(OTMA) framework for simulating large-scale ... - Semantic Scholar

A Generic Optimized Time Management Algorithms (OTMA) Framework for Simulating Large-Scale Overlay Networks

Syed S. Rizvi and Khaled M. Elleithy Department of Computer Science, University of Bridgeport, Bridgeport, CT 06604, USA {srizvi,elleithy}@bridgeport.edu

Distributed algorithms, discrete-event Keywords: simulation, overlay networks, synchronization algorithms.

achieving synchronization among huge number of nodes. This much needed synchronization results in high end-to-end delay, slower processing speed, large memory retirements, and an excessive amount of transmission overhead. Selection of nearby peers, redundant storage, efficient search/location of data items, data permanence or guarantees, hierarchical naming, trust and authentication, self-organizing, massively scalable, and load balancing are few of the many reasons for growth in the research and development of overlay networks [1]. As a result, the research and the developer community on distributed systems, and in particular on overlay networks, needs tools and frameworks for evaluating and developing their own protocols and services, as well as against other protocols with the same preconditions [2]. Packet-level simulators (e.g., NS-3 [3], OMNET++ [4]) and high-level simulators (e.g., p2psim [5], PeerSim [6], FreePastry [7], PlanetSim [8]) are example of some of the existing DES based simulators and frameworks. As the demand for developing the new protocols and services for overlay networks and peer-to-peer applications increases, the necessity for an efficient distributed DES framework for modeling and simulation is becoming apparent. Several challenges occur with designing an efficient DES framework for large-scale networks. Firstly, traditional, packet-level DES simulators have a true high cost in time and computer resources [2]. Currently, DES simulators like NS-3 and OMNET++ do not scale to thousands of nodes in a large overlay network [9]. Secondly, existing high-level DES simulators such as p2psim [5], PeerSim [6], FreePastry [7], and PlanetSim [8] provide better performance and enable big scale network evaluations but they do not support application level extension which is an essential element in the development and deployment of new protocols and services for large-scale networks. As a result, developing and simulating new overlay services and protocols, using the above mentioned DES simulators is not possible. Thirdly, as far as we know, there is no common DES framework available that combines the optimized forms of the underlying synchronization protocols. There are several reasons for this. In most DES simulators for large-scale networks, optimization of the

Abstract Recent evolutions in wireless networks will require more efficient use of the underlying parallel discrete-event simulation (PDES) synchronization protocols to accommodate the demand for large-scale network simulation. In this paper, we investigate underlying synchronization protocols to improve the performance of large-scale network simulators operating over PDES systems. We begin by proposing a generic optimized time management algorithms (OTMA) framework that combines the improved forms of synchronization protocols on a single platform. Particularly, for the proposed OTMA framework, we use the layered architecture approach to combine the optimized forms of conservative and optimistic time management algorithms. To support the implementation of the OTMA framework, a new m-LP (logical process) simulation model is proposed along with the varying parameters network topology that can show the implementation of different components of discrete-event simulation (DES). 1. INTRODUCTION Next generation networks typically consist of heterogeneous access networks with a high mobility rate of user terminals. A foreseen future of these networks could be the support of overlay networks by dedicated devices in the access networks to alleviate user terminals from overlay traffic. There has been much interest in emerging overlay networks because they provide a good substrate for creating largescale data sharing, content distribution and application-level multicast applications. Existing distributed DES systems do not support simulation of networks with these special properties. Conventional DES systems offer only limited support to collect statistics and has a very simplified underlying network layer without consideration of bandwidth and latency costs. This makes it difficult to simulate heterogeneous access networks and terminal mobility. Most of these DES systems use either optimistic or conservative algorithms as an underlying protocol for

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underlying protocols is not given a first class status. Both packet-level and high-level simulators use the TMA as an underlying synchronization protocol in its original form. Generally at the authoring stage, synchronization protocols such as conservative NMA and optimistic Time Wrap algorithms are imported in and complex functionality is added by external scripts or programs. This may not only be because of the absence of a common DES framework but also because on the lack of analytical and quantitative models present for these algorithms. Therefore, designing a generic synchronization framework that combines the optimized form of the underlying algorithms such as NMA and Time Wrap is challenging. Finally, optimizing the existing TMA requires the development of analytical and quantitative models for each protocol is also an open research problem. In addition to the above mentioned challenges, building a unified DES framework that can combine the optimized forms of conservative and optimistic algorithms on a single generic framework and developing their analytical and quantitative models is not possible unless there is consensus between network/protocol/services developers on a common platform and technology. This problem does not exist in traditional DES systems that model relatively small-scale networks by using an un-optimized form of a synchronization algorithm in its original form.

