Overclocked Load Scheduling in Large Clustered Reservation Systems

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					                                                             (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                       Vol. 8, No.4, 2010

    Overclocked Load Scheduling in Large Clustered
                Reservation Systems
       Tania Taami                      Amir Masoud Rahmani                             Ahmad Khademzade                       Ismail Ataie
  Islamic Azad University,              Islamic Azad University,                    Islamic Azad University,               Jam Petro. Complex,
Science and Research Branch,          Science and Research Branch,                Science and Research Branch,                  Tehran, Iran
          Tehran, Iran                        Tehran, Iran                                Tehran, Iran                   ataie.ismail@gmail.com
     t.taami@srbiau.ac.ir                 rahmani@sr.iau.ac.ir                          Zadeh@itrc.ac.ir


 Abstract—Advanced resource reservation has a great role in                   Physical architectural model of computing nodes is a
 maintaining QoS of requests. Resource allocation and                     cluster of nodes that connected by a shared back bone [12].
 management to reservation requests for optimal utilization               Any workload is divided in two subdivisions. In the first
 and guarantee of quality of service is challenging effort. When          division workload is deployed to node or nodes and in the
 a reservation request for a resource type fails although enough          second division workload(s) is started and continued up to
 free capacity might be available, there is not any chance for            its end. After transferring workload(s) to target(s),
 resolving conflicts. Inflexibility of reservation request in             computation starts and terminates until end of its workload.
 support of replacement on time axis, results in rigid resource           Two constraints exist on this model: computation capacity of
 utilization and even poor QoS of the system. But with the help           nodes and bandwidth capacity of infrastructure of network.
 of new overclocking technologies for doing over-clocking on
 some current scheduled reservation chunks, new chances                        Using overclocking any reservations or allocation on
 emerge to beat these restrictions [1]. Using strict overclocking         computing nodes could be relocated, finish times.
 schema with traditional processors in limited time in cluster of         Computing resources overclocking needs awareness of
 servers, simulation results show QoS of reservations could be            troubles that might be introduced in reliability of results and
 improved. This is came through with improvement to utilizing             on hardware chips. On the other hand, solving thermal
 of resources and increasing accepted reservations without any            equations of node material is costly in real time scheduler
 side effects on processing and reliability of computations.              [1]. So, for improving the schedulers we need a simple and
                                                                          dependable model to utilize capabilities of resources.
    Keywords-scheduling; overclocking; thermal behaviour;
 advance reservation; cluster; QoS;                                           The layout of this letter will be as follows: section ІІ will
                                                                          describe system model, reservation model, overclocking
                                                                          concepts and strict overclocking schema. In section ІІІ we
                       I.   INTRODUCTION
                                                                          will propose an algorithm that combined overclocking and
     In center of any collection system should be a scheduler             scheduling mechanisms into harmony. We will evaluate the
 to manage and allocate resources to the clients in appropriate           performance of proposed algorithm with the simulation and
 time. Once of most essential resources in any system, either             results in section IV. Finally, in section V we present our
 single or orchestrated system is processing unit. Accepting              conclusions of algorithms and proposed over-clocking
 and scheduling requests in appropriate time on appropriate               schema.
 nodes is challenging effort of scheduler. In this paper we
 concentrate on overclocking computing resource to beat                           II.    MODELS AND OVERCLOCKING CONCEPTS
 underutilized resources and improving QoS of reservations.
     Previously, many efforts have been done for scheduling               A. System Model
 in clusters or grid systems [2, 6, 7, 8, 9, 10, 11] and also                In this paper we choose system models of [12]. At this
 scheduling with over-clocking capabilities in single node                moment, briefly describe this model.
 systems for real-time (periodic and aperiodic) jobs [1, 5], but
 no studies about the integration of these yet.                               In this model we have one type of requests: reservation
                                                                          requests. according definition any reservation request R has
     In reliable overclocking, computing resource should be               five parameters: Rc, Rs, Re, n, Rio, where Rc is coming time
 controlled so that does not pass the thermal threshold of                of reservation request, Rs is start time of reservation, Re is
 equipment [1]. In this paper is introduced simple model of               end time of reservation, n is number of processing units that
 reliable overclocking processors, either overcome                        should be served for reservation and Rio is aspect of time is
 complexity of real thermal model of processors that impact               required to transferring reservation request to processing
 any algorithms in real time and either reduce complexity of              units. In this model requests should be guaranteed to
 computation of thermal radiated from processors that also                serviced with n processing unit, in interval Rs and Re.
 reduce computation time of any stage of algorithm.                       Reserves could not coming in system earlier than Rc time




