106 research outputs found
Agent-Based Cloud Computing
No full textAgent-based cloud computing is concerned with the design and development of software agents for bolstering cloud service
discovery, service negotiation, and service composition. The significance of this work is introducing an agent-based paradigm for
constructing software tools and testbeds for cloud resource management. The novel contributions of this work include: 1) developing
Cloudle: an agent-based search engine for cloud service discovery, 2) showing that agent-based negotiation mechanisms can be
effectively adopted for bolstering cloud service negotiation and cloud commerce, and 3) showing that agent-based cooperative problemsolving
techniques can be effectively adopted for automating cloud service composition. Cloudle consists of 1) a service discovery agent
that consults a cloud ontology for determining the similarities between providers’ service specifications and consumers’ service
requirements, and 2) multiple cloud crawlers for building its database of services. Cloudle supports three types of reasoning: similarity
reasoning, compatibility reasoning, and numerical reasoning. To support cloud commerce, this work devised a complex cloud
negotiation mechanism that supports parallel 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. Empirical results show that
using the complex cloud negotiation mechanism, agents achieved high utilities and high success rates in negotiating for cloud resources.
To automate cloud service composition, agents in this work adopt a focused selection contract net protocol (FSCNP) for dynamically
selecting cloud services and use service capability tables (SCTs) to record the list of cloud agents and their services. Empirical results
show that using FSCNP and SCTs, agents can successfully compose cloud services by autonomously selecting services
Evolving Fuzzy Rules for Relaxed-Criteria Negotiation
No full textIn the literature on automated negotiation, very few negotiation agents are designed with the flexibility to slightly relax their negotiation criteria to reach a consensus more rapidly and with more certainty. Furthermore, these relaxed-criteria negotiation agents were not equipped with the ability to enhance their performance by learning and evolving their relaxed-criteria negotiation rules. The impetus of this work is designing market-driven negotiation agents (MDAs) that not only have the flexibility of relaxing bargaining criteria using fuzzy rules, but can also evolve their structures by learning new relaxed-criteria fuzzy rules to improve their negotiation outcomes as they participate in negotiations in more e-markets. To this end, an evolutionary algorithm for adapting and evolving relaxed-criteria fuzzy rules was developed. Implementing the idea in a testbed, two kinds of experiments for evaluating and comparing EvEMDAs (MDAs with relaxed-criteria rules that are evolved using the evolutionary algorithm) and EMDAs (MDAs with relaxed-criteria rules that are manually constructed) were carried out through stochastic simulations. Empirical results show that: 1) EvEMDAs generally outperformed EMDAs in different types of e-markets and 2) the negotiation outcomes of EvEMDAs generally improved as they negotiated in more e-markets
BLGAN: Bayesian Learning and Genetic Algorithm for Supporting Negotiation With Incomplete Information
No full textAutomated negotiation provides a means for resolving
differences among interacting agents. For negotiation with
complete information, this paper provides mathematical proofs
to show that an agent’s optimal strategy can be computed using
its opponent’s reserve price (RP) and deadline. The impetus of
this work is using the synergy of Bayesian learning (BL) and
genetic algorithm (GA) to determine an agent’s optimal strategy
in negotiation (N) with incomplete information. BLGAN adopts:
1) BL and a deadline-estimation process for estimating an opponent’s
RP and deadline and 2) GA for generating a proposal
at each negotiation round. Learning the RP and deadline of an
opponent enables the GA in BLGAN to reduce the size of its search
space (SP) by adaptively focusing its search on a specific region
in the space of all possible proposals. SP is dynamically defined
as a region around an agent’s proposal P at each negotiation
round. P is generated using the agent’s optimal strategy determined
using its estimations of its opponent’s RP and deadline.
Hence, the GA in BLGAN is more likely to generate proposals
that are closer to the proposal generated by the optimal strategy.
