An Active Resource Management System for Computational Grid*

Transcription

1 An Active Resource Management System for Computational Grid* Xiaolin Chen 1, Chang Yang 1, Sanglu Lu 2, and Guihai Chen 2 1 Department of Computer Science, Chuxiong Normal University, Chuxiong , China {chenxl,yc}@cxtc.edu.cn 2 State Key Laboratory of Novel Software Tech., Nanjing University, Nanjing , China {sanglu,gchen}@nju.edu.cn Abstract. In this paper, we propose an active grid resource management system supported by active networks for computational grid. First, we construct a scalable two-level resource management architecture. In this architecture, resources in the system are divided into multiple autonomous domains. In each domain, an active resource tree (ART) is organized with resources as its leaf nodes and active routers as non-leaf nodes. Resource information is disseminated into each active router in ART, and nodes in ART work cooperatively to discover and schedule resources. Communications between domains are done via the root of ART. Second, the resource trade between consumers and providers is carried out by an improved barter marketing model. The proposed model reduces the complexity and cost of the trade, and provides desired soft quality of service as well. We illustrate that the proposed system exhibits good scalability, autonomy, flexibility, and fault-tolerance. 1 Introduction A grid [1] is a very large-scale network computing system that can scale up to Internet size environment, in which kinds of computing, storage and data resources, as well as scientific devices or implements, are distributed across multiple organizations and administrative domains. The computing resources can be supercomputers, SMPs, clusters, desktop PCs, or even mobile computing devices such as PDA. In the grid s architecture, grid resource management system (GRMS) is the central component, which is responsible for disseminating resources information across the grid, accepting requests for resources, discovering and scheduling the suitable resources that match the requests from the global grid resources, and executing the requests on scheduled resources [2]. As a grid is geographically distributed, heterogeneous and autonomous in nature, the design and implementation of RMS is challenging. In this paper, we focus on GRMS for computational grid. The scalability of GRMS determines the scalability of the grid, and the scalability of GRMS is determined by the resource organization structure, resource scheduling structure and economic model for resource trade. In this paper, we propose an active resource management system (AGRMS) supported by active networks for computa- * This research was supported by the National Natural Science Foundation of China under Grant No , the National High Technology Development 863 Program of China under Grant No.2001AA and the National Grand Fundamental Research 973 Program of China under Grant No.2002CB H. Jin, Y. Pan, N. Xiao, and J. Sun (Eds.): GCC 2004, LNCS 3251, pp , Springer-Verlag Berlin Heidelberg 2004

2 226 Xiaolin Chen et al. tional grid. AGRMS employs a two-level resource management architecture and an improved resource barter trade model, which demonstrate good scalability, autonomy, flexibility, extensibility and fault-tolerance, and provide soft quality of service. The remainder of this paper is organized as follows. In section 2 background and related works are reviewed. Section 3 presents the assumed application characteristics. The design of ARRMS is described in Section 4. Section 5 introduces the current status of the implementation. Conclusion and the future work are finally presented in Section 6. 2 Related Work Most GRMSs for computational grid, such as AppLeS [3], Condor [4], Globus [5], and Legion [6], lack an economic model for resource trade. Because the motivation for contributing resources to grid has been driven by public good, prize, fun or collaborative advantage, it makes these GRMSs not scale well. [7] presents a number of arguments for the need of market or economy-based mechanisms in GRMS. Economic Model for Resource trade can be classified into barter trade and trade with currency as mediate. The disadvantages of the former are that it is less flexible and it has limitation on single goods at a time, while the advantages of the latter is that it is flexible and it is suitable for multiple goods exchange, but its process is complicated and the cost of trade is too high, which are caused by the introduction of electronic cash, electronic bank or trade market due to pricing, accounting and payment mechanisms in the process of trade. Distributed computing systems that adopt the latter trade mode can t scale well, because there are some centralized electronic banks or trade markets existing to reduce the complication of trade. In this paper, we proposed an improved resource barter trade model for computational grid, which overcomes the disadvantages of traditional barter trade and reduces the complexity and cost of the trade. Active network [8, 9] is a novel approach to network architecture in which customized computation can be performed on the fly at routers as active packets pass through them. The user-defined computation carried by an active packet may extend and modify the network infrastructure. Active networks are composed of active nodes, which are either active routers or active terminal nodes (hosts). Active nodes provide execution environment (EE) for user-defined computation carried by the active packets. When an active packet that contains active code or references to code arrives at an active node, the code is extracted or retrieved and executed. The code can modify the content of the packet or the state of the active node, or transmit one or more packets. The active node also provides soft-state cache for the active code to store active data and other state information. 3 Basic Assumptions To focus our research on grid resource management, we made a simple assumption that applications running on AGRMS are long running SPMD (Single Program Multiple Data stream). In the SPMD model, each processor runs same program and operates on part of the overall data. There are many applications that are characteristic of SPMD model such as operation of two large-scale matrix, crypto attack by brute force, and so on. Furthermore we do not care for the issue of communication and

