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A Mobile Agent-based System for Dynamic Task Allocation in Clusters of Embedded Smart Camer

A Mobile Agent-based System for Dynamic Task Allocation in Clusters of Embedded Smart Camer
A Mobile Agent-based System for Dynamic Task Allocation in Clusters of Embedded Smart Camer

A Mobile Agent-based System for Dynamic Task

Allocation in Clusters of Embedded Smart

Cameras

Michael Bramberger1,Bernhard Rinner1and

Helmut Schwabach2

1Institute for Technical Informatics,

Graz University of Technology,Graz,Austria

{bramberger,rinner}@iti.tugraz.at

2Video&Safety Technology,

ARC seibersdorf research,Seibersdorf,Austria

helmut.schwabach@arcs.ac.at

Abstract—This paper presents a dynamic task allocation method for smart cam-

eras targeting traf?c surveillance.Since our target platforms are distributed em-

bedded systems with limited resources,the task allocation has to be light-weight,

?exible as well as scalable and has to support real-time requirements.Therefore,

surveillance tasks are not allocated to smart cameras directly,but to groups of smart

cameras,so called surveillance clusters.We formulate the allocation problem as a

distributed constraint satisfaction problem(DCSP)and present a distributed method

for?nding feasible allocations.Finally,a cost function is used to determine the

optimal allocation of tasks.We have realized this dynamic task allocation using het-

erogeneous,mobile agents which utilize their agencies and our embedded software

framework to?nd the most appropriate mapping of tasks in a distributed manner.The

dynamic task allocation has been implemented on our smart cameras(SmartCam)

which are comprised of a network processor and several digital signal processors

(DSPs)and provide a complex software framework.

1Introduction

Surveillance systems currently undergo a dramatic shift.Traditional surveillance systems of the?rst and second generation have employed analog CCTV cameras,which trans-ferred the analog video data to digital base stations where the video analysis,storage and retrieval takes place.Current semiconductor technology enables surveillance systems to leap forward to third generation systems which employ digital cameras with on-board video compression and communication capabilities.Smart cameras[1][2]even go one step further;they not only capture and compress the grabbed video stream,but also per-form sophisticated,real-time,on-board video analysis of the captured scene.Smart cam-

BRAMBERGER,RINNER,SCHWABACH

eras help in(i)reducing the communication bandwidth between camera and base station, (ii)decentralizing the overall surveillance system and hence to improve the fault toler-ance,as well as(iii)realizing more surveillance tasks than with traditional cameras. Typical tasks to be run in a surveillance system targeting traf?c surveillance include MPEG-4video compression,various video analysis algorithms such as accident detec-tion,wrong-way drivers detection and stationary vehicle detection.Additionally,traf?c parameters such as average speed,lane occupancy and vehicle classi?cation are often required.Since computing power is limited we may not allocate all tasks to the cameras. This paper presents a resource-aware dynamic task allocation system for smart cameras targeting traf?c surveillance.Since our target platforms are distributed embedded systems with limited resources,the task allocation has to be light-weight,?exible,scalable,and has to support real-time requirements.Therefore,surveillance tasks are not allocated to smart cameras directly,but to groups of smart cameras,so called surveillance clusters.We formulate the allocation problem as a distributed constraint satisfaction problem(DCSP) and present a distributed method for?nding feasible allocations.Finally,a cost function is used to determine the optimal allocation of tasks.We have realized this dynamic task allocation using heterogeneous,mobile agents which utilize their agencies and our em-bedded software framework to?nd the most appropriate mapping of tasks in a distributed manner

The main contributions of this ongoing research project include(i)the distributed agent-based approach for determining all feasible allocations of a DCSP,(ii)the evaluation of a complex cost function considering the limited resources of the embedded platform in detail,(iii)the integration of a mobile agent system in our embedded software framework. The dynamic task allocation has been implemented on our smart cameras(SmartCam[3]) which are comprised of a network processor and several digital signal processors(DSPs) and provide a complex software framework.

The remainder of this paper is organized as follows:Section1.1sketches related work. Section2describes the DCSP approach and focuses on?nding feasible allocations of tasks and the cost function.Section3and4present the implementation and the experi-mental results,respectively.Section5concludes the paper with a summary and an outlook on future work.

