Ladislau Bölöni

A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems

Mixed-machine heterogeneous computing (HC) environments utilize a distributed suite of different high-performance machines, interconnected with high-speed links, to perform different computationally intensive applications that have diverse computational requirements. HC environments are well suited to meet the computational demands of large, diverse groups of tasks. The problem of optimally mapping (defined as matching and scheduling) these tasks onto the machines of a distributed HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original study of each heuristic. Therefore, a collection of 11 heuristics from the literature has been selected, adapted, implemented, and analyzed under one set of common assumptions. It is assumed that the heuristics derive a mapping statically (i.e., off-line). It is also assumed that a metatask (i.e., a set of independent, noncommunicating tasks) is being mapped and that the goal is to minimize the total execution time of the metatask. The 11 heuristics examined are Opportunistic Load Balancing, Minimum Execution Time, Minimum Completion Time, Min?min, Max?min, Duplex, Genetic Algorithm, Simulated Annealing, Genetic Simulated Annealing, Tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will out-perform another. The evaluation procedure is specified, the heuristics are defined, and then comparison results are discussed. It is shown that for the cases studied here, the relatively simple Min?min heuristic performs well in comparison to the other techniques.

Survey Paper: Routing protocols in ad hoc networks: A survey

Ad hoc wireless networks perform the difficult task of multi-hop communication in an environment without a dedicated infrastructure, with mobile nodes and changing network topology. Different deployments exhibit various constraints, such as energy limitations, opportunities, such as the knowledge of the physical location of the nodes in certain scenarios, and requirements, such as real-time or multi-cast communication. In the last 15years, the wireless networking community designed hundreds of new routing protocols targeting the various scenarios of this design space. The objective of this paper is to create a taxonomy of the ad hoc routing protocols, and to survey and compare representative examples for each class of protocols. We strive to uncover the requirements considered by the different protocols, the resource limitations under which they operate, and the design decisions made by the authors.

A comparison study of static mapping heuristics for a class of meta-tasks on heterogeneous computing systems

Heterogeneous computing (HC) environments are well suited to meet the computational demands of large, diverse groups of tasks (i.e., a meta-task). The problem of mapping (defined as matching and scheduling) these tasks onto the machines of an HC environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. Selecting the best heuristic to use in a given environment, however, remains a difficult problem, because comparisons are often clouded by different underlying assumptions in the original studies of each heuristic. Therefore, a collection of eleven heuristics from the literature has been selected, implemented, and analyzed under one set of common assumptions. The eleven heuristics examined are opportunistic load balancing, user-directed assignment, fast greedy, min-min, max-min, greedy, genetic algorithm, simulated annealing, genetic simulated annealing, tabu, and A*. This study provides one even basis for comparison and insights into circumstances where one technique will outperform another. The evaluation procedure is specified, the heuristics are defined, and then selected results are compared.

A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems

The problem of mapping (defined as matching and scheduling) tasks and communications onto multiple machines and networks in a heterogeneous computing (HC) environment has been shown to be NP-complete, in general, requiring the development of heuristic techniques. Many different types of mapping heuristics have been developed in recent years. However, selecting the best heuristic to use in any given scenario remains a difficult problem. Factors making this selection difficult are discussed. Motivated by these difficulties, a new taxonomy for classifying mapping heuristics for HC environments is proposed (Purdue HC Taxonomy). The taxonomy is defined in three major parts: the models used for applications and communication requests; the models used for target hardware platforms; and the characteristics of mapping heuristics, Each part of the taxonomy is described, with examples given to help clarify the taxonomy. The benefits and uses of this taxonomy are also discussed.

Agent-based resource discovery

Presents a distributed discovery method allowing individual nodes to gather information about resources in a wide-area distributed system made up of autonomous systems linked together by a network technology substrate. We introduce an algorithm and a model for distributed awareness and a framework for the dynamic assembly of agents monitoring network resources. Whenever an agent needs detailed information about the individual components of another system, it uses the information gathered by the distributed awareness mechanism to identify the target system, then creates a description of a monitoring agent that is capable of providing the information about remote resources, and sends this description to the remote site. There, an agent factory dynamically assembles the monitoring agent. This solution is scalable and is suitable for heterogeneous environments where the architecture and the hardware resources of individual nodes differ, where the services provided by the system are diverse, and where the bandwidth and latency of the communication links cover a broad range.

