Kwang Mong Sim

Ant colony optimization for routing and load-balancing: survey and new directions

Although an ant is a simple creature, collectively a colony of ants performs useful tasks such as finding the shortest path to a food source and sharing this information with other ants by depositing pheromone. In the field of ant colony optimization (ACO), models of collective intelligence of ants are transformed into useful optimization techniques that find applications in computer networking. In this survey, the problem-solving paradigm of ACO is explicated and compared to traditional routing algorithms along the issues of routing information, routing overhead and adaptivity. The contributions of this survey include 1) providing a comparison and critique of the state-of-the-art approaches for mitigating stagnation (a major problem in many ACO algorithms), 2) surveying and comparing three major research in applying ACO in routing and load-balancing, and 3) discussing new directions and identifying open problems. The approaches for mitigating stagnation discussed include: evaporation, aging, pheromone smoothing and limiting, privileged pheromone laying and pheromone-heuristic control. The survey on ACO in routing/load-balancing includes comparison and critique of ant-based control and its ramifications, AntNet and its extensions, as well as ASGA and SynthECA. Discussions on new directions include an ongoing work of the authors in applying multiple ant colony optimization in load-balancing.

A MARKET-DRIVEN MODEL FOR DESIGNING NEGOTIATION AGENTS

Although there are many extant agent–based systems for negotiation in e–commerce, the negotiation strategies of agents in these systems are mostly static. This article presents a model for designing negotiation agents that make adjustable rates of concession by reacting to changing market situations. To determine the amount of concession for each trading cycle, these market–driven agents are guided by four mathematical functions of eagerness, trading time, trading opportunity, and competition. Trading opportunity is determined by considering: (i) number of trading partners, (ii) spreads—differences in utilities between an agent and its trading partners, and (iii) probability of completing a deal. Competition is determined by the probability that an agent is not considered the most preferred trader by other negotiating parties. Motivated by factors such as corporate policies and resource needs, eagerness represents an agent’s desire to complete a deal. Agents with different time sensitivity to deadlines employ different trading strategies by making different rates of concession at different stages of negotiation. In this article, three classes of strategies with respect to remaining trading time are discussed. Theoretical analyses show that market–driven agents are designed to make prudent and appropriate amounts of concession for a given market situation.

Towards complex negotiation for cloud economy

In a Cloud computing environment, the dynamic configuration of a personalized collection of resources often requires Cloud participants (consumers, brokers, and providers) to establish service-level agreements (SLAs) through negotiation However, to date, state-of-the-art approaches in Cloud computing provides limited or no support for dynamic SLAs negotiation This position paper 1) presents the design of a complex Cloud negotiation mechanism that supports negotiation activities in interrelated markets: a Cloud service market between consumer agents and broker agents, and multiple Cloud resource markets between broker agents and provider agents, 2) specifies the negotiation protocols and strategies of consumer and broker agents in a Cloud service market, and 3) presents the design of the contracting and coordination algorithms for the concurrent negotiation activities between broker and provider agents in multiple Cloud resource markets The complex Cloud negotiation mechanism is designed to support complex negotiation activities in interrelated markets in which the negotiation outcomes between broker and provider agents in a Cloud resource market can potentially influence the negotiation outcomes of broker and consumer agents in a Cloud service market To the best of the authors knowledge, this work is the earliest proposal for a complex Cloud negotiation mechanism.

Self-Organizing Agents for Service Composition in Cloud Computing

In Cloud service composition, collaboration between brokers and service providers is essential to promptly satisfy incoming Cloud consumer requirements. These requirements should be mapped to Cloud resources, which are accessed via web services, in an automated manner. However, distributed and constantly changing Cloud-computing environments pose new challenges to automated service composition such as: (i) dynamically contracting service providers, which set service fees on a supply-and-demand basis, and (ii) dealing with incomplete information regarding Cloud resources (e.g., location and providers). To address these issues, in this work, an agent-based Cloud service composition approach is presented. Cloud participants and resources are implemented and instantiated by agents. These agents sustain a three-layered self-organizing multi-agent system that establishes a Cloud service composition framework and an experimental test bed. The self-organizing agents make use of acquaintance networks and the contract net protocol to evolve and adapt Cloud service compositions. The experimental results indicate that service composition is efficiently achieved despite dealing with incomplete information as well as coping with dynamic service fees.

A Price- and-Time-Slot-Negotiation Mechanism for Cloud Service Reservations

When making reservations for Cloud services, consumers and providers need to establish service-level agreements through negotiation. Whereas it is essential for both a consumer and a provider to reach an agreement on the price of a service and when to use the service, to date, there is little or no negotiation support for both price and time-slot negotiations (PTNs) for Cloud service reservations. This paper presents a multi-issue negotiation mechanism to facilitate the following: 1) PTNs between Cloud agents and 2) tradeoff between price and time-slot utilities. Unlike many existing negotiation mechanisms in which a negotiation agent can only make one proposal at a time, agents in this work are designed to concurrently make multiple proposals in a negotiation round that generate the same aggregated utility, differing only in terms of individual price and time-slot utilities. Another novelty of this work is formulating a novel time-slot utility function that characterizes preferences for different time slots. These ideas are implemented in an agent-based Cloud testbed. Using the testbed, experiments were carried out to compare this work with related approaches. Empirical results show that PTN agents reach faster agreements and achieve higher utilities than other related approaches. A case study was carried out to demonstrate the application of the PTN mechanism for pricing Cloud resources.

