies associated with the secondary storage devices [4]-[6]. CAP based detection algorithm utilizes the patterns in the accesses made to buffer cache blocks maintained in the individual files that are accessed by real time applications. Pattern identification is achieved through program counters. CAP maintains a separate sub-cache partition that can be accessed in the virtualized environment for each of the identified patterns. it is a multi policy scheme that selecting the policy to be applied based upon the patterns held by the sub-cache partition. this helps in adjusting to the changing application behavior under different workloads. The paper is organized as follows. Section 2 provides an overview of scheduling and disk, buffer management techniques. Design of patter based real time disk scheduling algorithm is described in Section 3. Section 4 provides the implementation details and discussions about the obtained result. Section 5 concludes the paper.

Keywords—Buffer cache; Block Access Pattern; Disk scheduling; Future Request Prediction; Real time scheduling; Seek time

I.

INTRODUCTION

In real time environment scheduling the processes to achieve their deadline is the main constraint for improving the system performance. This is further complicated if the process issue the request to virtualized environment. This provides an opportunity to build pattern based real time disk scheduling algorithm for virtualized environment. The access request issued by the process to virtual environment is predicted in advanced by predicting the disk block access patterns through a non-work High Throughput Token Bucket (HTBS) conserving algorithm. These are then buffered into the buffer cache before assigning it to the processes and are accessed by the main memory. Future buffer cache block request is predicted using Cache Access Pattern (CAP) based detection algorithm. Thus pattern based prediction of block access requests issued to disk and buffer cache improves the performance of virtualized environment. And an adaptive scheduling algorithm improves the performance of real time system as the blocks accessed by the processes are readily available due to future prediction which helps the processes in meeting the deadlines.

978-1-4799-2560-5/13 $31.00 © 2013 IEEE 978-1-4799-2561-2/13 DOI 10.1109/ICETET.2013.51

Jashweeni Nandanwar Research Scholar GHRCE, Nagpur [email protected]

II.

OVERVIEW OF SCHEDULING, DISK AND BUFFER MANAGMENET TECHNIQUES

Scheduling is the process of deciding how to commit resources between varieties of possible tasks. The researcher has combined the static algorithm with EDF algorithm. RM is combined with EDF but it has some limitations. The EDF algorithm [7] and least laxity first [8] algorithms are the optimal dynamic priority scheduling algorithms. The modified LLF algorithm solves the problem by reducing the number of context switches [9]. The Particle Swarm Optimization [10] is a population based stochastic optimization method, inspired by social behavior of bird flocking or fish schooling. This algorithm consists of the particles called as agent who moves in a search space and find out the best solution. Another method for scheduling is the Fixed Priority until Zero Laxity in this algorithm process is schedule without calculating the load of a process it means if the execution time is equal to deadline then the process is schedule. 178 177

In the operating system virtualization there is a native operating system called as Host Operating System (OS) [11]. Multiple guest operating systems can be installed on a single host OS through the Virtual Machine Monitor (VMM). All the physical devices are shared amongst the host as well as guest OS through the VMM [12]. Each guest OS has a virtual dish share of the physical disk. Virtual disk brings more challenges for the scheduling [13].

For switching between the EDF and ACO algorithm three parameters are considered process count, priority of the process and load of each process. Under overloaded condition scheduling decision are taken by the ACO algorithm that scheduled the process in the ascending order of their execution time. The process pi is created as follows

The objective of using disk scheduling is to improve the access speed of disk by minimizing the seek time. Traditional disk scheduler concentrates on workloads running on native operating systems [14, 15]. Recent studies by Boutcher et.al. [16] about the disk scheduling in virtual environment, suggests the right combination of schedulers to maximize throughput and fairness between VMs. Seelam and Teller proposed the Virtual I/O scheduler [17]. Gulati, Ahmad, and Waldspurger suggested the PARDA system which uses proportional allocation of a distributed storage resource among virtualized environments [18]. In [19], the authors studies the impact of virtualization and shared disk usage in virtualized environments on the guest VM-level I/O scheduler, and its ability to continue to enforce isolation and fair utilization of the VM's.

pi = (di,ri,ei) Where p_i – process di - deadline of process p_i ri

- release time of process p_i

ei - execution time of process p_i Process pi is created and its load is calculated as Load (l) = ∑ m(i=1) ei/qi

(2)

The real time process scheduling algorithm improves the success ratio of the system which be stated as follows Success Ratio = Number of Processes Scheduled / Total Number of Process (3)

A brief overview of the categories and policies used for managing the buffer cache are discussed in [20]-[21]. Access pattern based take their replacement decision based on the identified patterns.

