To adapt to volatile edge environments this paper envisages distributed systems built from small units of coordi-nation called microcompositions. Microcompositions decompose a single goal into subgoals and declaratively express compo-sition and coordination constraints. These microcompositions are managed by agents that self-organise themselves into a distributed management overlay structure - a Goal Realisation Tree (GRT) - based on goal realisation (means-ends) links. Both microcompositions and GRTs serve as runtime models, allowing agents to dynamically restructure them in response to changes in goals, service provision and context. Results from our evaluation demonstrate the effectiveness and scalability of our approach.

This paper envisages an agent-based approach for creating resilient, self-managing microservice systems. The approach is based on a categorisation of the adaptation space for microservices across two dimensions – application/infrastructure and service-type/composition. The monitoring and adaptation actions in the quadrants defined by these dimensions are distinct, requiring controllers/agents with different management capabilities. Based on this division of responsibility, an agent-oriented architectural approach is proposed – the Agent Mesh. A service mesh provides the observability and control of a network overlay that connects agents and services, while agents with the different capabilities defined above close the control loops at the application and infrastructure levels. A core feature of the approach is that both domain (managed) services and managing agents are integrated as microservices within the same mesh, and communicate with each other via sidecar proxies, thereby allowing adaptation of both the domain services and self-adaptation of the agents.

Low-cost transient resources such as Amazon's Elastic Compute Cloud (EC2) Spot instances can be opportunistically leveraged to reduce the ongoing costs of cloud applications. However, they are susceptible to unilateral revocations by the vendor making them a risky proposition for long-running applications with strict performance requirements. It is challenging to effectively balance the cost savings that transient resources provide with the associated revocation risk which, if realised, can impact application performance. To address this challenge, we propose an approach for risk-aware cloud cost optimization that is inspired by the concept ofportfolio diversification.Contract diversificationmitigates the revocation risk by procuring the required compute capacity as a mixed portfolio of transient and non-transient resources.Resource diversificationfurther diversifies the risk by using multiple transient resource types. Using our approach, consumers can leverage contract and resource diversification to proactively (re-)configure their application's resource portfolio to handle workload and resource price fluctuations while minimizing ongoing cost and keeping ongoing revocation risk within tolerable limits. Simulative evaluation using three real-world workload traces and Amazon's EC2 offerings demonstrate that our proposed approach can achieve meaningful cost savings compared to the baseline costs, while significantly reducing the portfolio's exposure to revocation risk.

In this paper, we introduce a new meta-model that captures core concepts for constructing software architectures for general-purpose, autonomous middleware frameworks that realize internalized and externalized self-adaptivity at both a system- and meta-level in order to achieve antifragility. The proposed meta-model builds on, specializes, and complements existing multi-agent meta-models in line with a previously published reference model for antifragile systems in the cyber domain.

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 anunbounded knapsack problem, and resource scaling as anone-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.

In this paper, we study a large-scale heterogeneous task allocation problem in complex systems. Existing work on task allocation mainly tackles this well-known NP-hard problem from an optimisation perspective, where an exact or approximate solutions can be found after intensive computation. They have not been able to cater for the extra needs of scalability and robustness in large scale complex systems. In this work, we employ a game-theoretic framework to model the studied task allocation problem, and align the objective in task allocation (i.e., system optimality) with the concept of Nash equilibrium in game theory. Our formulation enables the expression of heterogeneity in both agents and tasks, and allows multiple agents to form teams or coalitions to cooperatively perform a task. Based on this formulation, we propose a novel GreedyNE algorithm to efficiently search for a Nash equilibrium solution. The proposed GreedyNE algorithm is a scalable, anytime, and monotonic algorithm, which in turn, makes it robust for the deployment in complex systems. GreedyNE is simple, easy to implement and flexible enough, so that it can also be used as a local search algorithm for improving the quality of any existing allocation solution. By conducting comprehensive experiments, we show that GreedyNE achieves a solution quality as good as the state-of-the-art approximation algorithms, yet with significantly lower computation time.

This paper explores the heterogeneous task allocation problem in Multi-robot systems. A game-theoretic formulation of the problem is proposed to align the goal of individual robots with the system objective. The concept of Nash equilibrium is applied to define a desired solution for the task allocation problem in which each robot can allocate itself to an appropriate task group. We also introduce a market-based distributed mechanism, called DisNE, to allow the robots to exchange messages with tasks and move between task groups, eventually reaching an equilibrium solution. We carry out comprehensive empirical studies to demonstrate that DisNE achieves near-optimal system utility in significantly shorter computation times when compared with the state-of-the-art mechanisms.

