Improved Load Balancing in Distributed Service Architectures

Transcription

1 Improved Load Balancing in Distributed Service Architectures LI-CHOO CHEN, JASVAN LOGESWAN, AND AZIAH ALI Faculty of Engineering, Multimedia University, 631 Cyberjaya, MALAYSIA. Abstract: - The advancement in the field of Distributed Object-oriented Computing has motivated the development of flexible telecommunication architectures for the current telecommunication market. The Object-oriented Distributed Service Architectures utilize the principles of distributed object-oriented programming such that these telecom architectures are open, object-oriented, distributed and allow rapid and flexible development of new applications on all kinds of software and hardware platforms. Despite the location transparency offered by these architectures, performance problems occur due to the distributed nature of the computational objects in several different physical nodes, which causes overloading in the nodes and unnecessary inter-node communication. In this paper, we propose a new load balancing algorithm, Random Sender Initiated Algorithm, that is able to improve the system performance of the distributed service architecture. This newly developed algorithm has shown a better performance as compared to the benchmark load balancing algorithms. Key-Words: - Object-oriented Distributed Service Architectures, Load Balancing, Performance, TINA, Computational Objects, CO distribution. 1. Introduction The latest telecommunications market has been influenced much by the advancement in the computer and Information Technology fields. The development in the area of distributed objectoriented computing has provided a greater room for flexible telecommunication architectures to be developed. The object-oriented distributed service architectures, utilizing the concept of distributed processing and object orientation, are expected to cater for rapid service creation and good management of telecommunication services. Examples of these architectures include Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA), and Open Service Access (OSA). The main idea of a good service architecture is the separation of the service logic and control from the actual call switching. Since the architectures are normally distributed, the service execution and control are carried out in several physical nodes in the network. In an object-oriented service architecture (see Fig. 1), a number of objects have to interact with one another during service execution, while instances of the same object type can be positioned on several different physical nodes. Different object distributions will result in different amounts of inter-node communication and different loading conditions on the nodes. This causes inconsistent system performance, an unresolved problem in object-oriented distributed service architectures (DSA). Three performance issues in DSA are [1]: Object Distribution, Overload Control Mechanism, and Load Balancing. The object distribution problem can be formulated as: How can the objects of the application be distributed in the network such that the service setup times and server loads can be optimised? The overload control problem may be devised as: What action should be taken during overload so that the Quality of Service (QoS) demands from both the services and users can be met. Consequently, the load balancing problem is very much related to a condition where given an object distribution, which object instance of a certain object type should be chosen during a task execution to ensure that no node is overloaded while another node has spare capacity [1].

2 a b c Network Fig. 1 An object-oriented distributed service architecture (DSA) Each of the performance issues above is a wide research topic and requires more in-depth study to explore possible solutions to improve system performance. Hence, in this paper, we only focus on the aspect of load balancing algorithm. Numerous papers have been published concerning load balancing and load sharing in computer networks. However, few of them discuss load balancing algorithm for DSA. This paper extends the work by Kihl et al [2] and McArdle et al. [3] with the aim to propose a load balancing algorithm which is able to perform better than the benchmark load balancing algorithms discussed in the above work. The proposed Random Sender Initiated Algorithm has been investigated under several simulation conditions and has exhibited better network performance than the benchmark algorithms, Shortest Queue Algorithm and Random Algorithm. The TINA architecture on which the simulation model is based on is described in the next section. 2. TINA The Telecommunications Information Networking Architecture (TINA), started in 199, is a proposed architecture to bring together two industries: telecommunications and information technology. The TINA Consortium (TINA-C) was formed in 1993, consisting of approximately 4 leading companies, including network operators, IT vendors and telecom vendors [4]. TINA combines a variety of new computing technologies and applies them coherently to a complete set of architectural principles to shape a future-safe telecommunication and information marketplace. TINA [5] provides a set of concepts and principles to be applied in the specification, design, implementation, deployment, execution, and operation of software for telecommunication systems. The overall architecture of TINA is c d d b partitioned into service architecture, network architecture, management architecture and computing architecture. More details on these architectures are available in Chapman and Montensi [6]. For this research work, the major architectures of interest are computing and service architectures. The service architecture defines a set of concepts and principles for the design, specification, implementation and management of telecommunication services. The computing architecture defines a set of concepts and principles for designing and building distributed software and software support environment. One important concept in TINA is session. A session represents the information used by all processes involved in the provision of a service for certain duration. Four types of sessions are available in TINA: a) Service session handles a single activation of service. b) User session deals with a user s interaction with a service session. c) Access session deals with user s authentication and authorisation. d) Communication session supports the communication needs of the service session and manages communication resources. Computational objects (COs) are programming and encapsulation units in TINA. The objects are able to interact with each other by exchanging information using two forms of computational interfaces. The operational interface allows the functions of the offering object to be invoked by other objects. The stream interface connects communication endpoints producing or consuming information flows, for example video or voice components. Another important concept in TINA is the Distributed Processing Environment (DPE). The DPE hides the complexity of distribution and heterogeneity from the service developer. Thus, the DPE provides access transparency, location transparency and failure transparency [6]. 3. Load Balancing Algorithms The main purpose of load balancing is to distribute the workload equally among all nodes in the network such that the situation where one node is idle or lightly loaded while others are heavily loaded, will not occur. Therefore, we expect that the average service time for a request would be minimised while the throughput is optimised by applying load balancing in a network. There are three different load balancing algorithms