1.1Related Work

Agents-based load distribution has been an active research in the last decade.An overview of agent standards and available platforms is presented in[4].WY A(While You’re Away)[5]is based on the NOMADS mobile agent system.It provides dynamic load balancing by mobile agents,which utilize a central coordinator to move between work-stations.Qin et al[6]identi?ed the amount I/O-traf?c as an important issue for dynamic load balancing beside CPU-load and memory.Chow and Kwok[7]introduced the af?nity of an agent to its machine and used a credit-based scheme to determine the agents to be migrated by using a central host station.Agents used in surveillance systems were pro-posed by the MODEST consortium[8],where mobile agents were used to track vehicles using PC based platforms.

Distributed constraint satisfaction problems have been analyzed by Yokoo et al.in [9]for variable-split CSPs.The presented asynchronous backtracking algorithm used

DYNAMIC TASK ALLOCATION IN CLUSTERS OF EMBEDDED SMART CAMERAS autonomous agents,however,the high communication effort between the agents is im-practical for real-time systems.

2Task Allocation

The goal of the task allocation system is to ?nd a mapping of tasks to smart cameras which satis?es all requirements and is optimal with respect to a speci?ed metric,i.e.some cost function.Since the surveillance tasks have ?rm real-time requirements,the task allocation system has to take care that no deadlines are missed due to an overload of a camera or a camera’s subsystem,respectively.

The re-allocation of tasks may be necessary due to events,raised by hardware or soft-ware:(1)Hardware events usually originate from changed resource availability due to added or removed cameras,hardware breakdown,or re-usability of recovered resources.

(2)Software events are caused by changes to resource requirements due to changes in the task set of the surveillance cluster,or because of changes in the quality-of-service level (QoS)of tasks,i.e.due to detected events in the observed scene.

The allocation of tasks to smart cameras is done in two steps.(1)In the ?rst step,all fea-sible allocations of tasks to smart cameras (allocations where no real-time requirements are violated)are determined.(2)In the second step,the optimal allocation of tasks is chosen by using a cost function.

2.1Find Feasible Allocations

The determination of feasible allocations of tasks to smart cameras is a distributed con-straint satisfaction problem (CSP)[9].CSPs are de?ned by a set of n variables T ={T 1,...,T n },which hold values of a ?nite domain D ,and a set of k constraints C ={C 1,...,C k }.In our case,the surveillance tasks to be allocated to the cameras of the surveillance cluster are the variables,and the values they hold is the identi?er of the allo-cated smart camera D ={1,...,m }.Therefore,T i =d indicates,that task i is currently allocated to smart camera d .

Equation 1determines all permutations with repetitions A ,denoted by R ,from the m cameras and n tasks.The subsets of A are allocations of tasks to cameras,which have to be checked for feasibility.The constraints (cp.eq.4are formulated using two functions which determine the resource requirements and availability of the tasks and the cameras.req (Res,i )determines the requirements of task i concerning resource Res ,while avail (Res,d )determines the availability of the resource Res on smart camera d .

A =

R (m,n )={A 1,...,A p };p =m n (1)A j ={a 1,...,a n }|a i ∈D (2)R ={CP U,MEM,DMA }(3)C ={?A j ∈A :?d ∈D :?r ∈R :?a i ∈A j ?a i =d :( i req (r,i ))

Equations 1to 4de?ne a feasible allocation,i.e.,any allocation A which does not violate any resource constraint C .Note that in our implementation we also considered the DSPs

BRAMBERGER,RINNER,SCHWABACH

memory hierarchy and several parameters of the DMA subsystem (cp.resource costs in section 2.2.1).

However,this approach requires a central host.A more scalable solution is to distribute the CSP to several cameras in the surveillance cluster.

2.1.1The Distributed CSP

In order to distribute the determination of feasible allocations to all smart cameras,we split the domain D into m sub-domains D 1,...,D m ,each comprised of a single value D l =l .The set of tasks T remains the same,while the complexity of the constraints is reduced slightly (cp.equation 5).