Cloud infrastructure providers are offering consumers a wide range of resource and contract options to choose from, yet most elasticity management solutions are incapable of leveraging this to optimize the cost and performance of cloudhosted applications. To address this problem, in this paper, we propose a novel resource scaling approach that exploits both resource and contract heterogeneity to achieve optimal resource allocations and better cost control. We model resource allocation as an Unbounded Knapsack Problem, and resource scaling as an one-step ahead resource allocation problem. Based on this, we present two scaling strategies, namely delta scale optimization and full scale optimization. Delta scale optimization supports the traditional notion of scaling resources horizontally, i.e., it computes an optimal allocation (or deallocation) of resources to increase (or decrease) the total compute capacity based on the current allocation and the forecast application workload. Full scale optimization, on the other hand, supports the notion of cost-optimal resource rescaling, i.e., the simultaneous allocation and deallocation of resources to meet the forecast workload irrespective of the decision to increase, decrease or maintain capacity. Both strategies provide users greater flexibility in managing trade offs between cost and performance. We motivate our research work by using a realistic and non-trivial scenario of resource scaling for a cloud-hosted IoT platform and use simple use cases to illustrate the benefit of our proposed approach.

Resilience is an important property of distributed cyber defence systems operating in complex, adversarial environments. While resilience can be realized through self-management, implementing distributed self-management poses several significant challenges. In this paper, we identify some of these challenges and present our initial work on an approach for cyber resilience called AWaRE. The novelty of AWaRE lies in the use of a conceptual, state-space-based design and reconstitution framework, combined with run-time models and distributed constraint satisfaction/optimization techniques for decision-making and coordination of system re-configurations. The run-time models are generated via an expressive domain-specific language enabling component, constraint and agent modeling as well as constraint problem decomposition and architectural self-organization. We realize AWaRE via a concrete software framework, focusing specifically on autonomic self-* properties pertaining to distributed system configuration, deployment and reliable operation. We demonstrate the value of AWaRE in the context of a simple, cloud-based enterprise scenario.

Consumers can realize significant cost savings by procuring resources from computational spot markets such as Amazon Elastic Compute Cloud (EC2) Spot Instances. They can take advantage of the price differentials across time slots, regions, and instance types to minimize the total cost of running their applications on the cloud. However, Spot markets are inherently volatile and dynamic, as a consequence of which Spot prices change continuously. As such, prospective bidders can benefit from intelligent insights into the Spot market dynamics that can help them make more informed bidding decisions. To enable this, we propose a descriptive statistics approach for the analysis of Amazon EC2 Spot markets to detect typical pricing patterns including the presence of seasonal components, extremes and trends. We use three statistical measures - the Gini coefficient, the Theil index, and the exponential weighted moving average. We also devise a model for estimating minimum bids such that the Spot instances will run for specified durations with a probability greater than a set value based on different look back periods. Experimental results show that our estimation yields on average a bidding strategy that can reliably secure an instance at least 80% of the time at minimum target guarantee between 50% and 95%.

Safety applications based on the dedicated short-range communication (DSRC) in vehicular networks have very strict performance requirements for safety messages (in terms of delay and packet delivery). However, there is a lack of systematic approach to achieve the performance requirements by leveraging the potential of multi-hop forwarding. This paper proposes a generic multi-hop probabilistic forwarding scheme that achieves these requirements for event-driven safety messages, is compatible with the 802.11 broadcasting protocol and inherits some of the best features of solutions proposed so far for vehicular safety applications. In addition, we develop a unified and comprehensive analytical model to evaluate the performance of the proposed scheme taking into account the effect of hidden terminals, vehicle densities, and the spatial distribution of the multiple forwarders, in a one-dimensional highway scenario. Our numerical experiments confirm the accuracy of the model and demonstrate that the proposed protocol can improve the packet delivery performance by up to 209 percent, while maintaining the delay well below the required threshold. Finally, the utility of the analytical model is demonstrated via an optimal design for the coefficients of a forwarding probability function in the proposed scheme.

