Adel Nadjaran Toosi

Interconnected Cloud Computing Environments: Challenges, Taxonomy, and Survey

A brief review of the Internet history reveals the fact that the Internet evolved after the formation of primarily independent networks. Similarly, interconnected clouds, also called Inter-cloud, can be viewed as a natural evolution of cloud computing. Recent studies show the benefits in utilizing multiple clouds and present attempts for the realization of an Inter-cloud or federated cloud environment. However, cloud vendors have not taken into account cloud interoperability issues, and each cloud comes with its own solution and interfaces for services. This survey initially discusses all the relevant aspects motivating cloud interoperability. Furthermore, it categorizes and identifies possible cloud interoperability scenarios and architectures. The spectrum of challenges and obstacles that the Inter-cloud realization is faced with are covered, a taxonomy of them is provided, and fitting enablers that tackle each challenge are identified. All these aspects require a comprehensive review of the state of the art, including ongoing projects and studies in the area. We conclude by discussing future directions and trends toward the holistic approach in this regard.

A new approach to intrusion detection based on an evolutionary soft computing model using neuro-fuzzy classifiers

An intrusion detection systems main goal is to classify activities of a system into two major categories: normal and suspicious (intrusive) activities. Intrusion detection systems usually specify the type of attack or classify activities in some specific groups. The objective of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network. Among the several soft computing paradigms, neuro-fuzzy networks, fuzzy inference approach and genetic algorithms are investigated in this work. A set of parallel neuro-fuzzy classifiers are used to do an initial classification. The fuzzy inference system would then be based on the outputs of neuro-fuzzy classifiers, making final decision of whether the current activity is normal or intrusive. Finally, in order to attain the best result, genetic algorithm optimizes the structure of our fuzzy decision engine. The experiments and evaluations of the proposed method were performed with the KDD Cup 99 intrusion detection dataset.

Resource Provisioning Policies to Increase IaaS Provider's Profit in a Federated Cloud Environment

Cloud Federation is a recent paradigm that helps Infrastructure as a Service (IaaS) providers to overcome resource limitation during spikes in demand for Virtual Machines (VMs) by outsourcing requests to other federation members. IaaS providers also have the option of terminating spot VMs, i.e, cheaper VMs that can be canceled to free resources for more profitable VM requests. By both approaches, providers can expect to reject less profitable requests. For IaaS providers, pricing and profit are two important factors, in addition to maintaining a high Quality of Service (QoS) and utilization of their resources to remain in the business. For this, a clear understanding of the usage pattern, types of requests, and infrastructure costs are necessary while making decisions to terminate spot VMs, outsourcing or contributing to the federation. In this paper, we propose policies that help in the decision-making process to increase resources utilization and profit. Simulation results indicate that the proposed policies enhance the profit, utilization, and QoS (smaller number of rejected VM requests) in a Cloud federation environment.

Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications

AFSA (artificial fish-swarm algorithm) is one of the best methods of optimization among the swarm intelligence algorithms. This algorithm is inspired by the collective movement of the fish and their various social behaviors. Based on a series of instinctive behaviors, the fish always try to maintain their colonies and accordingly demonstrate intelligent behaviors. Searching for food, immigration and dealing with dangers all happen in a social form and interactions between all fish in a group will result in an intelligent social behavior.This algorithm has many advantages including high convergence speed, flexibility, fault tolerance and high accuracy. This paper is a review of AFSA algorithm and describes the evolution of this algorithm along with all improvements, its combination with various methods as well as its applications. There are many optimization methods which have a affinity with this method and the result of this combination will improve the performance of this method. Its disadvantages include high time complexity, lack of balance between global and local search, in addition to lack of benefiting from the experiences of group members for the next movements.

SLA-based virtual machine management for heterogeneous workloads in a cloud datacenter

Efficient provisioning of resources is a challenging problem in cloud computing environments due to its dynamic nature and the need for supporting heterogeneous applications. Even though VM (Virtual Machine) technology allows several workloads to run concurrently and to use a shared infrastructure, still it does not guarantee application performance. Thus, currently cloud datacenter providers either do not offer any performance guarantee or prefer static VM allocation over dynamic, which leads to inefficient utilization of resources. Moreover, the workload may have different QoS (Quality Of Service) requirements due to the execution of different types of applications such as HPC and web, which makes resource provisioning much harder. Earlier work either concentrate on single type of SLAs (Service Level Agreements) or resource usage patterns of applications, such as web applications, leading to inefficient utilization of datacenter resources. In this paper, we tackle the resource allocation problem within a datacenter that runs different types of application workloads, particularly non-interactive and transactional applications. We propose an admission control and scheduling mechanism which not only maximizes the resource utilization and profit, but also ensures that the QoS requirements of users are met as specified in SLAs. In our experimental study, we found that it is important to be aware of different types of SLAs along with applicable penalties and the mix of workloads for better resource provisioning and utilization of datacenters. The proposed mechanism provides substantial improvement over static server consolidation and reduces SLA violations.

