Section: Scientific Foundations
Large System Modeling and Analysis
Participants : Bruno Gaujal, Derrick Kondo, Arnaud Legrand, Florence Perronnin, Brigitte Plateau, Olivier Richard, Corinne Touati, JeanMarc Vincent.
Understanding qualitative and quantitative properties of distributed systems and parallel applications is a major issue. The a posteriori analysis of the behavior of the system or the design of predictive models are notoriously challenging problems.
Indeed, large distributed systems contain many different features (processes, threads, jobs, messages, packets) with intricate interactions between them (communications, synchronizations). The analysis of the global behavior of the system requires to take into account large data sets.
As for a priori models, our current research focuses on capturing the distributed behavior of large dynamic architectures. Actually, both formal models and numerical tools are being used to get predictions on the behavior of large systems.
For large parallel systems, the nondeterminism of parallel composition, the unpredictability of execution times and the influence of the outside world are usually expressed in the form of multidimensional stochastic processes which are continuous in time with a discrete state space. The state space is often infinite or very large and several specific techniques have been developed to deal with what is often termed as the “curse of dimensionality”.
MESCAL deals with this problem using several complementary tracks:

Behavior analysis of highly distributed systems,

Simulation algorithms able to deal with very large systems,

Fluid limits (used for simulation, analysis and optimization),

Decomposition of the state space,

Structural and qualitative analysis,

Game theory methods for resolving autooptimization problems.
Behavior analysis of highly distributed systems
The development of highly distributed architectures running widely spread applications requires to elaborate new methodologies to analyze the behavior of systems. Indeed, runtime systems on such architectures are empirically tuned. Analysis of executions are generally manually performed on postmortem traces that have been extracted with very specific tools. This tedious methodology is generally motivated by the difficulty to characterize the resources of such systems. For example, big clusters, grids or peertopeer (P2P) (Our definition of peertopeer is a network (mainly the Internet) over which a large number of autonomous entities contribute to the execution of a single task.) networks present properties of size, heterogeneity, dynamicity that are usually not taken into account in classical system models. The asynchrony of the architecture also induces perturbations in the behavior of the application leading to significant slowdown that should be avoided. Therefore, when defining the workload of the system, the distributed nature of applications should be taken into account with a specific focus on problems related to synchronizations.
Simulation of distributed systems
Since the advent of distributed computer systems, an active field of research has been the investigation of scheduling strategies for parallel applications. The common approach is to employ scheduling heuristics that approximate an optimal schedule. Unfortunately, it is often impossible to obtain analytical results to compare the efficiency of these heuristics. One possibility is to conduct large numbers of backtoback experiments on real platforms. While this is possible on tightlycoupled platforms, it is infeasible on modern distributed platforms (i.e. Grids or peertopeer environments) as it is laborintensive and does not enable repeatable results. The solution is to resort to simulations . Simulations not only enable repeatable results but also make it possible to explore wide ranges of platform and application scenarios.
The SimGrid framework enables the simulation of distributed applications in distributed computing environments for the specific purpose of developing and evaluating scheduling algorithms. This software is the result of a longtime collaboration with Henri Casanova (University of California, San Diego).
Perfect Simulation
Using a constructive representation of a Markovian queuing network based on events (often called GSMPs), we have designed a perfect simulation tool computing samples distributed according to the stationary distribution of the Markov process with no bias. Two softwares have been developed. analyzes a Markov chain using its transition matrix and provides perfect samples of cost functions of the stationary state. ^{2} samples the stationary measure of Markov processes using directly the queuing network description. Some monotone networks with up to 10^{50} states can be handled within minutes over a regular PC.
Fluid models and mean field limits
When the size of systems grows very large, one may use asymptotic techniques to get a faithful estimate of their behaviors. One such tools is mean field analysis and fluid limits, that can be used on a modeling and simulation level. One recent significant application is call centers where . Another one is peer to peer systems. Web caches as well as peertopeer systems must be able to serve a set of customers which is both large (several tens of thousands) and highly volatile (with short connection times). These features make analysis difficult when classical approaches (like Markovian Models or simulation) are used. We have designed simple fluid models to get rid of one dimension of the problem. This approach has been applied to several systems of web caches (such as Squirrel) and to peertopeer systems (such as BitTorrent). This helps to get a better understanding of the behavior of the system and to solve several optimization problems. Another application concerns task brokering in desktop grids taking into account statistical features of tasks as well as of the availability of the processors. Mean field has also been applied to the performance evaluatin of work stealing in large systems.
Markov Chain Decomposition
The first class of models we will be using is Continuous time Markov chains (CTMC). The usefulness of Markov models is undisputed, as attested by the large number of modeling tools implementing Markov solvers. However their practical applications are limited by the statespace explosion problem, which puts excessive demands on memory and execution time when studying large reallife systems. Continuoustime Stochastic Automata Networks describe a system as a set of subsystems that interact. Each subsystem is modeled by a stochastic automaton, and some rules between the states of each automaton describe the interactions between subsystems. The main challenge is to come up with ways to compute the asymptotic (or transient) behavior of the system without ever generating the whole state space. Several techniques have been developed in our group based on bounds, lumpability, symmetry and properties of the Kronecker product. Most of them have been integrated in a software tool (PEPS ) which is openly available.
Discrete Event Systems
The interaction of several processes through synchronization, competition or superposition within a distributed system is a big source of difficulties because it induces a state space explosion and a nonlinear dynamic behavior. The use of exotic algebra, such as (min,max,plus) can help. Highly synchronous systems become linear in this framework and therefore are amenable to formal solutions. More complicated systems are neither linear in (max,plus) nor in the classical algebra. Several qualitative properties have been established for a large class of such systems called freechoice Petri nets (subadditivity, monotonicity or convexity properties). Such qualitative properties are sometimes enough to assess the class of routing policies optimizing the global behavior of the system. They are also useful to design efficient numerical tools computing their asymptotic behavior.
Game Theory Methods for Resolving Resource Contention
Resources in largescale distributed platforms (Grid computing platforms, enterprise networks, peertopeer systems) are shared by a number of users having conflicting interests who are thus prone to act selfishly. A natural framework for studying such noncooperative individual decisionmaking is game theory. In particular, game theory models the decentralized nature of decisionmaking.
It is well known that such noncooperative behaviors can lead to important inefficiencies and unfairness. In other words, individual optimizations often results in global resource waste. In the context of game theory, a situation in which all users selfishly optimize their own utility is known as a Nash equilibrium or Wardrop equilibrium . In such equilibria, no user has interest in unilaterally deviating from its strategy. Such policies are thus very easy to implement in a fully distributed system and have some stability properties. However, a possible consequence is the Braess paradox in which the increase of resource happens at the expense of every user. This is why, the study of the occurrence and degree of such inefficiency is of crucial interest. Up until now, little is known about general conditions for optimality or degree of efficiency of these equilibria, in a general setting.
Many techniques have been developed to enforce some form of collaboration and improve these equilibria. In this context, it is generally prohibitive to take joint decisions so that a global optimization cannot be achieved. A possible option relies on the establishment of virtual prices, also called shadow prices in congestion networks. These prices ensure a rational use of resources. Equilibria can also be improved by advising policies to mobiles such that any user that does not follow these pieces of advice will necessarily penalize herself (correlated equilibria ).