energy efficient task allocation over mobile...
TRANSCRIPT
Energy Efficient Task Allocation overMobile Networks
Carmela ComitoDEIS, University of Calabria
Rende (CS), [email protected]
Deborah FalconeDEIS, University of Calabria
Rende (CS), [email protected]
Domenico TaliaICAR-CNR
DEIS, University of CalabriaRende (CS), Italy
Paolo TrunfioDEIS, University of Calabria
Rende (CS), [email protected]
Abstract—In this paper we present an Energy-Aware Schedul-ing strategy that assigns computational tasks over a network ofmobile devices optimizing the energy usage. The main designprinciple of our scheduler is to find a task allocation thatprolongs network lifetime by balancing the energy load amongthe devices. We have evaluated the scheduler using a prototype ofthe system that includes smart phones and Android emulators.Experimental results show that significant energy savings canbe achieved by using our energy-aware scheduler comparedto classical time-based scheduler, while meeting the specifiedperformance constraints.
Index Terms—Mobile computing; Energy efficiency; TaskScheduling.
I. INTRODUCTION
A mobile ad hoc network (MANET) is a self-configuringnetwork of mobile devices connected by ad hoc wireless linksand equipped with networking capabilities. Recent develop-ments in the technologies of laptops and PDAs together withthe reduction of their costs have incredibly raised the interestin MANETs.
For mobile devices running on batteries, energy efficiency isa key concern to enable effective and reliable computing overthem. Efficient resource allocation and energy managementcan be achieved through clustering of mobile nodes intolocal groups. Clustering the network promotes and easescollaborations among mobile users. We refer to a cooperativeMANET architecture where mobile devices are organized intolocal groups (also termed clusters or mobile groups).
Such a cooperative MANET can be seen as a systemincluding a set of consumers, e.g. mobile applications or tasks,and a set of resources as energy and processing power. Tomake the most of all available resources, a proper distributionof tasks among the devices such as to optimize the energyconsumption, represents a key issue to be addressed. Thedesign and implementation of such task allocation (or taskscheduling) strategy is the objective of this paper. The maindesign principle of our scheduler is to find a task allocationin order to prolong network lifetime. We, thus, propose anenergy-aware task allocation scheme that distributes energyconsumption among clusters by balancing the energy loadamong them. To this aim, we designed and implemented a two-phase heuristic-based algorithm. This algorithm first tries toassign a task locally to the cluster that generated the execution
request by maximizing the cluster residual life. If the taskcannot be assigned locally, the second phase of the algorithmis performed by assigning the task to the most suitable node allover the network of clusters, maximizing this way the overallnetwork lifetime. We characterize the energy consumption ofmobile devices defining an energy efficiency model in whichthe energy costs of both computation and communicationactivities are taken into account.
There are several works in literature whose goal is minimiz-ing the overall energy dissipation of the system. However, thisgoal does not capture the nature of cooperative ad-hoc systems.The reason is that minimizing the overall energy dissipationcan lead to heavy use of energy-effective devices, regardless oftheir remaining energy. The consequent short lifetime of suchdevices will very likely compromise the system performance.This weakness is a major motivation of the proposed energy-balanced task allocation scheme.
We have evaluated our scheduler using a prototype of thesystem that includes smart phones and Android emulators. Theexperimental results show that a significant improvement canbe achieved using our energy-aware scheduler compared to thetime-based round robin scheduler. In details, our algorithm:(i) is effective in prolonging network lifetime by reducing theenergy consumption; (ii) is able to complete a greater numberof tasks in the same experimental settings; (iii) in all theexperiments performed, is able to keep alive all the devicesthanks to its energy load balancing scheme. Our approach,thus, enhances the energy-efficiency of the system comparedto classical time-based scheduling algorithms like round robin.
The remainder of the paper is organized as follows. Sec-tion II presents the reference mobile ad-hoc network archi-tecture. Section III describes the energy model. Section IVpresents the energy-aware task allocation scheme and relatedalgorithms. Section V presents the experimental results. Sec-tion VI discusses related work. Finally, Section VII concludesthe paper.
