Functionality and performance issues in an agent based software deployment framework

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1 Functionality and performance issues in an agent based software deployment framework Mario Kusek, Kresimir Jurasovic and Ignac Lovrek University of Zagreb Faculty of Electical Engineering and Computing Unska 3, HR Zagreb {mario.kusek, kresimir.jurasovic, Abstract. Deploying and maintaining software in a distributed system includes software delivery, remote installation, starting, stoping, and modifying in order to configure or re configure a system according to user needs. This paper deals with an agent based framework where intelligent and mobile agents provide the means to implement a distributed system and enable its evolution by taking partial or full responsibility for software deployment tasks. Agents are organised into agent teams, where one agent is the team leader responsible for planning, while the others are operational agents capable of executing a defined plan. The formal model, as well as functionality and performance issues, are elaborated. Special attention is paid to deployment strategies and their optimization, while taking into account characteristics of distributed system nodes and the network connecting them. Simulation-based evaluation of agent serialization, migration and deserialization parameters, and their influence on overall performance, is included. 1 Introduction Establishment and maintenance of distributed systems includes operations that provide software delivery to system nodes, remote installation, starting and stoping, and version handling. Furthermore, modification of software after delivery must be supported in order to correct faults, improve performance characteristics, adapt to a changed environment or improve maintainability. Distributed system software should be configured initially, and re configured if and when corrective, perfective, adaptive or preventive actions are required. Software deployment and maintenance strategies are important in order to configure or re configure distributed systems according to their requirements (i.e., where and what), and to achieve minimum configuration and re configuration setup times (i.e., when and how), taking into account the network and node characteristics, as well as operational conditions. Intelligent and mobile agents provide the means to implement a distributed system and enable its evolution, by taking partial or full responsibility for these resource intensive and costly tasks. Regarding system performance, configuration and re configuration should have minimum influence on system operation.

2 Consequently, software deployment and maintenance should be optimised in order to achieve acceptable total execution time. In this paper, functionality and performance issues of an agent-based framework are elaborated. Furthermore, system parameters related to agent migration and agent activation/deactivation, and their influence on overall performance, are discussed. The paper is organized as follows: Section 2 describes formal model of an agent based software deployment framework and introduces a prototype system MA RMS. Case study dealing with system parameters describing agent serialization/de serialization time and communication link capacity, including results of simulations is presented in Section 3. Section 4 concludes the paper. 2 The formal model of an agent based software deployment framework Software deployment has to take into account the functionality that a distributed system provides (i.e. services), as well as characteristics of nodes (i.e. capacity, operating system, agent platform, installed software) and the network connecting them (i.e. topology, link bandwidth). Furthermore, procedures specific to the system must be considered. Examples of distributed systems faced with such problems are network centric applications and networks themselves, with hundreds or thousands of nodes, e.g. access points in local networks or base stations in mobile networks [1, 2]. From the formal standpoint, a distributed system is defined by the tuple (S, N) where S denotes system nodes, S = {S 1, S 2,..., S i,..., S ns }, and N denotes a network connecting them. System nodes should be configured in order to provide support for a set of required elementary services, ES = {es 1, es 2,..., es j,..., es nes } provided by the system, i.e., a defined subset of elementary services s i = {es i1, es i2,..., es ij,..., es in } should be supported by each node S i. Distributed system configuration is defined with an initial set up where software components corresponding to elementary services from ES are delivered and activated at each node S i, according to its predefined functionality, s i. Re configuration is required when the ES changes (i.e. new elementary service is introduced or an existing one updated), when node S changes (i.e. new node is connected or an existing one functionally upgraded) or when network N changes (i.e. network topology or link capacity changes). An agent based software deployment system is organised as a multi agent system, A SD, which shares the set of nodes, S and the network N with the system under consideration, (A SD, S, N). A SD includes a planning agent a P and a team of operational agents, {a 1, a 2,..., a i,..., a na }. The planning agent a P is responsible for planning software deployment, allocating deployment tasks to operational agents in the team and co ordinating them. Operational agents are multi operational, capable of executing one or more deployment tasks to one or more nodes. Each individual task corresponds to an operation, such as software delivery to a system node, remote installation, starting or stoping, replacement/modification of the existing software, version handling and others.