languages, leads to substantial memory consumption in many cases [13]. The Georgia Tech Network Simulator (GTNetS) [13] is a component-based network simulator aimed to provide a structured simulation environment. GTNetS uses conservative TMA as an underlying synchronization algorithm. GTNetS uses many techniques used in PDNS to enable parallel computation. SSFNet [14] is a scalable high performance simulation platform which is designed to operate on a share–memory multiprocessor, with global state available to all simulation threads. It provides a unified interface for DES. SSF also provides a high-level modeling language to describe modeling environments. Reference [15] proposed a scalable simulation framework (GloMoSim) [16] to simulate wireless and wired network systems. The GloMoSim applies the PDES functionality provided by PARSEC [15]. GloMoSim uses a layered model similar to the OSI layers. PARSEC [15] is a DES language that adopts the process interaction approach to DES. In PARSEC, an object in the physical system is represented by a LP. Interactions among physical processes (events) are modeled by exchanging time-stamped event messages between the corresponding LPs. Several DES based simulators designed specifically for overlay systems that have an underlying conservative or optimistic TMA were studied. P2PSim [5] is an open-source discrete-event simulator developed for P2P system simulations. It supports several existing P2P protocols: Chord [17], Tapestry [18] etc. For underlying network topologies, P2PSim supports a number of topology models including constant distance topology, DV graph, end-to-end time graph, Euclidean graph, G2 graph, etc. However, it does not support distributed simulation. PeerSim [6] is a Java-based P2P simulator that can be used to simulate both structured and unstructured P2P protocols. PeerSim provides two simulation models: cycle-based model and event-driven model. PeerSim uses a simplified Internet model of the message passing and it can simulate networks with large sizes in the cycle based model (up to 1 million nodes). However, PeerSim also does not support distributed simulation and parallel execution of events. Overlay Weaver [19] is designed as a simulation tool for easy development and testing of overlay protocols and services. Overlay Weaver implements common APIs such as DHT and multicast so that users can study and evaluate services built on top of these APIs. It implements Pastry [7], Chord [17], and Tapestry [18], etc. Overlay Weaver has a modular architecture. The routing layer is composed of several components such as routing algorithm, message service, etc. Although, the Overlay Weaver is scalable to large size networks, it also does not support parallel execution of events. Lin, Pan, Guo, and Zhang [20] proposed a distributed simulation architecture for large-scale overlay networks that typically involves more than 1 million nodes. Their proposed architecture uses conservative synchronization as an

2. LARGE-SCALE NETWORK SIMULATORS Several algorithms on general purpose sequential DES systems and its variants were studied. The most popular object-oriented discrete-event network simulator is NS-3 [9]. It is a packet-level simulator to simulate network protocols, such as TCP, routing and multicast, on small networks. However, the design of ns is such that simulation of largescale networks is difficult, if not impossible, due to excessive memory and CPU time requirements. J-Sim [10] (formerly called JavaSim) is another component-based network simulation framework which is similar to NS-3. OPNET [11] modeler is the leading commercial network simulator for studying and evaluating networks and distributed systems. OMNeT++ [4] is an open-source simulation environment that is similar to OPNET. All of the above mentioned general purpose sequential DES systems can simulate small networks. However, they cannot scale to networks with more than a few hundreds of nodes. A number of PDES systems were identified that use TMA to provide synchronization among participating processors that perform concurrent execution of discreteevents in a distributed network. Reference [12] proposed PDNS (parallel distributed network simulator) which is an enhancement to NS-3 to utilize parallel computation on multiple machines. PDNS uses a conservative TMA for synchronization. The main objective of PDNS is to reduce the memory requirement by distributing the simulation load, and thus improve the overall latency. The design of both NS3 and its successor PDNS, using the hybrid of Tcl and C

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underlying TMA. To avoid the Lookahead problem of conservative algorithm and maximize the execution speed, they introduced a slow message relaxation scheme that handles straggler events that may arrive later in the simulation time due to network delays. Although, their proposed scheme may achieve the peak speedup for largescale networks, the statistical accuracy of the distributed simulation may compromise due to the occurrence of late messages (i.e., slow messages) [10]. Late messages are referred as transient messages that may result in an inaccurate GVT computation. As a result, it is hard to assure the statistical accuracy of the final simulation results produce by the simulation engine. As discussed above, the current DES systems (both sequential and parallel) are mostly focused on providing a simulation framework to study the network protocols and services. However, none of them are perfectly suitable for simulations of overlay networks in large-scale. The current overlay simulators are not scalable in simulating large overlay networks since they do not support distributed simulation such as P2PSim [5], PeerSim [6], Overlay Weaver [19] etc.