II. SYSTEM ARCHITECTURE
We consider a multi-hop wireless ad-hoc mobile networkarchitecture. In a wireless mobile ad-hoc network, whichchanges its topology dynamically, efficient resource allocation,energy management and routing can be achieved through
adaptive clustering of the mobile nodes. In a clustering schemethe mobile nodes are divided into virtual groups. Generally,geographically adjacent devices are assigned to the same clus-ter. Under a cluster structure, mobile nodes may be assigneda different function, such as cluster-head or cluster member.A cluster-head normally serves as the local coordinator forits cluster, performing intra-cluster transmission arrangement,data forwarding, and so on. A cluster member is usually calledan ordinary node, which is a non cluster-head node withoutany inter-cluster links.
We refer to the cooperative system, referred to as Mobile-to-Mobile (M2M) architecture, depicted in Figure 1, designedto allow on-demand collaborations among mobile nodes. Ex-amples of mobile-to-mobile collaborations occur in severaldomains such as disaster relief, construction management andhealthcare. In order to promote and ease collaborations whentwo or more mobile users, who are members of the sameorganization or simply collaborate, meet each other, we letthem grouping into clusters referred to as mobile groups.Consequently, the proposed architecture includes a numberof mobile groups or clusters. Figure 1 shows the interactions
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Fig. 1. The M2M system architecture. The arrows denote remote servicecalls.
among the different components of the architecture. Mobilenodes within a group interact trough ad-hoc connections (e.g.,wi-fi, bluetooth) that we refer to as M2M connections, repre-sented as dotted arrows in Figure 1. Interactions among mobilegroups (cluster-to-cluster connections) take place through ad-hoc connections among the cluster-heads of the groups and arerepresented as dot-dash arrows. All types of interactions takeplace either to ask for a computation request or to cooperatein order to collaboratively execute a computational task.
This paper focuses on an energy-aware scheduling strategyallowing to efficiently allocate tasks over the clustered M2Marchitecture. More details about the clustering scheme can befound in our previous work [1].
III. ENERGY MODEL
Energy consumption of mobile devices depends on thecomputation and the communication loads. We define Ei
as the rate of energy consumption of node i in the timeinterval δt, which is the sum of all energy consumption for
communication, ETi, and computation, ECi, of all the tasksassigned to node i within the time interval δt:
Ei = ECi + ETi (1)
Our approach is to estimate the energy consumption forcomputation and to analytically evaluate the energy consumedfor communication. This last issue is the main aim of thesection.
In MANET networks, nodes must always be ready toreceive traffic from neighbors due to the absence of basestation nodes. Thus, a network interface operating in ad-hoc mode has to continuously listen to the wireless channel,consuming this way a constant idle energy power. Therefore,each node overhears every packet transmission occurring inits transmission range consuming this way energy uselessly.This idle energy consumption is referred to as overhearing.Due to overhearing, a new cost in the computation of per-packet energy consumption is introduced and it is the cost fordiscarding overheard packets. Therefore, to model the energyconsumed for communication, the costs to send, receive anddiscard a packet must be included. Consequently, the energyconsumed by a node i for communication can be defined bythe following equation:
ETi = Esendi+ Ereceivei + Ediscardi
(2)
A packet may be sent through a broadcast or a point-to-pointchannel. With the former mode the packet is received by allhosts within the sender’s transmission range; whereas with thelatter mode it is discarded by non-destination hosts.
According to [3] we evaluate the energy consumption be-havior of the mobile ad hoc network based on a model wherethe cost for a node to send or receive a message is modeled as alinear function. In this function there is a fixed cost associatedwith channel acquisition and an incremental cost proportionalto the size of the message. The fixed channel access costs,denoted as bsend and brecv, and the incremental costs, msend
and mrecv, are the same for broadcast and point-to-point. Forpoint-to-point traffic, the fixed cost includes also the MACnegotiation. In the IEEE 802.11 MAC protocol, the sourcesends an RTS (request-to-send) control message, identifyingthe destination. The destination responds with a CTS (clear-to-send) message. Upon receiving the CTS, the source sendsthe data and awaits an ack from the destination. For simplicity,these small control messages are assumed to have the samefixed send (bsendctl) and receive (brecvctl) costs.
