A Dynamic Load-Balancing Approach for Efficient Remote Interactive Visualization

Transcription

1 A Dynamic Load-Balancing Approach for Efficient Remote Interactive Visualization Chen-Han Kuo and Damon Shing-Min Liu Department of Computer Science and Information Engineering National Chung Cheng University,Chiayi,Taiwan Abstract In this paper, we present a dynamic load-balancing scheme in a networked heterogeneous computing environment and apply it to a Web-based scientific visualization system, whose efficiency requires a much stronger support than what is needed for visualizations merely on a single computer To achieve the overall system resource utilization and to determine the most cost-effective strategy for such computing applications, we adopt a distributed asynchronous pipeline approach and a dynamic load-balancing algorithm at server side to distribute tasks among the whole system To achieve efficient pipelining, it essentially requires a network with high bandwidth and low latency, an efficient interprocess communication mechanism on the network, and proper adaptation and partitioning of the visualization computations through the pipeline By taking each client s capability into consideration, this dynamic approach can dispatch some stages of the visualization pipeline to client for executing Besides, we also present a mechanism for selecting a computing unit that best suits for executing a specific visualization computation in the incoming job We have demonstrated a number of static and dynamic configurations in task allocation and functional partitioning in order to realize the target application Keywords: Scientific visualization, load-balancing, Java RMI, visualization pipeline, Visualization Toolkit 1 Introduction Over the past several years,visualization in scientific and medical research has become a rapidly emerging discipline aimed at developing approaches and tools to facilitate the interpretation of and interaction with large amounts of data The overall thrust of visualization science has been to provide researchers with the ability to explore hidden relationships,patterns,or characteristics of data in a new and deeper manner The field of scientific visualization usually involves the use of high-performance computers (such as supercomputers) to accommodate orders-of-magnitude variations in processing,memory and storage However, the price of such computers is often far beyond the reach of most researchers and practitioners Furthermore,even if high-performance computers can be used remotely,these is still a demanding need of flexibility and adaptability in utilizing network bandwidth and data access mechanisms The increased capabilities of workstations have been used to dramatically increase the size and complexity of numerical simulations As the size of the simulations increases,the size of the solution data sets also increases and can result in immense data sets representing the physical characteristics of a studied field Much research seeks to design a novel distributed software architecture that enables the construction of visualizations,despite geographical distribution of both resources and users Particularly,the recently widespread popularity of Web technology has created a new visualization paradigm The ease of Web development enables the implementation of a distributed environment as a visualization system It indicates that the Web and its associated browsers can serve as an easily used and powerful front-end to effective remote interactive visualization In it,computational resources are extended beyond the machine running the HTTP server by using UNIX interprocess communication mechanism or Java RMI for communication with rendering servers on other machines Nevertheless,very often the traffic on the Web is very bursty In these Web-based,computation intensive applications, we need a dynamic load-balancing algorithm to reduce the overall system response time by selecting the least loaded server and utilizing the whole system s resource properly In the Internet environment,there are two common kinds of computational models The one simply utilizes the comput /03 $ IEEE