Real Time Process Scheduler (Adaptive Scheduling Algorithm)

Recent techniques are SHiP [22], RRIP [23] that took their replacement decision by classifying the reference patterns based on reference interval and associating them memory region and program counter signatures. Random writes in the disk were managed by predicting the access patterns on cache insertion and hits in last-level cache [24]-[25].The delay in the buffered was reduced by applying pre-fetching technique at the application level based on user access behavior [26]. III.

(1)

Disk Scheduler (HTTB Disk Scheduling)

Buffer Cache Pattern Detector (CAP Pattern Detection) Offline Processing

DESIGN OF PATTERN BASED DISK SCHEDULING ALGORITHM

Pattern based disk scheduling algorithm is combination of an adaptive scheduling algorithm used for managing real time processes, an HTBS algorithm for predicting the disk block requests and CAP pattern detecting algorithm for predicting buffer cache block access patterns. Modules of pattern based real time disk scheduling algorithm for virtualized environment is shown in “Fig.1.” The co-ordination among the modules is discussed in the following section

Fig. 1. Modules of Pattern Based Detection Algorithm

B. Non-Working Conserving Disk Scheduling Module Real time processes issue request to blocks of disk that are shared in the virtual environment. To reduce disk access latency HTBS is used by disk scheduler module. HTTS dispatches the disk requests by maintaining spatial locality, which improves the overall system performance in terms of seek time. A detailed description about HTTB can be obtained from [3]. These blocks after future request prediction are fed to the buffer cache from where they can be accessed by processes.

A. Real Time Adaptive Process Scheduling Real time process scheduler module uses adaptive scheduling algorithm which combines EDF and ACO algorithm to reduce the execution time and handle the overloaded condition efficiently. Following steps are taken by the algorithm

C. CAP Pattern Detection Buffer cache pattern detector module uses CAP pattern detection algorithm to identify the patterns of the buffer cache blocks and predict their future access request. CAP makes use of program counter and identifies once-identified, frequently identified, recently identified and unidentified patterns in the accessed blocks. It is a multi policy caching scheme. It makes use of ARC to manage shared cache partition holding the

The attribute considered for the user processes are start time, execution time, release time and load of the process. By default EDF algorithm schedules the processes with the nearest deadline.

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An Adaptive algorithm schedules more process than the EDF and ACO algorithm. Figure 2 shows the Success Ratio and CPU utilization by using an Adaptive algorithm when system is overloaded. As the load increased the success ratio of the process is also increased. Graph shows that when the CPU load is 10 then the success ratio is 23 i.e. the number of processes schedule and the CPU utilization is 7% by an Adaptive algorithm.

repeatedly and unidentified patterns. MRU manages the frequently identified pattern cache partition. Thus in this manner by using an adaptive scheduling algorithm in co-ordination with HTBS and CAP in virtual environment efficient real time disk scheduling can be achieved. IV.

RESULTS AND DISUSSIONS

A. Adaptive Process Scheduling CPU utilization is the total amount of work handle by the CPU. CPU utilization depends upon the task or processes. Table 1 gives the total description of each process depend on the load An Adaptive algorithm combines the EDF and ACO algorithm user has to specify the algorithm type which he has to apply for the created process. Table 2 shows the execution of process using an Adaptive algorithm. Now all the process entered into an Adaptive queue. All the entered processes are in waiting state and depend upon the release time the process status change to the running. Load of each process is calculated if the calculated load is greater than the given CPU load then the process is switch to ACO algorithm.

Fig. 2. Success Ratio and CPU Utilization of an Adaptive Scheduling Algorithm. TABLE I. CPU USAGE AND TOTAL MEMORY CONSUMPTION

In Table 3 processes P1, P3, P4, P5 and P6 contain the load < 10 or load ≥ 10 so the five processes start their execution because of their release time is earlier than other processes. Total five processes is schedule from that two processes is executed using EDF algorithm and the remaining process is switch to ACO algorithm depending upon the following condition: Load of process is greater than the given CPU load. The priority of the process is high it means the deadline of the process is nearer. The process count.