To achieve all possible allocations of tasks,we choose ?0

C ={?A i ∈A :?A i ν∈A i :?a j ∈A i ν:?r ∈R :( j req (r,a j ))

Normally,CSPs require that all variables are included in the solutions of the problem,however,in our case this rule would imply that only partial allocations are valid,which include all n tasks of the surveillance cluster.In order to accept task allocations which do not include all tasks,the CSP rules have to be weakened.Even the allocation of no task is a valid allocation,which is reasonable,if a surveillance cluster contains less tasks than smart cameras.

2.1.2Merging of Allocations

Finally,m sets of partial task allocations are available (cp.eq.6),which have to be merged in order to ?nd all feasible task allocations.

P ={P 1,...,P m };P j ={P j 1,...,P j ν}(6)

Consequently,valid task allocations (1)do not allocate tasks to more cameras concur-rently,and (2)include all surveillance tasks.

The merging of partial allocations can be done in parallel,as long as rule 1is not violated.However,the ?nal merging process has to obey rule 2too.

The merging step of the partial allocations ?nally provides a set of f feasible allocations F ,from which the most appropriate allocation has to be chosen.

2.2Find Optimal Allocation

For ?nding the most appropriate allocation of tasks a cost-scheme is used.This cost-calculation scheme includes ?ve cost classes:(1)Resource costs (C R ),(2)data-transfer

DYNAMIC TASK ALLOCATION IN CLUSTERS OF EMBEDDED SMART CAMERAS costs(C T),(3)migration costs(C M),(4)af?nity costs(C A),and(5)quality-of-service costs(C QoS).These costs are calculated for every task on every smart camera in the surveillance cluster.Finally,the overall costs are accumulated as enlisted in the set of feasible allocations F.The following section presents the?ve cost classes,however,a more detailed description of the cost calculation can be found in[10].

2.2.1Cost Calculation

To compute the overall cost C T ot of a surveillance task,the cost values of the?ve cost classes are weighted and added:

C T ot=k R?C R+k T?C T+k M?C M+k A?C A+k QoS?C QoS(7) Resource Cost The resource costs are composed of four resources:(1)CPU load,(2) memory usage(including memory hierarchy on DSPs),(3)DMA utilization(including channels,reload-tables,and transfer complete codes),and(4)memory bus utilization.

Data-Transfer Cost We have de?ned two different types of data transfers:(1)Data entering or leaving the system will usually be transmitted using a network interface,while (2)intermediate data transfers operate internally via PCI bus or directly to memory.To consider the bandwidth of the used interface,the data-transfer cost C T is calculated as the amount of megabytes(MB)transferred,multiplied by a slowness factor depending on the bandwidth of the connection.

Migration Cost The migration of tasks between smart cameras is accounted for by the migration costs depending on(1)required data transfer,and(2)the algorithms downtime due to migration.

Af?nity Cost Af?nity costs express the priority of tasks inside a surveillance cluster (cluster af?ne tasks),and additionally provide the possibility to bind a surveillance task to a speci?c camera(scene af?ne task),as it is required for tracking tasks,for example. Quality-of-Service Cost In oder to allow a more?ne-grained control over the system, surveillance tasks may feature several quality-of-service(QoS)levels with different qual-ity levels and,therefore,different resource https://www.doczj.com/doc/291818022.html,ually it is desired to run all tasks with highest possible quality,therefore,this cost adds penalty costs if the task should be run with lower QoS-levels(e.g.due to insuf?cient resource availability for running at best quality).These penalty costs and the number of QoS-levels are de?ned by the task designer.

2.3Reallocation Scenarios

As mentioned before,two scenarios are possible,where a reallocation of tasks is neces-sary.However,we handle the scenarios differently to improve the performance of the system.

BRAMBERGER,RINNER,SCHWABACH

Increased Requirements/Decreased Resource Availability Increased requirements or decreased resource availability indicates a higher system load.Therefore,the number of feasible allocations will decrease,since less combinations of tasks will satisfy the con-straints of the CSP.Therefore,the value of the changed resource or availability is updated in the set of feasible allocations F.Finally,the feasible allocations are re-checked and infeasible allocations are removed from F.This re-checking process does not require to recalculate all possible allocations,therefore,the time required for determination of the new allocation is reduced dramatically.

