Online Algorithms for Geographical Load Balancing

It has recently been proposed that Internet energy costs, both monetary and environmental, can be reduced by exploiting temporal variations and shifting processing to data centers located in regions where energy currently has low cost. Lightly loaded data centers can then turn off surplus servers. This paper studies online algorithms for determining the number of servers to leave on in each data center, and then uses these algorithms to study the environmental potential of geographical load balancing (GLB). A commonly suggested algorithm for this setting is “receding horizon control” (RHC), which computes the provisioning for the current time by optimizing over a window of predicted future loads. We show that RHC performs well in a homogeneous setting, in which all servers can serve all jobs equally well; however, we also prove that differences in propagation delays, servers, and electricity prices can cause RHC perform badly, So, we introduce variants of RHC that are guaranteed to perform as well in the face of such heterogeneity. These algorithms are then used to study the feasibility of powering a continent-wide set of data centers mostly by renewable sources, and to understand what portfolio of renewable energy is most effective

저전력 손실 네트워크에서 대규모 응용분야를 위한 전송전력 제어기법

학위논문 (석사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 8. 박세웅.Transmission power is an important factor which impacts on routing topology in low power and lossy networks (LLNs). LLNs have been designed for low rate traffic where use of maximum transmission power is the best choice for performance maximization since it results in reduced hop distance and transmission overhead. However, large scale applications also require LLNs to deliver very high rate traffic. In such large scale applications, the nodes which are near the root node will incur heavy traffic even though each node generates low rate traffic. As a result, it will cause severe link congestion. In this paper, we first investigate the effect of transmission power control on the performance of the routing protocol for LLNs (RPL) at heavy traffic load through testbed experiments. Our experiments show that, unlike LLNs in low rate applications, packet delivery performance at heavy load first increases and then decreases with transmission power. And we further investigate the reasons of what makes packet loss rate have a convex curve according to transmission power by per node analysis. We classify packet losses into link loss and queue loss. From the experiment results, we observe that link and queue losses are significantly unbalanced among nodes, which causes the load balancing problem of RPL. Furthermore, queue losses occur at the nodes which experience severe link loss. To solve this problem, we propose a simple power control mechanism, which allows each node to adaptively control its transmission power according to its own link and queue losses. Our proposal significantly improves the packet delivery performance by balancing the traffic load within a routing tree. We show performance improvement through experimental measurements on a real mutihop LLN testbed running RPL over IEEE 802.15.4.Contents Abstract i Contents iii List of Figures iii List of Tables v Chap 1 Introduction 1 Chap 2 Experimental Environments 4 2.1. IPv6 routing protocol for low power and lossy networks (RPL) 4 2.2. Experimental environments 5 Chap 3 Load Balancing Problem of RPL 7 3.1. Packet loss rate 7 3.2. Queue loss and link loss 8 3.3. Topology analysis 11 3.4. Per node analysis 12 Chap 4 Transmission Power Control Mechanism 15 4.1. Effect of proposed power control on load balancing 15 4.2. Power control mechanism 17 Chap 5 Experimental Results 20 5.1. Packet loss rate 20 5.2. Queue loss and link loss 22 5.3. Packet loss rate 23 Chap 6 Conclusions 25 References 26 초 록 30 감사의 글 32Maste

Characterizing the impact of the workload on the value of dynamic resizing in data centers

Energy consumption imposes a significant cost for data centers; yet much of that energy is used to maintain excess service capacity during periods of predictably low load. Resultantly, there has recently been interest in developing designs that allow the service capacity to be dynamically resized to match the current workload. However, there is still much debate about the value of such approaches in real settings. In this paper, we show that the value of dynamic resizing is highly dependent on statistics of the workload process. In particular, both slow time-scale non-stationarities of the workload (e.g., the peak-to-mean ratio) and the fast time-scale stochasticity (e.g., the burstiness of arrivals) play key roles. To illustrate the impact of these factors, we combine optimization-based modeling of the slow time-scale with stochastic modeling of the fast time-scale. Within this framework, we provide both analytic and numerical results characterizing when dynamic resizing does (and does not) provide benefits

Online optimization with switching cost

We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant of the class of "online convex optimization (OCO)" problems that is strongly related to the class of "metrical task systems", each of which have been studied extensively. Prior literature on these problems has focused on two performance metrics: regret and competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however no known algorithms achieve both. In this paper, we show that this is due to a fundamental incompatibility between regret and the competitive ratio -- no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear

A Tale of Two Metrics: Simultaneous Bounds on Competitiveness and Regret

We consider algorithms for “smoothed online convex optimization” (SOCO) problems, which are a hybrid between online convex optimization (OCO) and metrical task system (MTS) problems. Historically, the performance metric for OCO was regret and that for MTS was competitive ratio (CR). There are algorithms with either sublinear regret or constant CR, but no known algorithm achieves both simultaneously. We show that this is a fundamental limitation – no algorithm (deterministic or randomized) can achieve sublinear regret and a constant CR, even when the objective functions are linear and the decision space is one dimensional. However, we present an algorithm that, for the important one dimensional case, provides sublinear regret and a CR that grows arbitrarily slowly

