Mauro Andreolini

Dynamic Load Management of Virtual Machines in Cloud Architectures

Cloud infrastructures must accommodate changing demands for different types of processing with heterogeneous workloads and time constraints. In a similar context, dynamic management of virtualized application environments is becoming very important to exploit computing resources, especially with recent virtualization capabilities that allow live sessions to be moved transparently between servers. This paper proposes novel management algorithms to decide about reallocations of virtual machines in a cloud context characterized by large numbers of hosts. The novel algorithms identify just the real critical instances and take decisions without recurring to typical thresholds. Moreover, they consider load trend behavior of the resources instead of instantaneous or average measures. Experimental results show that proposed algorithms are truly selective and robust even in variable contexts, thus reducing system instability and limit migrations when really necessary.

Models and framework for supporting runtime decisions in Web-based systems

Efficient management of distributed Web-based systems requires several mechanisms that decide on request dispatching, load balance, admission control, request redirection. The algorithms behind these mechanisms typically make fast decisions on the basis of the load conditions of the system resources. The architecture complexity and workloads characterizing most Web-based services make it extremely difficult to deduce a representative view of a resource load from collected measures that show extreme variability even at different time scales. Hence, any decision based on instantaneous or average views of the system load may lead to useless or even wrong actions. As an alternative, we propose a two-phase strategy that first aims to obtain a representative view of the load trend from measured system values and then applies this representation to support runtime decision systems. We consider two classical problems behind decisions: how to detect significant and nontransient load changes of a system resource and how to predict its future load behavior. The two-phase strategy is based on stochastic functions that are characterized by a computational complexity that is compatible with runtime decisions. We describe, test, and tune the two-phase strategy by considering as a first example a multitier Web-based system that is subject to different classes of realistic and synthetic workloads. Also, we integrate the proposed strategy into a framework that we validate by applying it to support runtime decisions in a cluster Web system and in a locally distributed Network Intrusion Detection System.

Load prediction models in web-based systems

Run-time management of modern Web-based services requires the integration of several algorithms and mechanisms for job dispatching, load sharing, admission control, overload detection. All these algorithms should take decisions on the basis of present and/or future load conditions of the system resources. In particular, we address the issue of predicting future resource loads under real-time constraints in the context of Internet-based systems. In this situation, it is extremely difficult to deduce a representative view of a system resource from collected raw measures that show very large variability even at different time scales. For this reason, we propose a two-step approach that first aims to get a representative view of the load trend from measured raw data, and then applies a load prediction algorithm to load trends. This approach is suitable to support different decision systems even for highly variable contexts and is characterized by a computational complexity that is compatible to run-time decisions. The proposed models are applied to a multi-tier Web-based system, but the results can be extended to other Internet-based contexts where the systems are characterized by similar workloads and resource behaviors.

A Cluster-Based Web System Providing Differentiated and Guaranteed Services

In a world where many users rely on the Web for up-to-date personal and business information and transactions, it is fundamental to build Web systems that allow service providers to differentiate user expectations with multi-class Service Level Agreements (SLAs). In this paper we focus on the server components of the Web, by implementing QoS principles in a Web-server cluster that is, an architecture composed by multiple servers and one front-end node called Web switch. We first propose a methodology to determine a set of confident SLAs in a real Web cluster for multiple classes of users and services. We then decide to implement at the Web switch level all mechanisms that transform a best-effort Web cluster into a QoS-enhanced system. We also compare three QoS-aware policies through experimental results in a real test-bed system. We show that the policy implementing all QoS principles allows a Web content provider to guarantee the contractual SLA targets also in severe load conditions. Other algorithms lacking some QoS principles cannot be used for respecting SLA constraints although they provide acceptable performance for some load and system conditions.

Performance study of dispatching algorithms in multi-tier web architectures

The number and heterogeneity of requests to Web sites are increasing also because the Web technology is becoming the preferred interface for information systems. Many systems hosting current Web sites are complex architectures composed by multiple server layers with strong scalability and reliability issues. In this paper we compare the performance of several combinations of centralized and distributed dispatching algorithms working at the first and second layer, and using different levels of state information. We confirm some known results about load sharing in distributed systems and give new insights to the problem of dispatching requests in multi-tier cluster-based Web systems.