3 implemented in our simulations - two benchmark algorithms and the proposed algorithm. The benchmark algorithms, Shortest Queue Algorithm and Random Algorithm, as described in [3] have been chosen for our investigation to enable performance evaluation of the proposed algorithm. The advantage of these algorithms is that they allow the theoretical upper bounds (or close to the upper bounds) of the performance measures to be achieved. However, each of these algorithms has its weaknesses in the real world implementation. The following briefly describes the benchmark algorithms: a) Random Algorithm: An instance of a computational object (CO) is chosen randomly and fairly from the node that contains instances of the particular CO type. The benefits of this algorithm is that it does not require any exchange of load information which results in less inter-communication cost and this algorithm is simple in term of implementation. However, the shortcoming of this algorithm is it is possible for a job to be sent to a node which is more heavily loaded even if there are other lightly loaded nodes around, as no load information is taken into account in the job assignment. b) Shortest Queue Algorithm: A node that consists of the instance of the particular CO type with shortest job queue will always be selected. This algorithm can be assumed to be the ideal algorithm if the service times for all COs are almost the same. Nevertheless, the DPE does not have all the load information of all other nodes in the real life. DPE has to get the information from other nodes from time to time and frequent exchange of information is cost-intensive. This becomes the performance bottleneck for the system itself. The newly proposed algorithm is as follows: a) Random Sender Initiated Algorithm: This algorithm is proposed to overcome the drawback of the Random Algorithm. It is implemented based on the thresholding concept. A CO will be chosen randomly from each node that contains instances of that CO type. If the selected node has less than threshold value T, jobs in its queue, then the job will be sent to it. However, if the job queue of the selected node is more than T jobs, other nodes (which consist of the instances of that CO type) will be probed one at a time to determine its job queue until a node with less than T jobs in its job queue is found. The job will be transferred to this node with less that T jobs in its queue. The poll limit is M which means that the number of probing made should be less than M. The advantage of this algorithm is that most likely a job will not be transferred to a heavily loaded node because the load information of a node is taken into consideration when making job assignment decisions. The algorithm also further ensures that the probing activity will not cause performance bottleneck through the setting of a polling limit. 4. Simulation Model A generic TINA service described by [7] is used for our investigations. Ten different CO types belonging to the user and retailer domains are used in this service. The objects involved in the user domain are Provider Agent, User Application (UAP), Generic Session End Point (GSEP), and Terminal Communication Session Manager (TCSM). The retailer domain consists of Initial Agent (IA), User Agent (UA), Communication Session Manager (CSM), Service Session Manager (SSM), User Session Manager (USM), Service Factory and User Session Manager (USM). New Arrival CO type USER of user 1 CO type USER of user 2 CO type USER of user N CO type SSM Distributed Processing Environment (DPE) Fig. 2 Simulation Model CO type USM CO type CSM CO type SF CO type AGENT Node 1 Node 2 Node Y The objective of this paper is to investigate the performance of the proposed load balancing algorithm against the benchmark load balancing algorithm in a TINA network. The DPE is the interface between the objects and the nodes, handling all communication of nodes and between nodes. The model is further simplified by modelling the COs. Firstly, as all the COs in the user domain normally reside on the same physical