Fig.1. OTMA framework layered architecture and services

formulization layer (MFL) that allows a simulation designer to provide an abstract view of the model under consideration. Model formulization layer can adopt any third party abstract simulator for this task. We believe that the first layer is well researched and most of the existing third party software and tools can be employed at sub layers of MAL. Therefore, for this research work, we focused on the middle and the bottom layers of the OTMA framework. The middle layer, DES OPL, provides an optimized form of TMA that can be used to provide synchronization among the participating LPs. In particular, DES OPL consists of distributed simulation protocols (e.g., NMA or Time Wrap algorithms) that provide services (such as ordering of event messages, transmission of synchronization messages, maintaining the virtual time etc.) that are necessary for different components of the DES systems (such as inter LP communication) to correctly interoperate. For this particular layer of OTMA framework, our main contribution will be the development of an optimized form of Time Wrap algorithm. The main significance of this development is that it provides a new UML scheme that not only solves the well known transient message problem but also optimizes the performance by reducing the latency and memory requirements. The detail of UML scheme is presented in [22]. In addition, this layer provides a deterministic model for NMA that allows the simulation designer to choose one of the most appropriate DES protocols from OPL with respect to the model specified at MAL of OTMA framework. The details of the proposed deterministic model can be found in [23]. The bottom layer, SIL, of OTMA framework provides the implementation platform where the system that specified at the model abstraction layer (MAL) is implemented. In particular, for this layer, we propose a new internal architecture of an m-LP simulation model where m represents the total number of participating LPs in DES system. Our proposed m-LP simulation model will utilize one of the optimized protocols from the above layer (i.e.,

3.

OPTIMIZED TIME MANAGEMENT ALGORITHM (OTMA) FRAMEWORK System architecture for a generic OTMA framework is proposed in Fig.1 that combines different functionalities of PDES simulator in a three layer model. A complete cycle of modeling and simulation is decomposed into the proposed three layers architecture. In the next section, internal architecture of the proposed OTMA framework for PDES system is presented. 3.1. OTMA Generic System Architecture The proposed OTMA generic system architecture is presented in Fig. 1. For OTMA framework, we adopt a top to bottom layered architecture style to separate the distinct functionalities that are needed for modeling a large-scale Overlay network and executing the simulation protocols. The OTMA framework consists of the following three layers: Model Abstraction Layer (MAL), DES Optimized Protocol Layer (OPL), and System Implementation Layer (SIL). The top layer, MAL, provides an interface between a simulation designer/engineer and the OTMA framework. There are two main sub-layers of MAL: (i) DES interface and (ii) Model formalization layer (MFL). DES interface allows a simulation designer to interact with the layers of OTMA framework in order to represent and execute model components and simulation protocols in a distributed system. For instance, a simulation designer can use the DES interface to use an abstract simulator for specifying the components of the physical system and formally define the model. The bottom sub-layer of MAL is a model

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DES OPL) to optimize the overall performance of PDES system. A two dimensional clock (TDC) is proposed for this layer that controls the simulation/virtual time. The bottom layer is mainly responsible for all inter-LP communication, event messages execution, and event and clock management. Our second contribution for this layer is the design of a new LP controller that consists of three main sub components: (i) Simulation Executive (SE), (ii) Simulation Application (SA), and (iii) Inter LP Communication Interface (CI). The detail of these components will be provided subsequently.

2. Output Queues 3. Two Dimensional Clock (TDC) The input and output queues carry all inter LP communication in the proposed simulation model by sending and receiving the event messages. For the proposed model, we assume a mesh topology where each LP is connected to the other LP via a direct communication link for maximizing the internetworking and inter-LP communication. Each LP, therefore, maintains i number of input lines to receive the event messages from input neighbors and o number of output lines for scheduling the event messages for other neighboring LPs. For an m-LP simulation model, i input lines = o output lines = m-1 neighboring LPs. Input and output queues act as a communication channel for exchanging event messages to execute and progress the simulation as well as send synchronization messages to synchronize the participating LPs. We assume that each LP maintains a TDC which is mainly responsible to ensure that the local causality requirement must not be violated. In particular, TDC maintains two clock times: one for each of its input and output neighbors as shown in Fig. 3. The first clock time is the minimum receiving time (MRT) for the input neighbors LPs whereas the second clock time is the minimum sending time (MST) for the output neighbors LPs. The MRT indicates an earliest time when an

