WARM: Workload-Aware Multi-Application Task Scheduling for Revenue Maximization in SDN-Based Cloud Data Center

Yuan, Haitao; Bi, Jing; Zhou, MengChu; Sedraoui, Khaled

doi:10.1109/access.2017.2773645

Cited by 27 publications

(13 citation statements)

References 67 publications

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“…This study illustrates the applicability of the best practices and guidelines to a real DC and uses an ML approach to perform IT room thermal characteristics assessment. This work could be extended by incorporating an integrated thermal management with existing energy efficiency policies-related research (e.g., energy awareness [15]; job scheduling using AI [32], temporal-based job scheduling [33], work-load aware scheduling [34], and queue theory [35]; resource utilization [36] of multiple applications using annealing and particle swarm optimization [37]). Another direction that could be taken could be the energy efficiency policies and waste heat utilization [26].…”

Section: Discussionmentioning

confidence: 99%

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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The energy efficiency of Data Center (DC) operations heavily relies on a DC ambient temperature as well as its IT and cooling systems performance. A reliable and efficient cooling system is necessary to produce a persistent flow of cold air to cool servers that are subjected to constantly increasing computational load due to the advent of smart cloud-based applications. Consequently, the increased demand for computing power will inadvertently increase server waste heat creation in data centers. To improve a DC thermal profile which could undeniably influence energy efficiency and reliability of IT equipment, it is imperative to explore the thermal characteristics analysis of an IT room. This work encompasses the employment of an unsupervised machine learning technique for uncovering weaknesses of a DC cooling system based on real DC monitoring thermal data. The findings of the analysis result in the identification of areas for thermal management and cooling improvement that further feeds into DC recommendations. With the aim to identify overheated zones in a DC IT room and corresponding servers, we applied analyzed thermal characteristics of the IT room. Experimental dataset includes measurements of ambient air temperature in the hot aisle of the IT room in ENEA Portici research center hosting the CRESCO6 computing cluster. We use machine learning clustering techniques to identify overheated locations and categorize computing nodes based on surrounding air temperature ranges abstracted from the data. This work employs the principles and approaches replicable for the analysis of thermal characteristics of any DC, thereby fostering transferability. This paper demonstrates how best practices and guidelines could be applied for thermal analysis and profiling of a commercial DC based on real thermal monitoring data.

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Section: Discussionmentioning

confidence: 99%

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

et al. 2020

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“…RRT use a deviation value to determine to accept a worse solution or not, which is not use probability acceptance rules. SA has been successfully applied to solve various optimization problems [25][26][27], especially for vehicle routing problem [28，29]. Its performance and practicality prompt us to solve the mixed school bus routing problem by the variant of SA.…”

Section: Literature Reviewmentioning

confidence: 99%

A Metaheuristic Algorithm for Routing School Buses With Mixed Load

et al. 2020

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Designing a system to solve school bus routing problems (SBRP), especially in a large school district, is very complex and expensive. One of the challenges resides in designing routes for the school buses when they have mixed loads, where each bus transports students for one or more schools at the same time to save the total number of buses required. This article aims to explore whether the algorithm originally developed for pickup and delivery problem with time windows (PDPTW) can be employed for solving mixed load SBRP. We present a PDPTW-based algorithm to address the mixed load SBRP focusing on minimizing the number of buses required. Our algorithm combines a record-to-record travel framework with three neighborhood operators, single paired insertion, swapping pairs between routes, and within route insertion, to improve solution iteratively. Results from implementing this algorithm show that our PDPTW-based algorithm is feasible for mixed load SBRP. Moreover, we found that guided strategy is better than random for permuting nodes that would be selected to relocate positions in a route or among routes. In addition, the results also show that the spatiotemporal connectivity index used in our algorithm can reduce the computation time needed for searching for solutions without affecting the quality of the solutions.

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“…SDN-enabled cloud computing has been emerging as a future SDN-based cloud environment. Many studies have proposed a various methods to not only increase revenue for data center providers but also reduce the round-trip time of tasks for applications, for example, [11], [12]. The integration of NFV and SDN technologies is known as the SDN/NFV architecture [2], as illustrated in Figure 2.…”

Section: Background Knowledge a Sdn-based Cloudmentioning

confidence: 99%

ECSD: Enhanced Compromised Switch Detection in an SDN-Based Cloud Through Multivariate Time-Series Analysis

Dinh

Park

2020

IEEE Access

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Nowadays, Software-Defined Networks (SDNs) are increasingly being used in many practical settings, posing a variety of security risks, such as compromised switches. Once a switch is compromised by an attacker, the switch may be either malfunctioning or misconfigured, displaying some abnormal network behaviors, e.g., delaying, dropping, adding, or modifying the traffic. In our previous work, we proposed an efficient scheme for detecting compromised SDN switches based on chaotic analysis of network traffic using an autoregressive-integrated-moving-average model. This scheme showed good results overall; however, it still showed high false-alarm rates due to a hard-set threshold. In this paper, we propose an enhanced scheme to detect compromised SDN switches effectively and reliably. The scheme consists of two phases (online and offline), leveraging the advantages of a stochastic recurrent neural network variant of multivariate time-series-based anomaly detection. Our main idea is to capture the normal patterns of multivariate time series by learning strong representations with the key techniques, such as planar normalizing flow and stochastic variable connection, then reconstruct input data by the representations, and use the reconstruction probabilities to find anomalies. Evaluation results of our proposed scheme yield outstanding performance in comparison with our previous work and other solutions.

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WARM: Workload-Aware Multi-Application Task Scheduling for Revenue Maximization in SDN-Based Cloud Data Center

Cited by 27 publications

References 67 publications

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

A Machine Learning Solution for Data Center Thermal Characteristics Analysis

A Metaheuristic Algorithm for Routing School Buses With Mixed Load

ECSD: Enhanced Compromised Switch Detection in an SDN-Based Cloud Through Multivariate Time-Series Analysis

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