A PACS is a high speed local computer network (LAN), which captures images from diagnostic devices .... summarising network utilization and network services.
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! !" # $% & ' () * +*!+* , ! %*+ + % - () * !$!** !$ *) (*! ! % *+!+ % !,* ' . *! & + In this paper, we address the congestion problems occurring at high speed networks, mainly because of digital image transfer. Such problems can be efficiently solved only by proper design and intelligent management of the related networks. To that end, we present a traffic source and network model of a typical Picture Archiving and Communication System (PACS), useful for a performance evaluation during the design phase. We proceed gradually from a simple to a complex source model, and present results and conclusions based on simulation studies of the system. Analytical solutions to the problem are also presented to complement time consuming simulations. We handle firstly the communication network as a queueing system, with Poisson image arrivals and constant service time. Next, we consider the case of finite buffers, necessary for realistic dimensioning. From the network management point of view, we specify "agent" objects, (extensions to standard Management Information Bases, MIBs), giving network management the possibility of monitoring and controlling the PACS environment. These objects are functional aggregates of variables such as the number of image servers and image display workstations, the mean value and the distribution of the image size, the capacity of the transport network, buffer sizes, etc.
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A Picture Archive and Communication System (PACS) is a digital system for archiving, retrieving and displaying of diagnostic images in a medical environment. In this paper, we present traffic source and network models of typical PACS's, for performance evaluation and optimal design of this system. In fact, every network design problem is essentially a resource management problem, and can be more efficiently handled, if the resource requirements can be accurately predicted. These requirements can be estimated by simulation or
T.CHIOTIS, T. KAROUNOS, B. MAGLARIS
analytical studies of traffic source and network models, based on measurements and knowledge of the problem domain. By using such models, one can solve design problems, where resources use is one of the most fundamental considerations. In high speed networks congestion problems may arise, mainly because of digital image transfers in multimedia applications.
To address such problems, we present a traffic source and a PACS network model,
and we evaluate the performance of the system, particularly its response time. Our network model consists of an image server and several client image viewing workstations over an FDDI broad-band network. Firstly, we handle the network as a queueing system, where the images interarrival times are exponentially distributed. Next, we consider finite buffers in order to address the dimensioning problem, and predict suitable buffer sizes. From the network management point of view, we identify critical parameters for monitoring and controlling a PACS environment. We study specific "agent" objects (extensions to standard Management Information Bases, MIBs), which are functional aggregates of variables, such as the number of image servers and image display workstations, image size, capacity of the transport network, buffer sizes, etc. We also present a framework for specification of PACS network and session management.
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In the medical community a growing number of diagnostic examinations are being generated via digital imaging instrumentation. Archival and retrieval of digital, high volume images is an essential requirement for PACS. A PACS is a high speed local computer network (LAN), which captures images from diagnostic devices, archives them, and offers them back to user workstations. Having images easily available allows medical data to be reviewed by several specialists, thereby sharing expertise.
Image Display Workstation
Broad-Band Network
Image Storage Base
Diagnostic Device
$ * / As shown in Figure 1, a PACS includes three classes of nodes interconnected by a broad-band network [1]. These three classes are: (a)
. This class of nodes includes all the diagnostic devices found in a modern hospital. Examples include computer tomography scanners, nuclear medicine systems, ultrasound scanners, digital subtraction angiography systems, and nuclear magnetic resonance (NMR) systems.
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PERFORMANCE EVALUATION AND MANAGEMENT OF PACS
(b)
. This class of nodes includes optical disks, magnetic disks and others high density storage media. These devices may follow a centralised or a distributed architecture, where storage facilities are distributed in several nodes.
(c)
. This class of nodes includes workstations with high resolution displays of 16 inches or even more, image processing capabilities, a sophisticated user interface and a local storage facility.
In spite of the fact, that this architecture is similar to conventional
client-server
computer
network
architectures, there are some particular attributes. The most fundamental are: (a)
The volume of data being transferred. Every single image is about 1024x1024x8 = 1Mbyte long. Furthermore, a clinician may ask more than one image for a single diagnosis. Thus, every transaction involves bulk transfers of data.
(b)
The users requirements for end-to-end response times. For a typical clinical environment, access and display of a medical image must be as small as a few seconds.
(c)
Data integrity and access security.
Due to the condensed information in medical images and the
confidentiality of medical records, a PACS environment must exhibit stringent specifications in error control and tight security management.