Online tracking of ants based on deep association metrics: method, dataset and evaluation

Tracking movement of insects in a social group (such as ants) is challenging, because the individuals are not only similar in appearance but also likely to perform intensive body contact and sudden movement adjustment (start/stop, direction changes). To address this challenge, we introduce an online multi-object tracking framework that combines both the motion and appearance information of ants. We obtain the appearance descriptors by using the ResNet model for offline training on a small (N=50) sample dataset. For online association, a cosine similarity metric computes the matching degree between historical appearance sequences of the trajectory and the current detection. We validate our method in both indoor (lab setup) and outdoor video sequences. The results show that our model obtains 99.3% ± 0.5% MOTA and 91.9% ± 2.1% MOTP across 24,050 testing samples in five indoor sequences, with real-time tracking performance. In an outdoor sequence, we achieve 99.3% MOTA and 92.9% MOTP across 22,041 testing samples. The datasets and code are made publicly available for future research in relevant domains

Characteristics and variations of the picophytoplankton community in the Arctic Ocean

Picophytoplankton are responsible for much of the carbon fixation process in the Arctic Ocean, and they play an important role in active microbial food webs. The climate of the Arctic Ocean has changed in recent years, and picophytoplankton, as the most vulnerable part of the high-latitude pelagic ecosystem, have been the focus of an increasing number of scientific studies. This paper reviews and summarizes research on the characteristics of picophytoplankton in the Arctic Ocean, including their abundance, biomass, spatial distribution, seasonal variation, community structure, and factors influencing their growth. The impact of climate change on the Arctic Ocean picophytoplankton community is discussed, and future research directions are considered

Online optimization with switching cost

We consider algorithms for "smoothed online convex optimization (SOCO)" problems. SOCO is a variant of the class of "online convex optimization (OCO)" problems that is strongly related to the class of "metrical task systems", each of which have been studied extensively. Prior literature on these problems has focused on two performance metrics: regret and competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however no known algorithms achieve both. In this paper, we show that this is due to a fundamental incompatibility between regret and the competitive ratio -- no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear

Endometrial microbiota in women with and without adenomyosis: A pilot study

IntroductionThe endometrial microbiota plays an essential role in the health of the female reproductive system. However, the interactions between the microbes in the endometrium and their effects on adenomyosis remain obscure.Materials and methodsWe profile endometrial samples from 38 women with (n=21) or without (n=17) adenomyosis to characterize the composition of the microbial community and its potential function in adenomyosis using 5R 16S rRNA gene sequencing.ResultsThe microbiota profiles of patients with adenomyosis were different from the control group without adenomyosis. Furthermore, analysis identified Lactobacillus zeae, Burkholderia cepacia, Weissella confusa, Prevotella copri, and Citrobacter freundii as potential biomarkers for adenomyosis. In addition, Citrobacter freundii, Prevotella copri, and Burkholderia cepacia had the most significant diagnostic value for adenomyosis. PICRUSt results identified 30 differentially regulated pathways between the two groups of patients. In particular, we found that protein export, glycolysis/gluconeogenesis, alanine, aspartate, and glutamate metabolism were upregulated in adenomyosis. Our results clarify the relationship between the endometrial microbiota and adenomyosis.DiscussionThe endometrial microbiota of adenomyosis exhibits a unique structure and Citrobacter freundii, Prevotella copri, and Burkholderia cepacia were identified as potential pathogenic microorganisms associated with adenomyosis. Our findings suggest that changes in the endometrial microbiota of patients with adenomyosis are of potential value for determining the occurrence, progression, early of diagnosis, and treatment oadenomyosis

Altered gut microbiota profile in patients with perimenopausal panic disorder

IntroductionFemales in the perimenopausal period are susceptible to mood disorders. Perimenopausal panic disorder (PPD) is characterized by repeated and unpredictable panic attacks during perimenopause, and it impacts the patient's physical and mental health and social function. Pharmacotherapy is limited in the clinic, and its pathological mechanism is unclear. Recent studies have demonstrated that gut microbiota is strongly linked to emotion; however, the relation between PPD and microbiota is limitedly known.MethodsThis study aimed to discover specific microbiota in PPD patients and the intrinsic connection between them. Gut microbiota was analyzed in PPD patients (n = 40) and healthy controls (n = 40) by 16S rRNA sequencing.ResultsThe results showed reduced α-diversity (richness) in the gut microbiota of PPD patients. β-diversity indicated that PPD and healthy controls had different intestinal microbiota compositions. At the genus level, 30 species of microbiota abundance had significantly different between the PPD and healthy controls. In addition, HAMA, PDSS, and PASS scales were collected in two groups. It was found that Bacteroides and Alistipes were positively correlated with PASS, PDSS, and HAMA.DiscussionBacteroides and Alistipes dysbiosis dominate imbalanced microbiota in PPD patients. This microbial alteration may be a potential pathogenesis and physio-pathological feature of PPD. The distinct gut microbiota can be a potential diagnostic marker and a new therapeutic target for PPD