The increasingly growing supply and demand for infrastructure as a service (IaaS) makes cloud trading possible in an open exchange (OCX) marketplace. The mechanisms based on economic principles show promise in addressing the problem of efficient cloud resource provisioning in such a marketplace, including resources allocation and pricing. Therefore, this article proposes effective and efficient economics-inspired double-sided mechanisms, focussing on attaining the allocative efficiency and truthfulness. Given non-deterministic polynomial time (NP) complexity of the considered problem and the computational tractability requirement for the practical solutions, we design and evaluate the approximation mechanisms for such markets. We propose a combinatorial greedy allocation mechanism to determine the distribution of cloud resources based on the sorted order of allocation candidates. We design the pricing mechanisms that derive the buyer prices based on critical-value, and the seller payments are determined via a mixed surplus-distribution rule that relies on direct and proportional-value payment. The theoretical analysis of the economic properties proves that the proposed mechanisms maintain Budget-Balance (BB), Individual Rationality (IR), Computational Tractability (CT), and achieve Truthfulness (T) for single-minded buyers. The experimental investigation of the approximation quality and the seller strategic manipulation reveal near-optimal allocation performance and near-truthful strategic incentive in our pricing mechanisms.

Classic results in game theory state that private information is a cause for a negotiation to end with suboptimal outcome. Subse- quently, private information is a compelling explanation for the frequent occurrence of negotiation breakdowns or costly delays. In this paper, we propose a mechanism for improving efficiency of negotiation outcome for multilateral negotiations with incomplete information (i.e., negotiators holding private information). This objective is achieved by introducing biased distribution of the resulting surplus created by the negotiators’ joint offers to prevent negotiators from misrepresenting their valuations of the negotiation outcomes. Our mechanism is based on rewarding concession-making agents with larger shares of the obtainable surplus.We show that the probabilities that the agents with private information make concession are accordingly increased. This allows for better efficiency to be achieved.

Summary Cloud computing offers a realization of SOA in which IT resources are dynamically provisioned as services to consumers using flexible provisioning and pricing models. When provisioning such services, providers and consumers must first agree over the service usage terms and conditions, which are captured in Service Level Agreements (SLAs). In this paper, we propose a policy‐based framework with corresponding models, mechanisms and tools for the automated establishment of SLAs in open, diverse and dynamic cloud environments. The Automated SLA Management framework allows entities to specify their requirements and capabilities, and preferences over them in a flexible and expressive manner. It also supports multiple interaction models for SLA establishment, giving consumers and providers the flexibility to select the one that is most appropriate in a given context, while simultaneously participating in multiple concurrent SLA interactions using different interaction models. As part of the framework, we define a formal model for the underlying policies, a corresponding physical model WS‐SLAM that extends WS‐Policy and a reference architecture that can be easily implemented. We validate the practicability of our framework through the Smart CloudPurchaser prototype that can automatically purchase computing resources from Amazon EC2 under different scenarios and contexts. Copyright © 2013 John Wiley & Sons, Ltd.

Migration to the cloud offers several benefits including reduced operational costs, flexibility, scalability, and a greater focus on business goals, but it also has a flip side reduced visibility. Organizations only have a blackbox view of cloud servers and while pricing and specification information is publicly available, there is limited information about cloud performance. This necessitates the need for tools that can provide greater visibility into cloud insfrastructure performance so that consumers can objectively compare and contrast the offerings from different providers. Smart CloudBench [1][2][3] is a system that allows users to run automated, ondemand, real-time and customized benchmark tests on cloud infrastructure. In this paper, we present Smart CloudMonitor - a performance monitoring tool that provides multi-layer performance monitoring capabilities to Smart CloudBench. It provides greater visibility and insight into cloud performance by monitoring both application performance as well as the corresponding resource consumption. Experiments conducted on cloud infrastructure using Smart CloudBench show the add value that Smart CloudMonitor provides to the process of cloud performance evaluation.

Cloud resource allocation and pricing is a significant and challenging problem for modern cloud providers, which needs to be addressed. In this work, we propose an adaptive greedy mechanism, which is a new type of greedy market mechanism for efficient cloud resource allocation. The mechanism is combinatorial and it is designed to be operated by a single cloud provider. We prove that our proposed market mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation for the resource. Our experimental investigation showed that the proposed mechanism outperforms the conventional (single-shot) approach for solving combinatorial auction in terms of generated social welfare and resource utilization.

Belief merging is an active research field with many important applications. Most existing work addresses the belief merging issue using a centralised approach. In this paper, we investigate a distributed approach to the problem of belief merging. The contribution of this paper is two-fold: (i) we develop a negotiation-based model for belief merging, and (ii) we investigate the computational complexity of the belief merging problem within the proposed framework. Through the proposed model of negotiation-based belief merging, we will present and discuss several significant logical properties and computational complexity results.