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                                                                                                       ISSN 1947-5500
                                                                        (IJCSIS) International Journal of Computer Science and Information Security,
                                                                                                                                  Vol. 8, No.4, 2010
but could out of system earlier than Re time if all of works                            The parameter  and  relate power consumption of
have been done on computing nodes.                                                   processor to its speed. The  parameter has a value of
   The system model in this paper is considered as a cluster                         roughly 3.0 [1,3]. For safety of system, processor
of nodes that connected by a single and shared media                                 temperature should not reach to critical point of temperature,
backbone, similar to a LAN network. A cluster consist of                             due to damaging effects on chip operation.
one coordinator node and n agent nodes A1, A2, ..,An. the                                According to thermal model in the (1), we can derive
coordinator node receives requests, reservations, and                                following (2) for calculating temperature at any point of
possibly plans to schedule request on agent nodes by its                             time[1,3]:
scheduler module. In a different way, each agent node also
has two major parts: local scheduler and processor frequency                                                  TE =TF +(T TF )et / τ
                                                                                                                        0
                                                                                                                                                    (2)
controller. The coordinator's scheduler dispatches scheduling
timetables and requests that should be ran on node, to agent                         Where in general TF = Rs is steady state temperature at
                                                                                                                 F
schedulers. According received timetables local scheduler                            overclocking speed of sF and TE = R s  is temperature at
                                                                                                                               E
give control of processing unit to request, the reservation.                         between times with speed of sE after elapsed t unit of time,
Figure 1 shows structure of cluster of nodes with a master or                        and T0 is the temperature at lowest level at the start time.
coordinator for managing several agent nodes that all                                Parameter  is equal to R·C and t is elapsed time of time that
connected to single backbone.                                                        temperature was T0.
    According to this model of computation, there are two
                                                                                     By this equation, we can calculate the t value:
resource, computing resource and network resource. Based
on these two types of resources, there are conflicts on                                                                   To  T H
accessing and utilization them. First conflict appears when                                                  t = τ ln (            )                (3)
any two or more request want exclusively access the                                                                       TE  TH
network media for communicating and deploying workload
                                                                                         To avoiding complex and time consuming computations
to destination node. Only one of them could access the
                                                                                     at run time on scheduler, we utilize simple and effective
network and transfer its data to destination node. Another
                                                                                     strict overclocking schema. Consequently, in this schema,
resource is computing power of the nodes. When a request
                                                                                     we exploited three phases in support of CPU frequency
wants completely access to the node, intended for uses it for
                                                                                     scaling, under-clocked phase, normal clocked phase and
processing purposes in some time interval, other requests
                                                                                     overclocked phase. In under-clocking phase (i.e. idle mode)
could not access it until end of processing time of current
                                                                                     frequency of processor is reduced to minimum available
request on it.
                                                                                     value which results in reduced temperature to near the
                                                                                     minimum possible value. In the over-clocking phase
                                                                                     transiently frequency of processor is increased to maximum
                                                                                     value until temperature reach to normal point. Finally in the
                                              Local
                                            Scheduler
                                                          Frequency
                                                          controller
                                                                                     normal-clocking phase frequency backs to nominal it to
                                                                                     continue probably reminded workload of request.
                                                        Agent node 1                 Considering the temperature is not above normal, reliability
       Global                                                                        and continuity of computing operations are preserved. Also
      Scheduler
                                                                                     we cover two working modes in the schema, normal load
        Coordinator node                                                             mode and idle load mode. To reducing temperature more
                                              Local       Frequency
                                                                                     quickly in idle mode we never deploy any workload to the
                                            Scheduler     controller
                                                                                     processor that keeps temperature and frequency in lowest
                                                                                     limit, i.e. under-clocking phase. We exploit this situation due
                                                         Agent node n
                                                                                     to expanding succeeding overclocking interval to the
Figure 1. Topology of cluster of nodes with a coordinator and many agent             maximum possible value. Using the (3) we can calculate t
                                 nodes.                                              and ratio of under-clocking to over-clocking periods.