Decreased Requirements/Increased Resource Availability In contrast to the upper scenario,the decrease of requirements or increase of resource availability can be seen as a reduced load of the system.Consequently,the number of both,partial and complete, allocations will increase.Therefore,all feasible allocations have to be re-computed.This approach leads to longer execution times,however,as the service quality will rise by this reallocation,the real-time deadlines are not as?rm as in the upper scenario.

3Implementation

For the implementation of our surveillance system we have chosen to use mobile agent technology.Mobile agents are most suitable for our system,since they support mobility, autonomy,and platform independence.

3.1Agent System

We have chosen to use the diet-agents1system since it includes all required features like mobility,autonomy,and platform independence and is reasonable small,which is in-evitable for embedded systems.

However,extensions to the agent system were necessary to add the support for the DSPs,and the decentralized management of the surveillance clusters.

DSP Agencies Agencies provide the environment for agents to live in.An extension to standard agencies is required to support multiple surveillance clusters.Therefore,a logical grouping of agents within the agency is desirable.We have implemented a cluster-information agent(CIA),which hosts local knowledge of other cameras belonging to the surveillance cluster,and provides lookup services for agents within the surveillance cluster.The integration of the DSPs into the agent system is done by one DSP integration agent(DIA)per DSP in the system.This DIA is a stationary agent,which manages message registrations,message dispatching,and binary downloads to the DSP.

Two basic agent types are deployed within the agent system:(1)Management-oriented SmartCam-agents,which are not included in the task allocation system,and(2)DSP agents,which implement algorithmic functionality to be executed on a DSP.Conse-quently,DSP-agents represent the tasks allocated by the presented allocation system. Therfore the next paragraph focuses on these agents.

1see https://www.doczj.com/doc/291818022.html,

DYNAMIC TASK ALLOCATION IN CLUSTERS OF EMBEDDED SMART CAMERAS DSP Agents DSP agents are an enhancement of standard agents,as they are the base of the task allocation system.Therefore,DSP agents include their requirements to the system and their cost parameters.Additionally,the cost calculation functionality is added to these agents.As the computational intensive parts of these agents are executed on the DSPs,the agents contain a binary,which is downloaded to the DSP and dynamically integrated into the system.Therefore,the agents maintains communication channels to the DSP by using the DSP integration-agent(DIA).

In case of migration the agent noti?es the DSPs task to stop computation and to trans-mit the persistent intermediate results to the agent.After migration the agent loads the binary to the DSP and transmits the stored intermediate results to the DSP.This enables the algorithm to continue computation or to use the intermediate results as new starting values,respectively.

3.2Task Allocation System

This section outlines the actions taken during a recon?guration of a surveillance cluster. Since our goal is a?exible,scalable and distributed implementation we have chosen to realize the task allocation system using mobile agents.

A new allocation is always initiated by a smart camera,either due to a hardware event (changes of availability)or a software event(changes of requirements).This camera initiates the reallocation by broadcasting the request-for-requirements to all agents,that encapsulate the surveillance tasks,within the surveillance cluster.After the agents receive this request,they broadcast their requirements to all smart cameras in their surveillance cluster.Additionally,all agents calculate their costs for every smart camera and transmit the cost values to the initiating camera.The smart cameras determine in parallel the partial allocations,which have to be merged in the next step.As the smart cameras are comprised of more DSPs,the partial allocations for the DSPs are merged.To further merge the partial allocations,the half of the smart cameras(determined during the creation of the surveillance cluster)create allocation-merging agents(AMA),which pick up the merged partial allocation and migrate to predetermined smart cameras(which comprise the other half of the surveillance cluster),where they grab the partial allocation,and merge both,the local and the included partial allocations.After the merging process, the agents migrate to the next smart camera,as de?ned by the?xed itinerary,and merge their partial allocation with the partial allocation provided by another AMA.This way the partial allocations are merged to a?nal allocation,whereas the last allocation merge is done on the initiating smart camera.The merging process corresponds to a binary tree, therefore ld(m)(where m is the number of smart cameras)sequential steps have to be done.