A coordinator for scaling elastic applications across multiple clouds

Cloud computing allows customers to dynamically scale their applications, software platforms, and hardware infrastructures according to negotiated Service Level Agreements (SLAs). However, resources available in a single Cloud data center are limited, thus if a large demand for an elastic application is observed in a given time, a Cloud provider will not be able to deliver uniform Quality of Service (QoS) to handle such a demand and SLAs may be violated. One approach that can be taken to avoid such a scenario is enabling further growing of the application by scaling it across multiple, independent Cloud data centers, following market-based trading and negotiation of resources. This approach, as envisioned in the InterCloud project, is realized by agents called Cloud Coordinators and allows for an increase in performance, reliability, and scalability of elastic applications. In this paper, we propose both an architecture for such Cloud Coordinator and an extensible design that allows its adoption in different public and private Clouds. An evaluation of the Cloud Coordinator prototype running in a small-scale scenario shows the effectiveness of the proposed approach and its impact on elastic applications.

Financial Option Market Model for Federated Cloud Environments

Pay-per-use service by Cloud service providers has attracted customers in the recent past and is still evolving. Since the resources being dealt within Clouds are non-storable and the physical resources need to be replaced very often, pricing the service in a way that would return profit on the initial capital investments to the service providers has been a major issue. Moreover, to maintain Quality of Service (QoS) to customers who reserve the resources in advance and may or may not be using the resources at a future date makes the resources wasted, if not allocated to other on-demand users. Therefore, a need for a mechanism to guarantee the resources to reserved users whenever they need them, while keeping the resources busy all the time is in very high demand. The concept of federation of Cloud service providers has been proposed in the past wherein resources are traded between the providers whenever need arises. We propose a financial option based Cloud resources pricing model to address the above situation. This model allows a provider to hedge the critical and risky situation of reserved users requesting the resources while all the resources have been allocated to other users, by trading (buying or outsourcing) resources from other service providers in the Cloud federation. We show that using financial option based contracts between Cloud providers in a Cloud federation, providers are able to enhance profit and acquire the needed resources at any given time. It would also help creating a trust and goodwill from the clients on the Cloud service providers by less number of QoS violation.

Brokering Algorithms for Optimizing the Availability and Cost of Cloud Storage Services

In recent years, cloud storage providers have gained popularity for personal and organizational data, and provided highly reliable, scalable and flexible resources to cloud users. Although cloud providers bring advantages to their users, most cloud providers suffer outages from time-to-time. Therefore, relying on a single cloud storage services threatens service availability of cloud users. We believe that using multi-cloud broker is a plausible solution to remove single point of failure and to achieve very high availability. Since highly reliable cloud storage services impose enormous cost to the user, and also as the size of data objects in the cloud storage reaches magnitude of exabyte, optimal selection among a set of cloud storage providers is a crucial decision for users. To solve this problem, we propose an algorithm that determines the minimum replication cost of objects such that the expected availability for users is guaranteed. We also propose an algorithm to optimally select data centers for striped objects such that the expected availability under a given budget is maximized. Simulation experiments are conducted to evaluate our algorithms, using failure probability and storage cost taken from real cloud storage providers.

Fuzzy Adaptive Artificial Fish Swarm Algorithm

Artificial Fish Swarm Algorithm (AFSA) is a kind of swarm intelligence algorithms which is usually employed in optimization problems. There are many parameters to adjust in AFSA like visual and step. Through constant initializing of visual and step parameters, algorithm is only able to do local searching or global searching. In this paper, two new adaptive methods based on fuzzy systems are proposed to control the visual and step parameters during the AFSA execution in order to control the capability of global and local searching adaptively. First method uniformly adjusts the visual and step of all fish whereas in the second method, each artificial fish has its own fuzzy controller for adjusting its visual and step parameters. Evaluations of the proposed methods were performed on eight well known benchmark functions in comparison with standard AFSA and Particle Swarm Optimization (PSO). The overall results show that proposed algorithm can be effective surprisingly.

A Novel Soft Computing Model Using Adaptive Neuro-Fuzzy Inference System for Intrusion Detection

The main purpose of this paper is to incorporate several soft computing techniques into the classifying system to detect and classify intrusions from normal behaviors based on the attack type in a computer network. Some soft computing paradigms such as neuro-fuzzy networks, fuzzy inference approach and genetic algorithms are investigated in this work. A set of neuro-fuzzy classifiers are used to perform an initial classification. The fuzzy inference system would then be based on the outputs of neuro-fuzzy classifiers, making decision of whether the current activity is normal or intrusive. As a final point, in order to attain the best result, a genetic algorithm optimizes the structure of the fuzzy decision engine. The experiments and evaluations of the proposed method were done with the KDD Cup 99 intrusion detection dataset.