3 Distributed System (S,N) Software Deployment and Maintenance MA-RMS Request Information Model of (S,N) ES, S, N, S i Execution a P-RMS Deployment task ES, S, N, S i Strategy selection/ optimisation Task allocation a 1-RMS a 1-RMS a 1-RMS 1 na Team of a i-rms Fig. 1. Service deployment framework MA RMS When deploying software components on a large number of nodes at remote locations using software agents, one of the major challenges faced is determining the number of operational agents to use and distributing the required tasks among them. Software deployment efficiency depends on agent team organisation and the strategy used. The simplest strategy is when a single agent executes all tasks on all nodes, while the most promising one is when team size and task assignment are optimized according to the system and network conditions. As an example, the role of planning agent a P, when ES should be re configured to ES (ES ES), is defined as follows: (1) Identify a subset of nodes S S that should be re configured, i.e. for which s i s i; (2) Define deployment tasks required for re configuration of each node S i S, for each elementary service es ij s i ; (3) Define a sub network N N which connects nodes from S ; (4) Collect node and network parameters for (S, N ); (5) Decide which deployment strategy to use, organize the agent team and let them work; (6) If deployment results with ES, all tasks were completed successfully. Otherwise the resulting configuration is ES x ES, and the procedure for re configuring ES x ES should be repeated until completion. Related work includes different aspects of the agent based approach for software (re)configuration, updating and maintenance [3 5]. Using agents in service oriented architectures has been studied mostly for service composition and orchestration [6]. The practical implementation can be difficult for complex distributed systems, from functionality and performance point of view. Some preliminary results show that rational agents based on BDI paradigm can cope with a complexity issues related to frequent changes, deployment errors or inconsistency and rollback ability. A service deployment framework, called the Multi Agent Remote Maintenance Shell (MA RMS) [4, 7] shown in Fig. 1, is based on a formal model of a mobile agent network [8].

4 The main goal of the planning agent, a p RMS, is to optimize the deployment strategy. This goal is divided into two sub goals. The first sub goal is to gather and analyse information regarding the nodes and the network. The second sub goal is to select a previously defined fixed strategy or an optimized one, taking into account the node and network characteristics and conditions. This goal consists of a set of plans, defined by triggering conditions and plan implementation. Triggering conditions define when the plan is applicable, while plan implementation activates operational agents according to the selected strategy. Planning agent a p RMS selects the predefined strategy which gives minimum completion time. The strategies are the following: (R1) a single agent executes all services on all nodes; (R2) an agent executes a single service on one node only; (R3) an agent executes all services on one node only; (R4) an agent executes a specific service on all nodes; (R5) services are assigned to the agents in order to exploit maximal parallelism in service execution (mutually independent services are assigned to different agents, in order to execute them simultaneously); (R6) a hybrid solution combining R4 and R3. An agent is responsible for a specific service on all nodes while other agents execute the remaining services each on a different node. Strategies R1 R6 are static, i.e., they always generate the same distribution of tasks among agents for a specific network topology, regardless of available link bandwidth or other conditions. An additional strategy, R7, based on a genetic algorithm for agent team optimization uses the network topology and link bandwidth as input parameters [9]. Operational agents, a i RMS, support full functionality required for software deployment and maintenance on nodes in an IP network. These agents know how to migrate, install, configure, start, stop and uninstall software. A mobile agent network simulator is applied to access performance issues [10]. It can simulate agent execution in different networks (i.e. nodes, switches and links), different operation execution times and different strategies, including the genetic algorithm. 3 Evaluating performance of the agent based software deployment solution The basic performance measure evaluated in the paper is the total execution time of the software deployment and maintenance process. The first group of parameters influencing this process describe the software under consideration (ES) and its distribution to system nodes (S). This distribution is defined by software configuration requirements for each node (s i ). The second group of parameters is related to agents, their complexity (i.e. the operations assigned to them, their size) and their life cycle, as shown in Fig. 2. Agent migration between nodes S i and S j when peforming some deployment task is defined by an agent transfer time, as follows: T ij = t pi + t ij + t aj where t pi is the agent preparation time needed for agent serialization at the originating node S i ; t ij is communication time needed for agent transfer from S i to S j ; and t aj is the

e. the time an agent spends in a queue at S j waiting execution); and t sj is the serving time (i.e. the time needed for execution at S j ).