Load

Process

Thread

Handles

CPU Usage

10 15 20 25

40 42 47 51

908 879 820 789

22315 21619 21559 21499

2.34 3.00 4.56 5.89

Memory Used MB 35.09 39.33 40.11 43.33

30

57

756

21434

6.99

44.55

35 40 45

62 66 70

715 700 689

20994 21222 21455

10.77 15.33 18.32

46.33 48.33 50.00

B. HTBS Disk Scheduling Guest OS requires minimum of 6 GB of free disk to run on the host OS. VM used in this experiment is VMware player 5.0. Each guest operating system is viewed as a single process of the host operating system that runs in the user space. The virtual machine is configured with 40 GB of hard disk space and 512 MB of RAM. The host operating system used in this experiment is Windows 7 Ultimate.

Table 2 shows the execution of an Adaptive algorithm and the remaining five processes are executed when these process are release for execution. It depends upon their release time. Process P1, P3, P4, P5, P6 are started to execute, because all the process has nearest deadline and these processes are released. The given load of each process is set to 10. Processes whose load is greater than the given load these processes are executed using ACO algorithm and less than the given load then these processes are executed using EDF algorithm.

1) Execution Time When the file read write requests are given to the scheduler their starting LBNs are calculated. The scheduler dispatches the the requests with strongest spatial locality on the basis of LBNs. As the algorithm being used is non-work conserving, it will predict the arrival of future request i.e. instead of considering the requests from the pending queue only; the scheduler will also consider the future request. If the incoming future request is nearer than the request in the pending queue, the scheduler will dispatch the future request else the request from the pending queue.

Process P1 and P6 has a load less than 10 so these two processes are switch to EDF algorithm and the process P3, P4 and P5 has a load greater than given load then these processes are executed using ACO algorithm. Table 2 shows the execution of an Adaptive algorithm. The status of rest of the process is changed from waiting to running depend upon the release time. Switching is performed between the processes it depends upon the load of each process. If the load, of the process change then the algorithm also changes. An Adaptive algorithm removes the drawback of EDF and ACO algorithm.

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TABLE II. Process ID 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246

Process Name P1 P2 P3 P4 P5 P6 P7 P8 P9 P10

Start Time 10:14:26 10:15:10 10:16:57 10:17:44 10:18:23 10:19:56 10:20:34 10:21:22 10:22:46 10:23:06

ADAPTIVE SCHEDULING ALGORITHM QUEQUE Dead line 10:18:26 10:18:60 10:19:19 10:18:55 10:20:45 10:24:34 10:22:12 10:22:23 10:24:33 10:25:29

Execution Time 4 3 3 1 2 5 2 1 1 2

Release timein Sec 1 2 1 1 1 1 4 3 2 2

State

Priority

CPU Load

Run Wait Run Run Run Run Wait Wait Waiting Waiting

Normal Normal Normal Normal Normal Normal Normal Normal Normal Normal

9.19 5.68 11.22 14.55 14.55 7.89 11.22 12.34 13.34 10.00

“Fig 3”, show the result of the HTBS scheduler implemented in host and in guest OS. The scheduler has been tested in the host as well as in the guest. 12 files with different sizes has been given as input to the scheduler and time required for reading those files in the host as well as in the guest has been noted. In addition with the HTBS, NOOP and CFQ has also been implemented in the guest OS. The results show that, the HTBS algorithm improves the execution time as compared with the CFQ and NOOP. 2) Disk Read/Write Rate “Fig 4”, shows the graphical results of the average disk read/write rate. From the graph, it is observed that the disk read write rate using HTBS is high as compared to NOOP and CFQ. CFQ gives the lowest disk read/write rate.

Fig. 4. Average Disk Read/ Write Rate (MB/Sec) in Guest OS by NOOP, CFQ and HTBS

3) Seek Time “Fig 5”, shows the graphical results for the average seek time by three algorithms. The maximum queue limit of these schedulers is 10. The number of files is varied and for each one the three schedulers are tested. CFQ, as being fair, allocates equal time slice to each of the file and executes each file until its time slice is expired in a round robin fashion. From the graph, it is observed that the average seek time using CFQ is maximum as compared to NOOP. NOOP uses the FIFO policy, and executes the requests in the order of their arrival. So in the case of this scheduler the average seeks time is minimum as compared to CFQ. HTBS dispatches the request in order to maintain spatial locality, so in the case of HTBS, average seek time is lowest as compared to both NOOP and CFQ.

