A Systematic Literature Review on Cloud Computing Security: Threats and Mitigation Strategies

IEEE Access, Journal Year: 2021, Volume and Issue: 9, P. 57792 - 57807

Published: Jan. 1, 2021

Cloud computing has become a widely exploited research area in academia and industry. benefits both cloud services providers (CSPs) consumers. The security challenges associated with have been studied the literature. This systematic literature review (SLR) is aimed to existing studies on security, threats, challenges. SLR examined published between 2010 2020 within popular digital libraries. We selected 80 papers after meticulous screening of works answer proposed questions. outcomes this reported seven major threats services. results showed that data tampering leakage were among highly discussed topics chosen Other identified risks intrusion storage environment. SLR's also indicated consumers' outsourcing remains challenge for CSPs users. Our survey paper blockchain as partnering technology alleviate concerns. findings reveal some suggestions be carried out future bring confidentiality, integrity, availability.

Language: Английский

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Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

IEEE Access, Journal Year: 2020, Volume and Issue: 8, P. 37191 - 37201

Published: Jan. 1, 2020

The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead cloud computing, thereby achieving most the at network edge IoT devices. Fog can thus improve quality service delay-sensitive allowing such to take advantage low latency provided rather than high cloud. Therefore, tasks in various must be effectively distributed over nodes service, specifically task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm (PSO), are used propose different scheduling algorithms load balance under communication cost time considerations. experimental results proposed compared with those round robin (RR) algorithm. evaluations show that ACO-based scheduler achieves an improvement times PSO-based RR balances nodes.

Language: Английский

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Mobility-aware computational offloading in mobile edge networks: a survey

Cluster Computing, Journal Year: 2021, Volume and Issue: 24(4), P. 2735 - 2756

Published: April 9, 2021

Language: Английский

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A survey of data center consolidation in cloud computing systems

Computer Science Review, Journal Year: 2021, Volume and Issue: 39, P. 100366 - 100366

Published: Jan. 20, 2021

Language: Английский

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VNF and CNF Placement in 5G: Recent Advances and Future Trends

IEEE Transactions on Network and Service Management, Journal Year: 2023, Volume and Issue: 20(4), P. 4698 - 4733

Published: March 31, 2023

With the growing demand for openness, scalability, and granularity, mobile network function virtualization (NFV) has emerged as a key enabler most of operators. NFV decouples functions from hardware devices. This decoupling allows services, called Virtualized Network Functions (VNFs), to be hosted on commodity which simplifies enhances service deployment management providers, improves flexibility, leads efficient scalable resource usage, lower costs. The proper placement VNFs in hosting infrastructures is one main technical challenges. significantly influences network's performance, reliability, operating VNF NP-Hard. Therefore, there need methods that can cope with complexity problem find appropriate solutions reasonable duration. primary purpose this study provide taxonomy optimization techniques used tackle problems. We classify studied papers based performance metrics, methods, algorithms, environment. Virtualization not limited simply replacing physical machines virtual or VNFs, but may also include micro-services, containers, cloud-native systems. In context, second part our article focuses Containers (CNFs) edge/fog computing. Many issues have been considered traffic congestion, utilization, energy consumption, degradation, etc. For each matter, various are proposed through different surveys research addresses specific manner by suggesting single objective multi-objective types algorithms such heuristic, meta-heuristic, machine learning algorithms.

Language: Английский

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A systematic review of green-aware management techniques for sustainable data center

Sustainable Computing Informatics and Systems, Journal Year: 2024, Volume and Issue: 42, P. 100989 - 100989

Published: April 1, 2024

Language: Английский

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Efficient UAV-based mobile edge computing using differential evolution and ant colony optimization