5 Fig.2. Agent transfer and holding time agent activation time which includes agent reception and de serialisation at the destination node. Handling of some deployment task at node S j is described by an agent holding time: t qj = t cj + t wj + t sj, where t cj is the inter agent communication time (i.e. the time an agent spends at node S j searching for the result of a deployment task performed by another agent); t wj is waiting time (i.e. the time an agent spends in a queue at S j waiting execution); and t sj is the serving time (i.e. the time needed for execution at S j ). The third group of parameters describes the characteristics and conditions of a distributed system that affect software deployment and maintenance. The basic node characteristics taken into consideration are node processing power influencing serving time, and agent serialization and de serialization (t sj, t pi, t aj ). All deployment tasks are treated the same with respect to t sj, which equals t. The basic characteristics of the network include its topology and available link bandwidth. For analysis purposes, all parameters related to timing are expressed in discrete time units, t. This approach provides a tool for analyzing the performance of a single agent. When extended to a multi agent system and a mobile agent network [11], it allows for analysis of the total execution time required to fulfill a software deployment and maintenance request. The total execution time is the time required by an agent team to execute all deployment tasks on all nodes. The case study that follows deals with the impact of agent serialization/de serialization (t pi and t aj on Fig. 2) and available link bandwidth on the total execution time. The sub network under consideration consists of 12 nodes and 6 switch components connected with a link of certain bandwidth (Fig. 3). Node S0 is used as a staring node hosting the planning agents and from which all agents start their execution. The set of elementary services that should be deployed to all nodes except S0 consists of 4 services. Three network scenarios are simulated by varying two parameters: a) the available link bandwidth, and b) the ratio of agent execution time to agent serialization/de serialization time. The aim of the simulation is to explore the influence of bandwidth variation and agent complexity on strategy selection. In the first scenario the bandwidth of the links in the network was set to 5 Mbit/s, in the second to 10 Mbit/s and in the third to 100 Mbit/s.

6 Fig. 3. Network topology Total execution time [ms] R1 R2 R3 R4 R5 R6 R7 Distribution strategies Service execution time to serialization/deserialization time ratio Fig.4. Scenario 1: Simulation result for link bandwidth of 5 Mbit/s In all scenarios, the service agent execution time to agent serialization/deserialization time ratio varies from 1 to the 128. The results of the simulations are shown in Fig. 4 (Sc. 1), Fig. 5 (Sc. 2) and Fig. 6 (Sc. 3). The graphs for all scenarios and all strategies show the same basic characteristic: growth of the total execution time as the ratio of agent execution time to agent serialization/de serialization time increases. The reason for this is evident: holding time increases with agent complexity. The difference between results obtained from different strategies is the speed of its growth. Each agent should be activated before execution and the strategy R3 with a single agent responsible for one node only is the best one in this respect. Such a conclusion will lead to the planning agent to define the triggering condition for R3 as low bandwidth & wide execution time span and make it applicable after detecting such situations (Sc. 1 and 2). Strategies R1, R4, and R6 have poor performance in slow networks because of intensive agent migration, while strategies R3 and R7 give acceptable total execution times (Sc. 1 and 2). With higher bandwidth available, strategies R2 and R6 become comparable to R3, because migration time has far less impact due to fast links (Sc. 3). Furthermore, faster networks compensate with higher agent ratio, making complex agents more attractive. Strategy R5 requires an triggering condition parallel capability.

