Marco Guazzone

Energy-Efficient Resource Management for Cloud Computing Infrastructures

Cloud computing is growing in popularity among computing paradigms for its appealing property of considering Everything as a Service. The goal of a Cloud infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with service providers, and, at the same time, by lowering infrastructure costs. Among these costs, the energy consumption induced by the Cloud infrastructure, for running Cloud services, plays a primary role. Unfortunately, the minimization of QoS violations and, at the same time, the reduction of energy consumption is a conflicting and challenging problem. In this paper, we propose a framework to automatically manage computing resources of Cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.

Exploiting VM migration for the automated power and performance management of green cloud computing systems

Cloud computing is an emerging computing paradigm in which Everything is as a Service, including the provision of virtualized computing infrastructures (known as Infrastructure-as-a-Service modality) hosted on the physical infrastructure, owned by an infrastructure provider. The goal of this infrastructure provider is to maximize its profit by minimizing the amount of violations of Quality-of-Service (QoS) levels agreed with its customers and, at the same time, by lowering infrastructure costs among which energy consumption plays a major role. In this paper, we propose a framework able to automatically manage resources of cloud infrastructures in order to simultaneously achieve suitable QoS levels and to reduce as much as possible the amount of energy used for providing services. We show, through simulation, that our approach is able to dynamically adapt to time-varying workloads (without any prior knowledge) and to significantly reduce QoS violations and energy consumption with respect to traditional static approaches.

A Game-Theoretic Approach to Coalition Formation in Green Cloud Federations

Federations among sets of Cloud Providers (CPs), whereby a set of CPs agree to mutually use their own resources to run the VMs of other CPs, are considered a promising solution to the problem of reducing the energy cost. In this paper, we address the problem of federation formation for a set of CPs, whose solution is necessary to exploit the potential of cloud federations for the reduction of the energy bill. We devise an algorithm, based on cooperative game theory, that can be readily implemented in a distributed fashion, and that allows a set of CPs to cooperatively set up their federations in such a way that their individual profit is increased with respect to the case in which they work in isolation. We show that, by using our algorithm and the proposed CPs utility function, they are able to self-organize into Nash-stable federations and, by means of iterated executions, to adapt themselves to environmental changes. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

Peer-to-Peer Desktop Grids in the Real World: The ShareGrid Project

ShareGrid is a peer-to-peer desktop grid aimed at satisfying the computing needs of the small research laboratories located in the Piedmont area in Northern Italy. Share- Grid adopts a cooperative approach, in which each participant allows the other ones to use his/her own resources on a reciprocity basis. ShareGrid is based on the OurGrid middleware, that provides a set of mechanisms enabling participating entities to quickly, fairly, and securely share their resources. In this paper we report our experience in designing, deploying, and using ShareGrid, and we describe the applications using it, as well as the lessons we learned, the problems that still remain open, and some possible solutions to them.

FC2Q: exploiting fuzzy control in server consolidation for cloud applications with SLA constraints

Modern cloud data centers rely on server consolidation (the allocation of several virtual machines on the same physical host) to minimize their costs. Choosing the right consolidation level (how many and which virtual machines are assigned to a physical server) is a challenging problem, because contemporary multitier cloud applications must meet service level agreements in face of highly dynamic, nonstationary, and bursty workloads. In this paper, we deal with the problem of achieving the best consolidation level that can be attained without violating application service level agreements. We tackle this problem by devising fuzzy controller for consolidation and QoS (FC2Q), a resource management framework exploiting feedback fuzzy logic control, that is able to dynamically adapt the physical CPU capacity allocated to the tiers of an application in order to precisely match the needs induced by the intensity of its current workload. We implement FC2Q on a real testbed and use this implementation to demonstrate its ability of meeting the aforementioned goals by means of a thorough experimental evaluation, carried out with real‐world cloud applications and workloads. Furthermore, we compare the performance achieved by FC2Q against those attained by existing state‐of‐the‐art alternative solutions, and we show that FC2Q works better than them in all the considered experimental scenarios. Copyright

The ShareGrid Peer-to-Peer Desktop Grid: Infrastructure, Applications, and Performance Evaluation

Peer-to-Peer (P2P) Desktop Grids are computing infrastructures that aggregate a set of desktop-class machines in which all the participating entities have the same roles, responsibilities, and rights. In this paper, we present ShareGrid, a P2P Desktop Grid infrastructure based on the OurGrid middleware, that federates the resources provided by a set of small research laboratories to easily share and use their computing resources. We discuss the techniques and tools we employed to ensure scalability, efficiency, and usability, and describe the various applications used on it. We also demonstrate the ability of ShareGrid of providing good performance and scalability by reporting the results of experimental evaluations carried out by running various applications with different resource requirements. Our experience with ShareGrid indicates that P2P Desktop Grids can represent an effective answer to the computing needs of small research laboratories, as long as they provide both ease of management and use, and good scalability and performance.