The cost, Esendij , for a node i to send a point-to-point packetto a node j is described by the following equation:
Esendij= bsendctl + brecvctl +msend ∗ |MSG|+ bsend + brecvctl (3)
where |MSG| is the size (number of bits) of the messageexchanged among nodes i and j.
The cost to receive a point-to-point packet is modeledthrough the following equation:
Erecij = brecvctl + bsendctl +mrecv ∗ |MSG|+ brecv + bsendctl (4)
In case of broadcast transmission, the cost to send a packetis represented through equation 5 while the cost to receive a
packet is given by equation 6:
Esendbroadi= msend ∗ |MSG|+ bsend (5)
Erecbroadi= mrecv ∗ |MSG|+ brecv (6)
Thus, the energy cost of node i for sending (equation 7) andreceiving (equation 8) packets depends on the used transmis-sion mode:
Esendi=
{Esendij
if point-to-pointEsendbroadi
otherwise(7)
Ereceivei =
{Erecij if point-to-pointErecbroadi
otherwise(8)
Non-destination nodes within the transmission range ofeither the transmitting or receiving nodes overhear the traffic.The cost of discarding is comparable to the one of a broadcastreceiving:
Ediscardi= mdiscard ∗ |MSG|+ bdiscard (9)
IV. THE ENERGY-AWARE SCHEDULER
In this section we present an energy-efficient dynamic taskallocation strategy over the cooperative M2M architecture. Insuch an approach whenever a task has to be executed, anefficient task assignment is found such that the total consumedenergy in the network is minimized and, thus, the networklifetime is prolonged.
A. Scheduling Model
The scheduling problem is the process of mapping a givenapplication onto a target architecture by: (1) selecting whichtask of the application shall be considered; (2) allocating thistask to a resource; (3) computing start and execution times forthe task; (4) repeating these steps until all tasks are scheduled.It is common in the literature to use the terms task allocationand task scheduling interchangeably. However, scheduling iscommonly used to describe all of the above mentioned stepsas well as to describe the computation of start and executiontimes only. Task allocation is, therefore, a step of the moregeneral scheduling problem; it can also be seen as a globalscheduling or meta-scheduling that distributes the tasks amongthe devices. Once tasks have been allocated, the problembecomes one of defining a feasible local schedule that managestask execution for each node. In this paper we focus on thetask allocation problem and we refer to task allocation or taskscheduling interchangeably.
We introduce the following model to support the descriptionof the scheduling strategy.D= {d1,d2, ..., dm} is the set of devices in the system. A
device is described in terms of:• battery level;• processing capability;• time, memory and energy consumed for computation;• time and energy consumed for communication.
We refer to a task model with independent tasks, either atomicapplications or tasks without dependencies. We denote with
T = {t1, t2, ..., tn} the set of tasks to be executed. A generictask ti is characterized by the following features:• the amount of data to be processed;• execution time of task ti on a device dj over a data set
of size s;• energy consumption of task ti on a device dj over a data
set of size s;• memory consumption of task ti on a device dj over a
data set of size s;Before going into the details of the scheduling policy, somedefinitions and notations are introduced to support the pro-posed scheduler.• PCi(t): processing capacity of device di at time t.• Mi(t): memory availability of device di at time t.• EECi(tj, s): estimated energy consumed for computation
by device di to process a task tj over a data set of sizes.
• EETi(tj, s): estimated energy consumed for communica-tion by device di to process a task tj over a data set ofsize s.
• EMCi(tj, s): estimated memory consumption of devicedi to run a task tj over a data set of size s.
• EPCi(tj, s): estimated processing capacity required bydevice di to execute a task tj over a data set of size s.