Flexible negotiation agent with relaxed decision rules

The distinguishing feature of negotiation agents in this paper is that they are designed with the flexibility to make adjustable amounts of concession by reacting to changing market conditions, taking into account factors such as competition, deadlines, and trading options, and relaxing trading aspirations in face of (intense) negotiation pressure (e.g., stiff competition) using a set of fuzzy rules. Extensive amounts of stochastic simulations were conducted on a wide variety of test environments including dense, moderate, sparse, balanced, favorable, and unfavorable markets on stringent, as well as, relaxed constraints. Empirical results suggest that on average, when compared to Sims market-driven agents, agents in this research achieved higher success rates in reaching a deal, and higher expected utilities.

Equilibria, prudent Compromises,and the "Waiting" game

While evaluation of many e-negotiation agents are carried out through empirical studies, this work supplements and complements existing literature by analyzing the problem of designing market-driven agents (MDAs) in terms of equilibrium points and stable strategies. MDAs are negotiation agents designed to make prudent compromises taking into account factors such as time preference, outside option, and rivalry. This work shows that 1) in a given market situation, an MDA negotiates optimally because it makes minimally sufficient concession, and 2) by modeling negotiation of MDAs as a game /spl Gamma/ of incomplete information, it is shown that the strategies adopted by MDAs are stable. In a bilateral negotiation, it is proven that the strategy pair of two MDAs forms a sequential equilibrium for /spl Gamma/. In a multilateral negotiation, it is shown that the strategy profile of MDAs forms a market equilibrium for /spl Gamma/.

Agent-based Cloud service composition

Service composition in multi-Cloud environments must coordinate self-interested participants, automate service selection, (re)configure distributed services, and deal with incomplete information about Cloud providers and their services. This work proposes an agent-based approach to compose services in multi-Cloud environments for different types of Cloud services: one-time virtualized services, e.g., processing a rendering job, persistent virtualized services, e.g., infrastructure-as-a-service scenarios, vertical services, e.g., integrating homogenous services, and horizontal services, e.g., integrating heterogeneous services. Agents are endowed with a semi-recursive contract net protocol and service capability tables (information catalogs about Cloud participants) to compose services based on consumer requirements. Empirical results obtained from an agent-based testbed show that agents in this work can: successfully compose services to satisfy service requirements, autonomously select services based on dynamic fees, effectively cope with constantly changing consumers’ service needs that trigger updates, and compose services in multiple Clouds even with incomplete information about Cloud participants.

A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling

The scheduling and execution of bag-of-tasks applications (BoTs) in Clouds is performed on sets of virtualized Cloud resources that start being exhausted right after their allocation disregarding whether tasks are being executed. In addition, BoTs may be executed in potentially heterogeneous sets of Cloud resources, which may be either previously allocated for a different and fixed number of hours or dynamically reallocated as needed. In this paper, a family of 14 scheduling heuristics for concurrently executing BoTs in Cloud environments is proposed. The Cloud scheduling heuristics are adapted to the resource allocation settings (e.g., 1-hour time slots) of Clouds by focusing on maximizing Cloud resource utilization based on the remaining allocation times of Cloud resources. Cloud scheduling heuristics supported by information about BoT tasks (e.g., task size) and/or Cloud resource performances are proposed. Additionally, scheduling heuristics that require no information of either Cloud resources or tasks are also proposed. The Cloud scheduling heuristics support the dynamic inclusion of new Cloud resources while scheduling and executing a given BoT without rescheduling. Furthermore, an elastic Cloud resource allocation mechanism that autonomously and dynamically reallocates Cloud resources on demand to BoT executions is proposed. Moreover, an agent-based Cloud BoT scheduling approach that supports concurrent and parallel scheduling and execution of BoTs, and concurrent and parallel dynamic selection and composition of Cloud resources (by making use of the well-known contract net protocol) from multiple and distributed Cloud providers is designed and implemented. Empirical results show that BoTs can be (i) efficiently executed by attaining similar (in some cases shorter) makespans to commonly used benchmark heuristics (e.g., Max-min), (ii) effectively executed by achieving a 100% success execution rate even with high BoT execution request rates and executing BoTs in a concurrent and parallel manner, and that (iii) BoTs are economically executed by elastically reallocating Cloud resources on demand.

A survey of bargaining models for grid resource allocation

Whereas it is noted that various types of auctions, commodity market models, and contract-net (tendering) model are more widely used for managing Grid resources, this paper focuses on discussing bargaining (negotiation) models for Grid resource management. To this end, this survey supplements and complements existing surveys by reviewing, comparing, and highlighting the very few extant research initiatives on applying bargaining as a mechanism for managing Grid resources. The contributions of this paper are (i) discussing the motivations for considering bargaining models for Grid resource management, (ii) discussing the issues in building bargaining mechanisms for Grid resource management, (iii) comparing the strategies and protocols of state-of-the-art bargaining models for Grid resources, and (iv) discussing possible new directions.