SDN-enabled Resource Provisioning Framework for Geo-Distributed Streaming Analytics

Geographically distributed (geo-distributed) datacenters for stream data processing typically comprise multiple edges and core datacenters connected through Wide-Area Network (WAN) with a master node responsible for allocating tasks to worker nodes. Since WAN links significantly impact the performance of distributed task execution, the existing task assignment approach is unsuitable for distributed stream data processing with low latency and high throughput demand. In this paper, we propose SAFA, a resource provisioning framework using the Software-Defined Networking (SDN) concept with an SDN controller responsible for monitoring the WAN, selecting an appropriate subset of worker nodes, and assigning tasks to the designated worker nodes. We implemented the data plane of the framework in P4 and the control plane components in Python. We tested the performance of the proposed system on Apache Spark, Apache Storm, and Apache Flink using the Yahoo! streaming benchmark on a set of custom topologies. The results of the experiments validate that the proposed approach is viable for distributed stream processing and confirm that it can improve at least 1.64× the processing time of incoming events of the current stream processing systems.</p

Impact of Tangible or Intangible incentives on job satisfaction among workers

Incentives either tangible or intangible are the most important or convenient resources that became the basis of workers satisfaction with their jobs. The study attempts to explore the relationship between tangible or intangible incentives and job satisfaction among workers. To achieve the objective, a sample of 100 workers were selected from Pakistan Ordinance Factory, Wah Cantt, Pakistan on basis of purposive sampling technique. Data is collected through the use of reward system questionnaire and Minnesota Satisfaction questionnaire. Statistical Package for social sciences (version, 21) was used for the analyses of data. Results showed that both tangible or intangible incentives are positively related to job satisfaction among workers. Thus, at work settings the rise in the use of incentives is associated with high job satisfaction

Effects of a high-dose 24-h infusion of tranexamic acid on death and thromboembolic events in patients with acute gastrointestinal bleeding (HALT-IT): an international randomised, double-blind, placebo-controlled trial

Background: Tranexamic acid reduces surgical bleeding and reduces death due to bleeding in patients with trauma. Meta-analyses of small trials show that tranexamic acid might decrease deaths from gastrointestinal bleeding. We aimed to assess the effects of tranexamic acid in patients with gastrointestinal bleeding. Methods: We did an international, multicentre, randomised, placebo-controlled trial in 164 hospitals in 15 countries. Patients were enrolled if the responsible clinician was uncertain whether to use tranexamic acid, were aged above the minimum age considered an adult in their country (either aged 16 years and older or aged 18 years and older), and had significant (defined as at risk of bleeding to death) upper or lower gastrointestinal bleeding. Patients were randomly assigned by selection of a numbered treatment pack from a box containing eight packs that were identical apart from the pack number. Patients received either a loading dose of 1 g tranexamic acid, which was added to 100 mL infusion bag of 0·9% sodium chloride and infused by slow intravenous injection over 10 min, followed by a maintenance dose of 3 g tranexamic acid added to 1 L of any isotonic intravenous solution and infused at 125 mg/h for 24 h, or placebo (sodium chloride 0·9%). Patients, caregivers, and those assessing outcomes were masked to allocation. The primary outcome was death due to bleeding within 5 days of randomisation; analysis excluded patients who received neither dose of the allocated treatment and those for whom outcome data on death were unavailable. This trial was registered with Current Controlled Trials, ISRCTN11225767, and ClinicalTrials.gov, NCT01658124. Findings: Between July 4, 2013, and June 21, 2019, we randomly allocated 12 009 patients to receive tranexamic acid (5994, 49·9%) or matching placebo (6015, 50·1%), of whom 11 952 (99·5%) received the first dose of the allocated treatment. Death due to bleeding within 5 days of randomisation occurred in 222 (4%) of 5956 patients in the tranexamic acid group and in 226 (4%) of 5981 patients in the placebo group (risk ratio [RR] 0·99, 95% CI 0·82–1·18). Arterial thromboembolic events (myocardial infarction or stroke) were similar in the tranexamic acid group and placebo group (42 [0·7%] of 5952 vs 46 [0·8%] of 5977; 0·92; 0·60 to 1·39). Venous thromboembolic events (deep vein thrombosis or pulmonary embolism) were higher in tranexamic acid group than in the placebo group (48 [0·8%] of 5952 vs 26 [0·4%] of 5977; RR 1·85; 95% CI 1·15 to 2·98). Interpretation: We found that tranexamic acid did not reduce death from gastrointestinal bleeding. On the basis of our results, tranexamic acid should not be used for the treatment of gastrointestinal bleeding outside the context of a randomised trial

P4Flow: Monitoring Traffic Flows with Programmable Networks

Cloud providers perform flow monitoring to get insights from the network traffic flows, often using coarse-grained packet counters or packet probing. These approaches give partial information from ongoing flows or introduce significant overhead if the probe packets cross multiple hops with diverse delay and bandwidth to reach the traffic collector. Recently, In-band Network Telemetry (INT) offered by programmable networks, e.g., P4, has gained attention by providing fine-grained network telemetry. Current attempts on INT are inflexible in collecting telemetry for customs flows according to the desired interval. This letter proposes P4Flow, a flow monitoring tool for cloud provider networks implemented on programmable data planes. P4Flow allows the providers to monitor a set of desired flows according to their needs. It reduces at least 1.6x the overhead of telemetry packets compared with the existing approaches.BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Dat

