Computer Science

Mobile ad hoc networks communicate in a self-organized way without depending on any fixed infrastructure.
The issue of selfish nodes, which may refuse to cooperate, is a great challenge in such networks and may cause network
throughput to drastically reduce. Energy-based selfishness is a category of selfishness in which a selfish node shows nondeterministic
and probabilistic selfishness behaviors based on level of its energy. In this paper, we propose a mechanism
for coping with this kind of selfishness. This mechanism called CEMDEEM not only detects and isolates energy-based
and traditional selfish nodes, but also malicious behaviors like spoofing. We also evaluate performance of Dynamic
Source Routing (DSR) protocol fortified by CEMDEEM in the presence of different percentages of selfish nodes. Results
show that CEMDEEM noticeably improves network performance with a reasonable additional packet overhead and network
delay especially when percentage of selfish nodes is not more than 40.

This paper presents an analysis on quality driven approaches which embodies non-functional requirements into software architecture design. The analysis characterizes vocabularies and concepts of the area, with exhibiting a comparison of the two main techniques. In the first technique, architectural tactics are represented and their semantics is clearly defined as a UML-based pattern specification notation called RBML. Given a set of non-functional requirements, architectural tactics are selected and composed into an initial architecture for the application. The second technique designates some attribute primitives which are similar to architectural patterns. It then introduces a method called Attribute Driven Design, to involve attribute primitives for satisfying a set of general scenarios. In this analysis, we intend to give a brief description of the both approaches.

Infrastructure as a Service (IaaS) is one of the most significant and fastest growing fields in cloud computing. To efficiently
use the resources of an IaaS cloud, several important factors such as performance, availability, and power consumption need to be
considered and evaluated carefully. Evaluation of these metrics is essential for cost-benefit prediction and quantification of different
strategies which can be applied to cloud management. In this paper, analytical models based on Stochastic Reward Nets (SRNs) are
proposed to model and evaluate an IaaS cloud system at different levels. To achieve this, an SRN is initially presented to model a group
of physical machines which are controlled by a management layer. Afterwards, the SRN models presented for the groups of physical
machines in the first stage are combined to capture a monolithic model representing an entire IaaS cloud. Since the monolithic model
does not scale well for large cloud systems, two approximate SRN models using folding and fixed-point iteration techniques are
proposed to evaluate the performance, availability, and power consumption of the IaaS cloud. The existence of a solution for the
fixed-point approximate model is proved using Brouwer’s fixed-point theorem. A validation of the proposed monolithic and approximate
models against both an ad-hoc discrete-event simulator developed in Java and the CloudSim framework is presented. The
analytic-numeric results obtained from applying the proposed models to sample cloud systems show that the errors introduced by
approximate models are insignificant while an improvement of several orders of magnitude in the state space reduction of the
monolithic model is obtained.

In this paper, Stochastic Activity Networks (SANs) are used to model and evaluate the performance and power consumption of an Infrastructure-as-a-Service (IaaS) cloud. The proposed SAN model is scalable and flexible, yet encompasses some details of an IaaS cloud, such as Virtual Machine (VM) provisioning, VM multiplexing, and failure/repair behavior of VMs. Using the proposed SAN, a power-aware self-adaptive resource management scheme is presented for IaaS clouds that automatically adjusts the number of powered-on Physical Machines (PMs) regarding variable workloads in different time intervals. The proposed scheme respects user-oriented metrics by avoiding Service Level Agreement (SLA) violations while taking provider-oriented metrics into consideration. The behavior of the proposed scheme is analyzed when the arriving workload changes, and then its performance is compared with two non-adaptive baselines based on diverse performance and power consumption measures defined on the system. A validation of the proposed SAN model and the resource management scheme against an adapted version of the CloudSim framework is also presented.

In this paper, Stochastic Activity Networks (SANs) are used to model and evaluate the performance and power consumption of an Infrastructure-as-a-Service (IaaS) cloud. The proposed SAN model is scalable and flexible, yet encompasses some details of an IaaS cloud, such as Virtual Machine (VM) provisioning, VM multiplexing, and failure/repair behavior of VMs. Using the proposed SAN, a power-aware self-adaptive resource management scheme is presented for IaaS clouds that automatically adjusts the number of powered-on Physical Machines (PMs) regarding variable workloads in different time intervals. The proposed scheme respects user-oriented metrics by avoiding Service Level Agreement (SLA) violations while taking provider-oriented metrics into consideration. The behavior of the proposed scheme is analyzed when the arriving workload changes, and then its performance is compared with two non-adaptive baselines based on diverse performance and power consumption measures defined on the system. A validation of the proposed SAN model and the resource management scheme against an adapted version of the CloudSim framework is also presented.

Long and continuous running of software can cause software aging-induced errors and failures. Cloud data centers suffer from these kinds of failures when Virtual Machine Monitors (VMMs), which control the execution of Virtual Machines (VMs), age. Software rejuvenation is a proactive fault management technique that can prevent the occurrence of future failures by terminating VMMs, cleaning up their internal states, and restarting them. However, the appropriate time and type of VMM rejuvenation can affect performance, availability, and power consumption of a system. In this paper, an analytical model is proposed based on Stochastic Activity Networks for performance evaluation of Infrastructure-as-a-Service cloud systems. Using the proposed model,
a two-threshold power-aware software rejuvenation scheme is presented. Many details of real cloud systems, such as VM multiplexing, migration of VMs between VMMs, VM heterogeneity, failure of VMMs, failure of VM migration, and different probabilities for arrival of different VM request types are investigated using the proposed model. The performance of the proposed rejuvenation scheme is compared with two baselines based on diverse performance, availability, and power consumption measures defined on the system.