2 ing resource on the server side and is called server-based The other one simply performs visualization tasks on the client side and is called client-based However,amoreflexible and effective design of a visualization needs to manage more carefully the computing resources on client and server, and also consider the influence of their interconnecting networks To this end,we often need a dynamic system that splits visualization tasks so as to distribute workload among all involved computing units (server and clients) according to the static and dynamic system parameters Here our aim is to develop an effective load-balancing scheme in such system In it,we are mainly concerned with the management of resource allocation and configuration in computing units, storage systems and networks 2 Previously related work In this section we review some previously related work in the areas of visualization system models and load-balancing algorithms 21 Visualization system models In [1],a visualization process is considered as a pipeline consisting of 1) data source; 2) data filter; 3) mapping; 4) rendering of the final results (see Fig 1) The model is useful to analyze a typical Web-based visualization system A client-server based,distributed scientific visualization system for medical image analysis and generation was presented by PW Liu et al in [2] They developed the system with several pre-allocated computation servers constituting the processor pool for the client In the model,the server side provides the communication module,which channels communications among the servers and host PCs and the servers themselves are the processes running in the processor pool On the client sides are the GUI and several manager modules They used command queue,communication module and pre-allocated processor pool to enable the distributed architecture [3] presents a system architecture describing how technology for Web-programming applications can be utilized for supporting interactive visualizations over the Web They developed a data visualization system using a set of Java Applets that interface with VTK It was the first attempt to use hybrid approach to construct Web-based visualization systems But they only split the renderer s computing load and distributed it to clients 22 Distributed load-balancing algorithms In order to execute arriving tasks with unpredictable workload rates,in [2] they built a load-balancing tree (similar to a Huffman s coding tree) by combining the static and the dynamic index,in which every node represents a computing processor The static index indicates the workstation s computing power while the dynamic index refers to the workstation s current workload On the tree,they can favorably adjust the indices representing the computation servers to minimize the amount of data communication needed for image generation This is one of the early reference models for controlling the data transmission overhead In some load-balancing algorithms,the outdated information will cause the herd effect which can push the system into a severe performance condition [4][5] because clients always catch the least loaded server Many algorithms have been presented to remedy this problem In [5], instead of sending a request to the least loaded server among n computing servers,a client randomly selects a size k subset of the servers and sends the request to the least loaded one in the subset As described in [4],the optimal subset size varies with the update frequency of load information In our system,we keep the transmitted message size between kernel and computing servers as small as possible,aiming to provide the load-balancing algorithm with the most time-critical information 3 ARCHITECTURAL DESIGN Our visualization system architecture (see Fig 2) is based on a client-server framework [6] that enables visualization over the Web It is designed and developed using Java RMI (Remote Method Invocation) and VTK (Visualization Toolkit) to achieve portability and modularity In the following,we will discuss the system components at server and client,respectively The server is designed to be able to serve multiple requests by integrating several computational components They include: 1 Kernel: The kernel receives requests when sessions are established and then sends the tasks to the public queue The kernel decides to which computing unit (CU) the jobs will be dispatched by executing the loadbalancing module 2 Load-Balancing Module: The load-balancing module performs to evaluate the load of each CU and to select the least loaded CU for executing next task 3 : In our system we develop the visualization work by use of an existing visualization library called VTK [7][8] VTK is an open source,objectoriented software system for 3D computer graphics, image processing and visualization,and it is freely available VTK comprises C++ and Java class libraries and several other interfacing utilities It also supports a wide variety of data structures and visualization algorithms /03 $ IEEE

3 stage 1 stage 2 stage 3 Raw Data Filter Mapper Renderer Output to client Direction of data flow Fig 1 Visualization pipeline Kernel Request GUI client Dataset preprocess Public Queue CU CU CU Response server GUI client RMI Server RMI Environment RMI Client Fig 2 System architecture /03 $ IEEE