The initiating smart camera has to determine the most appropriate task allocation,there-fore,the costs,as submitted by the agents,are accumulated for every task allocation. Consequently,the task allocation with the lowest cost is selected as the new task alloca-tion.Since the selection of the most appropriate task allocation is based on the costs of the agents,the desired optimization goal like the number of migrations,degradation of quality-of-service,or balanced use of resources can be achieved by adapting the scaling factors of the cost classes.Finally,the new task allocation is broadcast to all smart cam-eras and agents,which may update their QoS level,or migrate to another smart camera

BRAMBERGER,RINNER,SCHWABACH

Figure1:The prototype of the smart camera

then.To enable the fast recon?guration by removing feasible task allocations,also the set of feasible task allocations is broadcast to all smart cameras in the surveillance cluster after the system has settled.

3.3Hardware Setup

To verify,test and evaluate the presented agent system,we have used two hardware plat-forms.A prototype of our smart camera and PCs equipped with DSP boards.

The prototype of our smart camera consists of an Intel IXDP425Development Board, which is equipped with an Intel IXP425network processor running at533MHz,as well as two Network Video Development Kits(NVDK)from ATEME.Each board is comprised of a TMS320C6416DSP from Texas Instruments,running at600MHz,with a total of 264MB of on-board memory.Image acquisition is done using the National Semiconduc-tor LM9618monochrome CMOS image sensor,which is connected to one of the DSP boards.

Due to the lack of additional smart camera prototypes,we are using two Pentium-III personal computers(PCs)running at1GHz,which are equipped with one Network Video Development Kit(NVDK)from ATEME.

4Experiments

In order to verify and evaluate the presented task allocation system,we used the smart camera prototype and two PCs.Consequently,we have setup a surveillance cluster con-taining these three“cameras”.We have used four different agent types for our experi-ments:(1)A MPEG-4video compression agent,(2)a stationary-vehicle detection(StVD) agent,(3)a vehicle count agent,and(4)a vehicle classi?er agent.The MPEG-4agent and the stationary-vehicle detection agent[2]are well tested and evaluated agents,how-ever,the vehicle count and vehicle classify agents only simulated real behavior.A total of six agents have been instantiated from these four classes,where we used three MPEG-agents(as every camera has to transmit a live video stream),and one agent of every other agent-type.

Table1enlists the execution times of the two stages of the task allocation.On the PCs

DYNAMIC TASK ALLOCATION IN CLUSTERS OF EMBEDDED SMART CAMERAS Case PC PC SmartCam SmartCam

Executed by JDK1.4.2JamVM JamVM Native/C++ 1a Partial Allocations(6ag.)20ms14ms79ms13ms

1b Partial Allocations(3ag.)14ms7ms55ms9ms

2a Merge Solutions(6ag.)766ms4852ms21363ms2360ms

2b Merge Solutions(3ag.)9ms5ms31ms4ms

3a Overall running time(6ag.)786ms4866ms21442ms2373ms

3b Overall running time(3ag.)23ms12ms86ms13ms

4Pruning of allocations(6ag.)14ms32ms172ms26ms

Table1:Running times of task allocation

we have used two Java virtual-machines:(a)Sun’s JDK1.4.2,which uses a just-in-time (JIT)compiler,and therefore reaches better performance values,while(b)the JamVM virtual machine2is designed as an interpreter.JamVM was also used on the prototype of the smart camera(c),and?nally we have implemented the core calculation functions (determine partial allocations,allocation merging)in C++to estimate the maximum per-formance(d).

Lines1a,2a,and3a represent the execution times if all six tasks were considered.In a second scenario we have removed the MPEG-4encoding agents from the surveillance cluster,and allocated them statically to the cameras.The results of the dynamic allocation of the resulting three agents is enlisted in lines1b,2b,and3b in table1.

Lines1a and1b show that the determination of partial allocations only requires a low amount of time.However,lines2a and2b enlist that the merging process requires most of the computation time.It can be seen,that merging using the JamVM requires up to 21seconds,which is unacceptable.The C++implementation,however,performs better, but also requires about2seconds for computation.The running time of the merging stage rises exponentially with the number of agents and cameras,since the number of feasible allocations rises exponentially,too(e.g.there are2880feasible allocations in case2a). Therefore,a reduction of feasible allocations would yield in better performance.