B. Thermal Model                                                                                           III.   ALGORITHM
   Relation between processor speed and thermal behavior                                 In this section we introduce a scheduling algorithm that
of any chip can be approximated by the following                                     uses described strict overclocking schema in situations
equation[1]:                                                                         where conflicts are appeared between current reservation
                                                                                     request and previous guaranteed and scheduled requests,
                                     κsα (t) T(t)                                reservations parts, is discovered.
                           T ' (t)=          
                                        C      R C
                                                                                         As previously described, for overclocking any time
    Where T(t) is temperature at time t and s(t) is speed of                         period of the processors, we elaborate the three step strict
processor at time t. the parameters R and C are the thermal                          overclocking schema: in first step, node processor get under-
resistance and capacitance of chips, respectively (with fan or                       clocking frequency with idle workload, in the second, the
any peripheral attached to chip, like heat sink).                                    node get overclocking frequency, and last, the node get
                                                                                     normal clocking frequency. Only the timeslots of processor



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could be overclocked if exists enough timeslot before it that
hasn't been allocated to any request.                                       // reserve nodes with overclocking
                                                                            15 for i=1 to R.n - #AvailabeNodes
    In following algorithms there are two overclocking                      16         RE=EligiblesAlloci.R
approach: other-overclocking and self-overclocking. In                      17          = min((RE.Re-RE.Rs-RE.Rio), maxOCTime)*OCRate;
other-overclocking approach, timeslot of processor belong to                18         EligibleAllocsi.interval.start -= Tidle;
other previous requests, the reservations, is overclocked. But              19         EligibleAllocsi.interval.end -= ;
in self-overclocking approach, current request on nodes is                  20         updateAllocOnNode(EligibleAllocsi.node,EligibleAllocsi);
overclocked.                                                                21         allocateNode(EligibleAllocsi.node, R.Rs, R.Re, R);
    The doReserve algorithm (Fig. 2) firstly tries to schedule              22 end for;
                                                                            23 return true;
reservation R in cluster of nodes, without over-clocking. If
                                                                            24 else
it could not proceed, tries to apply overclocking techniques.               // find nodes that have self OverClocking condition for
The doReserveWithOverClock algorithm (Fig. 3) implements a                  // Reservation R
strict overclocking schema that previously has been                         25 selfOCNodes  
explained. First it finds eligible nodes; the nodes could be                26 for i=1 to n
overclocked during period of some scheduled jobs or                         27        if (isFree(nodei, R.Rs- Tidle, R.Re-())
reservations. If it could schedule by available nodes with                  28          selfOCNodes +=nodei;
normal clocking and overclocking other possible nodes,                      29 end for
either self-overclocking or other-overclocking, it proceeds,                30 if (#EligibleAllocs+ #selfOCNodes+#AvailabeNodes
otherwise it fails. Value of  is amount of time that the end               R.n)
of request goes back because of overclocking. The Tidle                     31        reserveNodes(AvailabeNodes, R, # AvailabeNodes);
parameter is the required time for period of under-clocking                 // reserve nodes for R reservation with overclocking other
with idle workload.                                                         // scheduled requests
                                                                            32        for i=1 to R.n - #AvailabeNodes
                                                                            33          RE=EligiblesAlloci.R
 boolean doReserve (R)                                                      34           = min((RE.Re-RE.Rs-RE.Rio),maxOCTime)*OCRate;
 1 if (isFreeIO(R.Rs, (R.Re- R.Rs)·R.Rio) == false)                         35          EligibleAllocsi.interval.start -= Tidle;
 2 return false;                                                            36          EligibleAllocsi.interval.end -= ;
 3 AvailabeNodes  findAvailabeNodes(R.Rs, R.Re);                           37          updateAllocOnNode(EligibleAllocsi.node,
 4 if (#AvailableNodes < R.n)                                                                                                                         EligibleAllocsi);
 5 return doReserveWithOverClock(R);                                        38          allocateNode(EligibleAllocsi.node, R.Rs, R.Re, R);
 6 else reserveNodes(AvailabeNodes, R.Rs, R.Re, R.n);                       39        end for;
 7 return true;                                                             // reserve nodes for R Reservation with Overclocking R itself
                                                                            40         for i=1 to R.n- (#EligibleAllocs+ #AvailabeNodes)
                                                                            41            = min((R.Re-R.Rs-R.Rio), maxOCTime)*OCRate;
                                                                            42           allocStartTime = R.Rs - Tidle;
             Figure 2. Top level of reservation algorithm
                                                                            43           allocEndTime= R.Re - ;
                                                                            44           allocateNode(nodei, allocStartTime, allocEndTime, R);
                                                                            45         end for;
                                                                            46         return true;
 boolean doReserveWithOverClock (R)                                         47 end if;
 // find and set Eligible Allocation scheduled slot of nodes for            48 end if;
 // overcloking                                                             49 return false;
 1 EligibleAllocs  
 2 for i = 1 to n
 3       Alloci=null;                                                                    Figure 3. Strict over-clocking schedular algorithm
 4      =min((R.Re-R.Rs-R.Rio), maxOCTime)*OCRate;
 5      if (Rid = cpuOverlap(nodei, R.Rs, R.Re)) != null and                  Overclocking schema could be applied on start time of
 5.1              isFree(nodei, Rid.Rs-Tidle-Rid.Rio, Rid.Rs) and          computation until end time of it. That is to say, overclocking
 5.2              (Rid.Re - )  Rs and                                    couldn't be applied on communication part of request
 5.3              isFree(nodei, Rid.Re, R.Re) )                            because communication time of any request depended to
 6           TimeIntervalnodei, R( Rid.Rs - Tidl, R.Re-);                network specification of cluster (i.e. bandwidth) and could
 7           Alloci= (nodei, Rid, TimeIntervalnodei, R);                   not be altered or increased without changing physical
 8      end if;                                                            characteristics of underlying network's components.
 9       if (Alloci !=null)
 10          eligibleAllocs  eligibleAllocs + Alloci;
 11 end for                                                                                   IV.       PERFORMANCE EVALUATION
 12 AvailabeNodes  findAvailabeNodes(R.Rs, R.Re);                            For analysis of mentioned strict overclocking schema,
 13 if (#EligibleAllocs + #AvailabeNodes R.n)                            we simulate a cluster of nodes with varying processing
 14 reserveNodes(AvailabeNodes, R, #AvailabeNodes);                        nodes and reservation requests. In all simulations, maximum
                                                                           number of requested nodes by any reservation request is
                                                                           number of nodes in cluster. The reservation requests deploy