3 An Active Resource Management System for Computational Grid 227 collaboration between processes that run in parallel on multiple machines to implement the tasks of application. The assumption implies the following issues: Only computing resources are considered, so as to simplify resource description. We use a binary (C, M) (representing estimates of compute and memory) to describe resource provided by a machine at a certain moment. According to the computational capacity and time the application needs to run in a single machine, the user divides application data into chunks, and requests GRMS to schedule application code and data to multiple machines with resource chunk as a unit. A resource chunk is represented by a triple (Cr, Mr, Tr), which means it takes Tr scheduling intervals to finish the processing of the application data of the chunk with the computing speed of Cr and the memory of Mr. Because the type of resource needed is monotonous, only the CPU time and memory are used, it is suitable to adopt the barter trade model. For the convenience of calculating the amount of resource and guaranteeing the fairness of trade, we make a unified price standard, the price of 1MIPS per 1 scheduling interval is 1, and that of 1K memory per 1 scheduling interval is 1. 4 Active Grid Resource Management System 4.1 Architecture According to the behavior of autonomous system in the IP network, resources are divided into separate autonomous domains. In the domain, a hierarchical structure is constructed where the root node stands for the root of domain, the mediate node stands for sub-domain, and the leaf node stands for computing resources such as supercomputers, clusters, PCs, and etc. In the tree-structured domains, all the nodes are active routers (AR) except for the leaf nodes, root node can be the border router of IP autonomous system or not, and the connection between any two nodes can be either physical or logical. Thus, an active resource tree (ART) is built. Among domains, the root node is used to communicate with other domains, and circuits can exist in topology of domains. Resource information is disseminated to soft-state cache of each AR in ART, and the cooperation of nodes in ART realizes recourses discovery and schedule, thus building a hierarchical resource management structure within the domain. As the resource broker of each user in a domain, the root of ART (root node) trades on load and repayment with other domains. In the process of trading, the trade entity is domain but not the users of domain. Thus, a scalable two-level hierarchical architecture of resource management system is formed (see Fig.1). 4.2 Resource Dissemination In the domain, resource manager reports resource state within a week to resource active router (RAR) which neighbors to the resource by the way of resource state matrix RS[7][48]. Each element RS[i][j] of the matrix RS refers to the total number of available CPU and memory resource in a scheduling interval j in day i after today. A scheduling interval lasts for 30 minutes. In the last scheduling interval of each day, resource manager updates matrix RS which is stored in AR s soft-state cache. Each element of matrix RS is 8 bytes and the size of RS is less than 3k. Suppose that RAR