4 node close to the user, these objects are modelled as one USER object. Secondly, the IA and UA COs are modelled as AGENT object. The other objects still remain the same as USM, SSM, CSM and SF. The simplified simulation model is shown in Fig. 2 and is based on the simulation model implemented in [2] and [3]. The network contains Y fully connected nodes with the assumption that the underlying network is extremely fast such that the switching times are insignificant. The new arrivals (requests) to the system are modelled as a Poisson stream and each request is randomly sent to one of the USER objects. The communication between different nodes require additional transmission times, which have been modelled as the added execution times for the sender and receiver nodes. Each node acts as a processor, which executes tasks in the system. The tasks that are unable to be served immediately are put into a FIFO queue in the node. The execution of tasks is based on a simple timesharing method that runs simultaneously in all nodes. The execution time for a task is highly dependent on the type of computational object involved and also whether the DPE service is used. If the CO uses the DPE service, the execution time will be multiplied with a certain factor. 5. Investigation This section discusses the simulation conditions and simulation parameters used in this paper. A focused CO distribution is used in the simulation, with node 1 and node 2 containing the USER object, node 3 contains the objects AGENT, USM, SSM, SF and CSM, node 4 consists of AGENT and USM objects, Node 5 contains the objects AGENT, USM and SSM and consequently node 6 has AGENT and SF. Table 1: Signalling Model and Execution Times Object type Number of normal signals Number of DPE service request Execution time (in ms) USER 1-1 AGENT USM 11-1 SSM CSM 2-4 SF 5-2 The focused or unbalanced distribution mimics the realistic situation of a distributed network, where nodes of different computing power exists, denoted in the DSA simulation by the number and types of CO it possesses. The load in the network is to be balanced across these nodes. A load balancing algorithm is effectively needed in focused distributions as balanced distributions do not require further load balancing. The number of signals and DPE requests that is necessary for a call setup and also the execution times for each object type are given in Table 1. The load balancing algorithms discussed earlier have been evaluated under three different simulation conditions with the simulation parameters as shown above. The simulation conditions are discussed as follows: a) Case 1: The load balancing algorithms is tested under the free condition where the communication cost between 2 nodes for wrapping and unwrapping of the communications protocol is not taken into account. For this simulation condition, we denote the algorithms as Random Algorithm (), Shortest Queue Free Algorithm (SQF) and Random Sender Initiated Free Algorithm (RSIF) respectively. The simulation for the free condition is necessary to evaluate the performance of RSIF against the SQF in the ideal condition without including the communication cost between different nodes and the query cost. It is assumed that the DPE has immediate information on the loading of other nodes. Another condition for Case 1 is that the job queue for each node in the network has infinite capacity (similar to the condition with no overload control). b) Case 2: The algorithms are tested under the expensive condition where the communication cost between two nodes for wrapping and unwrapping of communications protocol is taken into consideration. The purpose of having the expensive condition is that this mimics a real network, which incurs communication cost and query cost.. The algorithms are denoted as Random Algorithm (), Shortest Queue Expensive Algorithm (SQEX) and Random Sender Initiated Expensive Algorithm (RSIEX) respectively. Since the selects a node randomly to transfer a job, thus, the presence of communication cost and query cost has no effect on its performance. For this test case, it is still assumed that the job queue of each node has infinite capacity. c) Case 3: The algorithms (, SQEX, and RSIEX) are still tested under the expensive