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its workload to the nodes by using multicasting approach,                                     70

aimed to maximize bandwidth utilization.                                                                    Overclocked
                                                                                                                              Nodes=100, Workload=
                                                                                                            Normal
    For simulating previous algorithms, we use following                                      60


parameters: Arrival time of reservation requests have
Poisson distribution with average of 50 unit of time. Initially                               50

we consider length of requests be near to overclocking




                                                                            Utilization(%)
period, i.e. in interval of [40 .. 50], with uniform distribution                             40
that is named . This value of  is nearly double of
overclocking time length. Secondly we studied multiples of
the  in system utilization and acceptance ratio of system.                                   30


For computing fractions of idle time to overclocking time,
we used Dell Latitude D810 with Centrino processor and (3).                                   20


Based in this provision, this ratio calculated as 3 to 2, 3 units
of time for idle time and 2 units of time for overclocking                                    10

time. As mentioned previously, number of requested nodes                                               5     10       15       20      25      30         35   40   45            50

in each reservation is in [1 .. number of nodes] interval, i.e.                                                               number of requests (103)
with increasing number of nodes, request of nodes for each
reservation will rise. Total simulation time, 11 hours was                                   80

considered. Yield of overclocking than normal operation of                                                                 Nodes=100, Workload=
processor is 0.5 (the OCRate in the algorithm 2). Also                                       75                                                                     Overclocked
                                                                                                                                                                    Normal
communication time ration or the Rio is 0.1 of total                                         70

workload. Although advance reservation is used for                                           65
guarantee of QoS of mixed typical job and reservation for
                                                                           Acceptance(%)
reservation request, in this model we detach start of service                                60


and start of request for adapting with future advance                                        55

reservation models, and simulation purposes (FIFO model).
                                                                                             50

    Results (Fig. 2) show that using strictly overclocking                                   45
schema improves utilization of resources and acceptance
ratio of reservation request in scalable form.                                               40




