We consider a three-tier architecture for mobile and pervasive computing scenarios, consisting of a local tier ofmobile nodes, a middle tier (cloudlets) of nearby\ud computing nodes, typically located at the mobile nodes access points but characterized by a limited amount of resources, and a remote tier of distant cloud servers, which have practically infinite resources. This architecture has been proposed to get the benefits of computation offloading from mobile nodes to external servers while limiting the use of distant servers whose higher latency could negatively impact the user experience.\ud For this architecture, we consider a usage scenario where no central authority exists and multiple non-cooperative mobile users share the limited computing resources of a close-by cloudlet and can selfishly decide to send their computations to any of the three tiers. We define a model to capture the users interaction and to investigate the effects of computation offloading on the users’ perceived performance. We formulate the problem as a generalized Nash equilibrium problem and show existence of an equilibrium.We present a distributed algorithm for the computation of an equilibrium which is tailored to the problem structure and is based on an in-depth analysis of the underlying equilibrium problem. Through numerical examples, we illustrate its behavior and the characteristics of the achieved equilibria

Abstract-In this paper we consider several Software as a Service (SaaS) providers, that offer a set of applications using the Cloud facilities provided by an Infrastructure as a Service (IaaS) provider. We assume that the IaaS provider offers a pay only what you use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. We propose a two stage provisioning scheme. In the first stage, the SaaS providers determine the number of required flat and on demand instances by means of standard optimization techniques. In the second stage the SaaS providers compete, by bidding for the spot instances which are instantiated using the unused IaaS capacity. We assume that the SaaS providers want to maximize a suitable utility function which accounts for both the QoS delivered to their users and the associated cost. The IaaS provider, on the other hand, wants to maximize his revenue by determining the spot prices given the SaaS bids. We model the second stage as a Stackelberg game, and we compute its equilibrium price and allocation strategy by solving a Mathematical Program with Equilibrium Constraints (MPEC) problem. Through numerical evaluation we study the equilibrium solutions as function of the system parameters.

No abstract

No abstract

No abstract

Abstract. The introduction of self-adaptation and self-management techniques in a service-oriented system can allow to meet in a changing environment the levels of service formally defined with the system users in a Service Level Agreement (SLA). However, a self-adaptive SOA system has to be carefully designed in order not to compromise the system scalability and availability. In this paper we present the design and performance evaluation of a brokering service that supports at runtime the self-adaptation of composite services offered to several concurrent users with different service levels. To evaluate the performance of the brokering service, we have carried out an extensive set of experiments on different implementations of the system architecture using workload generators that are based on open and closed system models. The experimental results demonstrate the effectiveness of the brokering service design in achieving scalability and high availability.

Abstract-Service selection has been widely investigated by the SOA research community as an effective adaptation mechanism that allows a service broker, offering a composite service, to bind at runtime each task of the composite service to a corresponding concrete implementation, selecting it from a set of candidates which differ from one another in terms of QoS parameters. In this paper we present a load-aware perrequest approach to service selection which aims to combine the relative benefits of the well known per-request and per-flow approaches. We present experimental results obtained with a prototype implementation of a service broker. Our results show that the proposed approach is superior to the traditional perrequest one and combines the ability of sustaining large volume of service requests, as the per-flow approach, while at the same time offering a finer customizable service selection, as the perrequest approach.

Abstract-In this paper we consider a set of Software as a Service (SaaS) providers, that offer a set of Web services using the Cloud facilities provided by an Infrastructure as a Service (IaaS) provider. We assume that the IaaS provider offers a pay only what you use scheme similar to the Amazon EC2 service, comprising flat, on demand, and spot virtual machine instances. We propose a two-stage provisioning scheme. In the first stage, the SaaS providers determine the number of required flat and on demand instances by means of standard optimization techniques. In the second stage, the SaaS providers compete by bidding for the spot instances which are instantiated using the unused IaaS capacity. We put our focus on the bidding decision process by the SaaS providers, which takes place during the second stage, and apply N-armed bandit problems, in which the player is faced repeatedly with a choice among N different options, and every time he submits his decision evaluating past feedbacks. Through numerical experiments, we analyze proposed strategies under different scenarios and prove the SaaS providers ability to refine their behavior round by round and to determine the best bid so to maximize their revenue and achieve as many spot resources as possible, also addressing the importance of a trade-off between exploration and exploitation, i.e., among greedy and non-greedy actions.