CloudScope: diagnosing and managing performance interference in multi-tenant clouds

© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%

A survey of intrusion detection techniques in Cloud

Cloud computing provides scalable, virtualized on-demand services to the end users with greater flexibility and lesser infrastructural investment. These services are provided over the Internet using known networking protocols, standards and formats under the supervision of different managements. Existing bugs and vulnerabilities in underlying technologies and legacy protocols tend to open doors for intrusion. This paper, surveys different intrusions affecting availability, confidentiality and integrity of Cloud resources and services. It examines proposals incorporating Intrusion Detection Systems (IDS) in Cloud and discusses various types and techniques of IDS and Intrusion Prevention Systems (IPS), and recommends IDS/IPS positioning in Cloud architecture to achieve desired security in the next generation networks

Introduction to the Computation Offloading from Mobile Devices to the Edge of Mobile Network

This paper introduces the concept of Small Cell Cloud (SCC) composed of multiple Cloud-enabled Small Cells (CeSCs), which provide radio connection for mobile User Equipment (UE) such as smart-phones or wearables such as smart glasses. Moreover, CeSCs host computations offloaded from UEs in a way similar to centralized cloud, yet different in its proximity to users. Proposed client-server architecture of SCC con-veys mechanisms for moving offloaded computations from the UEs to CeSCs. Real-life implementation of the SCC architecture relies on custom-developed Of-floading Framework which is responsible for low-level communication between the UE and the SCC. The Of-floading Framework is accompanied by an Augmented Reality (AR) app, which employs intensive computa-tions for discovery of places of interest. Such app is latency-sensitive, a criterion which makes computation offloading beneficial due to its ability to decrease la-tency. The combination of the O˜oading Framework and the AR app makes up an SCC testbed used for fur-ther performance evaluation. Numerous measurements are carried out to examine the impact of various pa-rameters. Based on Proof-of-concept implementation and thorough measurements, it has been revealed that computation offloading can decrease overall latency as much as to 47 % and energy consumption on the UE side to 56

Building Programmable Wireless Networks: An Architectural Survey

In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through co-located data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. The emergence of "programmable wireless networks", that allow greater flexibility, ease of management and configurability, is a step in the right direction to overcome the aforementioned shortcomings of the wireless networks. In this paper, we provide a broad overview of the architectures proposed in literature for building programmable wireless networks focusing primarily on three popular techniques, i.e., software defined networks, cognitive radio networks, and virtualized networks. This survey is a self-contained tutorial on these techniques and its applications. We also discuss the opportunities and challenges in building next-generation programmable wireless networks and identify open research issues and future research directions.Comment: 19 page

Challenges in real-time virtualization and predictable cloud computing

Cloud computing and virtualization technology have revolutionized general-purpose computing applications in the past decade. The cloud paradigm offers advantages through reduction of operation costs, server consolidation, flexible system configuration and elastic resource provisioning. However, despite the success of cloud computing for general-purpose computing, existing cloud computing and virtualization technology face tremendous challenges in supporting emerging soft real-time applications such as online video streaming, cloud-based gaming, and telecommunication management. These applications demand real-time performance in open, shared and virtualized computing environments. This paper identifies the technical challenges in supporting real-time applications in the cloud, surveys recent advancement in real-time virtualization and cloud computing technology, and offers research directions to enable cloud-based real-time applications in the future