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Computers introduction to medical environments, and particularly, the use of PACS for archive and communication of images, has made traffic source and network models an essential requisite for their performance evaluation. Figure 2 presents our network model structure. It includes a broad-band local area network (LAN), the FDDI [2],[3]. The Fiber Distributed Data Interface (FDDI) is a 100 Mbps LAN. Using optical fiber as the transmission medium, the FDDI protocol is based on a timed token access method. We assume FDDI as the communication network, because of its advantages. Due to the use of optical fiber, FDDI offers high data bandwidth, security, safety, immunity to electromagnetic interference, and reduced weight and size. The dual ring design of FDDI offers superior reliability, availability and serviceability, even in the face of physical damage to the network [4]. Our network model includes N image servers and n image display workstations, as we may see in Figure 2.
FDDI
$ * 0 We took into account only image transfers from the image server to the image display workstations, since all other traffic components are (in comparison) negligible. The image sizes are constants at 1 Mbyte. They arrive at the network at exponentially distributed time intervals, with mean value proportional to the inverse of the desired network utilization.
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T.CHIOTIS, T. KAROUNOS, B. MAGLARIS
Every PACS solution may be evaluated using two basic performance criteria [5]: the end-to-end system response time and its reliability. The first parameter manifests how fast a single image can be transferred from source to destination, while the second addresses the delivery of information with enough accuracy to satisfy the end-user needs. N.Pronios and G.Yovanof [5] study interconnected PACS, where the second parameter is very important. We shall examine image transfer delay, without paying attention to the reliability.
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PERFORMANCE EVALUATION AND MANAGEMENT OF PACS
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Firstly, we consider that the image server loads the network in exponentially distributed time intervals, with a constant rate ë. If these intervals are independent, we have a Poisson model for image arrivals in the network. We can model the transport network as a M/D/1 queueing model, where the deterministic (constant) service time 1/ì is equal to the quotient of the length of an image to the capacity of the network:
1 ì
=
L ( bits )
(1)
C ( bps )
The average delay is given by:
1 Average Delay
=
ì
(2 - ñ )
2 (1 - ñ )
(2)
where ñ is the transport network utilization [6]. Like the M/D/1 analysis, we assume in our simulation a single image server and 8 image viewing workstations. We simulate FDDI as a token local area network (IEEE 802.5), with 100 Mbps capacity, 4 ms Token Holding Time (THT) and 4096 bytes long frame. We set the physical ring latency equal to 0, otherwise our simulator would produce a 100% utilization. Critical parameters to this model are the capacity of the transport network (100Mbps), the network utilization, which depends on the number of image viewing workstations, the frequency of image retrieval requests, the number of images per request (according to the diagnosis, more than one images may be requested), etc. Due to the importance of these parameters to the end-to-end image transfer time, we have to propose them as managed objects for PACS management (see 3 below). We use CACI Products Company COMNET II.5 simulator [7], [8]. Its building blocks are the network topology (nodes and their connecting links), network traffic (source, destination, size of messages, etc.) and network operations (e.g. strategies for choosing message routes, etc.). COMNET II.5 produces several reports, summarising network utilization and network services. Network descriptions are created graphically, through a highly intuitive interface that speeds model formulation. Figure 3 presents the end-to-end image transfer time versus transport network utilization, based on the M/D/1 model and simulation. Although we simulate a token ring network, our results show that the existence of a server flooding the network makes the M/D/1 model valid.
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The simulation results above were obtained for infinite station buffers. The buffer capacity is the total amount of space available at a node for frames that are waiting for transmission over the outgoing link (FDDI). If we use finite buffers, the end-to-end delay is higher, as shown in Figure 4. This simulation model uses different buffer sizes at the image server, and keeps utilization at 50 %, by adjusting the image interarrival times. We conclude that, there is a nearly optimal buffer size for the image server. Beyond a certain point there is no substantial improvement. The buffer size of the server, is therefore a critical parameter for PACS management.
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Apart from a proper design, intelligent management is a fundamental element for PACS functionality. Efficient management can be performed if appropriate managed objects are selected. The current approach to network management follows the familiar client-server model, or equivalently the manager-agent model. The agent is the server of management information to every responsible network manager. In order to find this information, through an interaction scheme, an agent accesses and manipulates different data structures in all communication protocol layers. The repository of all these data structures is called Management Information Base (MIB). Network management models, proposed by various standards bodies [9], state the desirability of early detection of faults before significant effects are felt by the user. Degradation of service may be detected by monitoring error rates. Threshold mechanisms on counters and gauges have been proposed to detect trends and provide warnings to managers. A proper network management platform must handle such alarms and be expandable to new managed objects [10]. We propose a few managed objects for PACS management derived from the above analysis. These objects may be put into two categories, as we may see in Figure 5: Objects for network management and objects for session management.