3.2. Bottom Layer - System Implementation Layer (SIL) The system component view of OTMA framework was proposed in Fig. 1. As mentioned before, in the proposed OTMA architecture, SIL is mainly responsible for all inter LP communication, LP mapping, local and remote event message management/execution, TDC implementation, and LP synchronization. To address all these issues, we propose a new m-LP simulation model, architecture for an internal LP’s main controller, and TDC. For the sake of simplicity, we first present the proposed internal architecture of a generic simulation model that consists of m number of LPs where each LP has i number of input lines and o number of output lines. The internal architecture of an LP is presented subsequently to show the different components such as simulation executive and event pool. Finally, in this section we present the architecture of LP’s main controller. 3.2.1 Proposed m-LP simulation model The proposed internal architecture of a generic simulation model for m number of LPs is shown in Fig. 2. It consists of primarily three components: 1. Input Queues

Fig.2. m-LP Simulation model: m number of LPs with I number of input queues and O number of output queues per LP

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Fig.3. TDC implementation on m-LP simulation model with minimum receiving time (MRT) and minimum sending time (MST)

LP may receive an event message from one of its input neighboring LPs, where as the MST represents an earliest time by which an LP can send an event message to one of its neighboring LPs. TDC updates and adjusts the MST with respect to the current simulation time (Ts) of that LP whereas the MRT is updated with respect to the smallest value of LBTS. This can also be expressed as:

MRT = Min LBTS1 , LBTS2 ,......, LBTSM

for m-LP

simulation model where LBTS1 corresponds to LP1, LBTS2 corresponds to LP2 and LBTSm corresponds to LPm. Each LP computes its own LBTS value to synchronize itself with the other LP by exchanging this information. LBTS value for an LPi is computed as follows: 3.2.2 Proposed internal architecture of an LP An internal architecture of an LP is shown in Fig. 4. In m-LP simulation model, an LP is connected to other neighboring LPs via a direct input communication link to receive remote event messages scheduled by the other neighboring LPs. Similarly, the execution of an event message may schedule a new event message for one of the neighboring LPs. This event message is referred as a remote event message and it will be sent to the destination LP using one of the direct output lines as shown in Fig. 4. Each LP in the proposed architecture implements the TDC that maintains the MRT for the input lines and MST for the output lines to ensure that the

Fig.5. An illustration of internal structure of a main controller of an LP with the exchange of event-messages and procedure calls

LP must process the event messages strictly in the nondecreasing time-stamp order and thus avoids the violation of causality constraint requirement. The internal architecture of the main controller of Fig. 4 will be elaborated subsequently. 3.2.3 Proposed architecture for LP’s main controller The proposed internal structure of a main controller of an LP is shown in Fig. 5. Each LP is mapped to one of the processors and encapsulates the controller that consists of 3 components as mentioned below. 1. 2. 3.

Simulation Executive (SE) Simulation Application (SA) Inter LP Communication Interface (CI)

The Simulation Executive (SE) is completely independent to the model of the physical system and therefore can be used to simulate several different types of systems. SE consists of two main components: event scheduler (ES) and TDC. The main responsibility of SE is to ensure reliable exchange of remote event messages among m number of LPs as well as periodic exchange of synchronization messages between the participating LPs. In our proposed architecture, SE is the only sub component that can communicate with the other participating LPs via the CI. In other words, all inter LP communication (i.e., the exchange of both event and

Fig.4. Internal architecture of an LP with I number of input lines and O number of output lines per LP

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synchronization messages) has to be done using the corresponding SE and CI. For the proposed controller architecture, we use FIFO queues with a static mesh communication topology similar to what is assumed in [21]. ES maintains one FIFO queue for each of the input neighbors where all the event messages are stored according to their time-stamps. Since we are using FIFO queues for the proposed architecture, the head of each queue will have the event message that has the smallest time-stamp within that queue. The use of FIFO queue, therefore, avoids the use of out of order sequencing that may be required to select the event message with the smallest time-stamp for execution. In addition, the use of TDC ensures that the event messages are stored in each FIFO queue of an LP in strictly non decreasing time-stamp order. Each time when an LP receives a remote event message from one of the neighboring LPs, TDC compares the MRT to the time-stamp of the received message to verify whether the time-stamp of the received message is smaller or larger than MRT. If timestamp of the received event message is equal or greater than the MRT, the event message will be accepted and stored in the corresponding FIFO queue else it will be rejected since this will violate the causality constraint requirement. ES repeatedly removes the event containing the smallest timestamp from the FIFO queues, call the TDC to advance the