4 228 Xiaolin Chen et al. Fig. 1. The architecture of AGRMS. has n ports, and RS k stands for resource state matrix reported by port K. RAR computes the sub-domain s resource state matrix RS, n RS = ( K = RS k 1 ). (1) then reports the result to parent node. Similar to the RAR, each node reports to parent node about the sub-domain s RS except for root node. According to the number of child nodes and the soft-state cache s free space, AR can store each matrix RS k reported by each child node, or store its sub-domain s RS to save space. 4.3 Resource Trade Model Users are taken as trade entities of both sides in traditional barter trade model, which results in the following problems: In order to get the debt back, debtee may communicate with debtors that distributed on multiple machines of different domains. Demanding thousands of debtors to schedule the job at the same time is impossible, thus the deadline of completing the job cannot be guaranteed. Due to the great gap of the time to schedule the job of debtee among different debtors, the communication between processes running in parallel on multiple machines is complicated. This brings difficulty in designing grid application. In order to solve these problems, we use domain as trade entity to loan and repay resource among domains, and use user and domain as trade entities to loan and repay resource in the domain. Among different domains, root node is the resource agent of every user in the domain. It loans and repays resource from the outside for its clients, and maintains a debt database that records the transactions with other domains. When root node receives resource request from other domains, it will schedule the whole domain s idle resource to provide service; when root domain node receives repayment request from other domain, it will check the debt relation and schedule the whole domain s idle resources to repay. In the domain, each user maintains a balance which is initialized set to zero. When the user obtains resources from either inside or outside of the domain, it will subtract the price of resource from the balance, accordingly, when user provides resources for request which comes from either inside or outside of

5 An Active Resource Management System for Computational Grid 229 the domain, it will add the price of resource to the balance. For example, Alice and Mike are users of domain A. They borrow resources valued 100, 300 from domain B, then domain B schedules user John to provide resources valued 40, 100 to Alice and Mike respectively, and schedules user Martin to provide Alice and Mike resource valued 60, 200 respectively. Later, John from domain B requests resource valued 400 outside domain B, then domain B s root node asks domain A to pay back 400. Domain A schedules its user Alice, Mike and Merry to provide resource valued 100, 200, and 100 respectively to repay. If the balance of each above user is 0 at the beginning, when the trade ends, the balance of user Alice, Mike, Merry, John and Martin will be 0, -100, 100, -260 and 260 respectively. The process of trade is greatly simplified by making use of the methods aforementioned. Meanwhile the cost of communication and scheduling for realizing the trade is reduced, and a large amount of idle resource inside the domain can be scheduled by root node at a scheduling interval. This improves the system s efficiency and scalability; Moreover, it can provide soft quality of service. 4.4 Resource Discovery and Scheduling Resource Request The resource request sent from the user includes the following information: Rr (Cr, Rr, Tr): definition of a resource chunk. number: the number of resource chunks. balance: the current balance of requester. income: the average daily income that can be earned by requester within a week. deadline: the job requested should be completed before one scheduling internal ends. If there s no requirement for deadline, deadline is 0. bandwidth: minimum network bandwidth of resource provider. When receiving resource request, RAR forwards it to parent node. Nodes in ART forward the request to parent nodes until it reach to root node. As agent of user inside the domain, root node requests resource from inside and outside of the domain for its clients. If root node requests resource in the domain with a deadline, it computes the amount of resource each child node should sell according to resource state matrix RS k, and notifies each child node to sell a certain amount of resource between some scheduling interval and deadline. After the mediate node of ART receives notification of selling resource, the node does the same as root node does. When the user receives the above notification forwarded by RAR, it decides how much resource to be sold according to its own selling strategy. Meanwhile, it preserves resource and replies to RAR with the amount of resource to be sold and the updated resource state matrix RS k. Every user may independently designs its own selling strategy which defines how much resource to be sold according to such factors as risk coefficient, demand of network bandwidth and so on. Risk coefficient is defined as: Risk coefficient = (number * price( Rr ) balance) / income. (2) When receiving the responses for all the notifications, the mediate node of ART records the amount of resource to be sold by each child node in soft-state cache, and