PACS MANAGEMENT
Network Management
FDDI Management (FDDI-MIB)
Session Management
Nodes Management
Server (MIB II)
Resource Monitor/Control Quality of Service (QoS) * buffer utilization * system response time * number of open sessions * number of blocked sessions * bandwidth utilization Clients (MIB II) - number of image requests per session - frequency of image requests
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This group of objects includes all the managed objects for FDDI management, and managed objects for the image server and image display workstations management. FDDI managed objects are not a standard yet. The TCP/IP community has specified a FDDI-MIB [11]. These definitions are, to the maximum extent possible, identical to those identified by the ANSI X3T9.5 committee [12]. The FDDI-MIB includes objects for all protocol layers. There are managed objects for SMT (Station Management), the MAC layer, the Paths, the Ports, the Attachments, the Chipsets, etc. For the image server and the image display workstations management, appropriate groups of managed objects may be found in MIB II [13]. This management information covers all the communication protocols and parameters used by a node (e.g. throughput, error rates, operation time, etc. per interface of a node). Due to its focal functionality, we should manage the image server in a more tight way than user workstations.
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For PACS session management there are two essential categories of managed objects. Managed objects, which monitor and control resources utilization and managed objects which monitor and control the provided quality of service while they facilitate its maintenance.
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T.CHIOTIS, T. KAROUNOS, B. MAGLARIS
Appropriate managed objects for resource monitor and control are:
An alarm should be generated, whenever the number of items to be processed has exceed the maximum allowable
An alarm should be generated, whenever this number exceeds a predefined limit.
An alarm should be generated, whenever the available transmission bandwidth has decreased. In addition a Network Management System monitors session objects like:
•
•
Proper managed objects for Quality of Service (QoS) session management are:
An alarm should be generated, whenever the elapsed time between the end of an inquiry and the beginning of the service is outside of acceptable limits, or whenever the service time exceeds a predefined limit.
An alarm should be generated, whenever the number of failed session set-up attempt exceeds a predefined limit.
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We conclude that simple queueing and simulation models provide efficient approaches to obtain PACS performance results. The most critical parameter is the transport network utilization, which must be controlled for every session over the PACS network. This parameter depends on several others, such as the number of viewing workstations, the frequency of image requests, the number of images per request, etc., which can be set by the manager to a suitable value, in order to achieve the desirable performance effects. Another important parameter for PACS design and PACS management is the buffer size of the server. Consequently buffer occupancy has to be monitored by the network management platform and guide planning of the system (e.g. memory upgrades). The PACS management consists of two functions, network management and session management. Network management involves FDDI and server and image display workstations management. Session management includes monitoring and control of resource utilization, in conjunction with quality of service. Apart from digital image transfers, we may have in a PACS environment:
•
Voice Reports
•
Integrated Patient Folders
•
On-line Consultation, possibly with real-time video sessions
In this multimedia network, additional source characterization definitions are needed in order to provide network management flexibility to negotiate connections, perform congestion control, traffic enforcement and resource allocation.
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PERFORMANCE EVALUATION AND MANAGEMENT OF PACS
8 [1].
*.* *!+* Jerome R.Cox, Jr., G.James Blaine, et al., Some Design Considerations for Picture Archiving and Communication Systems, Computer, August 1983.
[2].
"A Primer to FDDI: Fiber Distributed Data Interface," U.S.A., EC - H0750 - 42 LKG, Copyright 1991, Digital Equipment Corporation.
[3]. [4].
A.S.Tanenbaum, "Computer Networks," 2nd Edition, Prentice Hall, 1988. F.E.Ross, An Overview of FDDI: The Fibre Distributed Data Interface, IEEE JSAC, Vol.7, No.7, Sept. 1989.
[5].
Nikos B.Pronios, Gregory S.Yovanof, Effects of transmission errors on medical images, SPIE, Vol. 1446, Medical Imaging V (1991)/1.
[6].
Leonard Kleinrock, "Queueing Systems, Volume 1: Theory", John Wiley & Sons, Inc., 1975.
[7].
CACI Products Company, "COMNET II.5 User's Manual," La Jolla, California, 1988b.
[8].
Law, A.M., and W.D.Kelton, "Simulation Modeling and Analysis," Second Edition, McGraw-Hill, New York, 1991.
[9].
Information technology - Open System Interconnection - Systems Management: Alarm reporting function - ISO/IEC 10164/4 - December 1992.
[10].
F.Stamatelopoulos, K.Stathatos, T.Karounos & B.Maglaris, Cerberus Network Management System, ERSIM International Workshop, Crete, Greece, October 1992.
[11].
Case J., FDDI Management Information Base, SNMP Research, Incorporated, RFC 1285, January 1992.
[12].
American
National
Standards
Institute,
FDDI
Station
Management
(SMT),
Preliminary
Draft
Proposed American National Standard, American National Standards Institute, X3T9/90 - X3T9.5/84 89 REV 6.2, May 18, 1990. [13].
McCloghrie K., and M.Rose, Management Information Base for Network Management of TCP/IP based internets, RFC 1213, March 1991.
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