Autonomic Request Management Algorithms for Geographically Distributed Internet-Based Systems

Supporting Web-based services through geographical distributed clusters of servers is a common solution to the increasing volume and variability of modern traffic. These architectures pose interesting challenges to request management strategies where the most important goal is not to achieve maximum performance, but to guarantee stable and robust results. In this paper, we propose novel request management algorithms that are based on autonomic principles that is, on loose collaboration among the closest nodes and no knowledge about the global system state. Experimental evaluation shows that our autonomic-enhanced algorithms can guarantee robust performance in a variety of settings and reduce standard deviations of the response times with respect to existing request management algorithms.

A Scalable Architecture for Real-Time Monitoring of Large Information Systems

Data centers supporting cloud-based services are characterized by a huge number of hardware and software resources often cooperating in complex and unpredictable ways. Understanding the state of these systems for reasons of management and service level agreement requires scalable monitoring architectures that should gather and evaluate continuosly large flows in almost real-time periods. We propose a novel monitoring architecture that, by combining a hierarchical approach with decentralized monitors, addresses these challenges. In this context, fully centralized systems do not scale to the required number of flows, while pure peer-to-peer architectures cannot provide a global view of the system state. We evaluate the monitoring architecture for computational units of gathering and evaluation in real contexts that demonstrate the scalability potential of the proposed system.

Adaptive, scalable and reliable monitoring of big data on clouds

Real-time monitoring of cloud resources is crucial for a variety of tasks such as performance analysis, workload management, capacity planning and fault detection. Applications producing big data make the monitoring task very difficult at high sampling frequencies because of high computational and communication overheads in collecting, storing, and managing information. We present an adaptive algorithm for monitoring big data applications that adapts the intervals of sampling and frequency of updates to data characteristics and administrator needs. Adaptivity allows us to limit computational and communication costs and to guarantee high reliability in capturing relevant load changes. Experimental evaluations performed on a large testbed show the ability of the proposed adaptive algorithm to reduce resource utilization and communication overhead of big data monitoring without penalizing the quality of data, and demonstrate our improvements to the state of the art. Real time monitoring of cloud resources is crucial for system management.We propose an adaptive algorithm for scalable and reliable cloud monitoring.Our algorithm dynamically balances amount and quality of monitored time series.We reduce monitoring costs significantly without penalizing data quality.

A Software Architecture for the Analysis of Large Sets of Data Streams in Cloud Infrastructures

System management algorithms in private and public cloud infrastructures have to work with literally thousands of data streams generated from resource, application and event monitors. This cloud context opens two novel issues that we address in this paper: how to design a software architecture that is able to gather and analyze all information within real-time constraints, how it is possible to reduce the analysis of the huge collected data set to the investigation of a reduced set of relevant information. The application of the proposed architecture is based on the most advanced software components, and is oriented to the classification of the statistical behavior of servers and to the analysis of significant state changes. These results guide model-driven management systems to investigate only relevant servers and to apply suitable decision models considering the deterministic or non-deterministic nature of server behaviors.

Analysis of peer-to-peer systems: workload characterization and effects on traffic cacheability

Peer-to-peer file sharing networks have emerged as a new popular application in the Internet scenario. We provide an analytical model of the resource size and of the contents shared at a given node. We also study the composition of the content workload hosted in the Gnutella network over time. Finally, we investigate the negative impact of oversimplified hypotheses (e.g., the use of filenames as resource identifiers) on the potentially achievable hit rate of a file-sharing cache. It is clear from our findings that file sharing traffic can be reduced by using a cache to minimize download time and network usage. The design and tuning of the cache server should take into account the presence of different resources sharing the same name and should consider push-based downloads. Failing to do so can result in reduced effectiveness of the caching mechanism.