Definition 1: Let be REi(t) the residual energy availableat node i at time t, and Pi(t) the instantaneous power; theresidual life of node i at time t, RLi(t), is defined as follows:
RLi(t) = REi(t)/Pi(t). (10)
The classical task allocation problem can be reformulated hereas the problem of finding the proper task assignment thatminimizes the energy dissipated in the system and can bedefined as follows:
Definition 2: Given a task model T and a device model D,determine a task allocation TA that maps each task to a devicesuch as to maximize the network lifetime,where the network lifetime can be defined as the period fromthe beginning of the application execution to the time when noenough devices are alive to deliver the required performance.
B. Scheduling Strategy
This section presents an energy-aware (EA) dynamic taskallocation scheme over a cooperative MANET able to dealwith a system where independent tasks arrive at each nodedynamically over time.
The task allocation problem has been proven to be NP-Complete in its general form [2]. However, some optimalalgorithms have been proposed for some restricted versionsof the problem and some heuristic-based algorithms havebeen proposed for the more general versions of the problemallowing to find good allocations in polynomial time [4]. Thus,the problem is finding the minimum cost task allocation.
We propose a two-phase heuristic-based, decentralized al-gorithm. When an assignment decision has to be made for atask, the first phase, referred to as local assignment phase, isresponsible for local task arbitration: it considers the energy
consumption of task execution on the different devices withinthe local cluster. The algorithm tries to minimize the totalconsumed energy in the cluster by assigning the task to thedevice that allows to extend the cluster residual life. If the firstphase is not feasible, the second phase, referred to as globalassignment phase, is responsible for task arbitration amongclusters: the task will be assigned to the most suitable device,all over the network of clusters, that maximizes the overallnetwork lifetime.
We formalize the problem of task allocation as an optimiza-tion problem. As said before, the aim of the optimization is tomaximally extend the life of all the nodes in the network bybalancing the load proportionally to the energy of each nodesuch as to maximize the network lifetime. We optimize theproblem by iteratively trying to improve a candidate solution.A feasible allocation is optimal if the corresponding groupresidual life (in case of local assignment) or system lifetime(in case of global assignment) is maximized among all thefeasible allocations.
The candidate nodes to which a task ta could be assignedhave to satisfy the following constraints:
1) a node di must have enough processing powerto perform the task over a data set of size s:EPCi(ta, s) < PCi(t)
2) a node di must have enough energy to perform the taskover a data set of size s: EECi(ta, s) < REi(t)
3) a node di must have enough memory to perform the taskover a data set of size s: EMCi(ta, s) < Mi(t)
During the local assignment phase, a cluster-head, or the setof neighboring cluster-heads in case of the global assignment,will choose the local node, among the ones satisfying theabove constraints, that will prolong the life of the correspond-ing local group by using the following objective function:
RLLGj(t) = Max
NLGj∑i=1
αiRLi(t) (11)
where RLLGjdenotes the residual life of local group LGj,
NLGj is the number of nodes within the local group LGj, RLi
is the residual life of node i in the group, and parameter αi
takes into account the importance of node i in the local group.The node associated with the maximum value in the objectivefunction will be selected by the cluster-head as candidatenode. Note that throughout the experimental evaluation theparameter αi is set to 1 thus, all the nodes have the same rolewithin the local group.
If the global assignment phase is activated, the final decisionis taken by considering all the candidate nodes proposed bythe neighboring clusters. The task will be assigned to the localgroup that maximizes the life of the whole network:
RLnet(t) = MaxN∑
j=1
αjRLLGj(t) (12)
where N is the number of groups in the network.The choice of pursuing first a local optimum is motivated
by the intent of reducing the transmission costs. As indicated
by several researches, the wireless communication is a majorsource of energy dissipation in MANETs. We have had thisconfirmed by the experimental evaluation of the proposed taskallocation strategy. In the original design of the algorithm,the global assignment phase is activated when the residuallife of the local group is lower than the 50% of its ini-tial value. We evaluated the algorithm over a prototype (acomplete description of the experimental evaluation can befound in Section V-A). We found that the communicationenergy highly degrades the algorithm performance. On thebasis of this result we set the threshold to 20% and limited thefrequency with which the nodes update their resources stateto the corresponding cluster-head. Figure 2 shows the averagedissipated energy, separately identifying the communication(ET) and computation terms (EC), for both the old and newversion of the algorithm. In particular, the curve ET OLDrepresents the communication energy of the original version ofthe algorithm, while ET NEW stands for the communicationenergy of the new version. Obviously, the computation energycomponent is the same for both versions. From the evaluationis evident the considerable improvement to the algorithm:Figure 2 shows that in the new version of the algorithm theenergy for communication is almost negligible compared tothe one for computation; conversely, in the original versionthe energy for communication represents the dominant cost.