PeerJ Computer Science, Journal Year: 2022, Volume and Issue: 8, P. e870 - e870

Published: Feb. 4, 2022

Internet of Things (IoT) tasks are offloaded to servers located at the edge network for improving power consumption IoT devices and execution times tasks. However, deploying could be difficult or even impossible in hostile terrain emergency areas where is down. Therefore, mounted on unmanned aerial vehicles (UAVs) support task offloading such scenarios. challenge that UAV has limited energy, delay-sensitive. In this paper, a UAV-based strategy proposed first, dynamically clustered considering energy UAVs, delays, second, hovers over each cluster head process The optimization problem determining optimal number clusters, specifying member cluster, modeled as mixed-integer, nonlinear constraint optimization. A discrete differential evolution (DDE) algorithm with new mutation crossover operators formulated problem, compared particle swarm (PSO) genetic (GA) meta-heuristics. Further, ant colony (ACO) employed identify shortest path heads traverse. simulation results validate effectiveness terms delays consumption.

Language: Английский

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LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

Tsinghua Science & Technology, Journal Year: 2024, Volume and Issue: 29(4), P. 1219 - 1231

Published: Feb. 9, 2024

Edge computing, which migrates compute-intensive tasks to run on the storage resources of edge devices, efficiently reduces data transmission loss and protects privacy. However, due limited computing capacity, devices fail support real-time streaming query processing. To address this challenge, first, we propose a Long Short-Term Memory (LSTM) network-based adaptive approach in intelligent end-edge-cloud system. Specifically, maximize Quality Experience (QoE) users by automatically adapting their resource requirements capacity through an event mechanism. Second, reduce uncertainty non-complete adaption device towards user's requirements, use LSTM network analyze real time. Finally, features are aggregated cloud reevaluate comprehensive capability ensure fast response user during dynamic adaptation matching process. A series experimental results show that proposed has superior performance compared with traditional centralized matrix decomposition based approaches.

Language: Английский

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Containerized Microservices Orchestration and Provisioning in Cloud Computing: A Conceptual Framework and Future Perspectives

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(12), P. 5793 - 5793

Published: June 7, 2022

Cloud computing is a rapidly growing paradigm which has evolved from having monolithic to microservices architecture. The importance of cloud data centers expanded dramatically in the previous decade, and they are now regarded as backbone modern economy. Cloud-based architecture incorporated by firms such Netflix, Twitter, eBay, Amazon, Hailo, Groupon, Zalando. Such arrangements deal with parallel deployment data-intensive workloads real time. Moreover, commonly utilized services web email require continuous operation without interruption. For that purpose, service providers must optimize resource management, efficient energy usage, carbon footprint reduction. This study presents conceptual framework manage high amount microservice execution while reducing response time, consumption, costs. proposed suggests four key agent services: (1) intelligent partitioning: responsible for classification; (2) dynamic allocation: used pre-execution distribution among containers then makes decisions allocation at runtime; (3) optimization: charge shifting ensuring optimal use; (4) mutation actions: these based on procedures will mutate center workloads. suggested was partially evaluated using custom-built simulation environment, demonstrated its efficiency potential implementation context. findings show engrossment can lead reduced number network calls, lower relatively dioxide emissions.

Language: Английский

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A Genetic Algorithm-Based Energy-Efficient Container Placement Strategy in CaaS

IEEE Access, Journal Year: 2019, Volume and Issue: 7, P. 121360 - 121373

Published: Jan. 1, 2019

Container placement (CP) is a nontrivial problem in as Service (CaaS). Many works the literature solve it by using linear server energy-consumption models. However, solutions of model makes different CPs indistinguishable with regard to energy consumption homogeneous host environment that has same amount active hosts. As such, these are inefficient. In this paper, we demonstrate an energy-saving gain can be achieved optimizing containers under nonlinear model. Specifically, leverage strategy based on genetic algorithm (GA) search optimal solution. Unfortunately, conventional GA incurs performance degradation when virtual machine (VM) resource utilization high. order problem, propose improved called IGA for efficiently searching CP solution introducing two exchange mutation operations and constructing function control parameter selectively usage operations. Extensive experiments carried out settings, their results show our better than existing strategies, i.e., spread binpack, efficiency target. addition, introduced experimentally proved more effective compared First Fit, Particle Swarm Optimization (PSO) GA. Moreover, validate proposed new fitness alleviate caused VM

Language: Английский

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