Fig. 5. Average Seek Time (Sec) in Guest OS by NOOP, CFQ and HTBS

C. CAP Pattern Detection The buffer cache is shared with the virtual environment. Modified Linux strace utility intercepts the system calls made by the processes and records the following information about the, file identifier (inode), request size, I/O triggering program counter, starting address. . The program counters are obtained by tracing the function call stack backwards in the trace file. The inodes of the files are stored on disk in virtualized environment. The cscope application was run in the virtualized environment and then application traces were tested on buffer cache using the shared files. The total references issued by gcc were 158667 and was tested on varying cache sizes. 1) Comparison of Hit ratio The future access request of the block is predicted by using the information contained in the I/O request. Thus when the

Fig. 3. Execution Time (Sec) in Guest OS by NOOP, CFQ and HTBS

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block request arises the referenced block is present in the buffer cache. Thus buffer cache misses are reduced. Table 4 shows the comparison of cscope hit ratio with CAP and various algorithms. And the comparison graph of hit ratio is shown in “Fig 8”,. From the graph it can be seen that hit ratio is maximum with CAP compared to other algorithms. 2) Comparison of Execution Time As the future access request is predicted by CAP the requested block is available in the buffer cache. As it is the main memory to main memory transfer the time spent in the I/O is reduced. The total time spent in the execution is minimum compared to the RACE and other non detection based scheme. The comparison with different application under varying cache size is shown in Table 5 and “Fig 9”, below 3) Comparison of I/O Time The hit ratio of the application is improved by CAP as the miss are reduced. As block is available in the buffer cache, when the request arises the time spent in the I/O is reduced. Table 6 and “Fig 10”, shows the time required by CAP for performing the I/O with various applications under varying cache sizes are shown below.

Fig. 8. Cscope Execution Time Comaprison HIT RATIO COMPARISON

TABLE III.

Algorithms % Hit Ratio Comparison

Cache size MB

CAP

RACE

ARC

LRU

LIRS

MQ

2Q

8 16 32

44 45 48

54 54 54

43 44 45

43 43 44

44 44 46

43 43 44

42 46 44

64 128

50 59

54 97

46 46

45 46

45 77

45 54

45 75

TABLE IV. Cache size MB

EXECUTION TIME COMPARISON

Algorithms Execution Time Comparison in secs CAP

RACE

ARC

LRU

LIRS

MQ

8

276 6.44

1691.6 5

2526. 7

2777 .44

2503. 47

2769 .93

16

266 1.96

2019.5

2457. 9

2695 . 28

2446. 82

2687 .69

32

242 3.77

2005 03

2423. 56

2561 .37

2406. 12

2569 .16

64

256 6.19

1990.2

2371. 47

2476 .43

2369. 77

2479 .74

128

133 9.05

494.19

2315. 67

2409 .16

1311. 65

2109 .95

Fig. 6. Cscope Hita Ratio Comparison

TABLE V. Cache size MB 8 16

Fig. 7. Cscope I/O Time Comparison

32 64 128

181 182

2Q 216 6.6 8 247 7.6 3 244 8.7 3 239 3.0 7 133 4.2

I/O TIME COMPARISON

Algorithms I/O time Comparison in secs CAP

RACE

ARC

LRU

LIRS

MQ

2Q

2037. 91 2037. 91 2037. 91 2037. 91 2037. 91

1656.0 3 1656.0 3 1656.0 3 1656.0 3 1656.0 3

2094 .39 2094 .39 2094 .39 2094 .39 2094 .39

2167 .09 2167 .09 2167 .09 2167 .09 2167 .09

2066. 29 2066. 29 2066. 29 2066. 29 2066. 29

2156 .94 2156 .94 2156 .94 2156 .94 2156 .94

2091.7 3 2091.7 3 2091.7 3 2091.7 3 2091.7 3

V.

CONCLUSION

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As shown from the results it can be concluded that an adaptive scheduling algorithms efficiently scheduled the real time processes. HTBS predicts the future access request for shared disk in the virtualized environment and effectively reduces the access latency and seek time as depicted from the graphs.cap shows improved performance compared by improving the hit ratio, and reducing the time spent in performing the i/o and overall execution. Use of program counter helps in correctly identifying the references made to the small file. As the policies to be applied are based on identified pattern, cap can better adjust itself to the changing workload. This leads to effective and efficient utilization of the available limited buffer cache space shared with virtualized environment. REFERENCES 1.

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