5 condition However, in the real network, each node has its own capacity, so the node gets overloaded once it exceeds its maximum capacity. Therefore for this test case, the job queue for each node is limited to a finite capacity, J, to enable performance investigation for each algorithm to be carried out in a realistic finite queue length condition. 6. Results and Discussions The performance outcomes for the load balancing algorithms are represented by the mean completion time for one successful session (request) and also system throughput. Random Sender Initiated Algorithm is capable of performing as well as the SQF, which assumed to be the ideal algorithm. Mean Completion Time (s) SQEX RSIEX Mean Completion Time (s) SQF RSIF (a) Mean Completion Time per Session (a) Mean Completion Time per Session Percentage (%) RSIEX (b) Throughput SQEX Percentage (%) RSIF SQF (b) Throughput Fig. 3: Results for Case 1 The results for Case 1 are shown in Fig. 3. From the results, we observed that the throughput remains 1% for all load balancing algorithms as no sessions are rejected when the job queue is of infinite length. Evaluating the performance of the algorithms, we see that the performs badly, as compared with the SQF and RSIF. The reason for this is that the does not use the load information in each node to make the job transfer decision. Thus, when the number of new arrivals to the system increases, becomes incapable of distributing the load equally in the network. The results also exhibit that the proposed algorithm, Fig. 4: Results for Case 2 The results in Fig. 4 illustrate the mean completion time per session and throughput for Case 2, the more practical simulation condition. As the job queue of each node has unlimited capacity, the throughput for all algorithms still remain at 1% at any arrival rate. Nevertheless, we observe that the mean completion times per session are higher for the SQEX and RSIEX, especially at higher arrival rates as compared to SQF and RSIF (in Case 1). Fig 4(a) shows that the SQEX has become unstable and unable to give the performance readings when arrival rates are more than 4s -1. This is because when the cost for communication between different nodes and cost for querying other nodes for information have to be considered in the Shortest Queue Algorithm, the frequent exchange of load information that required by this algorithm to find a node with shortest queue to transfer a task across will overload the system. On the hand, the RSIEX is able to perform relatively stable and results in the lowest (best) mean completion time per session especially at higher arrival rates among all algorithms. The reason for

6 the better RSIEX performance than is that RSIEX takes the load information into account when making job transfer decision, thus it is able to select a less heavily loaded node to send the job to for execution. Comparing with the SQEX, RSIEX is less costly as it does not make as many queries of load information. SQEX has to explicitly probe the job queue in each node of the network before the DPE can make the job assignment decision to a node. M ean Completion Time (s) Throughput (%) R A SQ EX R SIE X A rrival Rate (1/s) (a) Mean Completion Time per Session SQEX RSIEX (b) Throughput Fig. 5: Results for Case 3 Fig. 5 shows the simulation results for the realistic situation. We observe from Fig. 5(b) that the throughput is no longer 1% for all algorithms at higher arrival rates due to the limited processing capacity of each node. When there are more arrivals to the system, some nodes in the system reach their maximum capacity and get overloaded. To protect the node, some sessions (requests) have to be rejected. Therefore, we see that the throughput decreases when the arrival increases. From the results, we also observe that RSIEX is able to produce the highest throughput at all arrival rates compared to the other algorithms. At the same time, RSIEX still achieves a relatively low mean completion time per session for almost all arrival rates in comparison with SQEX and. 7. Conclusions This paper describes the proposal of a newly developed algorithm which could be utilized in a distributed service architecture (DSA) environment. From the results achieved and discussed in the paper, it is seen that the Random Sender Initiated Algorithm is capable of better performance than the benchmark load balancing algorithms. The advantage of this proposed algorithm is that it takes the load information of the nodes into account before a job assignment decision is made, and at the same time the mechanism used is not as costly as compared to the Shortest Queue Algorithm when implemented in a realistic network. The performance issue in DSA still remains an open issue. Further research needs to be carried out in this field to identify and explore possible solutions for the unresolved performance problems to fully realise the benefits of DSA. Acknowledgement: The authors wish to thank Dr. M. Kihl and Mr. N. Widell from Lund Institute of Technology (Sweden), Dr. S.W. Lee and Dr. I. Chai from Multimedia University (Malaysia), who have contributed with invaluable comments and support in this research. References: [1] N. Widell, Performance Simulation of TINA Network, published Lic. Thesis, Lund Institute of Technology, 22. [2] M. Kihl, N. Widell & C. Nyberg, Load Balancing Algorithms for TINA Networks, 16 th Int. Teletraffic Congress, Edinburg, United Kingdom, 1999, pp [3] C. McArdle, N. Widell, C. Nyberg, E. Lilja, E., J. Nystrom, & T. Curran, Simulation of a Distributed CORBA-based SCP. 7 th Intl. Conf. On Intelligence and Services in Networks, Athens, Greece, 2, pp [4] P. Coppo & Y. Inoue, The Four Dimensions OF TINA Success, TINA Intl. Conf., Paris, France, 2, pp [5] H. Berndt, P. Graubmann & T. Hamada, TINA: Its Achievements and its Future Directions, IEEE Comsoc, Comm. Surveys, March 2, pp [6] M. Chapman & S. Montesi, Overall Concepts and Principles of TINA, TINA Consortium [7] R. Minetti & E. Utsunomiya, The TINA Service Architecture, TINA Workshop 96, Heidelberg, Germany, 1996.