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Fig. 2. Average dissipated energy for two versions of the algorithm.
C. The Energy-Aware Task Allocation Algorithm
This section describes the algorithm performed by nodewhen a task has to be executed.
When a node, referred to as requesting node, wants toexecute a task it will ask its cluster-head to handle the taskassignment process (see Figure 3).
Upon receiving a task allocation request, the cluster-headtriggers, through the task allocation method (see Figure 4),the activation of the task allocation strategy allowing to findthe optimal allocation for that task. To this aim, the cluster-head verifies whether the residual life of its group is greaterthan the 20% of its peak value. If the check is successful, itstarts up the local assignment phase. If the check is negative orthe local assignment failed because none of the nodes withinthe cluster can execute the task, the cluster-head activates theglobal assignment phase.
Method allocateTaskReqInput: task t, dataset size s
beginif (this.isClusterHead()) then
allocateTask(t,s);else
myClusterHead.allocateTaskReq(t,s);end
end
Fig. 3. Task allocation request.
Method allocateTaskInput: task t, dataset size s
beginlocalAssignmentFailed ← false;if (RLLGi
> 20%) then< dcandidate,ERLLGi
>← localAssignment(t,s);if (< dcandidate,ERLLGi
>= ∅) thenif (dcandidate = this) then
startTask(t,s);else
dcandidate.executeTask(t,s);end
elselocalAssignmentFailed ← true;
endendif (RLLGi
≤ 20% or localAssigmentFailed) thenC ← determineNeighboringClusters();globalAssignment(t, s, C);
endend
Fig. 4. Task allocation by the cluster-head of local group LGi.
The scheduling starts with the local assignment phase wherethe cluster-head tries to assign the task to a node within thegroup that maximizes the cluster residual life (see Figure 5).In this way, the cluster-head determines a candidate nodein the group meeting the requirements to execute the taskin terms of energy, processing and memory constraints. Thelocal assignment phase can be executed either by the cluster-head of the requesting node as well as by the cluster-heads ofneighboring clusters involved in a global assignment.
The global assignment phase (Figure 6) also considers theneighboring clusters distant at most three hops from the re-questing cluster-head. This restriction is due to the assumptionthat collaborations are established only among neighboringgroups and also to limit the communication costs in thenetwork. Among the neighboring groups will be selected onlythe ones having a residual life greater than a given threshold(set to 30%), in order to avoid overloading of the local group.After that, in each of such local groups, included the contactinglocal group, is established a candidate node that best performsthe task. Finally, among all the candidate nodes, the task willbe assigned to the one that allows to extend the residual lifeof the whole network.
V. PERFORMANCE EVALUATION
In this section we present an experimental evaluation ofthe proposed energy-aware scheduler. We have implementeda prototype of the system by realizing a network of mobile
Method localAssignmentInput: task t, dataset size sOutput: candidate node dcandidate, estimated residual life ERLLGi
of thelocal group LGi if the task would be assigned to dcandidate
beginif (this = CHi or (this = CHi and RLLGi
> 30%)) thenRLLGcurr ← 0;ERLLGi
← 0;dcandidate ← ∅;foreach node di ∈ LGi do
if (di.hasSkill(t,s)) thenRLi ← di.estimateNodeResidualLife(t,s);RLLGcurr ←estimateGroupResidualLife(di,RLi);if (RLLGcurr > ERLLGi
) thenERLLGi
← RLLGcurr ;dcandidate ← di;
endend
endendreturn < dcandidate,ERLLGi
>;end
Fig. 5. Local assignment of task t within local group LGi.