7 Total execution time [ms] R1 R2 R3 R4 R5 R6 R7 Distribution strategies 1 10 Service execution time to serialization/deserialization time ratio Fig.5. Scenario 2: Simulation result for link bandwidth of 10 Mbit/s Total execution time [ms] R1 R2 R3 R4 R5 Distribution strategies R6 R Service execution time to serialization/deserialization time ratio Fig.6. Scenario 3: Simulation result for link bandwidth of 100 Mbit/s Optimized strategy R7 shows good performance in the worst conditions, i.e. in the case when the available bandwidth is low (Sc. 1 and 2). This discussion covers performance analysis assuming an ideal environment where software deployment and maintenance is completed as required. In reality, some agents might not perform as required due to network congestion, unavailable nodes, and errors or faults, leading to partial fulfilment of software deployment tasks. When detected, such events can be used as additional triggers for optimized strategies. 4 Conclusion An agent based framework for software deployment includes a multi-agent system with a planning agent and a team of operational agents. The intelligent planning agent needs to know the current state of system nodes and the network. Using this information, it can decide which strategy to choose in order

8 to obtain better performance characterists, i.e. acceptable total execution time. Simulations were performed to evaluate deployment strategies from the performance perspective. Three network scenarios were simulated with variations in the link bandwidth and the ratio of agent execution time to agent serialization/deserialization time. The obtained results demonstrate how bandwidth variation and agent complexity influence the total execution time. Future work will include further definition and implementation of the planning agent as a rational agent based on the BDI paradigm. Further on, robustness issues will be studied in order to exploit the full potential of an agent-based software deployment framework in a large scale environment. Acknowledgments. This work is part of research project Content Delivery and Mobility of Users and Services in New Generation Networks, supported by the Ministry of Science, Education and Sports of the Republic of Croatia. References 1. Sherif, M.H., Ho, S.: Evolution of operation support systems in public data networks. Computers and Communications, IEEE Symposium on 0 (2000) Houssos, N., Alonistioti, A., Merakos, L., Mohyeldin, E., Dillinger, M., Fahrmair, M., Schoenmakers, M.: Advanced adaptability and profile management framework for the support of flexible mobile service provision. Special Issue on (R)Evolution towards 4G Mobile Communication Systems 10(4) (August 2003) 3. Bettini, L., De Nicola, R., Loreti, M.: Software Update via Mobile Agent Based Programming. In: Proceedings of SAC, Special Track on Agents, Interactions, Mobility, and Systems, ACM Press (2002) Jezic, G., Kusek, M., Desic, S., Caric, A., Huljenic, D.: Multi agent remote maintenance shell for remote software operations. In: Grid Services Engineering and Management. Volume 2774 of LNAI., Springer-Verlag (2003) Dalpiaz, F., Giorgini, P., Mylopoulos, J.: Software self-reconfiguration: a bdibased approach. Proceedings of the 12th The Eight International Conference on Autonomous Agents and Multiagent Systems (2009) 6. Ventakesan, V., Portchlevic, V.: Architecture for services orchestration using bdi agent. ( ) 7. Lovrek, I., Caric, A., Huljenic, D.: Remote maintenance shell: Software operations using mobile agents. Proceedings of the International Conference on Telecommunications (2002) Sinkovic, V., Lovrek, I.: Generic model of a mobile agent network suitable for performance evaluation. In: KES. (2000) Jurasovic, K., Kusek, M.: Optimizing service distributions using a genetic algorithm. In: KES 08, Springer-Verlag (2008) Kusek, M., Jurasovic, K., Jezic, G.: Verification of the mobile agent network simulator - a tool for simulating multi-agent systems. International Journal of Software Engineering and Knowledge Engineering 18(5) (2008) Sinkovic, V., Kusek, M., Jezic, G., Lovrek, I.: Performance evaluation of a mobile agent network using network calculus. In: KES 08, Berlin, Heidelberg, Springer- Verlag (2008)

Performance Evaluation of Mobile Agent Network

Performance Evaluation of Mobile Agent Network Ignac LOVREK and Vjekoslav SINKOVIC University of Zagreb Faculty of Electrical Engineering and Computing, Department of Telecommunications HR-10000 Zagreb,