Fuzzy-QaE: Achieving QoS Guarantees and Energy Savings for Cloud Applications with Fuzzy Control

We address the problem of managing cloud applications, consisting of a set of virtual machines (VMs), characterized by bursty and dynamic workloads, in such a way to provide guarantees on their Quality-of-Services (QoS) and, at the same time, to minimize the energy consumption of the physical infrastructure running them. We propose a fuzzy controller, Fuzzy-Q& E, that is able to allocate to the VMs of each cloud application the minimum amount of physical capacity needed to meet its QoS requirements. In this way, the number of physical resources that must be switched-on at any given time is reduced with respect to the case in which physical machines are statically provisioned and, consequently, less energy is required to run a given cloud workload. We implement a prototype of our controller on a Xen-based testbed, and we perform a set of experiments using an E-Commerce benchmark in which we compare Fuzzy-Q&E against Dyna QoS, a state-of-the-art fuzzy controller for virtualized resources. Experimental results show that Fuzzy-Q&E out performs Dyna QoS both in terms of the ability of meeting the QoS level of the application, and of the amount of physical capacity allocated to each VM.

Maximizing profit in green cellular networks through collaborative games

We consider the problem of maximizing operators profit in green cellular networks.We model the problem as a cooperative game with transferable utility.We devise a distributed algorithm for the formation of maximum profits coalitions.We show its effectiveness through experimental analysis in realistic scenarios.We assess the impact of energy price and user population on the operators profits. In this paper, we deal with the problem of maximizing the profit of Network Operators (NOs) of green cellular networks in situations where Quality-of-Service (QoS) guarantees must be ensured to users, and Base Stations (BSs) can be shared among different operators.We show that if NOs cooperate among them, by mutually sharing their users and BSs, then each one of them can improve its net profit.By using a game-theoretic framework, we study the problem of forming stable coalitions among NOs. Furthermore, we propose a mathematical optimization model to allocate users to a set of BSs, in order to reduce costs and, at the same time, to meet user QoS for NOs inside the same coalition. Based on this, we propose an algorithm, based on cooperative game theory, that enables each operator to decide with whom to cooperate in order to maximize its profit.This algorithms adopts a distributed approach in which each NO autonomously makes its own decisions, and where the best solution arises without the need to synchronize them or to resort to a trusted third party.The effectiveness of the proposed algorithm is demonstrated through a thorough experimental evaluation considering real-world traffic traces, and a set of realistic scenarios. The results we obtain indicate that our algorithm allows a population of NOs to significantly improve their profits thanks to the combination of energy reduction and satisfaction of QoS requirements.

FCMS: A fuzzy controller for CPU and memory consolidation under SLA constraints

Cloud providers (CPs) rely on server consolidation (the allocation of several virtual machines [VMs] on the same physical server) to minimize their costs. Maximizing the consolidation level is thus become 1 of the major goals of cloud providers. This is a challenging task because it requires the ability of estimating, in a resource contention scenario, multidimensional resource demands for multitier cloud applications that must meet service‐level agreements (SLAs) in face of nonstationary workloads. In this paper, we cope with the problem of jointly allocating CPU and memory capacity to (a) precisely estimate their capacity required by each VM to meet its SLAs and (b) coordinate their allocation to limit the negative effects due to the interactions of dynamic allocation mechanisms, which, if ignored, can lead to SLA violations. We tackle this problem by devising FCMS, a feedback fuzzy controller that is able to dynamically adjust the CPU and memory capacity allocated to each VM in a coordinated way, to precisely match the needs induced by the incoming workload. By means of an extensive experimental evaluation, we show that FCMS is able to achieve the above goals and works better than existing state‐of‐the‐art alternative solution in all the considered experimental scenarios.

Forensic analysis of the ChatSecure instant messaging application on android smartphones

We present the forensic analysis of the artifacts generated on Android smartphones by ChatSecure, a secure Instant Messaging application that provides strong encryption for transmitted and locally-stored data to ensure the privacy of its users.We show that ChatSecure stores local copies of both exchanged messages and files into two distinct, AES-256 encrypted databases, and we devise a technique able to decrypt them when the secret passphrase, chosen by the user as the initial step of the encryption process, is known.Furthermore, we show how this passphrase can be identified and extracted from the volatile memory of the device, where it persists for the entire execution of ChatSecure after having been entered by the user, thus allowing one to carry out decryption even if the passphrase is not revealed by the user.Finally, we discuss how to analyze and correlate the data stored in the databases used by ChatSecure to identify the IM accounts used by the user and his/her buddies to communicate, as well as to reconstruct the chronology and contents of the messages and files that have been exchanged among them.For our study we devise and use an experimental methodology, based on the use of emulated devices, that provides a very high degree of reproducibility of the results, and we validate the results it yields against those obtained from real smartphones.