Learn-as-You-Go with Megh: Efficient Live Migration of Virtual Machines

Basu, Debabrota; Wang, Xiayang; Hong, Yang; Chen, Haibo; Bressan, Stéphane

doi:10.1109/icdcs.2017.173

Cited by 12 publications

(16 citation statements)

References 6 publications

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“…The live migration in cloud systems refers to the process of moving a virtual machine state and system properties from one host source to another host target while maintaining continues service [42]. All VM configurations, storage, operating system, and application state remain unchanged.…”

Section: Virtualisation and Live Migrationmentioning

confidence: 99%

“…If the migration process is unsuccess-ful, the process will be repeated. There are several techniques to reduce the total migration time, such as work presented in [42], [45]- [47]. In the post-copy migration method, the source host is suspended, the VMs minimal processor state is copied to target host and only on completion the service is resumed on a target host.…”

Section: Virtualisation and Live Migrationmentioning

confidence: 99%

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A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions

2020

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This paper reviews the background and related studies in the areas of cloud systems, intrusion detection and blockchain applications against cyber attacks. This work aims to discuss collaborative anomaly detection systems for discovering insider and outsider attacks from cloud centres, including the technologies of virtualisation and containerisation, along with trusting intrusion detection and cloud systems using blockchain. Moreover, the ability to detect such malicious attacks is critical for conducting necessary mitigation, at an early stage, to minimise the impact of disruption and restore cloud operations and their live migration processes. This paper presents an overview of cloud architecture and categorises potential stateof-the-art security events based on their occurrence at different cloud deployment models. Network Intrusion Detection Systems (NIDS) in the cloud, involving types of classification and common detection approaches, are also described. Collaborative NIDSs for cloud-based blockchain applications are also explained to demonstrate how blockchain can address challenges related to data privacy and trust management. A summary of the research challenges and future research directions in these fields is also explained.

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Section: Virtualisation and Live Migrationmentioning

confidence: 99%

Section: Virtualisation and Live Migrationmentioning

confidence: 99%

A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions

2020

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“…This may also require rewriting network policy configurations from the beginning, which is one of the reasons why administrators are reluctant to transfer their systems to software-defined environments. Exodus [137] suggests generating network topologies that are functionally similar to the original networks. This methodology helps to identify the network topology related changes made during a migration process.…”

Section: Policy Enforcement and Validation: Floodlight [90]mentioning

confidence: 99%

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

Abbasi

Shamshirband

et al. 2019

IEEE Access

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Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

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“…To provide quick and energy efficient solutions, many prior works focus on developing intelligent policies to schedule compute tasks on fog hosts [5], [6], [7]. These methods have been dominated by heuristic techniques [7], [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

“…These methods adapt to changing scenarios and offer promising avenues for dynamic optimization [11]. However, they too are unable to efficiently manage volatile fog environments because of their poor modelling accuracies and low scalability [3], [5]. For accurate and scalable modelling of the fog environment, there have been many works that use deep learning based local search or learning models with neural networks which approximate an objective function such as energy consumption or response time [3], [5], [12].…”

Section: Introductionmentioning

confidence: 99%

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

Tuli

Poojara

Srirama

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Intelligent task placement and management of tasks in large-scale fog platforms is challenging due to the highly volatile nature of modern workload applications and sensitive user requirements of low energy consumption and response time. Container orchestration platforms have emerged to alleviate this problem with prior art either using heuristics to quickly reach scheduling decisions or AI driven methods like reinforcement learning and evolutionary approaches to adapt to dynamic scenarios. The former often fail to quickly adapt in highly dynamic environments, whereas the latter have run-times that are slow enough to negatively impact response time. Therefore, there is a need for scheduling policies that are both reactive to work efficiently in volatile environments and have low scheduling overheads. To achieve this, we propose a Gradient Based Optimization Strategy using Back-propagation of gradients with respect to Input (GOBI). Further, we leverage the accuracy of predictive digital-twin models and simulation capabilities by developing a Coupled Simulation and Container Orchestration Framework (COSCO). Using this, we create a hybrid simulation driven decision approach, GOBI*, to optimize Quality of Service (QoS) parameters. Co-simulation and the back-propagation approaches allow these methods to adapt quickly in volatile environments. Experiments conducted using real-world data on fog applications using the GOBI and GOBI* methods, show a significant improvement in terms of energy consumption, response time, Service Level Objective and scheduling time by up to 15, 40, 4, and 82 percent respectively when compared to the state-of-the-art algorithms.

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Learn-as-You-Go with Megh: Efficient Live Migration of Virtual Machines

Cited by 12 publications

References 6 publications

A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions

A Review of Intrusion Detection and Blockchain Applications in the Cloud: Approaches, Challenges and Solutions

Software-Defined Cloud Computing: A Systematic Review on Latest Trends and Developments

COSCO: Container Orchestration Using Co-Simulation and Gradient Based Optimization for Fog Computing Environments

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