    Overall, because of multi node reservation request that is
                                                                                             35



responded through dynamic and elasticity of overclocking,                                    30


that impacts and results in more utilization in overclocked                                        5        10       15       20      25       30         35   40   45            50
                                                                                                                              number of requests (103)
schema than normal clocking schema, despite of reducing
and convergence of overclocked and normal schema                                                       Figure 4. Acceptance and utilization in 100 nodes.
together.
    Fig. 3 in comparison with Fig. 2 proves that increasing                                  80
                                                                                                                           Nodes=500, Workload=
number of nodes have not any impact on improving                                             75                                                                          Overclocked

utilization and acceptance ratio similar to normal clocking.                                 70
                                                                                                                                                                         Normal



    In other way, with increasing average length of                                          65

reservation workloads, overall overclocked utilization
                                                                          Acceptance(%)




improvement with respect to normal clocking, will be
                                                                                             60



increased. The reason is that, with increasing the workload,                                 55


side effects of idle time slice that happened before any                                     50

overclocking part of workload, is decreased. But with
growing number of requests at the constant workload rate,
                                                                                             45



this gain is starting to be decreased, because side effects of                               40


underutilized idle times before any overclocked time slices                                  35

will be raised.                                                                              30

                                                                                                   5        10       15       20      25       30         35   40   45            50

                                                                                                                              number of requests (103)




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                     70                                                                                                            55
                                                       Nodes=500, Workload=                                                                      Nodes=500, Workload=3                     Overclocked
                                   Overclocked
                                                                                                                                                                                             Normal
                                   Normal                                                                                          50
                     60


                                                                                                                                   45

                     50
  Utilization(%)




                                                                                                                   Acceptance(%)
                                                                                                                                   40


                     40
                                                                                                                                   35



                     30                                                                                                            30



                                                                                                                                   25
                     20


                                                                                                                                   20

                     10

                           5         10          15     20         25      30       35    40   45     50                           15

                                                         number of requests (103)                                                      5   10   15     20      25       30       35   40   45            50

                                                                                                                                                      number of requests (103)
                               Figure 5. Acceptance and utilization in 500 nodes

    In all cases, normal and over-clocked schema, increasing                                                                       65
                                                                                                                                                 Nodes=500, Workload=2                          Overclocked
average length of reservations will cause drop of acceptance                                                                       60
                                                                                                                                                                                                 Normal

ratio of reservation requests. Coming out such results is
obvious; because of increasing length of reservations, the                                                                         55


probability of facing of them with each other will increase                                                                        50

simultaneously.
                                                                                                                   Acceptance(%)
                                                                                                                                   45


                     80
                                                       Nodes=500, Workload=2                                                      40
                                   Overclocked
                                   Normal                                                                                          35
                     70

                                                                                                                                   30


                     60
  Utilization(%)




                                                                                                                                   25


                                                                                                                                   20
                     50
                                                                                                                                        5   10   15     20      25       30       35   40   45            50

                                                                                                                                                      number of requests (103)
                     40
                                                                                                                  Figure 6. Acceptance and utilization in 500 nodes with workload of 2
                                                                                                                                                 and 3.
                     30
                                                                                                                     With increasing the workload length of reservations
                                                                                                                 absolutely, both normal and overclocked schemas quickly
                                                                                                                 improve more than before until to reach saturation point. At
                     20

                           5        10           15     20         25      30       35    40   45     50

                                                        number of requests (103)                                this point, increasing number of requests, the overclocking
                                                                                                                 has no other influences. Fig. 5 with Fig. 6 shows this matter.
                                                      Nodes=500, Workload=3                                     Based on default value of ,2and 3Fig. 7 graphs show
                      80
                                    Overclocked                                                                  that increasing average workload of requests, peak point of
                                    Normal
                                                                                                                 improvement is shifted to left, i.e. towards to less reservation
                      70
                                                                                                                 request numbers. This means, with increasing workload,
                                                                                                                 collision between end time of requests and required idle time
    Utilization(%)




                      60
                                                                                                                 intervals before overclocking time of processor, will happen
                                                                                                                 sooner.
                      50




                      40




                      30




                      20

                               5     10          15      20        25       30       35   40   45     50
                                                             number of requests (103)




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                                                                                                                                                             ISSN 1947-5500
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                                     30                                                                                   [3]  N. Bansal and K. Pruhs, “Speed scaling to manage temperature”, in
                                                                                                                              Symposium on Theoretical Aspects of Computer Science, 2005.
                                                                                       Nodes=500               
                                                                                                                          [4] N. Bansal, T. Kimbrel, and K. Pruhs, “Dynamic speed scaling to
                                                                                                               