In vehicle-to-vehicle communication, safety messages could be broadcasted over one-hop or multi-hop using different transmission ranges to warn each other of changing conditions or dangers ahead. We investigate the broadcast performance considering one-hop and multi-hop transmissions and study the effect of different transmission ranges on the multi-hop broadcasting performance. Our results show that multi-hop transmission can provide significant performance improvement when the transmission range is chosen appropriately.

As the rate of cloud computing adoption grows, so does the need for consumption assistance. Enterprises that are looking to migrate their IT systems to the cloud, would like to quickly identify providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs. However, no two vendors offer the same resource configurations, pricing and provisioning models, making the task of selecting appropriate computing resources complex, time-consuming and expensive. In this paper, we present Smart CloudBench - a platform that automates the performance benchmarking of cloud infrastructure, helping potential consumers quickly identify the cloud providers that can deliver the most appropriate price/performance levels to meet their specific requirements. Users can estimate the actual performance of the different cloud platforms by testing representative benchmark applications under representative load conditions. Experimentation using the prototype implementation shows that higher price does not necessarily translate to better or more consistent performance, and benchmarking results can provide more information to help enterprises make better informed decisions.

As the rate of cloud computing adoption grows, so does the need for consumption assistance. Enterprises looking to migrate their IT systems to the cloud require assistance in identifying providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs. In this paper, we present Smart Cloud Broker - a suite of software tools that allows cloud infrastructure consumers to evaluate and compare the performance of different Infrastructure as a Service (IaaS) offerings from competing cloud service providers, and consequently supports selection of the cloud configuration and provider with the specifications that best meet the user's requirements. Using Smart Cloud Broker, prospective cloud users can estimate the performance of the different cloud platforms by running live tests against representative benchmark applications under representative load conditions.

Automated negotiation is one of the most common approaches used to make decisions and manage disputes between computational entities leading them to optimal agreements. Many existing works tackle single-issue negotiations and the negotiation environment is assumed to be static so that the agents can make decisions based solely on the proposals of the counterparts and their own fixed parameters. Most real-world scenarios, however, involve complex domains and dynamic environments. In such cases, it is no longer sufficient to consider negotiation as an isolated activity in a static environment. Therefore, a more general framework for automated negotiation is needed in which the negotiation agents can be very flexible and adaptive. In this paper, we describe a generic framework for automated negotiation, which captures descriptively the social dynamics of the negotiation process. The proposed framework enables the agents to behave responsively to the changes in the environment. Their strategies can adapt as the conditions outside of the negotiation change to ensure that their decisions remain rational. And the agents are proactive and responsive by searching for options, which are outside of the negotiation and which may improve their outcomes. The key ideas and the overall system architecture together with a specific negotiation instance in a basic bilateral setting are described, along with two illustrative examples. The first example is in the context of e-commerce, and the second example is an application scenario of service level agreement negotiation in service computing. We also describe a prototypical implementation of the proposed negotiation framework.

ABSTRACT This paper investigates the maximum achievable channel throughput in a single‐channel and single‐hop wireless sensor network using IEEE 802.15.4 Medium Access Control (MAC) protocol. We introduce a simple mean‐field approach to model the Carrier Sense Multiple Access with Collision Avoidance mechanism of the 802.15.4 MAC protocol under unsaturated conditions. We derive a set of expressions such as optimal sensing rate of a sensor node, its corresponding failure probability, and the channel throughput for both saturated and unsaturated networks. With those expressions, we propose several network designs to achieve the optimal throughput by choosing the appropriate MAC parameters. We validate the proposed optimal designs using ns‐2 simulations. Furthermore, we evaluate the network lifetime expectancy of the optimal designs and compare it with the lifetime of network settings under saturated conditions that use the default MAC parameters of IEEE 802.15.4. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a policy-based framework for the automated establishment of SLAs for cloud computing services. The proposed framework supports multiple interaction models for SLA establishment giving consumers and providers the flexibility to choose one that is most appropriate in a given context, while simultaneously supporting multiple concurrent SLA interactions using different interaction models. We describe the underlying policies, focussing on the key features and contributions of the framework. We also validate our framework through a real-world use-case scenario using the Amazon EC2 service.