Method globalAssignmentInput: task t, dataset size s, set of neighboring clusters C
beginD ←∅;foreach local group LGi ∈ C do
< dcandidate,RLLGi>← CHi.localAssignment(t,s);
D.add(< dcandidate,RLLGi>);
endERLnet ← 0;dbest ← ∅;foreach node di ∈ D do
RLnet ←estimateNetworkResidualLife(di,RLLGi);
if (RLnet > ERLnet) thenERLnet ← RLnet;dbest ← di;
endendCHbest ← getClusterHead(dbest);if (dbest = this) then
startTask(t,s);else
if (CHbest = this) thendbest.executeTask(t,s);
elseCHbest.requireTaskExecution(dbest, t, s);
endend
end
Fig. 6. Global assignment of task t.
devices composed of a set of smart phones and Androidemulators.
Unless otherwise specified, the parameters used in thesimulation are as follows. Tasks are generated on each node.The task arrival event is a Poisson process with variablefrequency. The simulation area was set to 250000 m2. Theinitial value of the transmission range was set to 100 metersand the initial energy level on each device is 24300 J. Eachdevice was equipped with a network interface 802.11 b/g, witha bandwidth of 11 Mbps. The transmission energy is based onthe model presented in Section III.
The scheduler model proposed is general and the consid-ered computational tasks may concern different application
scenarios. To the aim of the performance evaluation, wefocused on data mining algorithms. In all the experiments, werefer to the computational load as the energy consumption, apriori estimated, to execute a set of data mining tasks. Thesecosts have been estimated through experimental evaluation.We characterize the performance of a data mining algorithmrunning over a specific device and with respect to a given dataset, in terms of the following metrics: memory consumption,battery depletion and execution time assuming that the datamining tasks are CPU-bound.
A. Evaluation Results
Since the goal of our scheduling policy is to maximizethe energy levels of the nodes and, thus, extend the networklifetime, the simulation aims to study the behavior of thescheduler with respect to the energy depletion and networklifetime. Accordingly, we use as performance metrics theresidual life of the network, the number of devices poweredand the number of completed tasks. To assess the effectivenessof the proposed scheduler energy-aware (EA) we comparedits performance with that achieved by the well known roundrobin (RR) scheduling algorithm. This comparison aimed atevaluating the throughput, in terms of number of completedtasks, and the energy consumption of the two algorithms. Notethat the comparison through the residual life parameter issignificant only if the two algorithms execute the same numberof tasks. Otherwise, the comparison can be made with respectto the number of alive devices and the number of completedtasks.
In a first experiment, the initial energy level of the devicesin the network is on average the 75% of the peak value.The computational task used is the J48 data mining algorithmrunning over a data set of 800 Kbytes that takes 2 hours and 16minutes to be executed, with an average energy consumptionof about 2592 J. The total number of submitted tasks is70; these tasks arrive to the system following a Poissondistribution with a frequency λ equal to 20 tasks per hour. Thesimulation time is not fixed a priori, since the experiment endswhen all the scheduled tasks have been executed. The aim ofthis first experiment is to show that the proposed EA scheduleris effective in prolonging network lifetime compared to theRR algorithm. In particular, by executing the same number oftasks, the EA scheduler saves about 3 hours compared to RR(see Figure 7). The EA scheduler allocates only 55 tasks ofthe 70 submitted because recognizes that it is the maximumnumber of tasks that the devices in the network can execute.The EA scheduler completes 54 of such tasks because for thelast allocated task the scheduled node, at execution time, hasno longer the energy required to run it. Such node asks forthe re-allocation of the task but no other nodes in the networkhave the energy needed to execute it. Thus, the task executionfails. The RR algorithm executes also 54 tasks, however, it isnot aware of the energy availability of the nodes and, thus,allocates a number of tasks greater than the actual availabilityof the network. Consequently, it causes the turning off of 5devices (see Figure 8). We can conclude that the proposed
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Fig. 7. Average network residual life for both EA and RR algorithms.
