Summary
Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.

iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

Resource management in a cloud environment is a hard problem, due to: the scale of modern data centers; the heterogeneity of resource types and their interdependencies; the variability and unpredictability of the load; as well as the range of objectives of the different actors in a cloud ecosystem. Consequently, both academia and industry began significant research efforts in this area. In this paper, we survey the recent literature, covering 250+ publications, and highlighting key results. We outline a conceptual framework for cloud resource management and use it to structure the state-of-the-art review. Based on our analysis, we identify five challenges for future investigation. These relate to: providing predictable performance for cloud-hosted applications; achieving global manageability for cloud systems; engineering scalable resource management systems; understanding economic behavior and cloud pricing; and developing solutions for the mobile cloud paradigm .

Resource Management in Clouds: Survey and Research Challenges

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

/pdf/quality-of-service-in-cloud-computing-modeling-techniques-1ivbgaewet.pdf

Quality-of-service in cloud computing: modeling techniques and their applications

Since the phenomenon of cloud computing was proposed, there is an unceasing interest for research across the globe. Cloud computing has been seen as unitary of the technology that poses the next-generation computing revolution and rapidly becomes the hottest topic in the field of IT. This fast move towards Cloud computing has fuelled concerns on a fundamental point for the success of information systems, communication, virtualization, data availability and integrity, public auditing, scientific application, and information security. Therefore, cloud computing research has attracted tremendous interest in recent years. In this paper, we aim to precise the current open challenges and issues of Cloud computing. We have discussed the paper in three-fold: first we discuss the cloud computing architecture and the numerous services it offered. Secondly we highlight several security issues in cloud computing based on its service layer. Then we identify several open challenges from the Cloud computing adoption perspective and its future implications. Finally, we highlight the available platforms in the current era for cloud research and development.

/pdf/cloud-computing-features-issues-and-challenges-a-big-picture-32vbbraz53.pdf

Cloud Computing Features, Issues, and Challenges: A Big Picture

Fog computing is an emerging technology to address computing and networking bottlenecks in large scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed and possibly cooperative manner. These elements may be virtualized computing functions placed at edge devices or network elements on demand, realizing the “computing everywhere” concept. To put the current research in perspective, this paper provides an inclusive taxonomy for architectural, algorithmic and technologic aspects of fog computing. The computing paradigms and their architectural distinctions, including cloud, edge, mobile edge and fog computing are subsequently reviewed. Practical deployment of fog computing includes a number of different aspects such as system design, application design, software implementation, security, computing resource management and networking. A comprehensive survey of all these aspects from the architectural point of view is covered. Current reference architectures and major application-specific architectures describing their salient features and distinctions in the context of fog computing are explored. Base architectures for application, software, security, computing resource management and networking are presented and are evaluated using a proposed maturity model.

/pdf/fog-computing-a-comprehensive-architectural-survey-2sxms5zo7j.pdf

Fog Computing: A Comprehensive Architectural Survey

Applications are shifting into large scale, virtualized data centres that provide resources on a pay-per-usage basis. Data centres must minimize resource consumption while providing enough resources to meet application requirements. To meet highly variable application demands, a dynamic approach to virtual machine (VM) management is required. This involves three basic management operations: (i) placing newly arrived VMs, (ii) migrating (moving) VMs off of highly utilized machines to avoid performance degradation, and (iii) migrating VMs off of underutilized machines so that they may be shut down to save power. We define a management strategy to consist of a set of policies that guide these three operations. We consider the goals of minimizing Service Level Agreement violations and minimizing power consumption. Developing a management strategy to achieve both of these goals is challenging, as the goals are often in conflict. We propose achieving both goals through dynamically switching between two management strategies, each with a single goal, depending on current data centre state. We propose three methods of dynamically switching strategies, and evaluate these methods through simulation. Dynamic strategy switching offers improved results over a single management strategy.

The right tool for the job: Switching data centre management strategies at runtime

Developing algorithms to dynamically manage resources in a virtualized, multi-tenant data centre is challenging. Experimenting with such algorithms on the data centre scale is impractical due to size and complexity. Thus, there is a need for simulation tools to allow rapid development and evaluation of management techniques. We present DCSim, an extensible framework for simulating a multi-tenant, virtualized data centre.

DCSim: A data centre simulation tool

Computing today is increasingly moving into large-scale virtualized data centres, offering computing resources in the form of virtual machines (VMs) on a pay-per-usage basis. In order to minimize costs, VMs should be consolidated on as few physical machines (PMs) as possible, switching idle PMs into a power saving mode. It may be necessary to dynamically allocate and reallocate VMs to PMs in order to meet highly dynamic VM resource requirements. The problem of assigning VMs to PMs is known to be NP-Hard. Most solutions focus on a centralized approach, with a single management node making allocation decisions periodically. This approach suffers from poor scalability and the existence of a single point of failure. We present a fully distributed approach to dynamic VM management, and evaluate our approach using a simulation tool. Results indicate that the distributed approach can achieve similar performance to the centralized solution, while eliminating the single point of failure and reducing the network bandwidth required for management.

http://www.cnsm-conf.org/2013/documents/papers/CNSM/p166-tighe.pdf

A distributed approach to dynamic VM management

Computing is increasingly moving into large-scale data centres, providing resources on-demand for clients on a pay-per-usage basis. One form of such on-demand computing is an Infrastructure as a Service (IaaS) Cloud, which provides low level access to virtualized resources. Developing and evaluating data centre management techniques for such large-scale data centres presents a significant challenge. As such, most work turns to simulation tools as the test environment. We present a number of extensions and additional features to an existing simulation tool, DCSim. Our improvements to DCSim include work on the core of the simulator, improved event, communication and management mechanisms, and a more complete model of the structure of a data centre. We also present improved simulation configuration tools and output, including a unique visualization tool. We evaluate the usefulness of the simulator through a demonstration of its use in comparing dynamic VM management techniques.

/pdf/towards-an-improved-data-centre-simulation-with-dcsim-5b7pbi5qgk.pdf

Towards an improved data centre simulation with DCSim

Cloud computing continues to mature and more applications continue to be deployed in public clouds. Client applications deployed in the cloud should automatically scale up and down to match changing workload demands, though they must be careful to ensure that sufficient resources are provisioned to achieve performance objectives. The cloud provider, on the other hand, attempts to reduce costs by reducing power consumption by consolidating load onto fewer, highly utilized machines. In this work, we introduce an algorithm that integrates both application autoscaling and dynamic virtual machine (VM) allocation into a single algorithm in order to achieve the goals of both cloud provider and client. Further, we consider multi-VM applications, such as multi-tiered web-based applications, and extend the integrated algorithm to take the network topology into account when placing or migrating applications. The goal is to reduce VM-to-VM communication latency; our focus is on trying to contain applications within the same racks. We evaluate our work through simulation, showing that the integrated algorithm can achieve better application performance with a significant reduction in virtual machine live migrations, and the topology-aware extension successfully places applications within a single rack.

/pdf/topology-and-application-aware-dynamic-vm-management-in-the-1qf4arb7ws.pdf

Topology and Application Aware Dynamic VM Management in the Cloud

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