Lines3a and3b enlist the overall execution time,as the sum of?nding the partial allocations and the merging process.

Finally,we have also evaluated the performance of the task allocation system at in-creasing system load,in which case the previously determined allocations a rechecked for feasibility.Line4enlists the times required to recheck all2880allocations.Note that even running JamVM on our prototype delivers a new allocation within172ms,which is suf?cient.

5Conclusion

In this paper we have presented a resource-aware dynamic task-allocation system target-ing embedded smart cameras.Surveillance tasks are not allocated to the smart cameras directly,but to groups of smart cameras,surveillance clusters,within the tasks are allo-cated by the smart cameras in a distributed manner.We show that the task allocation can 2https://www.doczj.com/doc/291818022.html,

BRAMBERGER,RINNER,SCHWABACH

be formulated as a distributed constraint satisfaction problem(DCSP)and present a solu-tion for this DCSP.Therefore we combine the results of the DCSP with a cost function to retrieve the optimal allocation of tasks.Finally,we discuss the mobile-agent based im-plementation of the system and present results achieved by evaluation of the implemented system.

Future work includes(1)the further evaluation of the system,using more complex scenarios,(2)the tighter combination of?nding feasible allocations and calculating the costs for an allocation in order to delete expensive allocations at an early stage,(3)the test and evaluation of the system in real-world scenarios,and(4)the integration of learning agents into surveillance clusters,which in?uence the behavior of the system by adapting the cost function based on previous behavior,events,and actions.

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一、单项选择题 1.PN短码用于前向信道的调制,标识不同的________。B A.基站 B.小区 C.业务信道 D.控制信道 2.IS95 CDMA系统中使用的PN短码偏置共有个。A A. 512 B. 1024 C. 32768 D.32767 3.RAKE接收技术是一种_______分集技术。C A. 空间 B. 频率 C. 时间 D.极化 4.IS-95 CDMA移动台最多可以解调______多径信号。C A. 1个 B. 2个 C. 3个 D.4个 5.对于IS95 CDMA系统,寻呼信道数一般为。 A A. 1个 B. 7个 C. 8个 D. 12个 6.反向闭环功率控制比特的发射速率是______。D A. 1bps B. 20bps C.100bps D. 800bps 7.IS95 CDMA同步信道的比特率是。A A. 1200bps B. 2400bps C. 4800bps D.9600bps 8.从WASLH码的角度分析,IS95 CDMA系统的前向业务信道最多有个。B A. 55 B. 61 C. 64 D.48 10. IS95 CDMA小区的PN短码偏置在______信道发布。 A. 导频 B. 寻呼 C. 同步 D.前向业务 二、填空题 1.扩频通信理论中的香农公式为_______。 C=Wlog2(1+S/N) 2.在IS-95 CDMA系统中使用了三种扩频码,分别是_______、________和________。 PN短码、PN长码、WALSH码 3._______在反向信道用于扩频并区分不同的用户。PN长码 4. IS-95 CDMA系统的反向功率控制分为开环功率控制和_______功率控制。闭环 5.CDMA将导频信号分成____导频集、______导频集、_____导频集和剩余导频集。激活、候选、相邻 6.中国电信IS95 CDMA系统的工作频率为:_______(移动台发),________ (基站发);频道间隔为_______。825-835MHz 870-880MHz 1.23MHz 7.IS95 CDMA前向信道采用_____阶walsh码进行扩频。 8.IS95 CDMA手机的激活导频集中最多可以有_______个导频。 3 9.IS95 CDMA系统的导频信道使用_______进行扩频,同步信道使用_________进行扩频,寻呼信道使用__________进行扩频。WALSH0 WALSH32 WALSH1-7 10.IS95 CDMA系统业务信道采用________可变速率编码方式。8Kevrc 11. IS95 CDMA系统反向信道由______信道和反向业务信道组成。接入 12.一在通话状态的移动台测量到一个新的导频,其信号强度为-10dB(已知T_ADD=-13dB),此时移动台将申请将该导频加入__________。激活导频集 13. 一在通话状态的移动台测量到其正在使用的一个小区的导频强度为-16dB(已知

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