                                                                                                                               manage energy and temperature”, IEEE Syposium on Foundations of
                                     25
                                                                                                                               Computer Science, 2004.
                                                                                                                          [5] S. Wang, R. Bettati, “Reactive Speed Control in Temperature-
 Utilization Improvement(%)




                                                                                                                               Constrained Real-Time Systems”, Proceedings of the 18th Euromicro
                                                                                                                               Conference on Real-Time Systems (ECRTS 06), Dresden, Germany,
                                                                                                                               pp. 161-170, July 2006.
                                     20
                                                                                                                          [6] L. Eyraud-dubois , G. Mounié , D. Trystram, “Analysis of Scheduling
                                                                                                                               Algorithms with Reservations”, Proceedings of the 21st IEEE
                                                                                                                               International Parallel and Distributed Processing Symposium, USA,
                                                                                                                               2007.
                                     15
                                                                                                                          [7] J. Blazewicz, P. Dell’Olmo, M. Drozdowski, P. Maczka, “Scheduling
                                                                                                                               multiprocessor tasks on parallel processors with limited availability”,
                                                                                                                               European Journal of Operational Research, vol. 149, pp. 377–389,
                                                                                                                               2003.
                                     10
                                          5    10     15        20        25      30        35   40   45     50           [8] J. Blazewicz, M. Machowiak, J. Weglarz, M. Kovalyov, D. Trystram,
                                                                     number of requests (103)
                                                                                                                               “Schedulingmalleable tasks on parallel processors to minimize the
                                                                                                                               makespan”. Annals of Operations Research, vol. 129, pp. 65–80,
                                     16                                                                                        2004.
                                                                                                                         [9] K. Jansen. “Scheduling malleable parallel tasks: An asymptotic fully
                                                                                  Nodes=500
                                                                                                              
                                     14                                                                       
                                                                                                                               polynomial time approximation scheme”, Algorithmica, vol. 39, pp.
                                                                                                                               59–81, 2004.
                                     12                                                                                   [10] O.H. Kwon, K.Y. Chwa, “Scheduling parallel tasks with individual
         Acceptance Improvement(%)




                                                                                                                               deadlines”, 6th International Symposium on Algorithms and
                                     10                                                                                        Computation, Springer-Verlag, vol. 215, pp. 198–207, 1995.
                                                                                                                          [11] V. Subramani, R. Kettimuthu, S. Srinivasan, P. Sadayappan,
                                      8                                                                                        “Distributed Job Scheduling on Computational Grids Using Multiple
                                                                                                                               Simultaneous Requests”, IEEE Computer Society, p. 359, 2002.
                                      6
                                                                                                                          [12] A. Mamat, Y. Lu, J. Deogun, S. Goddard, “Real-Time Divisible
                                                                                                                               Load Scheduling with Advance Reservation”, Euromicro Conference
                                      4
                                                                                                                               on Real-Time Systems (ECRTS '08), Prague, pp. 37-46, 2008.
                                      2
                                                                                                                                             ACKNOWLEDGMENT
                                      0
                                                                                                                             This work was supported by Iran Telecommunication
                                          5    10     15        20        25      30        35   40   45     50
                                                                number of requests (103)
                                                                                                                          Research Center (ITRC).
             Figure 7. Acceptance and utilization improvement in 500 nodes with
                                workload of , 2 and 3


                                                           V.        CONCLUSIONS
    Study of results shows that by means of the proposed
strict overclocking schema in controlled boundary,
utilization absolutely increases than normal clocking. Also,
acceptance rate of system with limited conditions increase.
In addition, as temperature of processing nodes could not
reach to critical point, reliability of computation is
preserved. With preserving power of processor, economical
and commercial aspect of power consumption remains.
   Expanding networks and resources, we can use this
schema in larger grid networks than clusters. Since resources
exclusively are provided to requests, this model and
algorithms is very good for private grids that total resources
available for commercial purposes.

                                                                REFERENCES

[1]                                  Y. Ahn, R. Bettati, “Transient Overclocking for Aperiodic Task
                                     Execution in Hard Real-Time Systems”, Euromicro Conference on
                                     Real-Time Systems (ECRTS '08), Prague, p. 102, 2008.
[2]                                  D. G. Feitelson, “Scheduling parallel jobs on clusters”, High
                                     Performance Cluster Computing, vol. 1, Architectures and Systems,
                                     pp. 519–533, 1999.



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