LACE: A Locust-Inspired Scheduling Algorithm to Reduce Energy Consumption in Cloud Datacenters

Kurdi, Heba; AlIsmail, Shaden M.; Hassan, Mohammad Mehedi

doi:10.1109/access.2018.2839028

Cited by 20 publications

(13 citation statements)

References 25 publications

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“…More effective and adaptive load balancing algorithms should be developed to exploit performance, resource utilization, and energy conservation to deliver quality services to the users with the least cost. Some metrics were neglected in [19], [62], [69], [91], such as carbon emission, CADCloud sun sensitivity, cloud wind, green cloud rate, average heuristics performance, and rebalance time. This is because this paper has focused on the most popular metrics as in Table 5.…”

Section: Discussionmentioning

confidence: 99%

“…They had success in attaining an efficient utilization level for the resources and reducing the energy consumption while keeping to the service level agreement. In [66], [69] authors introduced an optimization algorithm. [66] extended the first fit decreasing optimization algorithm which optimized the CPU utilization and identified the effective capacity of a PM.…”

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

confidence: 99%

“…[66] extended the first fit decreasing optimization algorithm which optimized the CPU utilization and identified the effective capacity of a PM. [69] used an inspired optimization algorithm, namely Locust, which mapped and consolidated based on the CPU threshold.…”

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

confidence: 99%

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Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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The data-center is considered the heart of cloud computing. Recently, the growing demand for cloud computing services has caused a growing load on data centers. In terms of system behavior and workload, patterns of cloud computing are very dynamic; and that might serve to imbalance the load among data center resources. Eventually, some data-center resources could come to be over-loaded/under-loaded, which leads to an increase in energy consumption in addition to decreased functioning and wastage of resources. Just considering energy-efficiency (that can be attained efficiently by consolidate the servers) may not be enough for real applications because it may cause problems such as unbalanced load for each Physical Machine (PM). Therefore, this paper surveys published load balancing algorithms that achieved by server consolidation via a meta-analysis. Load balancing with server consolidation enriches the exploitation of resource utilization and can enhance Quality of Service (QoS) metrics, since data-centers and their applications are increasing exponentially. This meta-study, reviews the literature on load balancing and server consolidation and presents a ready reference taxonomy on the most efficient algorithms that achieve load balancing and server consolidation. This work attempts to present a taxonomy with a new classification for load balancing and server consolidation, such as migration overhead, hardware threshold, network traffic, and reliability.

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Section: Discussionmentioning

confidence: 99%

Section: Parameters In Consideration In the Systematic Survey 1) Hardware Thresholdsmentioning

confidence: 99%

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Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Ala’anzy

Othman

2019

IEEE Access

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“…The plaintext is the message that will be sent via unreliable media, and it will be encrypted before being sent. The encrypted communication is called code text, and it is received at the opposite end of the medium and decoded to reveal the plaintext message [11].…”

Section: Methodology and Approachmentioning

confidence: 99%

Compositional Defence of Application Privacy in Resistant to Physical and Software Attacks in Untrusted Cloud Environment

Chanodiya*¹,

Potey²

2021

IJEAT

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Cloud computing facilities have become an increasing rise in demand for presenting computing sources. Various programs like monetary transactions, fitness care systems, video streaming, IoT programs want on-call for provisioning of cloud sources to guarantee timeliness and excessive availability. A shared culture for data interchange has evolved as a result of the fast progress of networking technology. The data security has become a difficult issue as a result of the vast volume of data that is exchanged via the internet. Thus there is a requirement for security to protect data that is transmitted across an insecure connection. In our proposed work, privacy, confidentiality, availability, integrity, and accountability that contribute to the security of information are ensured by cryptographic technique. This paper also describes an exhaustive study on the usage of hybrid algorithms i.e. the combination of Advanced Encryption Standards (AES) and Elliptical Curve Cryptography (ECC) to protect data leakage and protect the privacy of end-users and SHA256 algorithm to ensure data integrity. This type of hybrid encryption will help not only in concentrating software as well as physical attacks but also provide the model to deal with and prevent the application from these types of attacks; thus making the cloud application system more secure. The implemented web application can be used as a defence system for privacy and security in organizations as well as institutions where members of the organization can share the file or confidential data to other team members or colleagues without data leakage thus providing data privacy and security.

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“…In this paper, we present an improved deep learning (DL) framework, driven by massive data, to offer a solution for improving energy efficiency by predicting next energy demand in the cloud. Considering the non‐linearity and complexity of various devices in DC and accuracy of the experiment [11], we focus on researching the power consumption of IT infrastructures, which is expressed as

P_{IT}

in (1). To this end, our goal is to improve the energy efficiency of cloud computing clusters by allocating resources legitimately using our power consumption model.…”

Section: Introductionmentioning

confidence: 99%

Power consumption model based on feature selection and deep learning in cloud computing scenarios

Yang

2020

IET Communications

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LACE: A Locust-Inspired Scheduling Algorithm to Reduce Energy Consumption in Cloud Datacenters

Cited by 20 publications

References 25 publications

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Load Balancing and Server Consolidation in Cloud Computing Environments: A Meta-Study

Compositional Defence of Application Privacy in Resistant to Physical and Software Attacks in Untrusted Cloud Environment

Power consumption model based on feature selection and deep learning in cloud computing scenarios

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