Fig.7. Interaction between simulation application (SA) and simulation executive (SE) to schedule and execute local and remote event-messages

current simulation time (Ts) and update the MST and MRT accordingly, and finally call the SA for event execution. The two sub components of SE are shown in Fig. 6. Simulation Application (SA) consists of two main components: Event Handler (EH) and local state variables. When an SA receives the execution call from SE, it calls the EH that processes the event. The processing of an event message may result two things: (i) the scheduling of new event messages for the future simulation time, and (ii) change the state of the physical system (modify the local state variables to reflect changes in the state of the physical system). EH has the capability to determine whether the newly generated event message(s) is local or remote. If the preprocessing of an event message results in the scheduling of one or more remote event messages, EH will call the SE which in turns send the remote event messages to CI. However, if EH determines that the newly generated event messages are the local event messages, it calls ES directly to schedule local events. If the execution of an event message changes the state of the physical system, that change in the state must be reflected by updating/modifying the local state variables. The interaction of two sub-components of SA with SE is shown in Fig. 7. In our proposed architecture, SE is directly connected to Communication Interface (CI) to send and receive timestamped event messages as well as synchronization messages. Each LP has its own CI which is connected to the

Fig.6. Internal structure of simulation executive (SE) with the two main subcomponents (TDC and ES) interacting with SA and CI

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of Time Wrap algorithm. The detail of the optimized form of Time Wrap algorithm is presented in [22]. In addition, this layer provides a deterministic model for NMA that allows the simulation designer to choose one of the most appropriate DES protocols from OPL with respect to the model specified at MAL of OTMA framework. The details of the proposed deterministic model can be found in [23]. 4.

CONCLUSION

In this paper, we presented a generic OTMA framework that combines the optimized forms of synchronization protocols on a single platform. Specifically, a layered architecture approach was proposed to combine the optimized forms of conservative/optimistic algorithms on a generic synchronization framework. To support the implementation of the proposed OTMA framework, a new m-LP simulation model was proposed. We adopted a layered architecture style for the proposed OTMA framework that allows the simulation designers to further contribute in each independent layer of the framework. In addition, our analysis showed that the proposed OTMA framework is protocol independent and modular in its architecture that results in a strong simulation model and consequently better simulation results. Our proposed OTMA framework can be used as commonly agreed reference architecture to provide a common platform for all TMA along with the other functionalities (such as DES interface and inter LP communications) that are needed for modeling and simulating a large-scale PDES system. Barring legal ramifications, OTMA framework can be used by the simulation engineers to develop a network simulator for modeling and simulating large-scale networks at the minimum cost (i.e., reduced transmission overhead, latency, processor idle time, and memory requirements). A direct application of OTMA framework would be to model and simulate a physical system in a rather simplified and efficient way using the three layers architecture. Alternatively, the middle and the bottom layers of OTMA framework can be adopted in any general purpose sequential and parallel DES based network simulators to provide effective synchronization among participating LPs. For future work, we will integrate other TMA in the DES OPL layer of the proposed OTMA framework to extend the synchronization platform for PDES systems.

Fig.8. Interaction of DES-OPL layer with Model Abstraction Layer (MAL) and System Implementation Layer (SIL) of OTMA framework

other CIs of the neighboring LPs. In the context of NMA, whenever TDC updates the current simulation time after the retrieval of an event message from FIFO queue, a synchronization message will be sent by SE to other LPs using the respective CI. The synchronization message (also referred as null message) will contain the time-stamp equal to MST of the sending LP. When CI of an LP receives an outgoing remote event message from the SE, it checks the destination and sends the message using the appropriate output line. When a CI receives an incoming remote event message from the other CI of an LP, it sends the message to the SE which in turns sends that event message to ES. This implies that EH directly calls SE for outgoing remote event messages whereas SE directly calls ES for incoming remote event messages. In addition, EH can directly call ES to schedule local event messages. 3.3 Middle Layer – DES Optimized Protocol Layer (OPL) DES OPL is a middle layer in the proposed OTMA framework architecture that provides optimized form of TMA such as conservative NMA and optimistic Time Wrap algorithm. When a simulation designer is done with the formal specification of the target system by developing an abstract model, he/she can choose the PDES simulation protocol from the lower layer (i.e., DES OPL) by analyzing that which one of the TMAs is most appropriate with respect to the abstract view of the model presented at the top layer (i.e., MAL). The internal architecture of DES OPL layer can be seen in Fig. 8. For this particular layer of OTMA framework, our main contribution will be the development of an optimized form

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