EA algorithm allocates and completes the execution of all thetasks that can be executed over the network, prolonging thenetwork lifetime by balancing the energy load and avoidingin such way devices turning off.
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Fig. 8. Number of alive devices after the execution of 54 tasks for both EAand RR algorithms.
In a second experiment the devices are less charged, withan average initial energy available of 30% of the peak value.The simulation time is not fixed, the experiment ends when allthe scheduled tasks are executed; by fixing the number of sub-mitted tasks to 15, this time is of 12 hours. Figure 9 shows thenumber of alive devices and the number of completed tasks forboth the EA and RR algorithms with respect to the simulationtime. From the graph one can see that EA balances the energyload and remains with all the devices alive whereas RR assignstasks also to device that do not have the necessary energy and,thus, causes the switching off of 6 devices. Moreover, at theend of the simulation time the EA algorithm completes 15tasks versus 9 of RR. This means that when the tasks arecompute demanding, compared to the overall network energy,the EA algorithm makes the best from the available energyoutperforming RR also in terms of number of completed tasks.We repeated the experiment by considering an increasingnumber of submitted tasks, ranging from 15 to 30. Withthe considered network energy configuration, the maximumnumber of J48 tasks, over a dataset of 800 KBytes, that can beexecuted are 17. Confirming the previous result, EA does notallocate a number of tasks greater than the maximum numbersupported by the network. It, thus, successfully executes allthe allocated tasks. Conversely, RR allocates all the submitted
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Fig. 9. Number of powered devices and number of completed tasks forbothth EA and RR algorithms.
tasks and, as shown in Figure 10, the number of devices turnedon decreases by increasing the number of submitted tasks,reaching zero when 30 tasks are submitted. In a scenariolike the one considered, where the energy configuration ofthe different devices is rather heterogeneous, the energy loadbalancing effect of the EA is particularly significant. Figure11 shows how the remaining energy is distributed among allthe devices in the network. In particular, it is shown how thisdistribution changes by increasing the number of submittedtasks, for both the EA and RR algorithms. More precisely,it is specified the percentage of devices with (i) no energy(RE = 0), (ii) low remaining energy (0 < RE ≤ 1000) (iii)medium remaining energy (1000 < RE < 5000) and (iv) highremaining energy (RE ≥ 5000). One can note the effectivenessof the EA algorithm in balancing the energy level amongthe devices. In the case of the EA algorithm the percentageof devices with medium remaining energy is always ratherhigh, regardless of the number of submitted tasks. Conversely,in the case of the RR algorithm, by increasing the numberof submitted tasks the percentage of devices without energygrows always more, reaching the 100% when 30 tasks havebeen submitted and all the devices are turned off.
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EA - Nr Completed Tasks RR - Nr Completed Tasks
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Fig. 10. Number of alive device and number of completed tasks for bothEA and RR algorithms w.r.t. the number of submitted tasks.
The efficiency of the EA scheduler is further confirmedwhen dealing with tasks of increasing computational load. Thegraph in Figure 12 shows that EA outperforms RR in termsof throughput and device lifetime. By increasing the compu-
Fig. 11. Distribution of devices remaining energy for both EA and RRalgorithms w.r.t the number of submitted tasks.
tational load the EA scheduler completes a lower number oftasks because it allocates only the tasks that can be executedavoiding this way the switching off of the devices. Conversely,RR turns off a number of devices that increases with thegrowing of the computational load. With a computational loadof 2500 J, the two algorithms execute the same number oftasks but EA remains with all the devices switched on whileRR turns off more than the 90% of the devices.
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EA - Nr Completed Tasks RR - Nr Completed Tasks
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Fig. 12. Number of alive devices and number of completed tasks for boththe EA and RR algorithms w.r.t. the computational load.