Requirements engineering (RE) is a coordinated effort to allow clients, users, and software engineers to jointly formulate assumptions, constraints, and goals about a software solution. However, one of the most challenging aspects of RE is the detection of inconsistencies between requirements. To address this issue, we have developed REInDetector, a knowledge-based requirements engineering tool, supporting automatic detection of a range of inconsistencies. It provides facilities to elicit, structure, and manage requirements with distinguished capabilities for capturing the domain knowledge and the semantics of requirements. This permits an automatic analysis of both consistency and realizability of requirements. REInDetector finds implicit consequences of explicit requirements and offers all stakeholders an additional means to identify problems in a more timely fashion than existing RE tools. In this paper, we describe the Description Logic used to capture requirements, the REInDetector tool, its support for inconsistency detection, and its efficacy as applied to several RE examples. An important feature of REInDetector is also its ability to generate comprehensive explanations to provide more insights into the detected inconsistencies.

SUMMARY Cloud Computing is the latest paradigm proposed toward fulfilling the vision of computing being delivered as an utility such as phone, electricity, gas and water services. It enables users to have access to computing infrastructure, platform and software as services over the Internet. The services can be accessed on demand and from anywhere in the world in a quick and flexible manner, and charged for based on their usage, making the rapid and often unpredictable expansion demanded by nowadays' business environment affordable also for small spin‐off and start‐up companies. In order to be competitive, however, Cloud providers need to be able to adapt to the dynamic loads from users, not only optimizing the local usage and costs but also engaging into agreements with other Clouds so as to complement local capacity. The infrastructure in which competing Clouds are able to cooperate to maximize their benefits is called a Federated Cloud. Just as Clouds enable users to cope with unexpected demand loads, a Federated Cloud will enable individual Clouds to cope with unforeseen variations of demand. The definition of the mechanism to ensure mutual benefits for the individual Clouds composing the federation, however, is one of its main challenges. This paper proposes and investigates the application of market‐oriented mechanisms based on the General Equilibrium Theory of Microeconomics to coordinate the sharing of resources between the Clouds in the Federated Cloud. Copyright © 2010 John Wiley & Sons, Ltd.

SUMMARY The end‐to‐end QoS negotiation for service level agreement establishment for composite services involves compound multi‐party negotiations in which the composite service provider concurrently negotiates with multiple candidates for each atomic service, selecting the one that best satisfies the atomic service QoS preferences while ensuring that the end‐to‐end QoS requirements are also fulfilled. In order to be able to negotiate with potential candidates, it is necessary to derive the atomic utility boundaries from the global utility boundary. Additionally, there has to be a mechanism for updating these boundaries in subsequent negotiation rounds based on the individual negotiation outcomes. In this paper, we propose an algorithm for the decomposition of global utility boundary into atomic service utility boundaries, and the surplus redistribution from successful negotiation outcomes among the remaining negotiations. The proposed mechanism is a practical approach to efficiently coordinate concurrent service negotiations within complex workflows, enabling the iterative and interactive adjustment of the negotiation boundaries for each atomic service in a composition based on the performance of other atomic negotiations. We demonstrate the feasibility of our approach by evaluating it with some popular negotiation strategies using the Specialized Property Search Scenario. Copyright © 2011 John Wiley & Sons, Ltd.

We propose a policy-based framework for the QoS differentiated provisioning of services. The proposed frame-work improves the state-of-the-art in policy-based preference specification by combining cardinal and ordinal preferences. We describe the underlying models, focussing on the key features and contributions of the proposed framework. We also show how, using our framework, the QoS evaluation problem can be translated to a Constraint Satisfaction Problem while preserving the semantics of the preference policies.

We present a policy-centered QoS meta-model which can be used by service providers and consumers alike to express capabilities, requirements, constraints, and general management characteristics relevant for SLA establishment in service aggregations. We also provide a QoS assertion model which is generic, domain-independent and conforming to the WS-Policy syntax and semantics. Using these two models, assertions over acceptable and required values for QoS properties can be expressed across the different service layers and service roles.

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

In this paper, we introduce an alternative approach to reasoning about action. The approach provides a solution to the frame and the ramification problem in a uniform manner. The approach involves keeping a (syntax-based) model of the world that is updated when actions are performed. Our approach is similar to the STRIPS system in which formulas are deleted and added as effects of an action. The presented framework however does not suffer from STRIPS' limitations in expressivity.

We present a uniform non-monotonic solution to the problems of reasoning about action on the basis of an argumentation-theoretic approach. Our theory is provably correct relative to a sensible minimisation policy introduced on top of a temporal propositional logic. Sophisticated problem domains can be formalised in our framework. As much attention of researchers in the field has been paid to the traditional and basic problems in reasoning about actions such as the frame, the qualification and the ramification problems, approaches to these problems within our formalisation lie at heart of the expositions presented in this paper.