4 4 Computing Unit (CU): Computing units are one or more computers connecting to visualization server by high-bandwidth networks They will get the required data,exploit appropriate VTK functions to process the data,and generate intermediate or final results 5 Public Queue: The public queue is where the client job requests will be placed and sent for further processing In client,there are two primary components: 1 a Java-based graphical user interface using AWT or Swing 2 a VTK function module Particularly client can also contribute its computing power to the system and acts as a CU 4 Approaches In our visualization system we propose a dynamic approach to operate on the Web-based visualization tasks We aim to decompose,distribute,and execute every individual stage of visualization pipeline on a least loaded CU Not only the CUs at server but also client itself should be considered as a computational resource in the system The information needed by the kernel for inserting the requesting tasks into the CU pool is collected from client as passed parameters under the authentication by RMI Running task codes are written in Java so that the visualization system can be developed on any hardware platform As soon as the kernel receives the request it inserts a job node (including all the information needed for executing the job) into the public queue The job node is later removed and dispatched to the least loaded CU selected by the load-balancing module to be executed (here task is always initiated from the first stage of the pipeline) After the CU finishes performing the first stage (including loading data from the data server and filtering) and produces the intermediate output (in our case,a vtk file),it creates and inserts another job node into the head of the public queue Then the new job is rescheduled by the load-balancing module again for the rest of the stages On completing every stage in the pipeline,the result (an image or a VRML file) is sent to the client 41 Load-balancing In the following,we will elaborate the concepts of load evaluation mechanism,the organization of CUs,updating policy and dispatching strategy 411 Load evaluation In our system,before the kernel can select one or more least loaded CUs to dispatch jobs,it has to estimate each CU s static system parameters and dynamic workload The static information includes the computing power expressed in term of MIPS in the processor,mb in memory size, and so on The static information is gathered while CUs or clients connecting to the server For a better estimation, dynamic information is evaluated with respect to some premeasured amounts For instance,we can sample with different data sizes and operational parameters beforehand,run and measure each pipeline stage s operation time on a single CU If the actual request value (ie isovalue) at run-time is not in sample values,we can utilize linear interpolation techniques to compute the data Or we can simply use the nearest value if the sampling precision is considered high enough 412 Updating policy The load information will be updated whenever a CU finishes executing the current stage and sends a new job node of next stage to the head of the public queue We always keep the most recent information possible to avoid the herd effect and ensure that the transmitted message overhead is small compared to the job request Owing to the frequent updates,we search for CU using hash table so as to increase the efficiency When a job is accepted or completed,the CU will adjust its load parameter by appropriate amounts (ie workload of the job) 413 Organization of CUs Aggregation of the static and dynamic load index can result in a single load parameter for each CU To choose a least loaded CU,instead of sorting CUs by traditional sorting mechanisms,we present a Ranging Buckets approach to increase the search efficiency and simplify the sorting steps Firstly,we classify CUs into several levels and each one is associated with a weighted value,the more powerful ones get larger values Furthermore,we separate the load parameter extent into several buckets and each represents a ranging segment In the initial phase,we place all CUs into ranging buckets depending on which bucket their static load parameter is in the range of In the updating state,a CU will change its load parameter and shift among the buckets A CU will move back to a higher range number bucket when it regains computing resource because of completing tasks Since we constantly shift the CUs positions in those buckets when updating the load information at run time,we can perform the search in the order of high to low range number bucket for the first nonempty bucket,and further explore /03 $ IEEE

5 in that bucket for the most efficient CU to execute the next task 414 Dispatching strategy Due to data migration overhead,we prefer to continue processing data on the same CU if possible To this end,we raise the priority of the CU which performs the previous stage and favor it when selecting the next CU candidate We do so by multiplying a factor to the load of the original CU and the factor is determined by the size of the intermediate output 5 Conclusions and future work [5] M Mitzenmacher, How useful is old information? in Proceedings of the Sixteenth Annual ACM Symposium on Principles of Distributed Computing ACM Press, 1997,pp [6] C-H Kuo,C-J Shiu,and D S-M Liu, A framework for Web-based scientific visualization, in Proceedings of the 2002 Computer Graphics Workshop,Tainan,Taiwan,2002,p 52 [7] W Schroeder,K Martin,and B Lorensen,The Visualization Toolkit, 2nd Edition,Prentice Hall,Upper Saddle River,New Jersey,1998 [8] W Schroeder, The VTK User s Guide Version 40 Kitware,Inc,2001 In this project,we have presented an effective loadbalancing scheme and applied it to our visualization system,which involves great computational and memory demands Our system divides each visualization tasks into several subtasks and assumes clients can contribute their computing resources too We evaluate using the static and dynamic load information of all computing units,and partition them into several ordered intervals so as to dynamically select a least loaded computing unit for executing each subtask We aim to achieve overall system effectiveness by exploring different computational techniques in a remote real time visualization environment In the future,we wish to port our system on Grid infrastructure By exploring techniques in Grid,we hope to get more precise information such as free memory size and processor utilization ratio, more globally and uniformly under authorization References [1] C Upson,T Faulhauber,D Kamins,D Laidlaw, D Schlegel,J Vroom,R Gurwitz,and A van Dam, The application visualization system: a computational environment for scientific visualization, IEEE Computer Graphics and Applications,vol 9,no 4,pp 30 42,July 1989 [2] P-W Liu, Distributed computing: new power for scientific visualization, IEEE Computer Graphics and Applications,vol 16,no 3,pp 42 51,1996 [3] A Alves,M Ferreira de Oliveira,R Minghim,and L Nonato, Interactive visualization over the WWW, IEEE Computer Graphics and Image Processing,pp ,Oct 2000 [4] M Dahlin, Interpreting stale load information, in Proceedings of the 19th IEEE International Conference,1999,pp /03 $ IEEE