In the last experiment four different network configurationsare considered. For all the configurations the average initialenergy level of the devices is the 30% of the peak value.The configurations differentiate only for the distribution ofthe energy among the devices. The EA scheduler with itsdynamic strategy adapts its behavior to the different configu-rations always assigning the tasks to the most energy powerfuldevices. Differently, RR allocates the tasks without takinginto account the energy availability of the devices. Thus, theperformance of RR depends on the specific combination ofdevices and tasks: each time a task has to be allocated, RRefficacy depends on the charge of the device at the beginningof the scheduling queue and on the load of the task to beallocated. For example, if the most charged device is at thebeginning of the scheduling queue when a high computationaltask has to be allocated, there is a chance of executing thetask without turning off the device. Figure 13 shows thatEA successfully executes all the submitted tasks in all theconsidered configurations (as EA performs in the same way
in all the configurations, in Figure 13 we use the notationEA [1,2,3,4] to refer to EA in the 4 different configurations).The graph also shows the bad behavior of the RR: in all theconsidered configurations the throughput of EA is higher thanthe RR one. This is particularly evident in configuration RR 4where the less charged devices are at the beginning of thescheduling queue. Consequently, a greater number of taskshave been allocated to such less energy powerful devices thatare not able to execute them; for example when 5 tasks havebeen submitted, RR is unable to complete even a single taskin correspondence of configuration RR 4.
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Fig. 13. Number of completed tasks in different energy configurations forboth EA and RR algorithms w.r.t. the number of submitted tasks.
In general, the behavior of the RR scheduler which assignstasks to each processors in equal portions and in circularorder, lets its effectiveness depending on the combination taskto be allocated and device at the beginning of the circularqueue. For example, in the case of Figures 10 and 12 thecurves representing the RR performance, are relative to themost convenient configuration for RR, the one where the mostenergy powerful devices are at the beginning of the circularqueue. It is, thus, the configuration which allows to achievethe best performance with RR. Despite this, EA is much moreefficient. If in those experiments we had considered one of theother possible configurations for RR, the improvement of ouralgorithm would have been even higher.
VI. RELATED WORK
Most of the existing research work in the area of energy-aware systems are hardware-based techniques focusing onreducing the energy consumption of the processor. One ofthe most adopted techniques is turning off idle components[5]. Dynamic Voltage Scaling (DVS) is another technique ofenergy conservation. DVS refers to the technique of simulta-neously varying the processor voltage and frequency as perthe energy performance level required by the tasks [6], [7],[8]. Remote execution is a software-based technique in whicha device with limited energy transfers a computational task toa nearby device which is more energy powerful.
Energy-aware task scheduling is another software methodwhere the scheduling policy aims at optimizing the energy.To the best of our knowledge, little work has been done onenergy-aware scheduling over a MANET network. In [9] is
proposed an energy-aware dynamic task allocation algorithmover MANETs. However, this work is different from oursin terms of the underlying architecture and cost functionto be optimized. We clustered the devices to promote localcooperation among nearby devices and minimize the trans-mission energy. This issue is particularly relevant because wehave experimentally found that the transmission energy highlyimpacts on the overall energy consumption. In contrast to ours,the solution proposed in [9] is effective for compute intensiveapplications and does not address the communication aspectsof the system. Furthermore, we adopt a different objectivefunction: we maximize the network residual life rather thanminimizing the energy consumption. Using the residual lifeparameter we are able to actually consider the real energyconsumption rate of single devices, single clusters and theoverall network. Conversely, [9] does consider only the localcomputation issues and works at a node level ignoring theworkload in the rest of the network. Thus, differently fromus, they do not take into account the actual load of thedevices with the possibility of assigning a task to a device thatconsumes less energy, but which is less charged compared toanother one that consumes more energy but it is more energypowerful and thus could efficiently execute that task.
VII. CONCLUSION
In this paper we presented a task allocation scheme formobile networks focusing on energy efficiency. To conser-vatively consume energy and maximize network lifetime wehave introduced a heuristic algorithm that balances the energyload among all the devices in the network. We have imple-mented a prototype of the system and evaluated the schedulingstrategy through simulation experiments. Results show that theproposed scheduler greatly enhances the performance of thesystem compared to time-based traditional schedulers like theround-robin. We achieved improvements in terms of networklifetime, number of alive devices and number of completedtasks.
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