The overall increase in traffic on the World Wide Web is augmenting user-perceived response times from popular Web sites, especially in conjunction with special events. System platforms that do not replicate information content cannot provide the needed scalability to handle large traffic volumes and to match rapid and dramatic changes in the number of clients. The need to improve the performance of Web-based services has produced a variety of novel content delivery architectures. This article will focus on Web system architectures that consist of multiple server nodes distributed on a local area, with one or more mechanisms to spread client requests among the nodes. After years of continual proposals of new system solutions, routing mechanisms, and policies (the first dated back to 1994 when the NCSA Web site had to face the first million of requests per day), many problems concerning multiple server architectures for Web sites have been solved. Other issues remain to be addressed, especially at the network application layer, but the main techniques and methodologies for building scalable Web content delivery architectures placed in a single location are settled now. This article classifies and describes main mechanisms to split the traffic load among the server nodes, discussing both the alternative architectures and the load sharing policies. To this purpose, it focuses on architectures, internal routing mechanisms, and dispatching request algorithms for designing and implementing scalable Web-server systems under the control of one content provider. It identifies also some of the open research issues associated with the use of distributed systems for highly accessed Web sites.

Popular Web sites cannot rely on a single powerful server nor on independent mirrored-servers to support the ever-increasing request load. Distributed Web server architectures that transparently schedule client requests offer a way to meet dynamic scalability and availability requirements. The authors review the state of the art in load balancing techniques on distributed Web-server systems, and analyze the efficiencies and limitations of the various approaches

Data Stream Processing (DSP) applications are widely used to timely extract information from distributed data sources, such as sensing devices, monitoring stations, and social networks. To successfully handle this ever increasing amount of data, recent trends investigate the possibility of exploiting decentralized computational resources (e.g., Fog computing) to define the applications placement. Several placement policies have been proposed in the literature, but they are based on different assumptions and optimization goals and,\ud as such, they are not completely comparable to each other. In this paper we study the placement problem for distributed DSP applications. Our contributions are twofold.\ud We provide a general formulation of the optimal DSP placement (for short, ODP) as an Integer Linear Programming\ud problem which takes explicitly into account the heterogeneity of computing and networking resources and which encompasses - as special cases - the different solutions proposed in the literature. We present an ODP-based scheduler for the Apache Storm DSP framework. This allows us to compare some well-known centralized and decentralized placement solutions. We also extensively analyze the ODP scalability with respect to various parameter settings

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-Architecting software systems according to the serviceoriented paradigm, and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves a greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.Index Terms-Service-oriented architecture, runtime adaptation, quality of service.

Runtime adaptation is recognized as a viable way for a serviceoriented system to meet QoS requirements in its volatile operating environment. In this paper we propose a methodology to drive the adaptation of such a system, that integrates within a unified framework different adaptation mechanisms, to achieve a greater flexibility in facing different operating environments and the possibly conflicting QoS requirements of several concurrent users. To determine the most suitable adaptation action(s), the methodology is based on the formulation and solution of a linear programming problem, which is derived from a behavioral model of the system updated at runtime by a monitoring activity. Numerical experiments show the effectiveness of our approach. Besides the methodology, we also present a prototype tool that implements it.

No abstract

No abstract