Utility-based Allocation of Resources to Virtual Machines in Cloud Computing

In recent years, cloud computing has gained a wide spread use as a new computing model that offers elastic resources on demand, in a pay-as-you-go fashion. One important goal of a cloud provider is dynamic allocation of Virtual Machines (VMs) according to workload changes in order to keep application performance to Service Level Agreement (SLA) levels, while reducing resource costs. The problem is to find an adequate trade-off between the two conflicting objectives of application performance and resource costs. In this dissertation, resource allocation solutions for this trade-off are proposed by expressing application performance and resource costs in a utility function. The proposed solutions allocate VM resources at the global data center level and at the local physical machine level by optimizing the utility function. The utility function, given as the difference between performance and costs, represents the profit of the cloud provider and offers the possibility to capture in a flexible and natural way the performance-cost trade-off. For global level resource allocation, a two-tier resource management solution is developed. In the first tier, local node controllers are located that dynamically allocate resource shares to VMs, so to maximize a local node utility function. In the second tier, there is a global controller that makes VM live migration decisions in order to maximize a global utility function. Experimental results show that optimizing the global utility function by changing the number of physical nodes according to workload maintains the performance at acceptable levels while reducing costs. To allocate multiple resources at the local physical machine level, a solution based on feed-back control theory and utility function optimization is proposed. This dynamically allocates shares to multiple resources of VMs such as CPU, memory, disk and network I/O bandwidth. In addressing the complex non-linearities that exist in shared virtualized infrastructures between VM performance and resource allocations, a solution is proposed that allocates VM resources to optimize a utility function based on application performance and power modelling. An Artificial Neural Network (ANN) is used to build an on- line model of the relationships between VM resource allocations and application performance, and another one between VM resource allocations and physical machine power. To cope with large utility optimization times in the case of an increased number of VMs, a distributed resource manager is proposed. It consists of several ANNs, each responsible for modelling and resource allocation of one VM, while exchanging information with other ANNs for coordinating resource allocations. Experiments, in simulated and realistic environments, show that the distributed ANN resource manager achieves better performance-power trade-offs than a centralized version and a distributed non-coordinated resource manager. To deal with the difficulty of building an accurate online application model and long model adaptation time, a solution that offers model-free resource management based on fuzzy control is proposed. It optimizes a utility function based on a hill-climbing search heuristic implemented as fuzzy rules. To cope with long utility optimization time in the case of an increased number of VMs, a multi-agent fuzzy controller is developed where each agent, in parallel with others, optimizes its own local utility function. The fuzzy control approach eliminates the need to build a model beforehand and provides a robust solution even for noisy measurements. Experimental results show that the multi-agent fuzzy controller performs better in terms of utility value than a centralized fuzzy control version and a state-of-the-art adaptive optimal control approach, especially for an increased number of VMs. Finally, to address some of the problems of reactive VM resource allocation approaches, a proactive resource allocation solution is proposed. This approach decides on VM resource allocations based on resource demand prediction, using a machine learning technique called Support Vector Machine (SVM). To deal with interdependencies between VMs of the same multi-tier application, cross- correlation demand prediction of multiple resource usage time series of all VMs of the multi-tier application is applied. As experiments show, this results in improved prediction accuracy and application performance

The Role of Mixed Criticality Technology in Industry 4.0

[EN] Embedded systems used in critical systems, such as aeronautics, have undergone continuous evolution in recent years. In this evolution, many of the functionalities offered by these systems have been adapted through the introduction of network services that achieve high levels of interconnectivity. The high availability of access to communications networks has enabled the development of new applications that introduce control functions with higher levels of intelligence and adaptation. In these applications, it is necessary to manage different components of an application according to their levels of criticality. The concept of "Industry 4.0" has recently emerged to describe high levels of automation and flexibility in production. The digitization and extensive use of information technologies has become the key to industrial systems. Due to their growing importance and social impact, industrial systems have become part of the systems that are considered critical. This evolution of industrial systems forces the appearance of new technical requirements for software architectures that enable the consolidation of multiple applications in common hardware platforms-including those of different criticality levels. These enabling technologies, together with use of reference models and standardization facilitate the effective transition to this approach. This article analyses the structure of Industry 4.0 systems providing a comprehensive review of existing techniques. The levels and mechanisms of interaction between components are analyzed while considering the impact that the handling of multiple levels of criticality has on the architecture itself-and on the functionalities of the support middleware. Finally, this paper outcomes some of the challenges from a technological and research point of view that the authors identify as crucial for the successful development of these technologies.This research was funded by the Spanish Science and Innovation Ministry MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN201786520-C3-1-R.Simó Ten, JE.; Balbastre, P.; Blanes Noguera, F.; Poza-Lujan, J.; Guasque Ortega, A. (2021). The Role of Mixed Criticality Technology in Industry 4.0. Electronics. 10(3):1-16. https://doi.org/10.3390/electronics1003022611610