6 230 Xiaolin Chen et al. reports to parent node about the total amount of resource to be sold and the updated resource state matrix of its child nodes. Finally, root node will know how much resource is actually sold to resource requester. If the requester is inside the domain, root node will inform the requester about the amount of resource to be sold. Otherwise, root node will calculate and record liquidated debt between the domain and the requester, and send the requester a notification which carries the information of the amount resource to be sold and the liquidated debt. After receiving the notification signed by resource provider, the requester signs an agreement about the liquidated debt and sends it to resource provider. If the resource requester send request without deadline, root node will put priority on scheduling users whose balance is negative to sell resource; if the total amount of resource provided by all such users cannot satisfy resource request, the remains should be processed according to the way of that of deadline, and the deadline is the last scheduling interval of that week. In order to implement schedule priority of debtors, each user reports to RAR about its balance. Each AR in ART maintains a debt bitmap in soft-state cache to indicate which child nodes have debtors Resource Repayment When receiving resource repayment notification from other domains, root node checks the validity of the notification base on the debt database. If the notification is not valid, root node demands the requester to send the last liquidated debt signed by root node to verify the sum root node owes. If the repayment requires a deadline, root node process in the way described in If there s no deadline, root node process in the similar way described in The difference is: in the latter case, the user can decide the amount of resource to be sold according to its own selling strategy after receiving selling resource notification; however, in the former case, the amount of resource to be sold is the maximum between the resource that is needed to repay off the debt and the resource that can be supplied within one week. When receiving resource request, root node first notify every debtors within the domain to repay to avoid that some debtors can t earn money for a long time and can t ask for loans Resource Scheduling After receiving response for resource request from root node, resource requester will send active codes and data which realize computing task to root node. Root node will distribute the active codes and data to child nodes according to the amount of resource preserved by child nodes, and actions of the rest mediate node in ART is the same as root node s. After receiving active code and application data, resource provider will executes active code on assigned scheduling intervals Resource Authorization In the domain, any sub-domain can authorize a user of another sub-domain to consume its resource free or on discount. And it is the same case between domains. If some domain or sub-domain authorizes a user of another sub-domains to consume its resource free or on discount, its administrator will send authorization statements to active router which charges that domain. The active router will store these statements in soft-state cache.

7 An Active Resource Management System for Computational Grid Fault-Tolerance When root node fails, a new root node will be selected from its child nodes. After logically connecting all child node of the old root, the new root node asks them to report resource information matrix RS, debtor bitmap and so on. It also requests resource provider to report records of trade with other domains. After receiving the response, the new root node computes out debt relation between its domain and other domain. Thus, the state of the new root is the same as that of the old one before it fails. When the mediate node fails, process in the similar way as the previous one does. In addition, root node can store redundant resource proportion in soft-state cache. When some resource provider or mediate node fail, the nodes which detect these failures will report to root node about the amount of resource planned to be provided by these failed nodes. Root node allocates corresponding resource from redundant resource. 5 Implementation Status We are currently implementing AGRMS on the ANTS [10] toolkit. ANTS is a javabased active network execution environment developed by MIT. It has three components: Capsule, Active Node and Code distribution System. A capsule encapsulates the application data and user customized program. When the capsules pass through active nodes that can be routers, switches or end system, user-defined codes carried in the capsule are executed and routed to destination. Code distribution System provides on-demand code distribution using mobile code. Most components of AGRMS are implemented by ten types of capsule. Some components have been finished or nearing completion. Other parts remain being developed. 6 Conclusions and Future Work In this paper, we propose an active grid resource management system (AGRMS) supported by active networks for computational grid. AGRMS employs a two-level resource management architecture and an improved resource barter trade model, which demonstrate good scalability, autonomy, flexibility, extensibility and faulttolerance, and provide soft quality of service. To simplify the model and concentrate on resource management strategy, we have not discussed the communication between processes distributed on multiple machines within a grid in this paper. As a supplement, we are doing some research work from the following two aspects. First, we are designing a layered process communication service based on MPI (Message Passing Interface) to accommodate layered scheduling. Second, network bandwidth will be further considered in the resource model, and AGRMS will guarantee soft qualify of service also on bandwidth. References 1. I. Foster and C. Kesselman (editors), The Grid: Blueprint for a New Computing Infrastructure, Morgan Kaufmann Publishers, USA, Klaus Krauter, et al, A Taxonomy and Survey of Grid Resource Management Systems, International Journal of Software, Practice and Experience, Volume 32, Issue 2, Pages: , Wiley Press, USA, 2002.

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