Network-aware worker placement for wide-area streaming analytics

Many organizations leverage Distributed Stream processing systems (DPSs) to get insights from the data generated by different users/devices, e.g., the Internet of Things (IoT) devices or user clicks on a website, on geographically distributed datacenters. The worker nodes in such environments are connected through Wide Area Network (WAN) links with various delays and bandwidth. Therefore, minimizing the execution latency of a task on the worker nodes while using the links with enough bandwidth and lower cost to steer the traffic of the applications is a challenging task. In this paper, we formulate the worker node placement for a geo-distributed DSPs network as a multi-criteria decision-making problem. Then, we propose an additive weighting-based approach to solve it. The users can prioritize the worker node placement according to the network-relevant parameters. We also propose a framework that can be integrated with the current DPSs to execute the tasks. We test our placement approach on three widely used stream processing systems, i.e., Apache Spark, Apache Storm, and Apache Flink, on three custom graphs adopted from the real cloud providers. We run the streaming query of the Yahoo! streaming benchmark on these three DPSs. The experimental results show that our approach improves the performance of Spark up to 2.2x-7.2x, Storm up to 1.2x-3.4x, and Flink up to 1.4x-3.3x compared with other placement approaches, which makes our framework useful for use in practical environments

SNR: Network-aware geo-distributed stream analytics

Emerging applications such as those running on the Internet of Things (IoT) devices produce constant data streams that need to be processed in real-time. Distributed stream processing systems (DSPs), with geographically distributed cluster networks interconnected via wide area network (WAN) links, have recently gained interest in handling these applications. However, these applications have stringent requirements such as low-latency and high bandwidth that must be guaranteed to ensure the quality of service (QoS). These application requirements raise fundamental DSPs resource management and scheduling challenge. In this paper, we formulate the problem of placement of worker nodes on a geo-distributed DSPs cluster network as a multi-criteria decision-making problem and propose an additive weighting-based approach to solve it. The proposed solution finds the trade-off among different network parameters and allows executing the tasks according to the desired performance metrics. We evaluated the proposed approach using the Yahoo! streaming benchmark on a testbed and compare it against mechanisms deployed in Apache Spark, Apache Storm, and Apache Flink. The results of the evaluation show that our approach improves the performance of Spark up to 2.2x-7.2x, Storm up to 1.2x-3.4x, and Flink up to 1.4x-3.3x compared to other approaches, which makes our approach useful for use in practical environments.BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and Dat

Contamination profile of aflatoxin M1 residues in milk supply chain of Sindh, Pakistan

Aflatoxin M1 (AFM1) is a potent carcinogen, teratogen and mutagen found in the milk when lactating animals consume feed contaminated with aflatoxin B1 (AFB1). In the present study, the contamination of AFM1 was evaluated in the milk supply chain of the province of Sindh, Pakistan. For the broader profiling of targeted toxin, enzyme-linked immunosorbent assay (ELISA) was used for the determination of AFM1 in both branded and non-branded milk samples. The results showed that 96.43% of samples (81 out of 84) were contaminated with AFM1 in the range of 0.01–0.76 μg/L. The average contamination level was 0.38 μg/L. The determined values of AFM1 in the collected milk samples were above the standard limit of the European Commission while 70% of the samples exceeded levels established by United States regulations. According to these results, the estimated daily intake of AFM1 for adults was determined as 3.1 ng/kg of body weight per day

PERFORMANCE ENHANCEMENT OF FACE RECOGNITION SYSTEM USING PRINCIPAL COMPONENT ANALYSIS MERGED WITH DISCRETE WAVELET TRANSFORMS

Performance of face recognition system can be enhanced by proposed technique titled as PCA merged with Discrete Wavelet Transform (DWT) instead of using the conventional PCA technique. In this technique to reduce the computational complexity of traditional PCA the size of the image is first reduced by taking the DWT of it. After applying the DWT the facial features of the image are extracted by calculating the Eigenface of the image with size already reduced by taking DWT. As a result of this process the size of database will reduce to one-fourth of the conventional PCA in which the facial features are extracted directly from the image by calculating the Eigenface. The size of the train database is reduced with the proposed technique which reduces the processing time of the face recognition without losing the accuracy. Performance of face recognition system is enhanced in terms of low processing time as shown by comparing the experimental results of conventional PCA and the proposed technique in this pape

Isolation of Bacillus cereus from botanical soil and subsequent biodegradation of waste engine oil

Waste engine oil causes a vital environmental pollution when it spill during change and transportation and products of waste engine oil causes lethal effects to the living systems. Thus, abiotic and biotic approaches are being extensively used for removal of waste engine oil pollution. Therefore in present study, waste engine oil degradation was accomplished by a new bacterial culture, isolated from the soil by an enrichment technique. Morphological, biochemical and gene sequence analysis revealed that isolate was Bacillus cereus. Subsequently, biodegradation potential of B. cereus for waste engine oil was studied. Experimental variables, such as pH, substrate concentration, inoculum size, temperature and time on the biodegradation, were checked in mineral salt medium. The biodegradation efficiency of B. cereus was determined by gravimetry, UV–visible spectrophotometry and gas chromatography. In addition, waste engine oil was also characterized by GC–MS and FTIR for its major constituents, which showed total 38 components in waste engine oil, including hopanes, benzopyrene, long-chain aliphatic hydrocarbons, dibenzothiophenes, biphenyl and their derivatives. Results of successive biodegradation indicated that B. cereus was capable to degrade 1% of waste engine oil with 98.6% degradation potential at pH 7 within 20 days. Hence, B. cereus presents an innovative tool for removing the engine oil from the contaminated area