Cloud consumers have access to an increasingly diverse range of resource and contract options, but lack appropriate resource scaling solutions that can exploit this to minimize the cost of their cloud-hosted applications. Traditional approaches tend to use homogeneous resources and horizontal scaling to handle workload fluctuations and do not leverage resource and contract heterogeneity to optimize cloud costs. In this paper, we propose a novel opportunistic resource scaling approach that exploits both resource and contract heterogeneity to achieve cost-effective resource allocations. We model resource allocation as an unbounded knapsack problem , and resource scaling as an one-step ahead resource allocation problem . Based on these models, we propose two scaling strategies: (a) delta capacity optimization , which focuses on optimizing costs for the difference between existing resource allocation and the required capacity based on the forecast workload, and (b) full capacity optimization , which focuses on optimizing costs for resource capacity corresponding to the forecast workload. We evaluate both strategies using two real world workload datasets, and compare them against three different scaling strategies. The results show that our proposed approach, particularly full capacity optimization, outperforms all of them and offers in excess of 70 percent cost savings compared to the traditional scaling approach.
We present an agent-based mechanism that acts as a mediator module between theorem proving systems and mathematical knowledge bases containing information that is necessary for the constructions of proofs. Unlike the more popular user-oriented mediators who work as information agents to provide the so-called value-added services to the collected data before presenting it to users or user applications, our (multi-) agents are more task-oriented. That is, our agents work in tandem with the user or user application on the tasks the user is trying to solve. This approach is particularly suitable to mathematical knowledge retrieval in theorem proving as (i) checking for applicable axioms/definitions/theorems from the knowledge base can be done independently from the proof search process concurrently carried out by the prover, and (ii) the prover and the mediator operate on two different search spaces and the search outcome brought about by the mediator can be of great benefit to the prover, e.g. to avoid the prover from exploring many unnecessary or irrelevant proof steps, to keep the prover's search space more manageable and the constructed proof more comprehensible.
There is great promise in the idea of having Web services available on the Internet, that can be flexibly composed to achieve more complex services, which can themselves then also be used as components in other contexts. However it is challenging to realise this idea, without essentially programming the composition using some process language such as WS-BPEL or OWL-S process descriptions. This paper presents a mechanism for specifying the external interface to composite and component services, and then deriving an appropriate internal model to realise a functioning composition. We present a conversation specification language for defining interaction protocols and investigate the issue of synchronous and asynchronous communication between the composite service and the component services
This paper investigates heterogeneous-cost task allocation with budget constraints (HCTAB), wherein heterogeneity is manifested through the varying capabilities and costs associated with different agents for task execution. In contrast with the centralized optimization-based method, the HCTAB problem is solved using a fully distributed framework, and a coalition formation game is introduced to provide a theoretical guarantee for this distributed framework. To solve the coalition formation game, a convergence-guaranteed log-linear learning algorithm based on heterogeneous cost is proposed. This algorithm incorporates two improvement strategies, namely a cooperative exchange strategy and a heterogeneous-cost log-linear learning strategy. These strategies are specifically designed to be compatible with the heterogeneous cost and budget constraints characteristic of the HCTAB problem. Through ablation experiments, we demonstrate the effectiveness of these two improvements. Finally, numerical results show that the proposed algorithm outperforms existing task allocation algorithms and learning algorithms in terms of solving the HCTAB problem.
This paper studies the problem of majority-rule-based collective decision-making where the agents' preferences are represented by CP-nets (Conditional Preference Networks). As there are exponentially many alternatives, it is impractical to reason about the individual full rankings over the alternative space and apply majority rule directly. Most existing works either do not consider computational requirements, or depend on a strong assumption that the agents have acyclic CP-nets that are compatible with a common order on the variables. To this end, this paper proposes an efficient SAT-based approach, called MajCP (Majority-rule-based collective decision-making with CP-nets), to compute the majority winning alternatives. Our proposed approach only requires that each agent submit a CP-net; the CP-net can be cyclic, and it does not need to be any common structures among the agents' CP-nets. The experimental results presented in this paper demonstrate that the proposed approach is computationally efficient. It offers several orders of magnitude improvement in performance over a Brute-force algorithm for large numbers of variables.
With the rapidly growing demand for the cloud services, a need for efficient methods to trade computing resources increases. Commonly used fixed-price model is not always the best approach for trading cloud resources, because of its inflexible and static nature. Market-based trading shows promise for more efficient resource allocation and pricing in the cloud. However, most of the existing mechanisms ignore the seller's costs of providing the resources. In order to address it, we design a single-sided market mechanism for trading virtual machine instances in the cloud, where the cloud provider can express the reservation prices for traded services. We prove that the proposed mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation of the services. We perform extensive experiments in order to investigate the impact of the reserve price on the market outcome. Our experiments show that the proposed mechanism yields near-optimal allocations and has a low execution time.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