Characterizing Resource Allocation Heuristics for Heterogeneous Computing Systems

In many distributed computing environments, collections of applications need to be processed using a set of heterogeneous computing (HC) resources to maximize some performance goal. An important research problem in these environments is how to assign resources to applications (matching) and order the execution of the applications (scheduling) so as to maximize some performance criterion without violating any constraints. This process of matching and scheduling is called mapping. To make meaningful comparisons among mapping heuristics, a system designer needs to understand the assumptions made by the heuristics for (1) the model used for the application and communication tasks, (2) the model used for system platforms, and (3) the attributes of the mapping heuristics. This chapter presents a three-part classification scheme ( 3PCS ) for HC systems. The 3PCS is useful for researchers who want to (a) understand a mapper given in the literature, (b) describe their design of a mapper more thoroughly by using a common standard, and (c) select a mapper to match a given real-world environment.

Ad hoc grids: communication and computing in a power constrained environment

We introduce ad hoc grids as a hierarchy of mobile devices with different computing and communication capabilities. An ad hoc grid allows a group of individuals to accomplish a mission, often in a hostile environment; examples of applications of ad hoc grids are disaster management, wild-fire prevention, and peacekeeping operations. We are concerned with the interplay between computing and communication in the power-constrained environment of an ad hoc grid.

Maximizing the Value of Sensed Information in Underwater Wireless Sensor Networks via an Autonomous Underwater Vehicle.

This paper considers underwater wireless sensor networks (UWSNs) for submarine surveillance and monitoring. Nodes produce data with an associated value, decaying in time. An autonomous underwater vehicle (AUV) is sent to retrieve information from the nodes, through optical communication, and periodically emerges to deliver the collected data to a sink, located on the surface or onshore. Our objective is to determine a collection path for the AUV so that the Value of Information (VoI) of the data delivered to the sink is maximized. To this purpose, we first define an Integer Linear Programming (ILP) model for path planning that considers realistic data communication rates, distances, and surfacing constraints. We then define the first heuristic for path finding that is adaptive to the occurrence of new events, relying only on acoustic communication for exchanging short control messages. Our Greedy and Adaptive AUV Path-finding (GAAP) heuristic drives the AUV to collect packets from nodes to maximize the VoI of the delivered data. We compare the VoI of data obtained by running the optimum solution derived by the ILP model to that obtained from running GAAP over UWSNs with realistic and desirable size. In our experiments GAAP consistently delivers more than 80% of the theoretical maximum VoI determined by the ILP model.

YAES: a modular simulator for mobile networks

Developing network protocols for mobile wireless systems is a complex task, and most of the existing simulator frameworks are not well suited for experimental development. The YAES simulation framework was specifically developed such that it allows the fast prototyping of networking protocols, and support real-time experimentation and refactoring. By providing a large set of abstractions and generic implementations, a number of frequently used techniques such as genetic algorithms or neural networks can be created in matter of minutes. Our experience shows that by requiring only Java programming skills which computer science and engineering students commonly possess, YAES can be a useful tool for classroom use, as well.This paper presents the considerations behind the YAES architecture and provides a description of the system. As a case study, we present the steps necessary for running experiments on the energy efficiency behavior of the Weighted Clustering Algorithm (WCA).

An object-oriented framework for building collaborative network agents

We are primarily interested in the design of software agents supporting interoperability in a heterogeneous computing system. We view an agent as a composite object consisting of several other objects including a finite state machine, a model of the world, strategies associated with every state and an agenda. We introduced an object- oriented framework for building collaborative network agents. Moreover, we introduce an agent definition language and describe a mechanism to create agents dynamically.