Using GA to search around a proposal generated by its current
strategy, an agent in BLGAN compensates for possible errors in
estimating its opponent’s RP and deadline. Empirical results show
that agents adopting BLGAN reached agreements successfully,
and achieved: 1) higher utilities and better combined negotiation
outcomes (CNOs) than agents that only adopt GA to generate their
proposals, 2) higher utilities than agents that adopt BL to learn
only RP, and 3) higher utilities and better CNOs than agents that
do not learn their opponents’ RPs and deadlines
Grid Commerce, Market-Driven G-Negotiation, and Grid Resource Management
No full textAlthough the management of resources is essential
for realizing a computational grid, providing an efficient resource
allocation mechanism is a complex undertaking. Since Grid
providers and consumers may be independent bodies, negotiation
among them is necessary. The contribution of this paper
is showing that market-driven agents (MDAs) are appropriate
tools for Grid resource negotiation.MDAs are e-negotiation agents
designed with the flexibility of: 1) making adjustable amounts of
concession taking into account market rivalry, outside options,
and time preferences and 2) relaxing bargaining terms in the
face of intense pressure. A heterogeneous testbed consisting of
several types of e-negotiation agents to simulate a Grid computing
environment was developed. It compares the performance
of MDAs against other e-negotiation agents (e.g., Kasbah) in a
Grid-commerce environment. Empirical results show that MDAs
generally achieve: 1) higher budget efficiencies in many market
situations than other e-negotiation agents in the testbed and
2) higher success rates in acquiring Grid resources under high
Grid loadings
Grid Resource Negotiation: Survey and New Directions
No full textSince Grid computing systems involve large-scale resource sharing, resource management is central to their operations. Whereas there are more Grid resource management systems adopting auction, commodity market, and contract-net (tendering) models, this survey supplements and complements existing surveys by reviewing, comparing, and highlighting existing research initiatives on applying bargaining (negotiation) as a mechanism to Grid resource management. The contributions of this paper are: 1) discussing the motivations for considering bargaining models for Grid resource allocation; 2) discussing essential design considerations such as modeling devaluation of Grid resources, considering market dynamics, relaxing bargaining terms, and co-allocation of resources when building Grid negotiation mechanisms; 3) reviewing the strategies and protocols of state-of-the-art Grid negotiation mechanisms; 4) providing detailed comparisons and analyses on how state-of-the-art Grid negotiation mechanisms address the design considerations mentioned in 3); and 5) suggesting possible new directions
A family of heuristics for agent-based elastic Cloud bag-of-tasks concurrent scheduling
No full textThe 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
Agent-based Cloud service composition
No full textService 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
Concurrent Negotiation and Coordination for Grid Resource Coallocation
No full textBolstering resource coallocation is essential for realizing
the Grid vision, because computationally intensive applications
often require multiple computing resources from different
administrative domains. Given that resource providers and consumers
may have different requirements, successfully obtaining
commitments through concurrent negotiations with multiple resource
providers to simultaneously access several resources is a
very challenging task for consumers. The impetus of this paper
is that it is one of the earliest works that consider a concurrent
negotiation mechanism for Grid resource coallocation. The
concurrent negotiation mechanism is designed for 1) managing
(de)commitment of contracts through one-to-many negotiations
and 2) coordination of multiple concurrent one-to-many negotiations
between a consumer and multiple resource providers. The
novel contributions of this paper are devising 1) a utility-oriented
coordination (UOC) strategy, 2) three classes of commitment management
strategies (CMSs) for concurrent negotiation, and 3) the
negotiation protocols of consumers and providers. Implementing
these ideas in a testbed, three series of experiments were carried
out in a variety of settings to compare the following: 1) the CMSs
in this paper with the work of others in a single one-to-many
negotiation environment for one resource where decommitment
is allowed for both provider and consumer agents; 2) the performance
of the three classes of CMSs in different resource market
types; and 3) the UOC strategy with the work of others [e.g.,
the patient coordination strategy (PCS)] for coordinating multiple
concurrent negotiations. Empirical results show the following:
1) the UOC strategy achieved higher utility, faster negotiation
speed, and higher success rates than PCS for different resource
market types; and 2) the CMS in this paper achieved higher final
utility than the CMS in other works. Additionally, the properties
of the three classes of CMSs in different kinds of resource markets
are also verified
Complex and Concurrent Negotiations for Multiple Interrelated e-Markets
No full textTo date, most of the existing bargaining models are
designed for supporting negotiation in only one market involving
only two types of participants (buyers and sellers). This work
devises a complex negotiation mechanism that supports negotiation
activities among three types of participants in multiple interrelated
markets. The complex negotiation mechanism consists
of: 1) a bargaining-position-estimation (BPE) strategy for the
multilateral negotiations between consumer and broker agents in
a service market and 2) a regression-based coordination (RBC)
strategy for concurrent negotiations between broker and provider
agents in multiple resource markets. The negotiation outcomes
between broker and provider agents in a resource market can
potentially influence the negotiation outcomes between broker and
consumer agents in a service market. Empirical results show that
agents adopting the BPE strategy can better respond to different
market conditions than agents adopting the time-dependent
strategy because they do not make excessive (respectively,
inadequate) amounts of concessions in favorable (respectively,
unfavorable) markets. In the concurrent negotiations in multiple
resource markets, empirical results show that broker agents
adopting the RBC strategy achieved significantly higher utilities,
higher success rates, and faster negotiation speed than broker
agents adopting the utility-oriented and patient coordination
strategies
A Price- and-Time-Slot-Negotiation Mechanism for Cloud Service Reservations
No full textWhen 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 onlymake 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
- …
